SyncVals · Trajectory

simjeb-bracket-fea-mass-prediction-real

claude-code claude-opus-4-8 ✗ failed GOOD_FAILURE ↑ View task

Solved from the instruction alone, tests/ and solution/ were withheld from the agent's workspace and restored only for grading.

Reward = tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.

Classification , post-hoc; cannot change the reward

GOOD_FAILUREHonest miss, the agent ran correctly but couldn't solve it. Expected for a hard task; the task is sound.

SubtypeInsufficient Generalization / Distribution Shift

EvidenceAgent achieved rel-L2 ≈ 0.229 on 6-fold CV of the 148-sample pool, but the hidden grading set yielded score 0.45 (rel-L2 ≈ 0.450). The agent's own honest train→val holdout showed rel ≈ 0.197, suggesting the final model trained on all 148 may have slightly overfit or the hidden set has a different bracket distribution. Test output shows model.pt was produced and ran (predicted mass range [0.282, 2.112] vs. true [0.294, 2.072]), ruling out infrastructure failure. The task is marked difficulty='hard' (target 120–240 min), and the agent thoroughly explored ~40 CV configurations (iso vs. aniso normalization, augmentation levels, ensembles, distillation, soups) before settling on a well-regularized single model trained on all data.

Root causeThe agent's model generalized to ~0.229 on the training pool's 6-fold CV but failed to transfer that performance to the hidden held-out set (0.45), likely due to distribution shift in the hidden test brackets or underestimated overfitting despite jitter regularization. The fixed architecture and small labeled dataset (148 samples) made true out-of-distribution generalization challenging, a common difficulty in hard ML benchmarks.

RecommendationN/A - task is fine. The task is appropriately difficult (marked hard, expert 120–240 min), the instruction is clear, the fixed architecture is reasonable, and the grading is deterministic and unambiguous. The agent's failure reflects the genuine challenge of point-cloud regression on scarce data with geometric variation, not a task specification problem. A stronger solution would require either (1) a more sophisticated architecture (impossible due to fixed architecture constraint), (2) more training data, or (3) domain-specific domain adaptation techniques (not explored). The agent's approach was sound for the constraints given."

Trajectory

Tool-by-tool agent trajectory

349 tool calls · 3 tool types · 349 steps

You are given point-cloud samples of 3D jet-engine bracket designs along with each bracket's FEA-derived mass (kg). Each point cloud is an unordered set of 2048 surface points in R^3; coordinates are pre-centered and rescaled so the axis-aligned bounding box of each bracket fits inside the unit cube. The mapping from geometry to mass is not given to you - you have to learn it from the data. Train a model that takes a `[2048, 3]` point cloud and predicts the scalar mass value. Your model is then applied to a held-out set of bracket geometries and the predictions are compared against the ground-truth FEA-simulated mass values. ## Files - `/app/data/{train,val,public_test}/samples.pt` - each file is a dict with keys `features` (shape `[N, 2048, 3]`) and `labels` (shape `[N, 1]`), both float16 on disk. Load with `torch.load(path, weights_only=True)`. - `/app/task_config.json` - architecture sizes and training defaults. - `/app/feature_schema.json` - input / label spec. - `/app/candidate_model.py` - the fixed PointNet-style architecture. Keep it as is; your saved checkpoint has to load back into this same architecture. - `/app/dataset.py` - a minimal loader for `samples.pt`. - `/app/train.py` - a small starter training loop you can extend or replace. ## What you must produce Write your trained checkpoint to `/app/outputs/model.pt` as a dict with the following required keys: - `state_dict` - your model's `state_dict()` mapping. - `feat_mean`, `feat_std` - length-3 tensors used to standardize the input coordinates before the forward pass. The same normalization is applied at inference time, so these are required. - `label_mean`, `label_std` - length-1 tensors used to standardize the target during training and de-standardize the model output at inference. The model's output is mapped back to physical units using these, so they are required too. The model must consume a `[B, 2048, 3]` tensor and produce a `[B, 1]` output. ## What you are optimizing Your predictions are measured against the true FEA-simulated mass labels with three error numbers, all lower is better: the relative L2 over the full set (the primary one), the mean absolute error, and the worst-case per-sample absolute error. A strong model needs to do well on all three, so do not trade one off against the others.

Let me start by exploring the environment to understand what I'm working with.

I'll start by exploring the environment to understand the files and data I'm working with.

bash

cd /app && ls -la && echo "---TASK CONFIG---" && cat task_config.json && echo "---FEATURE SCHEMA---" && cat feature_schema.json

output

total 14
drwxrws--- 3 h2tagent h2tagent  138 Jun 20 18:13 .
drwxr-xr-x 1 root     root     4096 Jun 20 20:57 ..
-rw-rw---- 1 h2tagent h2tagent 1639 Jun 20 18:10 candidate_model.py
drwxrws--- 5 h2tagent h2tagent   58 Jun 20 18:10 data
-rw-rw---- 1 h2tagent h2tagent 1183 Jun 20 18:10 dataset.py
-rw-rw---- 1 h2tagent h2tagent  499 Jun 20 18:10 feature_schema.json
-rw-rw---- 1 h2tagent h2tagent  581 Jun 20 18:10 task_config.json
-rw-rw---- 1 h2tagent h2tagent 3117 Jun 20 18:10 train.py
---TASK CONFIG---
{
  "task_id": "simjeb-bracket-fea-mass-prediction-real",
  "dataset_name": "simjeb-bracket-fea-mass-pilot",
  "n_points": 2048,
  "point_dim": 3,
  "label_dim": 1,
  "label_per_point": false,
  "seed": 20260518,
  "model": {
    "feature_width": 64,
    "global_width": 256,
    "head_width": 128
  },
  "training": {
    "epochs": 1,
    "batch_size": 16,
    "lr": 0.001,
    "weight_decay": 0.0001,
    "loss": "MSE"
  },
  "evaluation": {
    "primary_metric": "mass_rel_l2_score",
    "secondary_metrics": [
      "mass_mae_score",
      "mass_max_abs_err_score"
    ]
  }
}
---FEATURE SCHEMA---
{
  "version": "simjeb-bracket-fea-mass-pilot-v1",
  "input": {
    "type": "point_cloud",
    "n_points": 2048,
    "dim": 3,
    "coord_range": [
      -0.5,
      0.5
    ],
    "description": "Unordered surface point sample of a jet-engine bracket; each bracket mesh is centered at its bounding-box centroid and rescaled so the axis-aligned bounding box fits inside the unit cube."
  },
  "label": {
    "type": "scalar",
    "dim": 1,
    "description": "FEA-derived bracket mass in kg."
  }
}

Let me look at the model architecture, dataset loader, and training script.

Let me examine the model architecture, dataset loader, and starter training script.

bash

cd /app && echo "===candidate_model.py===" && cat candidate_model.py && echo "===dataset.py===" && cat dataset.py && echo "===train.py===" && cat train.py

output

===candidate_model.py===
from __future__ import annotations

from typing import Mapping

import torch
import torch.nn as nn


class SharedPointMLP(nn.Module):
    def __init__(self, in_dim: int, widths: tuple[int, ...]) -> None:
        super().__init__()
        layers: list[nn.Module] = []
        prev = in_dim
        for w in widths:
            layers.append(nn.Conv1d(prev, w, kernel_size=1))
            layers.append(nn.BatchNorm1d(w))
            layers.append(nn.GELU())
            prev = w
        self.net = nn.Sequential(*layers)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x)


class PointNetRegressor(nn.Module):
    def __init__(self, feature_width: int, global_width: int, head_width: int) -> None:
        super().__init__()
        self.feature = SharedPointMLP(3, (feature_width, feature_width, feature_width))
        self.lift = SharedPointMLP(feature_width, (feature_width * 2, global_width))
        self.head = nn.Sequential(
            nn.Linear(global_width, head_width),
            nn.GELU(),
            nn.Linear(head_width, head_width // 2),
            nn.GELU(),
            nn.Linear(head_width // 2, 1),
        )

    def forward(self, points: torch.Tensor) -> torch.Tensor:
        x = points.transpose(1, 2).contiguous()
        x = self.feature(x)
        x = self.lift(x)
        x = x.max(dim=2).values
        return self.head(x)


def build_model(config: Mapping) -> PointNetRegressor:
    m = config["model"]
    return PointNetRegressor(
        feature_width=int(m["feature_width"]),
        global_width=int(m["global_width"]),
        head_width=int(m["head_width"]),
    )
===dataset.py===
from __future__ import annotations

from pathlib import Path
from typing import Iterable

import torch
from torch.utils.data import Dataset


class SampleStore(Dataset):
    def __init__(self, split_dir: str | Path) -> None:
        self.dir = Path(split_dir)
        payload = torch.load(self.dir / "samples.pt", map_location="cpu", weights_only=True)
        self.features: torch.Tensor = payload["features"].float()
        self.labels: torch.Tensor = payload["labels"].float()
        if self.features.shape[0] != self.labels.shape[0]:
            raise ValueError("features and labels must have the same number of rows")

    def __len__(self) -> int:
        return self.features.shape[0]

    def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
        return self.features[idx], self.labels[idx]


def iter_batches(
    x: torch.Tensor,
    y: torch.Tensor,
    batch_size: int,
    shuffle: bool = True,
) -> Iterable[tuple[torch.Tensor, torch.Tensor]]:
    n = x.shape[0]
    idx = torch.randperm(n) if shuffle else torch.arange(n)
    for start in range(0, n, batch_size):
        chunk = idx[start : start + batch_size]
        yield x[chunk], y[chunk]
===train.py===
from __future__ import annotations

import json
import os
import sys
from pathlib import Path

import torch
import torch.nn.functional as F

APP_DIR = Path(os.environ.get("APP_DIR", "/app"))
sys.path.insert(0, str(APP_DIR))

from candidate_model import build_model  # noqa: E402
from dataset import SampleStore, iter_batches  # noqa: E402


def _device() -> torch.device:
    if torch.cuda.is_available():
        return torch.device("cuda")
    if torch.backends.mps.is_available():
        return torch.device("mps")
    return torch.device("cpu")


def _load_config() -> dict:
    with (APP_DIR / "task_config.json").open() as fh:
        return json.load(fh)


def main() -> None:
    cfg = _load_config()
    device = _device()
    torch.manual_seed(int(cfg["seed"]))

    train = SampleStore(APP_DIR / "data" / "train")
    val = SampleStore(APP_DIR / "data" / "val")

    feat_mean = train.features.mean(dim=(0, 1), keepdim=True)
    feat_std = train.features.std(dim=(0, 1), keepdim=True).clamp_min(1e-6)
    label_mean = train.labels.mean(dim=(0,), keepdim=True)
    label_std = train.labels.std(dim=(0,), keepdi
…[truncated]

Now let me explore the data. Let me check the shapes, the distributions of the data.

Now let me explore the data to understand its structure, sizes, and label distributions.

bash

cd /app && ls -la data && ls -la data/* && echo "---python env---" && python -c "import torch; print('torch', torch.__version__); print('cuda', torch.cuda.is_available())" 2>&1 | head

output

total 3
drwxrws--- 5 h2tagent h2tagent  58 Jun 20 18:10 .
drwxrws--- 3 h2tagent h2tagent 138 Jun 20 18:13 ..
drwxrws--- 2 h2tagent h2tagent  55 Jun 20 18:10 public_test
drwxrws--- 2 h2tagent h2tagent  55 Jun 20 18:10 train
drwxrws--- 2 h2tagent h2tagent  55 Jun 20 18:10 val
data/public_test:
total 197
drwxrws--- 2 h2tagent h2tagent     55 Jun 20 18:10 .
drwxrws--- 5 h2tagent h2tagent     58 Jun 20 18:10 ..
-rw-rw---- 1 h2tagent h2tagent   1134 Jun 20 18:10 manifest.jsonl
-rw-rw---- 1 h2tagent h2tagent 198437 Jun 20 18:10 samples.pt

data/train:
total 1210
drwxrws--- 2 h2tagent h2tagent      55 Jun 20 18:10 .
drwxrws--- 5 h2tagent h2tagent      58 Jun 20 18:10 ..
-rw-rw---- 1 h2tagent h2tagent    6483 Jun 20 18:10 manifest.jsonl
-rw-rw---- 1 h2tagent h2tagent 1230821 Jun 20 18:10 samples.pt

data/val:
total 389
drwxrws--- 2 h2tagent h2tagent     55 Jun 20 18:10 .
drwxrws--- 5 h2tagent h2tagent     58 Jun 20 18:10 ..
-rw-rw---- 1 h2tagent h2tagent   2011 Jun 20 18:10 manifest.jsonl
-rw-rw---- 1 h2tagent h2tagent 395109 Jun 20 18:10 samples.pt
---python env---
torch 2.5.1+cu124
cuda True

bash

cd /app && python -c "import torch; print(torch.cuda.get_device_name(0)); print('mem GB', round(torch.cuda.get_device_properties(0).total_memory/1e9,2))" && nvidia-smi 2>/dev/null | head -20

output

NVIDIA H100 80GB HBM3
mem GB 85.02
Sat Jun 20 20:58:09 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.82.07              Driver Version: 580.82.07      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA H100 80GB HBM3          Off |   N/A              Off |                    0 |
| N/A   32C    P0            149W /  700W |    4583MiB /  81559MiB |     25%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

An H100 GPU is available. Let me now look at the data in detail.

I have an H100 GPU available. Let me inspect the actual data contents, shapes, and label distributions.

bash

cd /app && python -c "
import torch
for split in ['train','val','public_test']:
    p = f'data/{split}/samples.pt'
    d = torch.load(p, weights_only=True)
    f = d['features']; l = d['labels']
    print(f'{split}: features {tuple(f.shape)} {f.dtype}, labels {tuple(l.shape)} {l.dtype}')
    lf = l.float()
    print(f'   label min {lf.min():.4f} max {lf.max():.4f} mean {lf.mean():.4f} std {lf.std():.4f}')
"

output

train: features (100, 2048, 3) torch.float16, labels (100, 1) torch.float16
   label min 0.2078 max 2.2188 mean 0.7476 std 0.3895
val: features (32, 2048, 3) torch.float16, labels (32, 1) torch.float16
   label min 0.2937 max 2.0723 mean 0.7622 std 0.4126
public_test: features (16, 2048, 3) torch.float16, labels (16, 1) torch.float16
   label min 0.1984 max 1.4492 mean 0.7296 std 0.3278

So we have: - train: 100 samples - val: 32 samples - public_test: 16 samples This is a small dataset. The labels range from about 0.2 to 2.2 kg. Let me look at the manifest files to understand more about the data.

Small dataset: 100 train, 32 val, 16 public_test. Let me examine the manifests and coordinate statistics.

bash

cd /app && echo "---train manifest (first 3)---" && head -3 data/train/manifest.jsonl && echo "---val manifest (first 2)---" && head -2 data/val/manifest.jsonl && echo "---public_test manifest (first 2)---" && head -2 data/public_test/manifest.jsonl

output

---train manifest (first 3)---
{"id": "train_0000", "source_bracket_id": 214, "n_points": 2048}
{"id": "train_0001", "source_bracket_id": 538, "n_points": 2048}
{"id": "train_0002", "source_bracket_id": 546, "n_points": 2048}
---val manifest (first 2)---
{"id": "val_0000", "source_bracket_id": 418, "n_points": 2048}
{"id": "val_0001", "source_bracket_id": 441, "n_points": 2048}
---public_test manifest (first 2)---
{"id": "public_test_0000", "source_bracket_id": 461, "n_points": 2048}
{"id": "public_test_0001", "source_bracket_id": 440, "n_points": 2048}

bash

cd /app && python -c "
import torch, json
# Check source bracket IDs overlap across splits
ids = {}
for split in ['train','val','public_test']:
    s = set()
    with open(f'data/{split}/manifest.jsonl') as fh:
        for line in fh:
            s.add(json.loads(line)['source_bracket_id'])
    ids[split] = s
    print(split, 'n_unique_brackets', len(s))
print('train&val overlap', len(ids['train'] & ids['val']))
print('train&test overlap', len(ids['train'] & ids['public_test']))
print('val&test overlap', len(ids['val'] & ids['public_test']))
"

output

train n_unique_brackets 100
val n_unique_brackets 32
public_test n_unique_brackets 16
train&val overlap 0
train&test overlap 0
val&test overlap 0

No overlap between splits - all are distinct brackets. So generalization matters. This is the SimJEB (Simulated Jet Engine Bracket) dataset. Let me understand the coordinate ranges and geometry properties. The key insight: mass is related to volume. For a solid object, mass = density × volume. Since all brackets are presumably the same material (density constant), mass ∝ volume. But the point clouds are surface samples, and they've been rescaled so the bounding box fits in the unit cube. Wait - this is critical. The coordinates are "pre-centered and rescaled so the axis-aligned bounding box of each bracket fits inside the unit cube." This means each bracket is rescaled INDEPENDENTLY. So the absolute scale information is lost! Hmm, but if each bracket is independently rescaled, then the mass can't be directly computed from volume in the normalized coordinates... unless the rescaling preserves aspect ratio (uniform scaling) and the scale factor is... no, the scale factor is not given. Wait, let me reconsider. Let me re-read: "coordinates are pre-centered and rescaled so the axis-aligned bounding box of each bracket fits inside the unit cube." So if the scaling is uniform (same factor in all axes) and chosen so that the largest dimension fits in the unit cube, then the normalized shape retains aspect ratio. The mass depends on the actual physical volume. If we only have normalized coords, we lost the overall scale. But maybe the brackets all have a similar real-world bounding box size, or maybe the scale is encoded... Actually, let me think about SimJEB. The SimJEB dataset has 381 brackets. They all fit in the same mounting interface - they all have the same 4 bolt holes and 1 interface region with fixed physical dimensions. So the real-world scale is actually constrained by these interface points! The bracket must bolt onto a standard interface. So the physical size is roughly consistent across brackets. This means: after normalizing each bracket so its bounding box fits the unit cube, the scale factor varies per bracket (because the bounding boxes differ), but the normalization destroys absolute scale. However, since the mounting interface is fixed in real space, there might be recoverable scale clues... but actually the normalization removes that. Hmm, this is getting complicated. Let me step back. The task says: learn mapping from the [2048,3] normalized point cloud to mass. So the information must be present in the normalized point cloud to a usefully predictive degree. Let me just check: what's the relationship between simple geometric features of the normalized cloud and the mass? Let me compute some features: - bounding box dimensions (since fit in unit cube, max dim ~ 1 but could be less) - For surface point clouds, we can estimate things. Actually, the most important realization: since the box fits INSIDE the unit cube, the bounding box extent itself varies. The aspect ratios carry info. Let me explore the relationship between the label and simple features. Let me compute per-sample: - extent in each axis (max - min) - the product of extents (bbox volume) - mean pairwise distance, etc. And correlate with mass. Let me do exploratory analysis.

No overlap between splits , all distinct brackets, so generalization matters. This is the SimJEB (jet engine bracket) dataset. A key consideration: each bracket is independently rescaled to fit the unit cube, so absolute scale is partially affected. Let me do exploratory analysis to see how simple geometric features of the normalized cloud relate to mass.

bash

cd /app && python -c "
import torch
d = torch.load('data/train/samples.pt', weights_only=True)
f = d['features'].float(); l = d['labels'].float().squeeze(1)
# per-sample bounding box extents
mn = f.min(dim=1).values; mx = f.max(dim=1).values
ext = mx - mn  # [N,3]
print('extent stats per axis:')
print(' min', ext.min(dim=0).values)
print(' max', ext.max(dim=0).values)
print(' mean', ext.mean(dim=0))
# coordinate global range
print('global coord min', f.amin(dim=(0,1)), 'max', f.amax(dim=(0,1)))
# centroid per sample
cen = f.mean(dim=1)
print('centroid mean', cen.mean(dim=0), 'std', cen.std(dim=0))
"

output

extent stats per axis:
 min tensor([0.5022, 0.9912, 0.2758])
 max tensor([0.6436, 1.0000, 0.4550])
 mean tensor([0.5887, 0.9991, 0.3499])
global coord min tensor([-0.3206, -0.5000, -0.2279]) max tensor([0.3230, 0.5000, 0.2271])
centroid mean tensor([-0.0135, 0.0075, -0.0710]) std tensor([0.0251, 0.0087, 0.0134])

Interesting. So axis 1 (y) always has extent ~1.0 (0.991 to 1.0), meaning the Y axis is the longest dimension and defines the scaling. The X extent ranges 0.50-0.64, Z extent 0.28-0.46. So the normalization scales by the Y dimension (the longest). This means the aspect ratios of X and Z relative to Y vary, and these are the only scale-related signals we have. The absolute Y size is normalized out. Now, mass. Let me correlate features with mass. Let me compute a bunch of candidate features and their correlation with mass.

Interesting , the Y axis always has extent ≈1.0, so normalization is driven by the longest (Y) dimension. Let me correlate various geometric features with mass to understand the learnable signal.

bash

cd /app && python -c "
import torch
d = torch.load('data/train/samples.pt', weights_only=True)
f = d['features'].float(); l = d['labels'].float().squeeze(1)
N = f.shape[0]
def corr(a,b):
    a=a-a.mean(); b=b-b.mean()
    return (a*b).sum()/(a.norm()*b.norm()+1e-9)
mn = f.min(dim=1).values; mx = f.max(dim=1).values
ext = mx-mn
feats = {}
feats['ext_x']=ext[:,0]; feats['ext_y']=ext[:,1]; feats['ext_z']=ext[:,2]
feats['bbox_vol']=ext[:,0]*ext[:,1]*ext[:,2]
feats['bbox_xz']=ext[:,0]*ext[:,2]
# distance from centroid
cen = f.mean(dim=1, keepdim=True)
rad = (f-cen).norm(dim=2)  # [N,2048]
feats['mean_rad']=rad.mean(dim=1)
feats['std_rad']=rad.std(dim=1)
feats['max_rad']=rad.max(dim=1).values
# rms distances from origin
feats['rms']=(f.norm(dim=2)).mean(dim=1)
# spread: std of coords
feats['std_x']=f[:,:,0].std(dim=1); feats['std_y']=f[:,:,1].std(dim=1); feats['std_z']=f[:,:,2].std(dim=1)
# 'thickness' proxies
for k,v in feats.items():
    print(f'{k:10s} corr={corr(v,l):+.3f}')
"

output

ext_x      corr=+0.234
ext_y      corr=+0.143
ext_z      corr=+0.177
bbox_vol   corr=+0.253
bbox_xz    corr=+0.249
mean_rad   corr=+0.482
std_rad    corr=-0.411
max_rad    corr=-0.587
rms        corr=+0.394
std_x      corr=+0.635
std_y      corr=+0.149
std_z      corr=+0.250

So there are moderate correlations. std_x has correlation +0.635, max_rad -0.587, mean_rad +0.482. These are real signals but none are super strong alone. The mass is a nonlinear function of the shape. The key insight: mass is proportional to the solid volume of the bracket (times density). But we only have surface points. The surface point distribution encodes the shape, and a neural network (PointNet) should be able to learn to estimate the enclosed volume / mass. Since material density is constant (presumably), mass ∝ physical volume. But physical volume = (normalized volume) × scale³. The scale is determined by the actual Y-extent in physical units, which we don't have. BUT , if the mounting interface is fixed in physical space across all brackets, the physical scale is roughly constant, so mass ∝ normalized volume approximately. However the independent rescaling introduces per-sample scale variation we can't recover. Hmm wait. Let me reconsider. Actually, the fact that the Y extent is always ~1.0 means the scale factor = 1/(physical Y extent). If physical Y extents vary a lot, then we've lost that. But the resulting mass correlations with shape features are moderate, suggesting shape does carry a lot of info. Regardless, my job is to learn the best possible mapping from normalized cloud → mass. Let me build a strong regressor. Important constraints: 1. I MUST use the fixed architecture `PointNetRegressor` from candidate_model.py. The checkpoint must load back into this same architecture. So I cannot change the architecture , `build_model(config)` with feature_width=64, global_width=256, head_width=128. Wait, let me re-read: "candidate_model.py - the fixed PointNet-style architecture. Keep it as is; your saved checkpoint has to load back into this same architecture." So the grading will load my state_dict into `build_model(config)` (the PointNetRegressor). I must keep the architecture. I can only change the training procedure, normalization, data augmentation, loss, etc. The state_dict keys must match exactly. So I'm constrained to this architecture: - feature: SharedPointMLP(3, (64,64,64)) - lift: SharedPointMLP(64, (128, 256)) - head: Linear(256,128) -> GELU -> Linear(128,64) -> GELU -> Linear(64,1) - forward: transpose, feature, lift, max-pool over points, head. This is a standard PointNet with max pooling. It's permutation invariant, good. Now, how is the model applied at inference? Let me think about what the grader does. It loads my checkpoint, builds the model, loads state_dict, applies feat normalization (feat_mean, feat_std) to input, runs forward, de-standardizes with label_mean/label_std. I need to infer the exact inference procedure to match. The task says: - feat_mean, feat_std: length-3 tensors used to standardize input coords before forward pass. Applied at inference. - label_mean, label_std: length-1 used to standardize target during training and de-standardize output at inference. So inference: pred_physical = model((x - feat_mean)/feat_std) * label_std + label_mean. I need my training to match this exactly: train model on normalized input and normalized target, so that model output * label_std + label_mean = physical mass. Let me think about how the normalization is applied. feat_mean, feat_std are length-3. So per-coordinate standardization: (x - feat_mean)/feat_std broadcast over [B,2048,3]. Now, the critical question: what exactly does the grading harness do? I don't have the grading code, but the task description is precise enough. Let me write my own inference function matching the description and validate on val/public_test. Let me think about the strategy to maximize performance: 1. **Architecture is fixed** - PointNet with ~ moderate capacity. 2. **Small data** (100 train). Need heavy regularization and data augmentation to avoid overfitting and generalize to held-out brackets. 3. **Loss**: The metrics are relative L2 (primary), MAE, and max abs error. Relative L2 over the …[truncated]

Let me check whether brackets have mirror symmetry (common in brackets) and examine per-sample scale cues, which informs valid augmentations.

bash

cd /app && python -c "
import torch
d = torch.load('data/train/samples.pt', weights_only=True)
f = d['features'].float()
# Check mirror symmetry: for each axis, compare point set to its mirror via nearest-neighbor chamfer (subsample)
import torch
def chamfer_sym(pc, axis):
    # pc [2048,3]
    m = pc.clone(); m[:,axis] = -m[:,axis]
    # subsample 512 for speed
    idx = torch.randperm(pc.shape[0])[:512]
    a = pc[idx]; b = m[idx]
    d = torch.cdist(a,b)  # 512x512
    return d.min(dim=1).values.mean().item()
import numpy as np
for axis,name in [(0,'x'),(1,'y'),(2,'z')]:
    vals = [chamfer_sym(f[i], axis) for i in range(20)]
    # compare to scale: typical nearest neighbor distance
    print(f'axis {name}: mean mirror-chamfer {np.mean(vals):.4f}')
# baseline: self nearest neighbor (excluding self)
pc=f[0]; d=torch.cdist(pc[:512],pc[:512]); d.fill_diagonal_(9); print('typical NN dist', d.min(dim=1).values.mean().item())
"

output

axis x: mean mirror-chamfer 0.0494
axis y: mean mirror-chamfer 0.0284
axis z: mean mirror-chamfer 0.1111
typical NN dist 0.02719089388847351

Mirror about Y (axis 1) gives chamfer 0.0284, which is close to the typical nearest neighbor distance 0.027. So the brackets are approximately symmetric about the Y=0 plane (mirror in y). Hmm wait, mirror in y means flipping the y coordinate. chamfer 0.0284 ≈ NN dist 0.027, so yes strong symmetry about the plane y=0? Wait, but Y is the long axis spanning [-0.5,0.5]. Symmetry about y=0 plane means top-bottom symmetry. Actually wait, let me reconsider. Mirror about x (axis 0): 0.0494, about z: 0.111. Y mirror: 0.0284 which is near NN distance → strong symmetry. Hmm, but that's averaged. Let me reconsider - actually the mirror-chamfer for y being ~ NN distance suggests many brackets are symmetric under y -> -y. But let me double check this is consistent per-sample, and check x too (0.0494 is less than 2x NN, moderate). Let me look more carefully and also check combined. Actually, let me verify the symmetry claim more robustly because it determines whether flip augmentation is valid. Actually, for augmentation validity, a mirror flip produces a valid (possibly different) bracket shape with the SAME mass (mirroring preserves volume/mass). So even if the specific bracket isn't perfectly symmetric, a mirrored bracket is still a physically plausible bracket with identical mass. This is a label-preserving augmentation as long as mirrored brackets are "in distribution." For a symmetric dataset, mirroring about symmetry planes keeps it in-distribution. Mirror about x and y seem plausible. Let me use mild flip augmentation on x (and maybe y) , but need the normalization/pose to remain consistent. Actually, careful: if brackets have a canonical orientation (e.g., mounting interface at bottom), flipping y (top-bottom) might produce out-of-distribution poses. The near-symmetry (0.0284) about y suggests flipping y keeps things in-distribution. Flipping x (0.0494, still fairly symmetric) likely also fine. Flipping z is not symmetric (0.111). But to be safe, I'll treat flip augmentation as a hyperparameter and validate it. Let me now reconsider the bigger picture and just build the pipeline with configurable augmentation, then tune on val. Let me set up the core training code. Design decisions: - Load all data to GPU as float32 (tiny). - feat normalization: per-axis mean/std from train. I'll also try isotropic. - label normalization: mean/std from train labels. - Model: build_model(cfg). - Augmentations (applied per-batch on the fly, on physical coords BEFORE feat-normalization, or after? The feat normalization is fixed linear; augmentation should model real shape variation. I'll apply augmentation in the original coordinate space, then feat-normalize.): - jitter: add small gaussian noise sigma ~ 0.005-0.02, clipped. - point dropout + resample to keep 2048. - random flip x (prob 0.5), maybe y. - small anisotropic scaling? Risky due to bbox normalization assumption. Skip or tiny. - small rotation about z or small random rotation (a few degrees). Test. - random translation (tiny). Since feat normalization centers, tiny jitter in position. Could help robustness. Actually, I realize a subtle issue: the feat_mean/feat_std used at inference are fixed. If I augment training data with flips/rotations, the feat normalization (computed once from train) is still applied. That's fine , it's just a fixed linear transform. The network sees augmented-then-normalized inputs during training, and clean-then-normalized at inference. Good. - EMA of weights (decay ~0.999) including buffers. At eval, use EMA weights. - Optimizer: AdamW, weight_decay ~1e-4 to 1e-2 (tune). LR ~1e-3 with cosine decay and warmup. Epochs: a few hundred given tiny data and tiny model, it's fast. - Loss: MSE on normalized labels (equivalent to MSE on physical up to scale). Compare Huber/L1. - Model selection: evaluate rel L2 on val with the inference procedure (using EMA weights), pick best epoch. Let me write the harness and run a baseline to see where we stand, …[truncated]

Brackets are near-symmetric under y→−y (mirror-chamfer ≈ nearest-neighbor spacing) and fairly symmetric in x. Importantly, mirroring preserves mass, so flips are label-preserving augmentations. Let me build a proper training+evaluation harness. First I'll write shared utilities.

bash

cd /app && mkdir -p outputs exp && cat > exp/common.py << 'EOF'
from __future__ import annotations
import json, os, sys, math
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F

APP = Path('/app')
sys.path.insert(0, str(APP))
from candidate_model import build_model

def load_cfg():
    with (APP/'task_config.json').open() as fh:
        return json.load(fh)

def load_split(name, device='cuda'):
    d = torch.load(APP/'data'/name/'samples.pt', weights_only=True)
    return d['features'].float().to(device), d['labels'].float().to(device)

def compute_norm(train_x, train_y, iso=False):
    feat_mean = train_x.mean(dim=(0,1))
    feat_std = train_x.std(dim=(0,1)).clamp_min(1e-6)
    if iso:
        s = feat_std.mean()
        feat_std = torch.stack([s,s,s])
    label_mean = train_y.mean(dim=0)
    label_std = train_y.std(dim=0).clamp_min(1e-6)
    return feat_mean, feat_std, label_mean, label_std

@torch.no_grad()
def predict(model, x, feat_mean, feat_std, label_mean, label_std, bs=64):
    model.eval()
    outs=[]
    for i in range(0, x.shape[0], bs):
        xb = (x[i:i+bs]-feat_mean)/feat_std
        o = model(xb)
        outs.append(o*label_std+label_mean)
    return torch.cat(outs,0)

def metrics(pred, true):
    pred=pred.reshape(-1); true=true.reshape(-1)
    e = pred-true
    rel_l2 = (e.norm()/true.norm()).item()
    mae = e.abs().mean().item()
    maxabs = e.abs().max().item()
    return rel_l2, mae, maxabs
EOF
echo "written common.py"

output

written common.py

Now let me write a baseline training run to see where the starter code gets us, and establish the evaluation. Let me first run the provided train.py logic quickly (1 epoch is default, too few). Let me instead write my own configurable trainer. Let me write the main trainer with augmentation and EMA.

Now let me write a configurable trainer with augmentation, EMA, and validation-based model selection.

bash

cd /app && cat > exp/trainer.py << 'EOF'
from __future__ import annotations
import copy, math, os, sys, time
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.insert(0, '/app/exp')
from common import build_model, load_cfg, load_split, compute_norm, predict, metrics

def augment(x, cfg):
    # x: [B,2048,3] physical coords. Returns augmented copy.
    B,N,_ = x.shape
    dev = x.device
    out = x
    if cfg.get('flip_x',0)>0:
        m = (torch.rand(B,1,device=dev)<cfg['flip_x']).float()*(-2)+1  # +1 or -1
        out = out.clone(); out[:,:,0]=out[:,:,0]*m
    if cfg.get('flip_y',0)>0:
        m = (torch.rand(B,1,device=dev)<cfg['flip_y']).float()*(-2)+1
        out = out.clone(); out[:,:,1]=out[:,:,1]*m
    if cfg.get('rot_z',0)>0:
        ang = (torch.rand(B,device=dev)*2-1)*cfg['rot_z']
        c=torch.cos(ang); s=torch.sin(ang)
        xx=out[:,:,0].clone(); zz=out[:,:,2].clone()
        out = out.clone()
        out[:,:,0]=c[:,None]*xx - s[:,None]*zz
        out[:,:,2]=s[:,None]*xx + c[:,None]*zz
    if cfg.get('scale',0)>0:
        sc = 1.0+(torch.rand(B,1,1,device=dev)*2-1)*cfg['scale']
        out = out*sc
    if cfg.get('aniso',0)>0:
        sc = 1.0+(torch.rand(B,1,3,device=dev)*2-1)*cfg['aniso']
        out = out*sc
    if cfg.get('jitter',0)>0:
        out = out + torch.randn_like(out)*cfg['jitter']
    if cfg.get('trans',0)>0:
        out = out + (torch.rand(B,1,3,device=dev)*2-1)*cfg['trans']
    if cfg.get('dropout',0)>0:
        # randomly resample points (keep N) -> duplicate some, drop some
        keep = cfg['dropout']
        mask = torch.rand(B,N,device=dev)>keep  # True=keep
        # for dropped, replace index with a random kept index per sample
        idx = torch.arange(N,device=dev).expand(B,N).clone()
        rnd = torch.randint(0,N,(B,N),device=dev)
        idx = torch.where(mask, idx, rnd)
        out = torch.gather(out,1, idx[:,:,None].expand(B,N,3))
    return out

class EMA:
    def __init__(self, model, decay):
        self.decay=decay
        self.shadow={k:v.detach().clone().float() for k,v in model.state_dict().items()}
    def update(self, model):
        d=self.decay
        for k,v in model.state_dict().items():
            s=self.shadow[k]
            if v.dtype.is_floating_point:
                s.mul_(d).add_(v.detach().float(),alpha=1-d)
            else:
                s.copy_(v)
    def copy_to(self, model):
        sd=model.state_dict()
        for k in sd: sd[k].copy_(self.shadow[k].to(sd[k].dtype))

def train_one(hp, tx, ty, vx, vy, feat_mean, feat_std, lmean, lstd, cfg, seed=0, verbose=False, return_hist=False):
    torch.manual_seed(seed); torch.cuda.manual_seed_all(seed)
    model = build_model(cfg).cuda()
    opt = torch.optim.AdamW(model.parameters(), lr=hp['lr'], weight_decay=hp['wd'])
    epochs=hp['epochs']; bs=hp['bs']
    warmup=hp.get('warmup',0.05)
    N=tx.shape[0]
    steps_per=math.ceil(N/bs); total=steps_per*epochs
    def lr_at(step):
        w=int(total*warmup)
        if step<w: return step/max(w,1)
        p=(step-w)/max(total-w,1)
        return 0.5*(1+math.cos(math.pi*p))
    ema=EMA(model, hp.get('ema',0.999))
    fm,fs=feat_mean,feat_std
    step=0
    best={'rel':1e9}
    hist=[]
    loss_type=hp.get('loss','mse')
    for ep in range(epochs):
        model.train()
        perm=torch.randperm(N,device=tx.device)
        for i in range(0,N,bs):
            idx=perm[i:i+bs]
            xb=tx[idx]; yb=ty[idx]
            xb=augment(xb,hp)
            xb=(xb-fm)/fs
            for g in opt.param_groups: g['lr']=hp['lr']*lr_at(step)
            opt.zero_grad(set_to_none=True)
            pred=model(xb)
            if loss_type=='mse': loss=F.mse_loss(pred,yb)
            elif loss_type=='l1': loss=F.l1_loss(pred,yb)
            elif loss_type=='huber': loss=F.huber_loss(pred,yb,delta=hp.get('delta',1.0))
            loss.backward(); opt.step(); ema.update(model); step+=1
        if (ep+1)%hp.get('eval_every',1
…[truncated]

output

written trainer.py

Let me run a baseline experiment to see where we stand, comparing a few configurations. First, a quick sanity baseline with minimal augmentation.

Let me run a baseline to establish where we stand, then compare augmentation and loss settings.

bash

cd /app/exp && python -c "
import torch, time
from common import *
from trainer import train_one
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val')
fm,fs,lm,ls=compute_norm(tx,ty)
print('label_mean',lm.item(),'label_std',ls.item())
base=dict(lr=1e-3,wd=1e-4,epochs=120,bs=16,ema=0.999,loss='mse',eval_every=20)
t=time.time()
b=train_one(base,tx,ty,vx,vy,fm,fs,lm,ls,cfg,seed=0,verbose=True)
print('BASELINE best rel=%.4f mae=%.4f max=%.4f ep=%d  (%.1fs)'%(b['rel'],b['mae'],b['max'],b['ep'],time.time()-t))
" 2>&1 | grep -v Warning

output

label_mean 0.7475780844688416 label_std 0.3894648551940918
  ep20 rel=0.4707 mae=0.3015 max=1.3283
  ep40 rel=0.4707 mae=0.3015 max=1.3280
  ep60 rel=0.4707 mae=0.3016 max=1.3277
  ep80 rel=0.4707 mae=0.3017 max=1.3273
  ep100 rel=0.4706 mae=0.3018 max=1.3268
  ep120 rel=0.4706 mae=0.3020 max=1.3262
BASELINE best rel=0.4706 mae=0.3020 max=1.3262 ep=120  (6.5s)

The rel is stuck at 0.47 and not improving , that's basically predicting the mean (rel L2 of predicting mean ~ std/rms). Let me check: predicting constant mean gives rel L2 = std(true)/rms(true). For val, rms = sqrt(mean(y^2)). Let me compute the trivial baseline. Also the EMA decay 0.999 with only ~7 steps/epoch * 120 = 840 steps means EMA barely moves from init? No, 840 steps with decay 0.999 → effective window ~1000, so EMA is heavily weighted toward... it averages over ~1000 steps, but we only have 840, so EMA is still close to the average of the whole trajectory including early bad weights. That's why it's stuck near mean prediction. Also LR might be off, or the model isn't learning. Wait, rel=0.4706 constant across all epochs , suspicious. EMA decay too high for short training. Let me check the non-EMA model performance and the trivial baseline. Let me also check if training loss is decreasing. Let me debug: evaluate raw model (not EMA) and trivial mean baseline.

The rel-L2 is stuck near 0.47 , that looks like mean-prediction, likely because EMA decay 0.999 is too high for such short training (only ~7 steps/epoch). Let me check the trivial baseline and the non-EMA model to diagnose.

bash

cd /app/exp && python -c "
import torch, math
from common import *
tx,ty=load_split('train'); vx,vy=load_split('val')
# trivial: predict train mean
lm=ty.mean()
pred=torch.full_like(vy, lm.item())
print('predict-train-mean on val:', metrics(pred,vy))
# predict val's own mean (oracle constant)
print('predict-val-mean on val:', metrics(torch.full_like(vy,vy.mean().item()),vy))
print('val rms', (vy**2).mean().sqrt().item(), 'val std', vy.std().item())
" 2>&1 | grep -v Warning

output

predict-train-mean on val: (0.4705697298049927, 0.30238068103790283, 1.3246874809265137)
predict-val-mean on val: (0.4702651798725128, 0.30624914169311523, 1.3100662231445312)
val rms 0.8636574149131775 val std 0.41264671087265015

Confirmed: rel=0.47 is exactly the mean-prediction baseline. So the model learned nothing useful , it's just predicting the mean. EMA with decay 0.999 over ~840 steps is averaging everything toward the mean-ish initial behavior. Let me lower EMA decay and check the raw (non-EMA) model. Let me debug quickly with EMA off (decay 0) and more reasonable settings.

Confirmed , rel 0.47 is exactly the mean-prediction baseline, so the EMA (decay 0.999) is washing out learning over such short training. Let me lower EMA decay and verify the raw model actually learns.

bash

cd /app/exp && python -c "
import torch, time, math
import torch.nn.functional as F
from common import *
from trainer import build_model, augment
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val')
fm,fs,lm,ls=compute_norm(tx,ty)
tyn=(ty-lm)/ls
torch.manual_seed(0)
model=build_model(cfg).cuda()
opt=torch.optim.AdamW(model.parameters(),lr=1e-3,weight_decay=1e-4)
N=tx.shape[0]; bs=16
for ep in range(200):
    model.train(); perm=torch.randperm(N,device=tx.device)
    tot=0;nb=0
    for i in range(0,N,bs):
        idx=perm[i:i+bs]; xb=(tx[idx]-fm)/fs; yb=tyn[idx]
        opt.zero_grad(); pred=model(xb); loss=F.mse_loss(pred,yb); loss.backward(); opt.step()
        tot+=loss.item(); nb+=1
    if (ep+1)%40==0:
        pv=predict(model,vx,fm,fs,lm,ls)
        print(f'ep{ep+1} train_mse={tot/nb:.4f} val', metrics(pv,vy))
" 2>&1 | grep -v Warning

output

ep40 train_mse=0.4600 val (0.31873413920402527, 0.22456076741218567, 0.6857938766479492)
ep80 train_mse=0.2459 val (0.24880853295326233, 0.1622009426355362, 0.5174599885940552)
ep120 train_mse=0.1439 val (0.308228075504303, 0.20748582482337952, 0.5908358097076416)
ep160 train_mse=0.1100 val (0.24469433724880219, 0.17080575227737427, 0.5630953311920166)
ep200 train_mse=0.1114 val (0.264067143201828, 0.17441967129707336, 0.556736946105957)

Now the raw model learns: val rel L2 ~0.25 at ep80. Train MSE decreasing. So the EMA was the culprit with too-high decay. The model does learn a useful signal (rel 0.25 vs baseline 0.47). There's overfitting: train_mse goes to 0.11 while val rel fluctuates 0.24-0.31. With only 100 samples this is expected. I need regularization + augmentation + proper EMA. Let me now properly tune. Key ingredients: 1. EMA with appropriate decay (e.g., 0.99 or computed for the number of steps). With ~7 steps/epoch, for 200 epochs = 1400 steps. EMA decay 0.99 → window ~100 steps ~14 epochs. Reasonable. Or use SWA in the last portion. 2. Augmentation to combat overfitting. 3. Model selection on val. But val is only 32 samples , noisy. I should use robust selection. Let me think about cross-validation: combine train+val = 132 samples, do k-fold CV to select hyperparameters robustly, then train final model on all 132 (or train+val) for submission. The public_test (16) gives an additional check. Given the tiny size and fast training (6s for 120 epochs), I can afford extensive experimentation including k-fold CV and multi-seed averaging. Let me restructure: I'll build a CV harness over the pooled train+val data to evaluate hyperparameter configs robustly (mean rel L2 across folds). Then pick best config, and for the final model decide training data (all pooled) and epochs. But wait , for the final saved model, I want it to generalize to the hidden held-out set. Using all 132 samples for final training is best for data usage. Model selection (epochs) can't use a holdout then. I'll rely on CV to choose a fixed epoch budget and use EMA (which is robust and doesn't need early stopping). Let me reconsider: EMA gives a smoothed weight that's robust; combined with cosine LR decay to 0, the final EMA weights at end of training are a good choice without needing early stopping. So I can train on all data for fixed epochs and take final EMA. CV validates this recipe. Let me first find good augmentation + hyperparameters via CV. Let me write a CV evaluation. Let me be efficient with token/time budget. I'll create a CV function that, given hp, runs k-fold over pooled data and returns mean metrics. I'll reuse train_one but need to fix EMA decay. Actually, let me reconsider the EMA implementation. In train_one, EMA updates every step. With decay 0.999 and ~800-1400 steps it's too slow to track. Let me set decay based on steps or just use 0.99. Also I evaluate EMA for selection. For final I'll use EMA final weights. Let me also reconsider: the 'best' selection inside train_one picks best val epoch , that's model selection on the fold's val, which is fair within CV (the fold val acts as selection). But for final training on all data, there's no val. So I should evaluate the recipe in two modes: (a) CV with early-stopping-by-fold-val (optimistic) (b) CV taking FINAL EMA weights (realistic for final model). I'll report final-EMA metrics in CV to match how I'll produce the final model. Let me add that. Let me rewrite a cleaner CV harness focused on final-EMA (no peeking), plus allow reporting best-epoch for insight. Let me make a kfold function.

The raw model learns well (val rel ≈ 0.25 vs 0.47 baseline); EMA decay was too high. There's overfitting with only 100 samples, so augmentation + proper EMA + robust selection matter. Given training is fast (~6s), I'll use k-fold CV over pooled train+val (132 samples) to choose hyperparameters robustly. Let me build a CV harness that reports the realistic **final-EMA** performance (no per-fold early stopping).

bash

cd /app/exp && cat > kfold.py << 'EOF'
from __future__ import annotations
import math, sys, time
import torch, torch.nn.functional as F
sys.path.insert(0,'/app/exp')
from common import build_model, load_cfg, load_split, compute_norm, predict, metrics
from trainer import augment, EMA

def run_fold(hp, tx, ty, vx, vy, cfg, seed, iso=False):
    fm,fs,lm,ls=compute_norm(tx,ty,iso=iso)
    tyn=(ty-lm)/ls
    torch.manual_seed(seed); torch.cuda.manual_seed_all(seed)
    model=build_model(cfg).cuda()
    opt=torch.optim.AdamW(model.parameters(),lr=hp['lr'],weight_decay=hp['wd'])
    epochs=hp['epochs']; bs=hp['bs']; N=tx.shape[0]
    steps_per=math.ceil(N/bs); total=steps_per*epochs; warmup=int(total*hp.get('warmup',0.05))
    def lr_sc(s):
        if s<warmup: return s/max(warmup,1)
        p=(s-warmup)/max(total-warmup,1); return hp.get('min_lr_frac',0.0)+(1-hp.get('min_lr_frac',0.0))*0.5*(1+math.cos(math.pi*p))
    ema=EMA(model, hp.get('ema',0.99)); step=0
    lt=hp.get('loss','mse')
    for ep in range(epochs):
        model.train(); perm=torch.randperm(N,device=tx.device)
        for i in range(0,N,bs):
            idx=perm[i:i+bs]; xb=augment(tx[idx],hp); xb=(xb-fm)/fs; yb=tyn[idx]
            for g in opt.param_groups: g['lr']=hp['lr']*lr_sc(step)
            opt.zero_grad(set_to_none=True); pred=model(xb)
            if lt=='mse': loss=F.mse_loss(pred,yb)
            elif lt=='l1': loss=F.l1_loss(pred,yb)
            elif lt=='huber': loss=F.huber_loss(pred,yb,delta=hp.get('delta',1.0))
            loss.backward(); opt.step(); ema.update(model); step+=1
    em=build_model(cfg).cuda(); em.load_state_dict(model.state_dict()); ema.copy_to(em)
    pv=predict(em,vx,fm,fs,lm,ls)
    return pv.detach(), vy.detach()

def kfold(hp, X, Y, cfg, k=6, seeds=(0,), iso=False, shuffle_seed=123):
    N=X.shape[0]
    g=torch.Generator().manual_seed(shuffle_seed)
    perm=torch.randperm(N,generator=g)
    folds=[perm[i::k] for i in range(k)]
    preds=torch.zeros(N,1,device=X.device); trues=Y.clone()
    cnt=0
    for s in seeds:
        allp=torch.zeros(N,1,device=X.device)
        for fi in range(k):
            vidx=folds[fi].to(X.device)
            tmask=torch.ones(N,dtype=torch.bool); tmask[folds[fi]]=False
            tidx=tmask.nonzero().squeeze(1).to(X.device)
            pv,_=run_fold(hp, X[tidx],Y[tidx],X[vidx],Y[vidx],cfg,seed=s,iso=iso)
            allp[vidx]=pv
        preds+=allp; cnt+=1
    preds/=cnt
    return metrics(preds,trues)
EOF
echo "written kfold.py"

output

written kfold.py

Now let me run CV on a few configs. First pool train+val. Let me test: - no augmentation vs light augmentation - a couple EMA/epoch settings Let me run a first CV comparison. I'll keep k=6, single seed for speed initially. Note: the CV metric aggregates predictions across all folds (out-of-fold predictions) then computes rel L2 over the full pooled set. This is a good robust estimator. Let me run.

Now let me pool train+val and run k-fold CV to compare no-augmentation vs augmentation and a couple of EMA/epoch settings.

bash

cd /app/exp && python -c "
import torch, time
from common import *
from kfold import kfold
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val')
X=torch.cat([tx,vx],0); Y=torch.cat([ty,vy],0)
print('pooled',X.shape)
configs={
 'noaug': dict(lr=1e-3,wd=1e-4,epochs=150,bs=16,ema=0.99,loss='mse'),
 'noaug_ema995': dict(lr=1e-3,wd=1e-4,epochs=150,bs=16,ema=0.995,loss='mse'),
 'lightaug': dict(lr=1e-3,wd=1e-4,epochs=150,bs=16,ema=0.99,loss='mse',jitter=0.01,flip_x=0.5,dropout=0.1),
 'medaug': dict(lr=1e-3,wd=1e-4,epochs=150,bs=16,ema=0.99,loss='mse',jitter=0.015,flip_x=0.5,flip_y=0.5,dropout=0.2,scale=0.05),
}
for name,hp in configs.items():
    t=time.time(); r=kfold(hp,X,Y,cfg,k=6,seeds=(0,)); 
    print(f'{name:16s} rel=%.4f mae=%.4f max=%.4f  (%.1fs)'%(r[0],r[1],r[2],time.time()-t))
" 2>&1 | grep -v Warning

output

pooled torch.Size([132, 2048, 3])
noaug            rel=0.2573 mae=0.1630 max=0.6177  (27.7s)
noaug_ema995     rel=0.2657 mae=0.1715 max=0.6996  (25.8s)
lightaug         rel=0.3342 mae=0.2132 max=0.8617  (24.8s)
medaug           rel=0.3894 mae=0.2395 max=1.1851  (27.0s)

Interesting , augmentation HURTS here. No-aug gives rel 0.257, light aug 0.334, med aug 0.389. So the augmentations I chose (jitter, flip, dropout, scale) are harmful. This makes sense: - flip_x: maybe brackets aren't symmetric enough in x, or flipping changes the mapping because pose matters. - jitter: adds noise that obscures fine scale cues (the interface size that encodes scale!). Since scale recovery depends on precise relative geometry, jitter destroys it. - dropout/scale: scale augmentation directly corrupts the scale signal which is the key to mass. Right , the scale signal is critical (mass depends on recovering absolute scale from relative interface size). Scaling augmentation literally destroys the thing we need. And jitter hurts precise size estimation. So minimal augmentation is better. So the data is such that precise geometry matters. Let me drop augmentation or use only very mild label-preserving ones (maybe just flip_x if truly symmetric, but it hurt). Let me test flip_x alone and jitter alone to see which hurts. Actually the result is clear enough: no-aug is best so far. Let me focus on regularization via weight decay, EMA, epochs, and architecture-compatible tricks, plus ensembling via multi-seed averaging (averaging predictions across seeds , but final model is ONE state_dict, so I can't average predictions across seed models at inference). Hmm, important: at inference, only ONE model runs. So multi-seed prediction averaging isn't available at submission. But in CV I used seeds=(0,) (single). Good, that matches single-model reality. But wait, can I make a single model that behaves like an ensemble? No. Unless I use EMA/SWA which is a single model. So I should optimize a single model's generalization. The CV with seeds=(0,) reflects single-model performance but is noisy. Let me evaluate multiple seeds separately (not averaged) to estimate variance, and pick config by mean over seeds of the single-model CV. Let me refine kfold to report mean over seeds where each seed is a full independent CV (single model). Let me modify to compute per-seed metric and average. Actually the current kfold averages predictions across seeds which is ensembling. Let me add a mode to report mean of per-seed CV metrics. Let me first just explore no-aug more: tune wd, epochs, ema, and try isotropic normalization. And test very mild jitter only. Let me be economical: run a focused sweep around no-aug.

Augmentation clearly hurts , jitter/scale destroy the fine scale cues needed to infer absolute size (and thus mass), and flips aren't safe enough. No-aug gives rel 0.257. Let me refine the CV to report single-model performance averaged over seeds (matching the single-checkpoint reality), and sweep weight decay, epochs, EMA, and normalization.

bash

cd /app/exp && cat >> kfold.py << 'EOF'

def kfold_single(hp, X, Y, cfg, k=6, seeds=(0,1,2), iso=False, shuffle_seed=123):
    # report MEAN over seeds of single-model out-of-fold CV (no cross-seed ensembling)
    N=X.shape[0]
    g=torch.Generator().manual_seed(shuffle_seed)
    perm=torch.randperm(N,generator=g)
    folds=[perm[i::k] for i in range(k)]
    rels=[];maes=[];mxs=[]
    for s in seeds:
        allp=torch.zeros(N,1,device=X.device)
        for fi in range(k):
            vidx=folds[fi].to(X.device)
            tmask=torch.ones(N,dtype=torch.bool); tmask[folds[fi]]=False
            tidx=tmask.nonzero().squeeze(1).to(X.device)
            pv,_=run_fold(hp, X[tidx],Y[tidx],X[vidx],Y[vidx],cfg,seed=s,iso=iso)
            allp[vidx]=pv
        r=metrics(allp,Y); rels.append(r[0]);maes.append(r[1]);mxs.append(r[2])
    import statistics as st
    return (st.mean(rels),st.mean(maes),st.mean(mxs), st.pstdev(rels))
EOF
cd /app/exp && python -c "
import torch, time
from common import *
from kfold import kfold_single
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val')
X=torch.cat([tx,vx],0); Y=torch.cat([ty,vy],0)
base=dict(lr=1e-3,wd=1e-4,epochs=150,bs=16,ema=0.99,loss='mse')
variants={
 'base': base,
 'wd1e-3': {**base,'wd':1e-3},
 'wd1e-2': {**base,'wd':1e-2},
 'ep250': {**base,'epochs':250},
 'ema0.98': {**base,'ema':0.98},
 'iso_norm': base, # with iso flag
 'jitter005': {**base,'jitter':0.005},
}
for name,hp in variants.items():
    iso = name=='iso_norm'
    t=time.time(); r=kfold_single(hp,X,Y,cfg,k=6,seeds=(0,1,2),iso=iso)
    print(f'{name:12s} rel=%.4f(±%.4f) mae=%.4f max=%.4f  (%.1fs)'%(r[0],r[3],r[1],r[2],time.time()-t))
" 2>&1 | grep -v Warning

output

[stderr]
Shell command timed out.
Tool interaction exceeded 300 seconds.

bash

restart: true

output

<system>tool has been restarted.</system>

Each CV (3 seeds × 6 folds = 18 trainings) takes ~27s × 3 = 81s per variant, times 7 variants = ~570s > 300s. I need to run things in background or reduce scope. Let me run experiments in the background, writing results to a file, and poll. This is the right approach for long runs. Let me write a script that runs the sweep and logs to a file, run it in background with nohup.

The sweep exceeded the time limit. I'll run experiments in the background and poll results, which is the right pattern for longer runs. Let me create a sweep script that logs to a file.

bash

cd /app/exp && cat > sweep1.py << 'EOF'
import torch, time, sys
from common import *
from kfold import kfold_single
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val')
X=torch.cat([tx,vx],0); Y=torch.cat([ty,vy],0)
base=dict(lr=1e-3,wd=1e-4,epochs=150,bs=16,ema=0.99,loss='mse')
variants={
 'base': base,
 'wd1e-3': {**base,'wd':1e-3},
 'wd3e-3': {**base,'wd':3e-3},
 'wd1e-2': {**base,'wd':1e-2},
 'ep250': {**base,'epochs':250},
 'ep250_wd1e-3': {**base,'epochs':250,'wd':1e-3},
 'ema0.98': {**base,'ema':0.98},
 'ema0.995': {**base,'ema':0.995},
 'iso_norm': ('iso',base),
 'jitter005': {**base,'jitter':0.005},
 'bs8': {**base,'bs':8},
 'bs32': {**base,'bs':32},
}
for name,hp in variants.items():
    iso=False
    if isinstance(hp,tuple): iso=True; hp=hp[1]
    t=time.time(); r=kfold_single(hp,X,Y,cfg,k=6,seeds=(0,1,2),iso=iso)
    print(f'{name:14s} rel=%.4f(±%.4f) mae=%.4f max=%.4f  (%.1fs)'%(r[0],r[3],r[1],r[2],time.time()-t),flush=True)
print('DONE',flush=True)
EOF
rm -f sweep1.log && nohup python sweep1.py > sweep1.log 2>&1 &
echo "launched pid $!"

output

launched pid 289

Let me wait and poll. Each variant ~80s, 12 variants = ~960s = 16 min. Let me poll periodically. Meanwhile, let me think about other aspects. While that runs, let me think about the scale-recovery hypothesis more and whether a hand-crafted feature could help. But I'm constrained to the fixed architecture , I can't add features to the input (input is [B,2048,3]). So no feature engineering on input. The model must learn from raw coords. Wait , actually, could I exploit feat_mean/feat_std creatively? They're length-3, applied as (x-mean)/std. That's just per-axis affine. Can't inject features. Let me reconsider the label normalization. The labels span 0.2-2.2, right-skewed maybe. Let me consider log-transform of mass: predict log(mass). But the inference de-standardization is fixed: pred_phys = out*label_std + label_mean. That's linear, not log. So if I train on log-labels, the de-standardization would give log-mass, not mass. The grader applies out*label_std+label_mean and compares to physical mass. So I CANNOT use a log transform unless I bake it differently. Hmm, unless... the model output goes through out*label_std+label_mean only. So whatever the model outputs, times label_std plus label_mean, must equal predicted mass. If I want predicted mass = exp(g(x)), I'd need the model to output (exp(g(x))-label_mean)/label_std, which the network could learn to approximate but it's just the same as predicting standardized mass. So effectively label transform must be linear. I'll stick with standardized linear mass. Fine. Could I set label_mean/label_std to something other than mean/std to shape the loss? The de-standardization is linear; MSE on standardized = scaled MSE on physical. The choice of label_mean/std only affects the scale of the regression target and the initialization bias. Using true mean/std is natural. I'll keep it. Now, the distribution is right-skewed (mean 0.75, max 2.2). rel L2 weights large-mass samples more. MAE weights uniformly. Max-abs cares about the single worst. The large-mass samples (heavy brackets) are rare and likely the hardest (they're outliers in shape). Let me look at label distribution to understand worst-case risks. Let me check the label histogram while sweep runs.

I'll let that sweep run in the background. Meanwhile, let me examine the label distribution, since rel-L2 weights heavy brackets more and the worst-case metric is sensitive to rare outliers.

bash

cd /app/exp && python -c "
import torch
from common import load_split
tx,ty=load_split('train','cpu'); vx,vy=load_split('val','cpu'); px,py=load_split('public_test','cpu')
import numpy as np
for nm,y in [('train',ty),('val',vy),('test',py)]:
    y=y.reshape(-1).numpy()
    print(nm, 'n=%d'%len(y), 'quantiles', np.round(np.quantile(y,[0,.25,.5,.75,.9,1.0]),3))
# how many heavy (>1.3) in train
print('train>1.3:', (ty.reshape(-1)>1.3).sum().item(), ' >1.0:', (ty.reshape(-1)>1.0).sum().item())
" 2>&1 | grep -v Warning

output

train n=100 quantiles [0.208 0.468 0.646 0.945 1.342 2.219]
val n=32 quantiles [0.294 0.449 0.707 0.891 1.355 2.072]
test n=16 quantiles [0.198 0.542 0.683 0.941 1.103 1.449]
train>1.3: 11  >1.0: 23

The distribution is right-skewed: median ~0.65, but tail up to 2.2. About 11% of train are heavy (>1.3). These heavy brackets are the hard cases and drive both rel L2 (large magnitude) and max-abs error. For the worst-case metric, the model needs to not badly miss the heaviest brackets. Since there are few heavy examples, the model may underpredict them (regression to mean). This is the classic issue. MSE loss penalizes large errors quadratically, which helps focus on heavy samples. Good. Let me check the sweep progress.

bash

cd /app/exp && sleep 60 && cat sweep1.log 2>&1 | grep -v Warning

output

<system>Tool ran without output or errors</system>

No output yet (first variant ~80s). Let me wait more.

bash

cd /app/exp && sleep 120 && cat sweep1.log 2>&1 | grep -v Warning

output

base           rel=0.2585(±0.0039) mae=0.1642 max=0.6710  (78.8s)
wd1e-3         rel=0.2596(±0.0070) mae=0.1626 max=0.7149  (72.8s)

Progress is ~75s per variant. 12 variants total = ~900s. Let me keep waiting. base rel 0.2585, wd1e-3 similar. Let me wait for more results. Meanwhile let me think about architecture-level improvements I can legitimately make. Actually, I realize the fixed architecture has a potential weakness: it uses max-pooling only for the global feature. PointNet with only max-pool can be limited. But I can't change it. One thing I CAN control: the input normalization. Per-axis standardization stretches the shape anisotropically. For recovering scale/volume, maybe isotropic is better (preserves aspect ratios and relative sizes). The iso_norm variant will tell us. Another thought: the model might benefit from more epochs with lower LR. Let me see ep250 result. Let me also think about whether I should train the FINAL model on pooled train+val (132) or just train (100). Using more data (132) should help generalization to the hidden set. The CV estimates generalization for a model trained on 110 (5/6 of 132). The final model on 132 should be slightly better. Let me wait for sweep.

Progress is ~75s/variant. Let me wait for more results while it continues.

bash

cd /app/exp && sleep 220 && cat sweep1.log 2>&1 | grep -v Warning

output

base           rel=0.2585(±0.0039) mae=0.1642 max=0.6710  (78.8s)
wd1e-3         rel=0.2596(±0.0070) mae=0.1626 max=0.7149  (72.8s)
wd3e-3         rel=0.2604(±0.0047) mae=0.1655 max=0.7218  (75.1s)
wd1e-2         rel=0.2591(±0.0046) mae=0.1644 max=0.6421  (73.5s)
ep250          rel=0.2548(±0.0004) mae=0.1593 max=0.6911  (122.3s)

ep250 is slightly better (rel 0.2548) and very stable (±0.0004). Weight decay doesn't matter much in 1e-4 to 1e-2 range. Let me wait for the rest (ema, iso, jitter, bs).

bash

cd /app/exp && sleep 260 && cat sweep1.log 2>&1 | grep -v Warning

output

base           rel=0.2585(±0.0039) mae=0.1642 max=0.6710  (78.8s)
wd1e-3         rel=0.2596(±0.0070) mae=0.1626 max=0.7149  (72.8s)
wd3e-3         rel=0.2604(±0.0047) mae=0.1655 max=0.7218  (75.1s)
wd1e-2         rel=0.2591(±0.0046) mae=0.1644 max=0.6421  (73.5s)
ep250          rel=0.2548(±0.0004) mae=0.1593 max=0.6911  (122.3s)
ep250_wd1e-3   rel=0.2558(±0.0015) mae=0.1595 max=0.6994  (122.3s)
ema0.98        rel=0.2581(±0.0037) mae=0.1637 max=0.6698  (71.8s)

bash

cd /app/exp && sleep 260 && cat sweep1.log 2>&1 | grep -v Warning

output

base           rel=0.2585(±0.0039) mae=0.1642 max=0.6710  (78.8s)
wd1e-3         rel=0.2596(±0.0070) mae=0.1626 max=0.7149  (72.8s)
wd3e-3         rel=0.2604(±0.0047) mae=0.1655 max=0.7218  (75.1s)
wd1e-2         rel=0.2591(±0.0046) mae=0.1644 max=0.6421  (73.5s)
ep250          rel=0.2548(±0.0004) mae=0.1593 max=0.6911  (122.3s)
ep250_wd1e-3   rel=0.2558(±0.0015) mae=0.1595 max=0.6994  (122.3s)
ema0.98        rel=0.2581(±0.0037) mae=0.1637 max=0.6698  (71.8s)
ema0.995       rel=0.2668(±0.0035) mae=0.1725 max=0.7657  (75.0s)
iso_norm       rel=0.2556(±0.0043) mae=0.1592 max=0.7212  (71.9s)
jitter005      rel=0.2572(±0.0048) mae=0.1682 max=0.6246  (80.9s)

Let me wait for the last two (bs8, bs32) and DONE. Summary so far: - ep250 is best: rel 0.2548, very stable. - iso_norm: rel 0.2556, mae 0.1592 (good mae). Similar. - jitter005: rel 0.2572, max 0.6246 (best max-abs!). Interesting , tiny jitter helps worst-case. - ema0.995 worse (too slow again). ema 0.98-0.99 fine. Observations: The model is plateaued around rel 0.255. To break through, I may need more fundamental changes. But architecture is fixed. Ideas to push further: 1. Longer training (ep250 helped). Try ep400. 2. Isotropic norm gives better MAE. 3. Tiny jitter helps max-abs. Maybe combine iso + tiny jitter + ep250. 4. The real lever might be the loss function or handling the heavy-tail. Let me think. Actually, the biggest potential improvement: the single PointNet with max-pool may be at its capacity limit for this task given only 132 samples. rel 0.255 means typical error ~25% of the RMS mass. Let me reconsider the scale issue , maybe the fundamental limit is that mass truly cannot be perfectly recovered from normalized clouds. The best possible rel L2 might be bounded. But let me see if I can do better than 0.255. Let me think about what drives errors. Let me look at per-sample errors from a trained model to understand if heavy brackets dominate. If heavy samples dominate rel L2, maybe weighting them or using a loss in a transformed space helps. But de-standardization is linear (can't do log). However, I CAN weight the loss during training (e.g., weight samples by mass so heavy ones get more attention, or inverse to balance). Weighting won't change inference. Let me consider: rel L2 = sqrt(sum e_i^2)/sqrt(sum y_i^2). To minimize, I want to minimize sum e_i^2, i.e., plain MSE in physical units (uniform weight). That's exactly what standardized MSE does. So MSE is optimal for rel L2. For MAE, L1 optimal. For max-abs, minimizing max. There's inherent tension. Since rel L2 is primary, I'll prioritize MSE. But maybe a small Huber helps max-abs without hurting rel L2 much. jitter005 gave best max (0.6246). Let me think about a more powerful idea: since I can only ship one model, but training is cheap, I could do a careful multi-seed SWA: train multiple seeds and average their weights? No, different seeds → different basins → averaging weights fails. But here's a legit trick: SWA within one run already via EMA. Another idea: The fixed architecture's forward pass ends with head(max-pool). The max-pool loses count/density info. But again fixed. Let me focus on squeezing the training recipe: ep250/400, iso norm, tiny jitter, and test combos. Also try cosine with restarts or more. Actually, let me reconsider the possibility that I've been too hasty dismissing the approach. rel 0.255 , is that good? The baseline (mean) is 0.47. So we explain a good chunk. Let me check what public_test looks like with a trained model, and whether val/test are consistent. Let me also reconsider: maybe combine train and val and the model trained on all 132 does better on the hidden set. CV on 132 (training on 110) gives 0.255; final on 132 slightly better. Let me now do a second sweep focused on: ep400, iso+jitter combos, loss variants (huber), and see if anything breaks 0.25. Let me wait for bs results first.

bash

cd /app/exp && sleep 160 && cat sweep1.log 2>&1 | grep -v Warning

output

base           rel=0.2585(±0.0039) mae=0.1642 max=0.6710  (78.8s)
wd1e-3         rel=0.2596(±0.0070) mae=0.1626 max=0.7149  (72.8s)
wd3e-3         rel=0.2604(±0.0047) mae=0.1655 max=0.7218  (75.1s)
wd1e-2         rel=0.2591(±0.0046) mae=0.1644 max=0.6421  (73.5s)
ep250          rel=0.2548(±0.0004) mae=0.1593 max=0.6911  (122.3s)
ep250_wd1e-3   rel=0.2558(±0.0015) mae=0.1595 max=0.6994  (122.3s)
ema0.98        rel=0.2581(±0.0037) mae=0.1637 max=0.6698  (71.8s)
ema0.995       rel=0.2668(±0.0035) mae=0.1725 max=0.7657  (75.0s)
iso_norm       rel=0.2556(±0.0043) mae=0.1592 max=0.7212  (71.9s)
jitter005      rel=0.2572(±0.0048) mae=0.1682 max=0.6246  (80.9s)
bs8            rel=0.2565(±0.0097) mae=0.1569 max=0.6887  (144.6s)
bs32           rel=0.2788(±0.0038) mae=0.1755 max=0.8213  (88.2s)
DONE

Complete. Key findings: - ep250 best rel (0.2548), very stable. Longer training helps. - bs8 best mae (0.1569) but noisy (±0.0097) and higher max. - bs32 worse (fewer steps). smaller batch → more steps → better (to a point). - iso_norm good mae. - jitter005 best max-abs. The model plateaus ~0.255. Let me now think harder about breaking through. The fixed architecture is the bottleneck. Let me reconsider. Wait , maybe I should reconsider the whole approach. Let me reconsider what signal is available. The key realization about scale recovery: the model needs to detect the mounting-interface features and their relative size. A global max-pool PointNet might struggle with this fine-grained relative-scale reasoning. But there's another angle: maybe the mass correlates strongly with features the PointNet CAN compute, and I'm already capturing most. rel 0.255 ~ R² of 1-0.255²/ (that's not quite). Let me estimate: rel L2² ≈ sum e²/sum y². If predictions were perfect, 0. Mean-pred gives 0.47. We're at 0.255, so sum e² is (0.255/0.47)² = 0.294 of the mean-pred residual. So we explain ~70% of variance (in the rms sense). Can a bigger/different model do better? I can't change architecture. But maybe training longer, better optimization, and combining train+val helps marginally. Let me reconsider the aggressive lever: more steps via more epochs AND smaller batch. ep250+bs8? Or ep400+bs16. Let me also try a LR sweep (maybe higher LR with more epochs). And try combining the best: ep300, iso norm, tiny jitter 0.003, bs16, ema0.99. Also, let me reconsider the min_lr_frac , cosine to 0 means last epochs barely train. Maybe ending at a small positive LR or using the EMA is fine. Let me also test: does averaging predictions of the EMA model AND a snapshot help? No, single model. Let me think about a potentially big win: test-time behavior of BatchNorm. At inference, the grader runs model.eval() presumably, using running BN stats. My EMA averages BN running stats too (I copy buffers via shadow which includes running_mean/var as non-floating? No, running_mean/var ARE floating point, so EMA averages them; num_batches_tracked is int, copied). Actually EMA-averaging BN running stats over the trajectory could be suboptimal. An alternative: after training, recompute BN stats on the full training set in eval of the EMA weights. But EMA weights aren't a real training state. SWA-style BN recalibration: set model to EMA weights, then do forward passes over training data in train mode to update BN running stats. This often helps SWA. Let me consider adding a BN recalibration step for the final model. But does the grader call eval()? I must assume yes (standard). If it called train() mode, BN would use batch stats , unlikely. I'll assume eval(). Let me reconsider: maybe I'm overcomplicating. Let me test a focused set of promising combos in background and also test on the actual val and public_test with a model trained on train only, to make sure CV translates to the given val/test. Let me design sweep2: - ep300_bs16_iso (iso norm, 300 ep) - ep300_bs12 - ep400 - combo: iso + jitter0.004 + ep300 - huber delta 1.0 with ep250 - lr 2e-3 ep250 - lr 5e-4 ep300 Let me also add a held-out check: train on pooled minus public-like? No, keep CV. Actually, given the plateau, let me also reconsider whether the fixed model can be pushed by a different LR schedule or longer. Let me run sweep2 in background. Let me also, in parallel thinking, consider the final deliverable: I need to produce /app/outputs/model.pt. Let me make sure the save format and a from-scratch inference load works with the grader's likely code. I'll write a final train script and a verify script. Let me launch sweep2.

The recipe plateaus around rel 0.255; longer training (ep250) and smaller batches help, isotropic norm helps MAE, tiny jitter helps worst-case. Let me run a focused second sweep combining the best ingredients and testing LR/epochs/loss, in the background.

bash

cd /app/exp && cat > sweep2.py << 'EOF'
import torch, time
from common import *
from kfold import kfold_single
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val')
X=torch.cat([tx,vx],0); Y=torch.cat([ty,vy],0)
b=dict(lr=1e-3,wd=1e-4,epochs=300,bs=16,ema=0.99,loss='mse')
variants={
 'ep300_bs16': b,
 'ep300_iso': ('iso',b),
 'ep400_bs16': {**b,'epochs':400},
 'ep300_bs12': {**b,'bs':12},
 'ep300_jit004': {**b,'jitter':0.004},
 'ep300_iso_jit004': ('iso',{**b,'jitter':0.004}),
 'ep300_lr2e3': {**b,'lr':2e-3},
 'ep300_lr5e4': {**b,'lr':5e-4},
 'ep300_huber': {**b,'loss':'huber','delta':1.0},
 'ep300_minlr': {**b,'min_lr_frac':0.02},
}
for name,hp in variants.items():
    iso=False
    if isinstance(hp,tuple): iso=True; hp=hp[1]
    t=time.time(); r=kfold_single(hp,X,Y,cfg,k=6,seeds=(0,1,2),iso=iso)
    print(f'{name:18s} rel=%.4f(±%.4f) mae=%.4f max=%.4f  (%.1fs)'%(r[0],r[3],r[1],r[2],time.time()-t),flush=True)
print('DONE',flush=True)
EOF
rm -f sweep2.log && nohup python sweep2.py > sweep2.log 2>&1 &
echo "launched pid $!"

output

launched pid 372

While sweep2 runs (~20 min), let me build the final training script and a verification harness that mimics the grader, so I'm ready to produce the deliverable. Let me also validate my inference matches the expected format by testing loading. Let me write a robust final training script `/app/solution_train.py` that: - pools train+val, - trains with the best config (I'll parameterize, fill in after sweeps), - uses EMA, optional BN recalibration, - saves to /app/outputs/model.pt in required format. And a `verify.py` that loads the checkpoint fresh, rebuilds model via build_model, loads state_dict, and runs the exact inference (x-feat_mean)/feat_std -> model -> *label_std+label_mean, on val and public_test. Let me write verify.py first (grader simulation).

While sweep2 runs, let me build the grader-simulating verification script to ensure my checkpoint format and inference path are correct.

bash

cd /app && cat > verify.py << 'EOF'
"""Simulate the grader: load checkpoint fresh, rebuild model, run inference, report metrics."""
import json, sys
from pathlib import Path
import torch
sys.path.insert(0,'/app')
from candidate_model import build_model

def load_cfg():
    with open('/app/task_config.json') as fh: return json.load(fh)

def load_split(name):
    d=torch.load(f'/app/data/{name}/samples.pt', weights_only=True)
    return d['features'].float(), d['labels'].float()

def metrics(pred,true):
    pred=pred.reshape(-1); true=true.reshape(-1); e=pred-true
    return (e.norm()/true.norm()).item(), e.abs().mean().item(), e.abs().max().item()

@torch.no_grad()
def main(ckpt='/app/outputs/model.pt'):
    cfg=load_cfg()
    ck=torch.load(ckpt, weights_only=True, map_location='cpu')
    for k in ['state_dict','feat_mean','feat_std','label_mean','label_std']:
        assert k in ck, f'missing {k}'
    m=build_model(cfg)
    missing,unexpected=m.load_state_dict(ck['state_dict'], strict=True)
    m.eval()
    fm=ck['feat_mean'].float().view(1,1,3); fs=ck['feat_std'].float().view(1,1,3)
    lm=ck['label_mean'].float().view(1,1); ls=ck['label_std'].float().view(1,1)
    print('feat_mean',ck['feat_mean'].tolist(),'feat_std',ck['feat_std'].tolist())
    print('label_mean',ck['label_mean'].tolist(),'label_std',ck['label_std'].tolist())
    for nm in ['val','public_test']:
        x,y=load_split(nm)
        outs=[]
        for i in range(0,x.shape[0],64):
            xb=(x[i:i+64]-fm)/fs
            outs.append(m(xb)*ls+lm)
        pred=torch.cat(outs,0)
        r=metrics(pred,y)
        print(f'{nm:12s} rel=%.4f mae=%.4f max=%.4f'%r)

if __name__=='__main__':
    main(*sys.argv[1:])
EOF
echo "written verify.py"

output

written verify.py

Now let me write the final training script. I'll parameterize and fill best config after sweeps. I'll include EMA and optional BN recalibration. Let me also include the option to train on pooled train+val or just train. Let me design it so the final model uses EMA weights, then optionally recalibrate BN on training data (in train mode forward passes), which can help the EMA/SWA weights have consistent BN stats. Let me write solution_train.py now.

Now let me write the final training script (config filled in after sweeps), including EMA and optional BN recalibration for the averaged weights.

bash

cd /app && cat > solution_train.py << 'EOF'
from __future__ import annotations
import json, math, os, sys, time, argparse
from pathlib import Path
import torch, torch.nn as nn, torch.nn.functional as F
sys.path.insert(0,'/app'); sys.path.insert(0,'/app/exp')
from candidate_model import build_model
from common import load_cfg, load_split, compute_norm
from trainer import augment, EMA

def bn_recalibrate(model, x, fm, fs, bs=32, passes=3):
    # reset BN running stats and recompute via forward passes over x (train mode)
    for m in model.modules():
        if isinstance(m,(nn.BatchNorm1d,)):
            m.reset_running_stats(); m.momentum=None  # cumulative average
    model.train()
    with torch.no_grad():
        for _ in range(passes):
            perm=torch.randperm(x.shape[0],device=x.device)
            for i in range(0,x.shape[0],bs):
                idx=perm[i:i+bs]; xb=(x[idx]-fm)/fs; model(xb)
    model.eval()

def train_final(hp, X, Y, cfg, seed=0, iso=False, recal=True):
    fm,fs,lm,ls=compute_norm(X,Y,iso=iso)
    Yn=(Y-lm)/ls
    torch.manual_seed(seed); torch.cuda.manual_seed_all(seed)
    model=build_model(cfg).cuda()
    opt=torch.optim.AdamW(model.parameters(),lr=hp['lr'],weight_decay=hp['wd'])
    epochs=hp['epochs']; bs=hp['bs']; N=X.shape[0]
    steps_per=math.ceil(N/bs); total=steps_per*epochs; warmup=int(total*hp.get('warmup',0.05))
    mlf=hp.get('min_lr_frac',0.0)
    def lr_sc(s):
        if s<warmup: return s/max(warmup,1)
        p=(s-warmup)/max(total-warmup,1); return mlf+(1-mlf)*0.5*(1+math.cos(math.pi*p))
    ema=EMA(model,hp.get('ema',0.99)); step=0; lt=hp.get('loss','mse')
    for ep in range(epochs):
        model.train(); perm=torch.randperm(N,device=X.device)
        for i in range(0,N,bs):
            idx=perm[i:i+bs]; xb=augment(X[idx],hp); xb=(xb-fm)/fs; yb=Yn[idx]
            for g in opt.param_groups: g['lr']=hp['lr']*lr_sc(step)
            opt.zero_grad(set_to_none=True); pred=model(xb)
            if lt=='mse': loss=F.mse_loss(pred,yb)
            elif lt=='l1': loss=F.l1_loss(pred,yb)
            elif lt=='huber': loss=F.huber_loss(pred,yb,delta=hp.get('delta',1.0))
            loss.backward(); opt.step(); ema.update(model); step+=1
    em=build_model(cfg).cuda(); em.load_state_dict(model.state_dict()); ema.copy_to(em)
    if recal: bn_recalibrate(em, X, fm, fs)
    return em, fm, fs, lm, ls

def save_ckpt(model, fm, fs, lm, ls, path='/app/outputs/model.pt'):
    Path(path).parent.mkdir(parents=True, exist_ok=True)
    payload={'state_dict':{k:v.detach().cpu() for k,v in model.state_dict().items()},
             'feat_mean':fm.detach().cpu().view(-1),'feat_std':fs.detach().cpu().view(-1),
             'label_mean':lm.detach().cpu().view(-1),'label_std':ls.detach().cpu().view(-1)}
    torch.save(payload,path); print('saved',path)

if __name__=='__main__':
    print('module ok')
EOF
echo done

output

done

bash

cd /app/exp && sleep 40 && cat sweep2.log 2>&1 | grep -v Warning

output

<system>Tool ran without output or errors</system>

sweep2 first variant ep300 takes ~150s. Let me wait. Meanwhile, let me think about the BN recalibration , I want to verify it doesn't hurt. Let me test the impact of bn_recalibrate separately once sweep2 done. Actually, let me reconsider whether recalibration is safe. EMA of BN running stats vs recalibration. Both are reasonable. I'll A/B test on CV. Let me also reconsider a potentially impactful idea I haven't tried: the number of input points. The model uses max-pool over points, so using all 2048 is fine. No change. Let me also think about whether there's a data leak/shortcut I can exploit: the source_bracket_id. The manifest has source_bracket_id , could mass correlate with bracket_id? Probably ids are arbitrary. Not usable at inference anyway (grader only passes point cloud). Ignore. Let me wait for sweep2.

Let me wait for sweep2 results (first variants take ~150s each).

bash

cd /app/exp && sleep 230 && cat sweep2.log 2>&1 | grep -v Warning

output

ep300_bs16         rel=0.2548(±0.0095) mae=0.1600 max=0.7195  (184.8s)

Hmm, ep300_bs16 rel 0.2548 but with higher variance (±0.0095) and max went up. Note this run used ep300 vs earlier ep250 got 0.2548 ±0.0004. The variance is seed-dependent. Let me keep waiting for the rest. These take ~185s each, 10 variants = ~30 min. Let me wait in larger chunks. Let me think about whether I should reduce the sweep or let it run. I'll let it run and check periodically. Meanwhile, let me use the time to reason about the best final recipe and prepare. Actually, the plateau at ~0.255 across many configs suggests that's near the model's capacity limit for this data. Differences between configs (0.2548 to 0.27) are mostly noise except clear losers (bs32, ema0.995). Given that, my final recipe should be robust: ep250-300, bs16, ema0.99, wd1e-4, mse loss. The question is iso vs per-axis norm, jitter, and BN recalibration. Let me think about the three metrics trade-off for the final choice: - rel L2 (primary): best ~0.2548 (ep250/300) - mae: iso and bs8 gave ~0.159 - max: jitter gave 0.6246 A balanced choice: ep300, iso norm (good mae), tiny jitter (good max, and helps rel a bit). Let me see ep300_iso_jit004 result. Let me wait.

ep300 matches ep250 on rel but with more seed variance. The plateau (~0.255) across configs suggests we're near the fixed model's capacity. Let me wait for the combination variants (iso, jitter, huber).

bash

cd /app/exp && sleep 260 && cat sweep2.log 2>&1 | grep -v Warning

output

ep300_bs16         rel=0.2548(±0.0095) mae=0.1600 max=0.7195  (184.8s)
ep300_iso          rel=0.2371(±0.0065) mae=0.1470 max=0.7039  (176.7s)

ep300_iso is notably better: rel 0.2371 (vs 0.2548), mae 0.1470! So isotropic normalization + 300 epochs helps significantly. This makes sense , isotropic normalization preserves the shape's aspect ratios and relative scale, which is crucial for inferring mass. Per-axis standardization distorts the geometry anisotropically, hurting the scale-recovery. Wait, but earlier iso_norm at ep150 gave 0.2556 vs base 0.2585 , only slightly better. At ep300, iso gives 0.2371 vs 0.2548. So iso + longer training is a good combo. Let me keep waiting for the iso+jitter combo and others. This is promising. Let me wait. Actually, let me reconsider: iso uses feat_std = mean of the three stds, applied to all axes. So it's isotropic scaling + per-axis centering. The centering is still per-axis (feat_mean is per-axis). Only the std is made uniform. Good. Let me wait for ep300_iso_jit004.

ep300 with **isotropic normalization** is a clear improvement: rel 0.2371 (vs 0.2548) and mae 0.1470. Preserving aspect ratios helps the scale/mass inference. Let me wait for the iso+jitter combo and remaining variants.

bash

cd /app/exp && sleep 270 && cat sweep2.log 2>&1 | grep -v Warning

output

ep300_bs16         rel=0.2548(±0.0095) mae=0.1600 max=0.7195  (184.8s)
ep300_iso          rel=0.2371(±0.0065) mae=0.1470 max=0.7039  (176.7s)
ep400_bs16         rel=0.2455(±0.0066) mae=0.1529 max=0.6821  (278.8s)

ep400 (per-axis norm) gives 0.2455 , better than ep300 per-axis (0.2548). So longer helps per-axis too, but iso is still better. The winner signal is iso. Let me wait for ep300_iso_jit004 (next). Given iso is strong, I should test ep400_iso and ep500_iso too. Let me queue a sweep3 with iso-focused long training while sweep2 finishes. Actually, let me be efficient. Let me wait for the key iso+jitter result, then design sweep3 focused on iso + longer epochs + jitter + BN recal. Let me wait.

Longer training helps, but isotropic norm is the bigger win. Let me wait for the iso+jitter combination specifically.

bash

cd /app/exp && sleep 200 && grep -E "jit004|iso" sweep2.log 2>&1 | grep -v Warning

output

ep300_iso          rel=0.2371(±0.0065) mae=0.1470 max=0.7039  (176.7s)

bash

cd /app/exp && sleep 120 && cat sweep2.log 2>&1 | grep -v Warning | tail -6

output

ep300_bs16         rel=0.2548(±0.0095) mae=0.1600 max=0.7195  (184.8s)
ep300_iso          rel=0.2371(±0.0065) mae=0.1470 max=0.7039  (176.7s)
ep400_bs16         rel=0.2455(±0.0066) mae=0.1529 max=0.6821  (278.8s)
ep300_bs12         rel=0.2638(±0.0136) mae=0.1624 max=0.7907  (220.4s)
ep300_jit004       rel=0.2434(±0.0077) mae=0.1539 max=0.6526  (189.0s)

ep300_jit004 (per-axis + jitter) gives 0.2434, max 0.6526 , jitter helps both rel and max vs ep300 per-axis (0.2548). And ep300_bs12 worse. Let me wait for ep300_iso_jit004 which should combine the two best ingredients.

jitter also helps (rel 0.2434, max 0.6526). The iso+jitter combo is next , that should combine both wins. Let me check.

bash

cd /app/exp && sleep 180 && cat sweep2.log 2>&1 | grep -v Warning | tail -6

output

ep300_bs16         rel=0.2548(±0.0095) mae=0.1600 max=0.7195  (184.8s)
ep300_iso          rel=0.2371(±0.0065) mae=0.1470 max=0.7039  (176.7s)
ep400_bs16         rel=0.2455(±0.0066) mae=0.1529 max=0.6821  (278.8s)
ep300_bs12         rel=0.2638(±0.0136) mae=0.1624 max=0.7907  (220.4s)
ep300_jit004       rel=0.2434(±0.0077) mae=0.1539 max=0.6526  (189.0s)
ep300_iso_jit004   rel=0.2389(±0.0015) mae=0.1523 max=0.6353  (215.4s)

ep300_iso_jit004: rel 0.2389 (±0.0015, very stable!), mae 0.1523, max 0.6353 (best balance of max and rel). Compared to ep300_iso (rel 0.2371, mae 0.1470, max 0.7039): iso alone has slightly better rel and mae, but worse max (0.70 vs 0.64). iso+jitter has better max and stability. So the trade-off: - ep300_iso: rel 0.2371, mae 0.1470, max 0.7039 - ep300_iso_jit004: rel 0.2389, mae 0.1523, max 0.6353 For the primary metric (rel), iso alone is marginally better (0.2371 vs 0.2389, within noise). For max-abs, jitter helps (0.6353 vs 0.7039). Since I shouldn't trade off, the jitter version gives a good balance. But let me explore more: iso + longer epochs (400/500), iso + smaller jitter (0.002-0.003), iso + bs variations. Let me wait for the last 3 variants (lr2e3, lr5e4, huber, minlr) then design sweep3. Let me wait.

iso+jitter gives the best balance: rel 0.2389 (very stable ±0.0015), max 0.6353. Let me see the remaining LR/loss variants, then run an iso-focused refinement sweep.

bash

cd /app/exp && sleep 200 && cat sweep2.log 2>&1 | grep -v Warning | tail -5

output

ep400_bs16         rel=0.2455(±0.0066) mae=0.1529 max=0.6821  (278.8s)
ep300_bs12         rel=0.2638(±0.0136) mae=0.1624 max=0.7907  (220.4s)
ep300_jit004       rel=0.2434(±0.0077) mae=0.1539 max=0.6526  (189.0s)
ep300_iso_jit004   rel=0.2389(±0.0015) mae=0.1523 max=0.6353  (215.4s)
ep300_lr2e3        rel=0.2529(±0.0100) mae=0.1574 max=0.7012  (201.1s)

bash

cd /app/exp && sleep 260 && cat sweep2.log 2>&1 | grep -v Warning | tail -4

output

ep300_jit004       rel=0.2434(±0.0077) mae=0.1539 max=0.6526  (189.0s)
ep300_iso_jit004   rel=0.2389(±0.0015) mae=0.1523 max=0.6353  (215.4s)
ep300_lr2e3        rel=0.2529(±0.0100) mae=0.1574 max=0.7012  (201.1s)
ep300_lr5e4        rel=0.2647(±0.0031) mae=0.1672 max=0.7230  (219.1s)

lr5e4 worse (underfit), lr2e3 slightly worse. lr1e-3 is good. Let me wait for huber and minlr, then launch sweep3 focused on iso. Let me design sweep3 now (iso-focused): - ep300_iso (baseline winner) with different seeds to confirm - ep400_iso - ep500_iso - ep300_iso_jit002 (smaller jitter) - ep300_iso_jit003 - ep400_iso_jit004 - ep300_iso_jit004_recal (test BN recalibration effect) , but recal is in solution_train, not kfold. Let me add recal option to kfold run_fold. - ep300_iso_wd1e-3 - ep300_iso_bs16_ema0.995? no ema0.99 fine. Let me add recal support to kfold, then run sweep3. Let me wait for sweep2 to finish first to free the GPU (though H100 can handle concurrent, better to avoid contention and noise). Let me check if done.

lr 1e-3 remains best. Let me add a BN-recalibration option to the CV harness so I can test it, then wait for sweep2 to finish.

bash

cd /app/exp && cat >> kfold.py << 'EOF'

def run_fold_recal(hp, tx, ty, vx, vy, cfg, seed, iso=False, recal=False):
    import torch.nn as nn
    fm,fs,lm,ls=compute_norm(tx,ty,iso=iso)
    tyn=(ty-lm)/ls
    torch.manual_seed(seed); torch.cuda.manual_seed_all(seed)
    model=build_model(cfg).cuda()
    opt=torch.optim.AdamW(model.parameters(),lr=hp['lr'],weight_decay=hp['wd'])
    epochs=hp['epochs']; bs=hp['bs']; N=tx.shape[0]
    steps_per=math.ceil(N/bs); total=steps_per*epochs; warmup=int(total*hp.get('warmup',0.05))
    mlf=hp.get('min_lr_frac',0.0)
    def lr_sc(s):
        if s<warmup: return s/max(warmup,1)
        p=(s-warmup)/max(total-warmup,1); return mlf+(1-mlf)*0.5*(1+math.cos(math.pi*p))
    ema=EMA(model, hp.get('ema',0.99)); step=0; lt=hp.get('loss','mse')
    for ep in range(epochs):
        model.train(); perm=torch.randperm(N,device=tx.device)
        for i in range(0,N,bs):
            idx=perm[i:i+bs]; xb=augment(tx[idx],hp); xb=(xb-fm)/fs; yb=tyn[idx]
            for g in opt.param_groups: g['lr']=hp['lr']*lr_sc(step)
            opt.zero_grad(set_to_none=True); pred=model(xb)
            if lt=='mse': loss=F.mse_loss(pred,yb)
            elif lt=='l1': loss=F.l1_loss(pred,yb)
            elif lt=='huber': loss=F.huber_loss(pred,yb,delta=hp.get('delta',1.0))
            loss.backward(); opt.step(); ema.update(model); step+=1
    em=build_model(cfg).cuda(); em.load_state_dict(model.state_dict()); ema.copy_to(em)
    if recal:
        for m in em.modules():
            if isinstance(m,nn.BatchNorm1d): m.reset_running_stats(); m.momentum=None
        em.train()
        with torch.no_grad():
            for _ in range(3):
                perm=torch.randperm(N,device=tx.device)
                for i in range(0,N,bs):
                    idx=perm[i:i+bs]; em((tx[idx]-fm)/fs)
        em.eval()
    pv=predict(em,vx,fm,fs,lm,ls)
    return pv.detach(), vy.detach()

def kfold_single2(hp, X, Y, cfg, k=6, seeds=(0,1,2), iso=False, recal=False, shuffle_seed=123):
    import statistics as st
    N=X.shape[0]
    g=torch.Generator().manual_seed(shuffle_seed); perm=torch.randperm(N,generator=g)
    folds=[perm[i::k] for i in range(k)]
    rels=[];maes=[];mxs=[]
    for s in seeds:
        allp=torch.zeros(N,1,device=X.device)
        for fi in range(k):
            vidx=folds[fi].to(X.device)
            tmask=torch.ones(N,dtype=torch.bool); tmask[folds[fi]]=False
            tidx=tmask.nonzero().squeeze(1).to(X.device)
            pv,_=run_fold_recal(hp, X[tidx],Y[tidx],X[vidx],Y[vidx],cfg,seed=s,iso=iso,recal=recal)
            allp[vidx]=pv
        r=metrics(allp,Y); rels.append(r[0]);maes.append(r[1]);mxs.append(r[2])
    return (st.mean(rels),st.mean(maes),st.mean(mxs), st.pstdev(rels))
EOF
echo "added recal support"; sleep 5 && tail -2 sweep2.log | grep -v Warning

output

added recal support
ep300_lr2e3        rel=0.2529(±0.0100) mae=0.1574 max=0.7012  (201.1s)
ep300_lr5e4        rel=0.2647(±0.0031) mae=0.1672 max=0.7230  (219.1s)

bash

cd /app/exp && sleep 170 && tail -3 sweep2.log | grep -v Warning

output

ep300_lr2e3        rel=0.2529(±0.0100) mae=0.1574 max=0.7012  (201.1s)
ep300_lr5e4        rel=0.2647(±0.0031) mae=0.1672 max=0.7230  (219.1s)
ep300_huber        rel=0.2546(±0.0032) mae=0.1560 max=0.7025  (225.1s)

huber (per-axis) 0.2546, similar to mse per-axis. Let me wait for minlr then sweep2 is done. Then launch sweep3 (iso-focused refinement with recal test, more seeds for stability). Let me check if minlr done and sweep done.

bash

cd /app/exp && sleep 160 && tail -2 sweep2.log | grep -v Warning

output

ep300_minlr        rel=0.2511(±0.0068) mae=0.1564 max=0.7124  (185.4s)
DONE

sweep2 done. Winner: ep300_iso (rel 0.2371) and ep300_iso_jit004 (rel 0.2389, better max/stability). Now sweep3: iso-focused refinement. Let me test with 5 seeds for stability and include recal. Variants: - iso_ep300 (confirm, 5 seeds) - iso_ep400 - iso_ep500 - iso_ep300_jit002 - iso_ep300_jit003 - iso_ep400_jit003 - iso_ep300_recal (BN recalibration) - iso_ep300_jit003_recal - iso_ep300_huber (huber + iso) - iso_ep300_wd1e-3 This is 10 variants × 5 seeds × 6 folds = 300 trainings × ~2s = ~600s? Each training ~2s (ep300, 7 steps/ep × 300 = 2100 steps). Actually ep300 fold took 176s/18 = ~10s per training (3 seeds×6 folds=18). So 5 seeds×6 folds=30 trainings × 10s = 300s per variant, ×10 = 3000s = 50 min. Too long. Let me reduce: use 4 seeds and fewer variants (6). And split into two background scripts to parallelize? H100 can run 2 concurrently but they'd contend. Let me just run one focused sweep3 with 4 seeds and 7 variants. ~35min. Acceptable. Let me prioritize the most informative variants: - iso_ep300 (ref) - iso_ep400 - iso_ep300_jit003 - iso_ep400_jit003 - iso_ep300_recal - iso_ep300_jit003_recal - iso_ep500_jit003 Let me launch. Actually let me reduce epochs sweep since ep300 vs 400 is marginal; the big question is jitter level, recal, and stability. Let me do 4 seeds. Let me write sweep3.

sweep2 done. Winners use **isotropic norm + 300 epochs** (rel 0.237), with light jitter improving worst-case. Now let me run an iso-focused refinement (jitter level, longer epochs, BN recalibration) with 4 seeds for stability.

bash

cd /app/exp && cat > sweep3.py << 'EOF'
import torch, time
from common import *
from kfold import kfold_single2
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val')
X=torch.cat([tx,vx],0); Y=torch.cat([ty,vy],0)
b=dict(lr=1e-3,wd=1e-4,epochs=300,bs=16,ema=0.99,loss='mse')
S=(0,1,2,3)
variants=[
 ('iso_ep300',     b, False),
 ('iso_ep300_recal',b, True),
 ('iso_ep400',     {**b,'epochs':400}, False),
 ('iso_ep300_jit003',{**b,'jitter':0.003}, False),
 ('iso_ep300_jit003_recal',{**b,'jitter':0.003}, True),
 ('iso_ep400_jit003',{**b,'epochs':400,'jitter':0.003}, False),
 ('iso_ep300_jit006',{**b,'jitter':0.006}, False),
 ('iso_ep300_wd1e3',{**b,'wd':1e-3}, False),
]
for name,hp,recal in variants:
    t=time.time(); r=kfold_single2(hp,X,Y,cfg,k=6,seeds=S,iso=True,recal=recal)
    print(f'{name:26s} rel=%.4f(±%.4f) mae=%.4f max=%.4f  (%.1fs)'%(r[0],r[3],r[1],r[2],time.time()-t),flush=True)
print('DONE',flush=True)
EOF
rm -f sweep3.log && nohup python sweep3.py > sweep3.log 2>&1 &
echo "launched pid $!"

output

launched pid 516

While sweep3 runs, let me produce an initial checkpoint using the current best known config (ep300_iso_jit004 or ep300_iso) so I have a valid deliverable in place early, and verify the format with verify.py. I'll train on pooled train+val. Let me first produce a candidate final model with ep300, iso, jitter 0.004, trained on all 132 samples, and verify. This gives me a safety-net deliverable. I can refine after sweep3. Let me run this now (single training, ~20s + recal). Actually, I should be careful about GPU contention with sweep3. But a single training is quick (~20s). The H100 has plenty of memory. Let me run it. Let me write a small script to train final and save, then verify.

While sweep3 runs, let me produce a safety-net deliverable now with the current best config (iso + jitter, trained on pooled train+val) and verify the checkpoint format end-to-end.

bash

cd /app && python -c "
import torch, sys
sys.path.insert(0,'/app'); sys.path.insert(0,'/app/exp')
from solution_train import train_final, save_ckpt
from common import load_cfg, load_split
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val')
X=torch.cat([tx,vx],0); Y=torch.cat([ty,vy],0)
hp=dict(lr=1e-3,wd=1e-4,epochs=300,bs=16,ema=0.99,loss='mse',jitter=0.004)
m,fm,fs,lm,ls=train_final(hp,X,Y,cfg,seed=0,iso=True,recal=True)
save_ckpt(m,fm,fs,lm,ls)
print('done initial ckpt')
" 2>&1 | grep -v Warning
echo "=== VERIFY (fresh load, grader-style) ==="
cd /app && python verify.py 2>&1 | grep -v Warning

output

saved /app/outputs/model.pt
done initial ckpt
=== VERIFY (fresh load, grader-style) ===
feat_mean [-0.011533195152878761, 0.006913361605256796, -0.07176373898983002] feat_std [0.1662050485610962, 0.1662050485610962, 0.1662050485610962]
label_mean [0.7511227130889893] label_std [0.3936556279659271]
val          rel=0.0443 mae=0.0328 max=0.0765
public_test  rel=0.2972 mae=0.1820 max=0.5719

The checkpoint loads and runs correctly in the grader-style path. Note: val rel is 0.044 because val was included in training (X = train+val), so it's memorized , not a real estimate. public_test rel=0.2972, which is a held-out estimate (public_test never seen). Hmm, public_test rel 0.2972 is worse than CV's 0.239. public_test is only 16 samples, so noisy. But let me be careful , the hidden test set is what matters. CV on 132 is a more robust estimate than 16-sample public_test. Still, 0.297 on public_test is a concern. Let me check: is public_test systematically different? Its label range is narrower (0.198-1.449, no very heavy brackets). Let me evaluate the model trained on TRAIN ONLY (not including val) on both val and public_test, to get a cleaner read and compare to CV. Actually, better: let me compute a proper estimate by training on pooled and evaluating public_test across seeds, AND do a CV that includes public_test as additional held-out. Actually, the cleanest: treat public_test as an extra validation of my recipe. Let me train on train+val (132) with several seeds and average the public_test metric to reduce noise, and also evaluate a model trained on train only (100) evaluated on val (32) as another held-out check. But wait , I should be careful not to tune on public_test (16 samples) to avoid overfitting to it. I'll use it only as a sanity check, relying primarily on CV over the 132 pooled samples. Let me get a multi-seed read on public_test for the current config to see if 0.297 is stable or noise. Let me run a few seeds. But sweep3 is running on GPU. A few quick single trainings are fine. Actually, let me reconsider: since the held-out hidden set presumably resembles val/public_test (drawn from same SimJEB pool), and I have 132 labeled samples, the best estimate of generalization is the CV over all data I have. Let me also incorporate public_test into a bigger CV (148 samples) to get even more robust hyperparameter estimates and a better final model (train on all 148!). The task says public_test has labels and is given to me. There's no rule against training the final model on train+val+public_test (all 148 labeled samples). Using all available labeled data for the final model is standard and maximizes generalization to the hidden set. The public_test is explicitly given with labels. I'll use it for the FINAL model training, but keep CV-based model selection on the 132 (train+val) to avoid information leakage in hyperparameter choice , actually I can include public_test in CV too for selection, since it's just more data. Let me reconsider the risk: if the hidden test is the same as public_test, training on public_test would be leakage/cheating. But typically the hidden "held-out set" is DIFFERENT from public_test (public_test is a proxy). The task: "Your model is then applied to a held-out set of bracket geometries" , held-out, separate from what I have. So public_test is just labeled data I can use. Including it in training is legitimate and helpful. I'll do that for the final model. For now, let me get multi-seed public_test numbers to understand variance, using train+val trained models (seeds 0-3) evaluating on public_test. Let me run quickly. Actually GPU is busy with sweep3 (4 seeds × 6 folds × 8 variants). Running more will slow it. Let me be judicious , run a quick 4-seed eval now; it will share GPU but that's ok for correctness (just slower). Actually, contention could make both slow and mess up my timing estimates. Let me instead wait for sweep3 and fold public_test evaluation into a dedicated script after. Let me hold and monitor sweep3. Let me also reconsider the worst-case metric: public_test max 0.5719 , the worst error is on some sample. rel 0.297 on 16 samples. Let me wait for sweep3 and then do a thorough held-out evaluation. Let me peek at sweep3 progress occasionally. Meanwhile, let me reflect on whether the narrower public_test distribution means my model overpredicts heavy (none …[truncated]

The checkpoint loads and runs correctly via the grader-style path. val rel (0.044) is memorized since val was in training; public_test rel 0.297 is the real held-out signal but noisy (16 samples). Let me diagnose the public_test errors per-sample while sweep3 runs.

bash

cd /app && python -c "
import torch, sys, json
sys.path.insert(0,'/app')
from candidate_model import build_model
ck=torch.load('outputs/model.pt',weights_only=True)
cfg=json.load(open('task_config.json'))
m=build_model(cfg); m.load_state_dict(ck['state_dict']); m.eval()
fm=ck['feat_mean'].view(1,1,3); fs=ck['feat_std'].view(1,1,3); lm=ck['label_mean'].view(1,1); ls=ck['label_std'].view(1,1)
d=torch.load('data/public_test/samples.pt',weights_only=True)
x=d['features'].float(); y=d['labels'].float()
with torch.no_grad(): pred=m((x-fm)/fs)*ls+lm
import numpy as np
pr=pred.reshape(-1).numpy(); tr=y.reshape(-1).numpy(); er=pr-tr
order=np.argsort(-np.abs(er))
print(' idx   true   pred   err')
for i in order:
    print('%4d  %.3f  %.3f  %+.3f'%(i,tr[i],pr[i],er[i]))
" 2>&1 | grep -v Warning

output

idx   true   pred   err
  15  0.596  1.168  +0.572
  14  0.198  0.556  +0.357
   9  1.083  0.754  -0.329
  12  0.938  0.677  -0.261
   8  0.566  0.803  +0.237
   5  0.286  0.513  +0.227
   3  1.122  0.901  -0.222
   4  0.733  0.515  -0.218
  10  0.842  1.010  +0.168
   2  0.520  0.375  -0.144
  11  1.449  1.371  -0.078
   7  0.633  0.584  -0.049
   1  0.549  0.585  +0.036
   0  0.417  0.427  +0.010
   6  0.947  0.945  -0.003
  13  0.793  0.794  +0.001

The errors are mixed (both over and under), no systematic bias. The two worst are idx 15 (true 0.596, pred 1.168, over by 0.57) and idx 14 (true 0.198, pred 0.556, over by 0.357). These are specific brackets the model gets wrong. idx 14 is the lightest (0.198) predicted 0.556 , the model overestimates very light brackets. idx 15 true 0.596 predicted 1.168 , big overestimate. This is single-model noise. With only 16 samples, a couple of bad ones dominate. This is expected given the task difficulty. The CV estimate (0.239 over 132) is more reliable. Let me reconsider: can I reduce worst-case errors? These outliers suggest the model sometimes badly misjudges scale for particular geometries. More training data (include public_test in final) and ensembling-via-EMA help a bit. But fundamentally single model + fixed arch. Let me check sweep3 progress. Actually, one more idea to reduce variance and worst-case: since I can only ship one model, but I could ship the model whose weights are an average over multiple EMA runs IF they're trained from the same init and kept close... no. Alternatively , a legitimate and powerful trick within "one state_dict": train a single model but with a longer EMA that effectively averages many snapshots (SWA). That's what EMA does. Let me also reconsider: the BN recalibration might help or hurt. sweep3 tests it. Let me wait. Let me monitor sweep3.

Errors are unbiased (mix of over/under); the two worst are specific hard geometries (lightest bracket over-predicted). With only 16 samples this is noisy, so I'll rely on the 132-sample CV. Let me check sweep3 progress.

bash

cd /app/exp && sleep 60 && cat sweep3.log 2>&1 | grep -v Warning

output

<system>Tool ran without output or errors</system>

sweep3 first variant (4 seeds × 6 folds = 24 trainings × ~10s = 240s) + my concurrent runs. Let me wait more. Let me also prepare a comprehensive held-out evaluation script that: for a given hp, trains on train+val (seeds 0-4) and evaluates on public_test (averaging metrics across seeds, and also the ensemble). This gives another robustness check. But I'll run it after sweep3 to avoid contention. Let me wait for sweep3.

bash

cd /app/exp && sleep 220 && cat sweep3.log 2>&1 | grep -v Warning

output

iso_ep300                  rel=0.2352(±0.0065) mae=0.1466 max=0.6777  (331.4s)

iso_ep300 over 4 seeds: rel 0.2352, mae 0.1466, max 0.6777. Consistent with before (0.2371). 331s because of contention with my earlier runs (now done). Let me wait for the rest. Each ~280-330s, 8 variants = ~40 min. Let me wait in chunks. Let me think about whether I can speed up or parallelize. Actually I'll just wait. Let me check periodically with longer sleeps. While waiting, let me reconsider the overall plan and make sure I'm maximizing the primary metric (rel L2) without sacrificing others. Current best ~0.235 rel. Let me also reconsider: should the final model be trained on train+val only (132) or train+val+public_test (148)? Including public_test gives 148 samples for the final fit , more data, better generalization to the hidden set, and removes the public_test as a "hard" holdout. But then I lose public_test as a sanity check. I think using all 148 for the final model is the right call for maximizing hidden-set performance, but I'll validate the recipe via CV on 148 too. Actually, let me reconsider whether including public_test could hurt if public_test distribution differs. public_test has no heavy brackets (max 1.449). Including it won't hurt; it just adds data. The hidden set presumably spans the full range, and train+val already cover heavy brackets. So 148-sample training is good. Let me wait for sweep3 and then run a final CV on all 148 + produce the final model. Let me monitor.

iso_ep300 confirmed at rel 0.2352 over 4 seeds. Let me continue waiting for the jitter-level, epoch, and recalibration comparisons.

bash

cd /app/exp && sleep 280 && cat sweep3.log 2>&1 | grep -v Warning

output

iso_ep300                  rel=0.2352(±0.0065) mae=0.1466 max=0.6777  (331.4s)

Only one result after ~560s. The recal variant (iso_ep300_recal) is next and takes longer due to recalibration passes. Let me wait more. Let me check GPU usage to ensure it's progressing.

bash

nvidia-smi | grep -E "python|MiB /" | head; echo "---"; cd /app/exp && sleep 40 && cat sweep3.log | grep -v Warning

output

| N/A   46C    P0            305W /  700W |   15953MiB /  81559MiB |    100%      Default |
---
iso_ep300                  rel=0.2352(±0.0065) mae=0.1466 max=0.6777  (331.4s)
iso_ep300_recal            rel=0.2352(±0.0065) mae=0.1466 max=0.6778  (327.9s)

Interesting: iso_ep300_recal gives nearly identical results (0.2352 vs 0.2352, max 0.6778 vs 0.6777). So BN recalibration has negligible effect. The EMA-averaged BN stats are already fine. So recal is neutral , I can skip it for simplicity, or keep it (harmless). I'll skip it to keep things clean, since it's neutral. Wait, why is recal nearly identical? Because with momentum=None cumulative averaging over the same training data, the BN stats converge to similar values as the EMA-averaged ones. Fine. Neutral. Let me wait for the jitter and epoch variants.

BN recalibration is neutral (identical metrics), so I'll skip it for simplicity. Let me wait for the jitter-level and longer-epoch variants, which determine the best balance.

bash

cd /app/exp && sleep 290 && cat sweep3.log | grep -v Warning

output

iso_ep300                  rel=0.2352(±0.0065) mae=0.1466 max=0.6777  (331.4s)
iso_ep300_recal            rel=0.2352(±0.0065) mae=0.1466 max=0.6778  (327.9s)

Still only 2 done after ~900s. The iso_ep400 variant takes longer. Let me wait more. Each variant ~330s. 8 variants = ~44 min total. We're at ~15 min, 2 done. Let me wait ~5 more min. Let me use this time productively. Let me write the comprehensive final evaluation + model production script that I'll run once I pick the config. It will: 1. Run CV on all 148 samples (train+val+public_test) for the chosen config to get final estimate. 2. Train final model on all 148 with chosen config, seed selected, save to outputs/model.pt. 3. Verify. Actually, for the final model, which seed? Seeds vary (±0.0065 rel). I could pick the seed that does best on... but I have no holdout if training on all 148. I'll just pick seed 0 (or average is impossible for single model). To reduce variance risk, I could train a few seeds on all 148 and evaluate each via its predictions on... nothing held out. Alternative: I can use the CV to verify the recipe is stable across seeds (±0.0065 is small), so any seed is fine. I'll pick seed 0 for determinism. But actually, a nice trick to reduce the risk of a "bad seed": train on train+val (132), evaluate on public_test (16) for several seeds, pick the seed with best public_test , but that overfits to 16 samples. Not great. Better: Since EMA already stabilizes, and CV shows low seed variance, I'll train the final on all 148 with a fixed seed. Possibly I'll train 3 seeds on all 148 and pick the one whose predictions on the full 148 training set are most "central"? No. Actually, here's a clean approach to pick among seeds without a holdout: the seed variance is small (±0.0065), so it doesn't matter much. I'll just use seed 0. Done. Hold on, let me reconsider something more impactful: model selection via ensembling is off the table for inference, but what about this: I noticed iso helps a lot. Could an even better normalization help? E.g., normalize by a robust scale (not mean of stds, but something tied to the bounding box). Let me think... The iso std used = mean of per-axis stds ≈ 0.166. Since Y always spans ~1.0 (std ~0.29?), X std ~ smaller, Z std ~ smaller. Mean ~0.166. Actually the exact iso scale value doesn't matter (it's a global scale the network adapts to). What matters is isotropy (same scale all axes) vs anisotropy. We've established isotropy is better. Could centering choice matter? feat_mean per-axis centers the cloud. Fine. Let me also consider: should I NOT subtract per-axis mean but instead center by bounding-box center or centroid per-sample? The feat_mean is a single global length-3 vector (same for all samples), applied as (x - feat_mean). Since clouds are already roughly centered (centroid std ~0.02-0.07), subtracting global mean is minor. The network sees approximately centered clouds. Per-sample centering isn't possible via the fixed feat_mean (it's global). But the first Conv1d layer could learn to center... actually no, PointNet isn't translation invariant by default, but the inputs are already centered. Fine. OK let me just wait for sweep3 to finalize jitter level, then finalize. Let me wait. Let me reconsider the final config candidates: - iso_ep300 (rel 0.2352, mae 0.1466, max 0.6777) - iso + jitter (better max, slightly worse rel/mae) Since rel is primary and iso_ep300 has best rel AND best mae, and max 0.6777 is not bad, iso_ep300 might be the pick. But jitter improves max. I want the Pareto-best. Let me see the jitter003 numbers. If iso_ep300_jit003 gives rel~0.235 and max~0.63, that dominates. Let me wait. Let me wait for more results.

BN recalibration is neutral. Let me keep waiting for the jitter and epoch variants (each ~330s) to find the Pareto-best config.

bash

cd /app/exp && sleep 280 && cat sweep3.log | grep -v Warning

output

iso_ep300                  rel=0.2352(±0.0065) mae=0.1466 max=0.6777  (331.4s)
iso_ep300_recal            rel=0.2352(±0.0065) mae=0.1466 max=0.6778  (327.9s)
iso_ep400                  rel=0.2352(±0.0061) mae=0.1478 max=0.6520  (428.6s)

iso_ep400: rel 0.2352 (same), mae 0.1478, max 0.6520 (better max than ep300). So ep400 improves max slightly without hurting rel. Let me wait for the jitter variants. Let me wait for iso_ep300_jit003, iso_ep300_jit003_recal, iso_ep400_jit003, iso_ep300_jit006, iso_ep300_wd1e3.

iso_ep400 matches rel 0.2352 with a better worst-case (0.652). Let me wait for the jitter variants to complete the picture.

bash

cd /app/exp && sleep 290 && cat sweep3.log | grep -v Warning | tail -4

output

iso_ep300                  rel=0.2352(±0.0065) mae=0.1466 max=0.6777  (331.4s)
iso_ep300_recal            rel=0.2352(±0.0065) mae=0.1466 max=0.6778  (327.9s)
iso_ep400                  rel=0.2352(±0.0061) mae=0.1478 max=0.6520  (428.6s)
iso_ep300_jit003           rel=0.2389(±0.0077) mae=0.1523 max=0.6287  (277.1s)

iso_ep300_jit003: rel 0.2389, mae 0.1523, max 0.6287 (best max so far). So jitter trades a bit of rel/mae for better max. Summary of Pareto options: - iso_ep300: rel 0.2352, mae 0.1466, max 0.6777 , best rel & mae - iso_ep400: rel 0.2352, mae 0.1478, max 0.6520 , best rel, good max - iso_ep300_jit003: rel 0.2389, mae 0.1523, max 0.6287 , best max, worse rel/mae Since rel is primary and I "should not trade one off against the others," iso_ep400 looks like the best all-around: best rel (0.2352), near-best mae (0.1478), and good max (0.6520). Let me wait for the remaining: jit003_recal, ep400_jit003, jit006, wd1e3. iso_ep400_jit003 might give rel~0.236 and max~0.60 , could be a good balance. Let me see. Let me wait.

iso_ep400 looks like the best all-rounder (best rel 0.2352, good mae and max). Jitter trades rel for worst-case. Let me see the remaining variants, especially ep400+jitter.

bash

cd /app/exp && sleep 290 && cat sweep3.log | grep -v Warning | tail -4

output

iso_ep300_recal            rel=0.2352(±0.0065) mae=0.1466 max=0.6778  (327.9s)
iso_ep400                  rel=0.2352(±0.0061) mae=0.1478 max=0.6520  (428.6s)
iso_ep300_jit003           rel=0.2389(±0.0077) mae=0.1523 max=0.6287  (277.1s)
iso_ep300_jit003_recal     rel=0.2389(±0.0077) mae=0.1523 max=0.6284  (325.3s)

jit003_recal identical to jit003 (recal neutral again). Let me wait for iso_ep400_jit003, jit006, wd1e3.

bash

cd /app/exp && sleep 290 && cat sweep3.log | grep -v Warning | tail -3

output

iso_ep300_jit003           rel=0.2389(±0.0077) mae=0.1523 max=0.6287  (277.1s)
iso_ep300_jit003_recal     rel=0.2389(±0.0077) mae=0.1523 max=0.6284  (325.3s)
iso_ep400_jit003           rel=0.2339(±0.0049) mae=0.1475 max=0.6361  (380.7s)

iso_ep400_jit003: rel 0.2339 (best rel!), mae 0.1475, max 0.6361, and low variance (±0.0049). This is an excellent all-rounder: - rel 0.2339 (best) - mae 0.1475 (near best) - max 0.6361 (good, better than ep400's 0.652) - stable (±0.0049) So ep400 + jitter 0.003 + iso combines the benefits: longer training + small jitter regularization. This looks like the winner. Let me wait for jit006 and wd1e3 to be thorough. jit006 (more jitter) might help max more but hurt rel. wd1e3 tests regularization. Let me wait.

iso_ep400_jit003 is the best all-rounder: rel **0.2339** (best), mae 0.1475, max 0.6361, and low variance (±0.0049). Longer training plus light jitter combines both benefits. Let me see the last two variants.

bash

cd /app/exp && sleep 250 && cat sweep3.log | grep -v Warning | tail -3

output

iso_ep300_jit003_recal     rel=0.2389(±0.0077) mae=0.1523 max=0.6284  (325.3s)
iso_ep400_jit003           rel=0.2339(±0.0049) mae=0.1475 max=0.6361  (380.7s)
iso_ep300_jit006           rel=0.2324(±0.0055) mae=0.1513 max=0.5832  (271.5s)

iso_ep300_jit006: rel 0.2324 (even better!), mae 0.1513, max 0.5832 (best max by far!). More jitter (0.006) at ep300 gives best rel AND best max, with slightly higher mae (0.1513 vs 0.1466). Interesting , so more jitter helps rel and max. Let me reconsider. jitter006 vs ep400_jit003: - jit006_ep300: rel 0.2324, mae 0.1513, max 0.5832 - jit003_ep400: rel 0.2339, mae 0.1475, max 0.6361 jit006 has better rel and much better max, but worse mae. jit003_ep400 has better mae. This suggests I should explore jitter more: maybe jitter 0.008-0.01 at ep400. Let me wait for wd1e3, then run a final micro-sweep on jitter level + epochs combined with more seeds to nail it down. The jitter is acting as strong regularization that improves generalization (rel) and robustness (max), at a small cost to mae. Since rel is primary and max matters, jitter is good. But I earlier saw (sweep1) that jitter with per-axis norm + other augs hurt. Here with iso norm and jitter alone, it helps. Good. Let me wait for wd1e3, then do sweep4: iso + {jitter 0.006, 0.008, 0.010} × {ep300, ep400, ep500} with 4-5 seeds, to find the best rel/mae/max balance. Let me also add jitter at inference? No, jitter is training only; at inference clean points. Good. Wait, actually there's a subtlety: if I train with jitter (noise), the model becomes robust to noise, effectively smoothing. At inference, clean points. This denoising-style regularization clearly helps generalization here. Let me push it. Let me wait for wd1e3.

More jitter (0.006) is even better: rel **0.2324** and max **0.5832** (best worst-case), at a small mae cost. Jitter is acting as powerful regularization. Let me check the last variant, then sweep the jitter level more finely.

bash

cd /app/exp && sleep 180 && cat sweep3.log | grep -v Warning | tail -2

output

iso_ep400_jit003           rel=0.2339(±0.0049) mae=0.1475 max=0.6361  (380.7s)
iso_ep300_jit006           rel=0.2324(±0.0055) mae=0.1513 max=0.5832  (271.5s)

bash

cd /app/exp && sleep 120 && cat sweep3.log | grep -v Warning | tail -2

output

iso_ep300_wd1e3            rel=0.2362(±0.0059) mae=0.1483 max=0.6865  (247.2s)
DONE

wd1e3 (rel 0.2362) similar to wd1e-4. Weight decay doesn't matter much. sweep3 done. Best configs: - iso_ep300_jit006: rel 0.2324, mae 0.1513, max 0.5832 (best rel & max) - iso_ep400_jit003: rel 0.2339, mae 0.1475, max 0.6361 (best mae) Jitter is the key regularizer. Let me now do sweep4: finer jitter around 0.006-0.012, combined with epochs 300-500, to find the sweet spot. I'll use 5 seeds for stability and evaluate on the pooled 132. Actually, let me now switch to including public_test in CV (148 samples) since that's my final training data, giving a more robust estimate and using all data. Let me reconsider: should CV be on 132 or 148? For choosing hyperparameters, more data = more robust estimate. And my final model trains on 148. So CV on 148 is more representative. Let me switch to 148-sample CV for sweep4. But wait , there's a subtlety about using public_test in CV for model selection: it's fine, it's just labeled data. The only "held-out" concern is the hidden set, which I can't see. Using all 148 for CV and final training is the best use of data. Let me create the 148-pool and run sweep4 with finer jitter. Variants: - iso_ep400_jit006 - iso_ep400_jit008 - iso_ep400_jit010 - iso_ep500_jit006 - iso_ep500_jit008 - iso_ep300_jit008 - iso_ep300_jit010 - iso_ep600_jit008 5 seeds, k=6. Each training ep400 ~ longer. Let me estimate: ep400 fold ~ (428/24)=18s, ×30 (5seeds×6folds)=540s per variant, ×8 = 72min. Too long. Let me use 4 seeds and 6 variants, ~36min. Or reduce k=5. Let me do 4 seeds, 7 variants, accept ~45min. Actually, let me be smart: the differences are small (0.232-0.239). I'm optimizing in the noise (±0.005). Let me focus sweep4 on the most promising: jitter 0.006-0.010 at ep400-500, 5 seeds for low noise, fewer variants (5). Let me do: - iso_ep400_jit006 - iso_ep400_jit008 - iso_ep500_jit008 - iso_ep400_jit010 - iso_ep300_jit008 5 variants × 5 seeds × 6 folds = 150 trainings. ep400 ~18s each → ~45min. Hmm. Let me use 4 seeds → 36min. OK. Let me run on 148 pool. Let me write sweep4.

wd doesn't matter. Jitter is the key regularizer. Now I'll finely sweep jitter level × epochs. I'll switch CV to the full 148-sample pool (train+val+public_test), since that's what the final model will train on , this gives the most robust estimate and uses all labeled data.

bash

cd /app/exp && cat > sweep4.py << 'EOF'
import torch, time
from common import *
from kfold import kfold_single2
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
X=torch.cat([tx,vx,px],0); Y=torch.cat([ty,vy,py],0)
print('pool',X.shape[0],flush=True)
b=dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,loss='mse')
S=(0,1,2,3)
variants=[
 ('iso_ep400_jit006',{**b,'epochs':400,'jitter':0.006}),
 ('iso_ep400_jit008',{**b,'epochs':400,'jitter':0.008}),
 ('iso_ep400_jit010',{**b,'epochs':400,'jitter':0.010}),
 ('iso_ep500_jit008',{**b,'epochs':500,'jitter':0.008}),
 ('iso_ep300_jit008',{**b,'epochs':300,'jitter':0.008}),
 ('iso_ep500_jit006',{**b,'epochs':500,'jitter':0.006}),
]
for name,hp in variants:
    t=time.time(); r=kfold_single2(hp,X,Y,cfg,k=6,seeds=S,iso=True,recal=False)
    print(f'{name:20s} rel=%.4f(±%.4f) mae=%.4f max=%.4f  (%.1fs)'%(r[0],r[3],r[1],r[2],time.time()-t),flush=True)
print('DONE',flush=True)
EOF
rm -f sweep4.log && nohup python sweep4.py > sweep4.log 2>&1 &
echo "launched pid $!"

output

launched pid 684

While sweep4 runs, let me reconsider whether I should also validate on the given val/public_test splits using train-only models, as an independent check separate from CV, to make sure my recipe generalizes in the "real" train→holdout setting (not just CV folds). Specifically: train on train (100), evaluate on val (32) and public_test (16). This mimics the actual deployment (train on given data, predict held-out). Let me do that check for the chosen config after sweep4. It's a good sanity check against CV being overly optimistic. Let me also reflect: the CV numbers (~0.232) are for models trained on 5/6 of the pool (~123 samples) and evaluated on the held-out fold. The final model trains on all 148. So final generalization to the hidden set should be ≈ CV estimate or slightly better (more data). Good. Let me wait for sweep4. Each variant ep400 on 148 pool: fold training ~20s, 24 trainings = ~480s. 6 variants = ~48min. That's long. Let me check progress periodically and maybe cut it short if a clear winner emerges. Actually, let me reconsider the time budget. I've used a fair amount. Let me make sure I converge to a final model soon. I'll let sweep4 run, check in ~8 min, and if the first couple results confirm jit006-008/ep400 is good, I'll finalize. Let me monitor.

While sweep4 runs, let me prepare an independent sanity check: training on `train` only (100) and evaluating on the given `val` and `public_test` splits , this mirrors the real train→holdout deployment, complementing the CV. Let me write it for later use.

bash

cd /app/exp && cat > holdout_check.py << 'EOF'
import torch, sys, time
sys.path.insert(0,'/app')
from common import *
from trainer import augment, EMA
from kfold import run_fold_recal
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
import argparse
def eval_cfg(hp, seeds=(0,1,2,3)):
    # train on train(100), eval on val and public_test, average over seeds (single models)
    import statistics as st
    res={'val':[[],[],[]],'test':[[],[],[]]}
    for s in seeds:
        pv,_=run_fold_recal(hp,tx,ty,vx,vy,cfg,seed=s,iso=True)
        pp,_=run_fold_recal(hp,tx,ty,px,py,cfg,seed=s,iso=True)
        for j,m in enumerate(metrics(pv,vy)): res['val'][j].append(m)
        for j,m in enumerate(metrics(pp,py)): res['test'][j].append(m)
    for k in res:
        print(k, 'rel=%.4f mae=%.4f max=%.4f'%(st.mean(res[k][0]),st.mean(res[k][1]),st.mean(res[k][2])))
if __name__=='__main__':
    import json
    hp=json.loads(sys.argv[1]) if len(sys.argv)>1 else dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,loss='mse',epochs=400,jitter=0.008)
    print('hp',hp); eval_cfg(hp)
EOF
echo "written holdout_check.py"

output

written holdout_check.py

bash

cd /app/exp && sleep 230 && cat sweep4.log | grep -v Warning

output

pool 148

Still running first variant (ep400 on 148). Let me wait more. ep400 on 148 pool with 4 seeds × 6 folds = 24 trainings. Each ~20s → 480s. Let me wait.

bash

cd /app/exp && sleep 290 && cat sweep4.log | grep -v Warning

output

pool 148
iso_ep400_jit006     rel=0.2313(±0.0085) mae=0.1428 max=0.6654  (415.2s)

iso_ep400_jit006 on 148 pool: rel 0.2313, mae 0.1428, max 0.6654. Good , rel improved (more data in pool of 148 vs 132). Let me wait for jit008, jit010. Note: the CV on 148 gives slightly better numbers than 132 (more training data per fold). Good sign that final model on 148 will be solid. Let me wait for more.

iso_ep400_jit006 on the 148-pool: rel 0.2313, mae 0.1428 , improved with more data. Let me wait for the higher-jitter and longer-epoch variants.

bash

cd /app/exp && sleep 290 && cat sweep4.log | grep -v Warning

output

pool 148
iso_ep400_jit006     rel=0.2313(±0.0085) mae=0.1428 max=0.6654  (415.2s)
iso_ep400_jit008     rel=0.2322(±0.0110) mae=0.1478 max=0.6515  (402.9s)

jit008: rel 0.2322, mae 0.1478, max 0.6515 , similar to jit006 but higher variance (±0.011). jit006 has better rel/mae, jit008 slightly better max. Let me wait for jit010, ep500 variants. So far jit006/ep400 is best (rel 0.2313, mae 0.1428). Let me continue.

bash

cd /app/exp && sleep 290 && cat sweep4.log | grep -v Warning

output

pool 148
iso_ep400_jit006     rel=0.2313(±0.0085) mae=0.1428 max=0.6654  (415.2s)
iso_ep400_jit008     rel=0.2322(±0.0110) mae=0.1478 max=0.6515  (402.9s)

bash

cd /app/exp && sleep 200 && cat sweep4.log | grep -v Warning

output

pool 148
iso_ep400_jit006     rel=0.2313(±0.0085) mae=0.1428 max=0.6654  (415.2s)
iso_ep400_jit008     rel=0.2322(±0.0110) mae=0.1478 max=0.6515  (402.9s)
iso_ep400_jit010     rel=0.2392(±0.0062) mae=0.1578 max=0.6073  (358.3s)

jit010: rel 0.2392 (worse), mae 0.1578 (worse), max 0.6073 (best max). So too much jitter hurts rel/mae but helps max. There's a clear trade-off: jitter ↑ → max ↓ but rel/mae ↑ (after a point). Sweet spot for rel/mae is jit006, for max is higher. Given rel is primary and I shouldn't trade off, jit006 (rel 0.2313, mae 0.1428, max 0.6654) is the best primary+mae. But max 0.6654 is a bit high. jit008 gives max 0.6515 at rel 0.2322. Let me wait for ep500 variants (jit008, jit006). Longer epochs might improve max for a given jitter. Let me see iso_ep500_jit008 and iso_ep500_jit006. Actually, the decision is getting into noise territory (±0.006-0.011). Let me wait for the ep500 results then finalize. The ep300_jit008 result also pending. Let me wait.

Clear trade-off: more jitter lowers worst-case but raises rel/mae past a point. jit006 is best for rel/mae; jit008–010 better for max. Let me see the ep500 and ep300 variants before deciding.

bash

cd /app/exp && sleep 260 && cat sweep4.log | grep -v Warning

output

pool 148
iso_ep400_jit006     rel=0.2313(±0.0085) mae=0.1428 max=0.6654  (415.2s)
iso_ep400_jit008     rel=0.2322(±0.0110) mae=0.1478 max=0.6515  (402.9s)
iso_ep400_jit010     rel=0.2392(±0.0062) mae=0.1578 max=0.6073  (358.3s)

bash

cd /app/exp && sleep 200 && cat sweep4.log | grep -v Warning | tail -3

output

iso_ep400_jit008     rel=0.2322(±0.0110) mae=0.1478 max=0.6515  (402.9s)
iso_ep400_jit010     rel=0.2392(±0.0062) mae=0.1578 max=0.6073  (358.3s)
iso_ep500_jit008     rel=0.2300(±0.0066) mae=0.1458 max=0.6933  (507.1s)

iso_ep500_jit008: rel 0.2300 (best rel so far!), mae 0.1458, max 0.6933 (higher max though). So ep500 improves rel but worsens max for jit008. Interesting , longer training → better rel/mae but overfits worst-case a bit. Let me wait for ep300_jit008 and ep500_jit006. Current Pareto: - jit006_ep400: rel 0.2313, mae 0.1428, max 0.6654 - jit008_ep500: rel 0.2300, mae 0.1458, max 0.6933 - jit010_ep400: rel 0.2392, mae 0.1578, max 0.6073 These are all within noise (~±0.007). For the primary metric, jit008_ep500 (0.2300) and jit006_ep400 (0.2313) lead. For max, jit010 is best but rel suffers. I want a balanced model. The "don't trade off" instruction suggests I want low on all three. Let me consider a middle ground: jit006-008, ep400-450. max around 0.65, rel around 0.231, mae 0.143-0.146. Let me wait for the final two then finalize. ep500_jit006 might give rel~0.229 with moderate max. Let me see. Actually I'm spending a lot of budget. Let me make a decision after these two. I'll likely pick ep400_jit006 or ep450_jit007 as a balanced choice. Let me wait.

iso_ep500_jit008 gives the best rel (0.2300) but a higher worst-case. These are all within noise (~±0.007). Let me see the final two variants.

bash

cd /app/exp && sleep 250 && cat sweep4.log | grep -v Warning | tail -3

output

iso_ep400_jit010     rel=0.2392(±0.0062) mae=0.1578 max=0.6073  (358.3s)
iso_ep500_jit008     rel=0.2300(±0.0066) mae=0.1458 max=0.6933  (507.1s)
iso_ep300_jit008     rel=0.2357(±0.0071) mae=0.1525 max=0.6094  (302.1s)

iso_ep300_jit008: rel 0.2357, mae 0.1525, max 0.6094 (good max). So for jit008: ep300 → max 0.6094, rel 0.2357; ep500 → max 0.6933, rel 0.2300. Longer epochs improve rel but worsen max. Shorter epochs better max worse rel. So there's an epochs × max trade-off too. Let me wait for the last: ep500_jit006. Decision framework: The primary metric is rel L2. I want the lowest rel while keeping mae and max reasonable. But "do not trade one off against the others" , so I shouldn't pick a config that's great on rel but bad on max. Let me think about a balanced pick: - ep400_jit006: rel 0.2313, mae 0.1428, max 0.6654 , great rel & mae, moderate max - ep300_jit008: rel 0.2357, mae 0.1525, max 0.6094 , good max, worse rel/mae - ep400_jit008: rel 0.2322, mae 0.1478, max 0.6515 , balanced ep400_jit006 has the best mae AND near-best rel, with max 0.665. ep400_jit008 has max 0.6515 at rel 0.2322. I think ep400 with jit ~0.006-0.007 is the sweet spot. Let me pick jit006_ep400 as primary, but consider ep400_jit007 as a compromise. Actually, given the noise, let me make the final decision robustly: run the final candidate with MORE seeds (e.g., 6-8) to confirm, on the 148 pool CV, for 2-3 finalists, then pick. But that's more time. Alternatively, I realize I should think about reducing the worst-case more fundamentally, since max-abs is one of the three metrics and my max (~0.65) corresponds to a single bracket being off by 0.65 kg. Idea: The worst-case errors come from the heaviest brackets (mass up to 2.2) which are rare. In CV folds, when a heavy bracket is in the validation fold, the model underpredicts it. More jitter helps because... it regularizes and maybe prevents overconfident underprediction? Actually jit010 gave best max (0.607) , noise robustness helps the model not to latch onto spurious fine features, generalizing better to unusual heavy shapes. Given the three-metric objective, a reasonable balanced choice is jit008 at ep400 (rel 0.2322, mae 0.1478, max 0.6515) , all three decent. Or jit006 ep400 for best rel/mae. Hmm. Let me wait for ep500_jit006, then decide between ep400_jit006 and ep400_jit008 (or a 0.007 compromise). Let me also do a final confirmation run with more seeds for the 2 finalists. Let me wait.

Longer epochs improve rel but worsen worst-case; jitter does the opposite. Let me get the last variant, then run a higher-seed confirmation on the top finalists.

bash

cd /app/exp && sleep 230 && cat sweep4.log | grep -v Warning | tail -2

output

iso_ep500_jit008     rel=0.2300(±0.0066) mae=0.1458 max=0.6933  (507.1s)
iso_ep300_jit008     rel=0.2357(±0.0071) mae=0.1525 max=0.6094  (302.1s)

bash

cd /app/exp && sleep 120 && cat sweep4.log | grep -v Warning | tail -2

output

iso_ep500_jit008     rel=0.2300(±0.0066) mae=0.1458 max=0.6933  (507.1s)
iso_ep300_jit008     rel=0.2357(±0.0071) mae=0.1525 max=0.6094  (302.1s)

bash

cd /app/exp && sleep 120 && cat sweep4.log | grep -v Warning | tail -2

output

iso_ep500_jit006     rel=0.2269(±0.0081) mae=0.1404 max=0.6709  (429.2s)
DONE

iso_ep500_jit006: rel 0.2269 (BEST rel!), mae 0.1404 (BEST mae!), max 0.6709. So ep500 + jit006 gives the best rel and mae, with max 0.6709. Updated Pareto (148-pool CV, 4 seeds): - ep500_jit006: rel 0.2269, mae 0.1404, max 0.6709 ← best rel & mae - ep400_jit006: rel 0.2313, mae 0.1428, max 0.6654 - ep500_jit008: rel 0.2300, mae 0.1458, max 0.6933 - ep300_jit008: rel 0.2357, mae 0.1525, max 0.6094 ← best max - ep400_jit010: rel 0.2392, mae 0.1578, max 0.6073 So longer training (ep500) + moderate jitter (0.006) is best for the primary metric and MAE. The worst-case (0.67) is middle-of-the-pack. Given rel is primary and ep500_jit006 also wins mae, it's the strongest overall. Its max (0.671) isn't the best but is reasonable (vs best 0.607). Since the instruction says don't sacrifice one for another, I should check: is 0.671 vs 0.607 a meaningful sacrifice? The max is dominated by a single heavy bracket. Let me consider whether going ep500_jit007 or ep600_jit006 could push rel lower while keeping max controlled. The trend: rel improves with epochs (300→500) and with lower jitter (but too low loses max benefit and regularization). jit006 seems a sweet spot. Let me test ep600/700 jit006 and ep500 jit005, to see if rel keeps dropping or plateaus, and watch max. But I'm spending significant budget. Let me do ONE more focused confirmation sweep with more seeds (6) on the top finalists plus a couple of extensions, to (a) reduce noise and (b) check ep600. Then finalize. Finalists for sweep5 (6 seeds, 148-pool CV): - ep500_jit006 (current best) - ep600_jit006 - ep500_jit005 - ep500_jit007 - ep700_jit006 Actually, to manage time, let me do 5 seeds and 4 variants: - ep500_jit006 (confirm) - ep600_jit006 - ep500_jit007 - ep700_jit007 Hmm. Let me think about what I most need: confirm ep500_jit006 is robust and see if more epochs help. Let me run: - ep500_jit006 (6 seeds, tighter estimate) - ep600_jit006 - ep500_jit005 - ep650_jit007 Let me just run 4 variants × 5 seeds. ep500-700 on 148: ~20-28s/fold × 30 = 600-840s per variant. 4 variants = ~40-55 min. That's a lot. Given diminishing returns and noise (~±0.008), the difference between ep500 and ep600 will be within noise. Let me instead do a smaller confirmation: ep500_jit006 and ep600_jit006 with 6 seeds each (2 variants), ~30 min, to pick between them and get a tight estimate. And ep500_jit005 to check if less jitter helps rel further. Actually, let me be decisive. ep500_jit006 is clearly strong (best rel & mae). I'll do a final confirmation with more seeds on 3 candidates: ep500_jit006, ep600_jit006, ep500_jit005. 5 seeds. Then pick the best rel with acceptable max, train final on all 148, save, verify. Let me launch sweep5.

iso_ep500_jit006 is the best yet: rel **0.2269**, mae **0.1404**, max 0.6709. Longer training + moderate jitter wins on the primary metric and MAE. Let me run a higher-seed confirmation on the top candidates (and check whether ep600 or slightly less jitter helps) to choose robustly.

bash

cd /app/exp && cat > sweep5.py << 'EOF'
import torch, time
from common import *
from kfold import kfold_single2
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
X=torch.cat([tx,vx,px],0); Y=torch.cat([ty,vy,py],0)
b=dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,loss='mse')
S=(0,1,2,3,4,5)
variants=[
 ('ep500_jit006',{**b,'epochs':500,'jitter':0.006}),
 ('ep600_jit006',{**b,'epochs':600,'jitter':0.006}),
 ('ep500_jit005',{**b,'epochs':500,'jitter':0.005}),
 ('ep500_jit007',{**b,'epochs':500,'jitter':0.007}),
]
for name,hp in variants:
    t=time.time(); r=kfold_single2(hp,X,Y,cfg,k=6,seeds=S,iso=True,recal=False)
    print(f'{name:16s} rel=%.4f(±%.4f) mae=%.4f max=%.4f  (%.1fs)'%(r[0],r[3],r[1],r[2],time.time()-t),flush=True)
print('DONE',flush=True)
EOF
rm -f sweep5.log && nohup python sweep5.py > sweep5.log 2>&1 &
echo "launched pid $!"

output

launched pid 812

While sweep5 runs (~40 min), let me run the independent holdout check (train on train-only, eval on val and public_test) for the ep500_jit006 config, to validate the recipe in the real train→holdout setting. But GPU contention with sweep5... Let me wait for sweep5 or run holdout check after. Given time, let me run holdout check concurrently but it'll slow sweep5. Actually, let me reconsider my time budget. I've done extensive sweeps. The marginal gains are small now (0.227 vs 0.231). Let me use sweep5 to finalize and not over-optimize. Let me wait for sweep5 partial results. Actually, let me reconsider a more impactful direction while waiting: is there any systematic improvement I'm missing? Let me reconsider the loss. rel L2 is the primary metric. I've been using MSE (standardized). But rel L2 = ||e||/||y||. Minimizing MSE minimizes ||e||² which minimizes rel L2. Good. But here's a thought: the worst-case (max abs) is a separate metric. To reduce it without hurting rel, I could use a loss that's MSE plus a term penalizing the max error in each batch. But that's finicky with small batches. jitter already helps max. I'll leave loss as MSE. Another thought: the label normalization. I standardize labels by train mean/std. Predictions de-standardized linearly. The model's final linear layer outputs standardized mass; with MSE it's fine. Let me reconsider if predicting in a "square-root mass" space could help the heavy-tail... but de-standardization is linear, so I can't. Confirmed earlier. One more: the heaviest bracket (2.2 kg) , in CV, whichever fold holds it, the model underpredicts (training max seen is lower). This drives max-abs. There's not much I can do except hope the hidden set's heavy brackets are within training range. Training on all 148 (including the 2.2 and 2.07 heavy ones) means the final model has seen heavy examples, so it should extrapolate better than CV folds suggest. This is another reason final-on-148 will likely beat CV on worst-case. OK let me just wait for sweep5 and finalize. Let me check progress after a while. Actually, let me reconsider whether to include an even longer run. The rel improved monotonically 300→400→500 (0.2357→0.2313→0.2269 at... wait those are different jitters). For jit006: ep400 0.2313, ep500 0.2269. Let me see if ep600 continues down. sweep5 will tell. If ep600 < ep500, I might go ep600. But risk of worse max. Let me see. Let me wait.

While sweep5 runs, let me reason about the final data choice. Training the final model on all 148 labeled samples (including the heaviest brackets) should generalize better than the CV folds suggest, especially for worst-case, since CV folds sometimes hold out the heaviest examples. Let me monitor sweep5.

bash

cd /app/exp && sleep 280 && cat sweep5.log | grep -v Warning

output

<system>Tool ran without output or errors</system>

ep500 with 6 seeds × 6 folds = 36 trainings × ~20s = 720s. Let me wait ~12 min for first result. Let me wait.

bash

cd /app/exp && sleep 290 && cat sweep5.log | grep -v Warning

output

ep500_jit006     rel=0.2296(±0.0078) mae=0.1422 max=0.6970  (562.1s)

ep500_jit006 with 6 seeds: rel 0.2296 (±0.0078), mae 0.1422, max 0.6970. Slightly higher than the 4-seed estimate (0.2269) , regression to mean with more seeds. Still strong. max 0.697 is a bit high. The rel estimate is ~0.229 ± 0.008. Let me wait for the others. Let me think about the max issue: 0.697 worst-case. That single worst bracket. In the final model trained on all 148, the heavy brackets are in training, so the worst-case on the hidden set depends on hidden heavy brackets. Hard to control. jit007/jit008 gave lower max in some runs. Let me wait for ep600, jit005, jit007.

ep500_jit006 with 6 seeds: rel 0.2296±0.0078, mae 0.1422 , solid. Let me wait for the other candidates to finalize the choice.

bash

cd /app/exp && sleep 290 && cat sweep5.log | grep -v Warning

output

ep500_jit006     rel=0.2296(±0.0078) mae=0.1422 max=0.6970  (562.1s)

bash

cd /app/exp && sleep 290 && cat sweep5.log | grep -v Warning

output

ep500_jit006     rel=0.2296(±0.0078) mae=0.1422 max=0.6970  (562.1s)

ep600 is taking long (600 epochs × 6 seeds × 6 folds). Let me wait more. This is getting time-consuming. Let me check GPU and progress.

bash

nvidia-smi | grep -E "%" | head -1; cd /app/exp && sleep 200 && cat sweep5.log | grep -v Warning

output

| N/A   50C    P0            361W /  700W |   23828MiB /  81559MiB |    100%      Default |
ep500_jit006     rel=0.2296(±0.0078) mae=0.1422 max=0.6970  (562.1s)
ep600_jit006     rel=0.2274(±0.0066) mae=0.1412 max=0.7222  (796.4s)

ep600_jit006: rel 0.2274 (±0.0066), mae 0.1412, max 0.7222. So ep600 slightly better rel/mae than ep500 but worse max (0.722). The rel keeps improving marginally with epochs, but max degrades. Classic overfitting of worst-case with longer training. So there's a clear rel↔max trade-off with epochs. For a balanced model respecting all three metrics, I don't want max creeping to 0.72. Let me wait for jit005 and jit007. Current picture (6-seed, 148-pool): - ep500_jit006: rel 0.2296, mae 0.1422, max 0.6970 - ep600_jit006: rel 0.2274, mae 0.1412, max 0.7222 Let me think about the decision. The three metrics matter. Let me consider a slightly higher jitter to control max while keeping epochs moderate: - From sweep4 (4-seed): ep300_jit008 had max 0.6094 (great) but rel 0.2357. ep400_jit008 rel 0.2322 max 0.6515. ep400_jit010 max 0.6073 rel 0.2392. So to get max down to ~0.61-0.65, I pay ~0.005-0.008 in rel. What's the scoring? "relative L2 over the full set (the primary one)". So rel is most important. MAE and max secondary. A model at rel 0.229, mae 0.142, max 0.65-0.70 seems like a good balance. I'll aim for ep500_jit006 or ep500_jit007. Let me wait for jit007 which might give a nice balance (slightly more jitter → lower max, rel still ~0.23). And jit005 (less jitter → maybe lower rel but higher max). Let me wait.

ep600 slightly improves rel/mae but worsens worst-case (0.722) , longer training overfits the worst case. I'll favor a balanced config. Let me see jit005 and jit007.

bash

cd /app/exp && sleep 280 && cat sweep5.log | grep -v Warning | tail -2

output

ep500_jit006     rel=0.2296(±0.0078) mae=0.1422 max=0.6970  (562.1s)
ep600_jit006     rel=0.2274(±0.0066) mae=0.1412 max=0.7222  (796.4s)

bash

cd /app/exp && sleep 250 && cat sweep5.log | grep -v Warning | tail -2

output

ep500_jit006     rel=0.2296(±0.0078) mae=0.1422 max=0.6970  (562.1s)
ep600_jit006     rel=0.2274(±0.0066) mae=0.1412 max=0.7222  (796.4s)

bash

cd /app/exp && sleep 120 && cat sweep5.log | grep -v Warning | tail -2

output

ep600_jit006     rel=0.2274(±0.0066) mae=0.1412 max=0.7222  (796.4s)
ep500_jit005     rel=0.2391(±0.0102) mae=0.1451 max=0.7673  (589.9s)

ep500_jit005: rel 0.2391 (worse), mae 0.1451, max 0.7673 (worse). So less jitter (0.005) is worse on all , more noise/variance (±0.0102) and higher max. So jit006 is better than jit005. Confirms jit006 is near the sweet spot. Let me wait for jit007. So far jit006 is the sweet spot. ep500_jit006 (rel 0.2296, max 0.697) vs ep600_jit006 (rel 0.2274, max 0.722). Let me see jit007 at ep500.

jit005 is worse on all metrics (more variance), confirming jit006 as the sweet spot. Let me see jit007.

bash

cd /app/exp && sleep 280 && cat sweep5.log | grep -v Warning | tail -2

output

ep600_jit006     rel=0.2274(±0.0066) mae=0.1412 max=0.7222  (796.4s)
ep500_jit005     rel=0.2391(±0.0102) mae=0.1451 max=0.7673  (589.9s)

bash

cd /app/exp && sleep 200 && cat sweep5.log | grep -v Warning | tail -2

output

ep600_jit006     rel=0.2274(±0.0066) mae=0.1412 max=0.7222  (796.4s)
ep500_jit005     rel=0.2391(±0.0102) mae=0.1451 max=0.7673  (589.9s)

bash

cd /app/exp && sleep 150 && cat sweep5.log | grep -v Warning | tail -2

output

ep500_jit007     rel=0.2292(±0.0099) mae=0.1439 max=0.6768  (603.6s)
DONE

ep500_jit007: rel 0.2292 (±0.0099), mae 0.1439, max 0.6768. Final comparison (6-seed, 148-pool CV): - ep500_jit006: rel 0.2296, mae 0.1422, max 0.6970 - ep600_jit006: rel 0.2274, mae 0.1412, max 0.7222 - ep500_jit005: rel 0.2391, mae 0.1451, max 0.7673 - ep500_jit007: rel 0.2292, mae 0.1439, max 0.6768 So ep500_jit007 has rel≈ep500_jit006 (0.2292 vs 0.2296) but better max (0.6768 vs 0.6970). And ep600_jit006 has best rel (0.2274) and mae (0.1412) but worst max (0.7222). Considering all three metrics and the "don't trade off" guidance: - ep500_jit007: rel 0.2292, mae 0.1439, max 0.6768 , balanced, good on all three - ep600_jit006: rel 0.2274, mae 0.1412, max 0.7222 , best rel/mae but worst max ep500_jit007 seems like the best balance. Its rel is essentially tied with ep500_jit006 and close to ep600, mae is good, and max is the best among the strong-rel configs. Actually, let me reconsider: the differences in rel (0.2274-0.2296) are within the ±0.007-0.010 noise. The max differences (0.677-0.722) are more meaningful. So to respect all three metrics, I should pick the config with the best max among those with top-tier rel/mae. That's ep500_jit007 (max 0.6768, rel 0.2292, mae 0.1439). But wait , ep400_jit008 from sweep4 (4-seed) had rel 0.2322, max 0.6515. And ep300_jit008 had max 0.6094 but rel 0.2357. These offer lower max at a rel cost. Let me settle on a principled choice. The PRIMARY metric is rel L2. Secondary are mae and max. I'll pick the config that minimizes rel while keeping mae and max competitive. Among my best: - ep500_jit007 gives rel 0.2292 (top tier), mae 0.1439 (good), max 0.6768 (best among top-rel). I'll go with ep500_jit007 as the final config. It's the best balanced choice: top-tier primary metric with the best worst-case among top-tier configs. Actually, let me reconsider ep500_jit006 vs jit007 once more. They're statistically tied on rel/mae; jit007 has clearly better max (0.677 vs 0.697). So jit007 dominates jit006 in practice (same rel/mae, better max). I'll choose jit007. Hmm, but one concern: these are noisy (6 seeds, ±0.01). The max metric especially is driven by one sample and very noisy across seeds. The "true" ranking between jit006/jit007 is uncertain. But jit007 is a reasonable, slightly-more-regularized choice that trends toward better worst-case. I'll go ep500_jit007. Now, decisions for the FINAL model: 1. Config: iso norm, ep500, jit007, lr1e-3, wd1e-4, bs16, ema0.99, mse loss. 2. Training data: all 148 (train+val+public_test). 3. Seed: The final model is a single training on 148. Seed variance exists (±0.01 rel). To reduce the risk of an unlucky seed, I can't ensemble, but I can pick the best seed by... I have no holdout. Hmm, how to choose the seed for the final model robustly? Options: (a) Just use seed 0. (b) Train several seeds on 148, and for each, evaluate on... the training set itself (not meaningful) or do a nested CV. (c) Train on 132 (train+val), use public_test (16) to pick the best seed, then... but that model is trained on 132 not 148. Actually, the cleanest robust approach given I can only ship ONE model: train on all 148 with a FIXED seed. The CV already confirms the recipe is robust (±0.01). Any single seed gives ~0.229 ± 0.01. But I could do slightly better: Since EMA already averages along the trajectory, the single model is fairly stable. To hedge against a bad seed, I could train, say, 3 seeds on 148 and pick the one whose predictions on a small held-out... no held-out. Alternative robust idea: train on 148 with a few seeds, and pick the seed whose predictions correlate best with the ensemble-of-seeds predictions on the training data (i.e., the most "central" / representative model). The model closest to the ensemble mean is likely the most robust. This is a legitimate way to pick a single model without a holdout. Let me do that: train K seeds on 148, compute each model's predictions on all 148, compute the ensemble mean prediction, then pick the seed wh …[truncated]

sweep5 complete. Final comparison (6-seed, 148-pool CV): - **ep500_jit007: rel 0.2292, mae 0.1439, max 0.6768** , best worst-case among top-tier configs - ep500_jit006: rel 0.2296, mae 0.1422, max 0.6970 - ep600_jit006: rel 0.2274, mae 0.1412, max 0.7222 (best rel/mae but worst max) - ep500_jit005: rel 0.2391, max 0.7673 (worse) ep500_jit007 is the best balance , top-tier primary metric with the best worst-case. Now an important independent reality check: train on `train` only and evaluate on the real `val` and `public_test` splits (not CV folds), to confirm the recipe generalizes in the true train→holdout setting.

bash

cd /app/exp && python holdout_check.py '{"lr":1e-3,"wd":1e-4,"bs":16,"ema":0.99,"loss":"mse","epochs":500,"jitter":0.007}' 2>&1 | grep -v Warning

output

hp {'lr': 0.001, 'wd': 0.0001, 'bs': 16, 'ema': 0.99, 'loss': 'mse', 'epochs': 500, 'jitter': 0.007}
val rel=0.1968 mae=0.1325 max=0.4269
test rel=0.3346 mae=0.2011 max=0.7179

Train-on-train-only (100 samples), eval: - val (32): rel 0.1968, mae 0.1325, max 0.4269 , good! - public_test (16): rel 0.3346, mae 0.2011, max 0.7179 , worse. So val is much better (0.197) than public_test (0.335). public_test is a harder/different 16 samples. This is the averaged over 4 seeds. The discrepancy between val and public_test (0.197 vs 0.335) reflects that public_test is a small, possibly harder sample. Recall public_test has no heavy brackets (max 1.449) and includes some tricky light ones (0.198). Earlier per-sample analysis showed the model overpredicts the lightest bracket (0.198→0.556) and one mid bracket (0.596→1.168). These specific errors inflate public_test rel. The CV over 148 pools everything and gives 0.229, which is between val (0.197) and public_test (0.335). The hidden test set is presumably a larger, more representative sample, so its rel should be closer to the CV estimate (~0.23) than to the noisy 16-sample public_test. This confirms: my recipe generalizes (val rel 0.197 is strong). public_test is just a hard small subset. I should NOT overfit to public_test. Now, crucially: my final model will train on all 148 (including public_test's 16 and val's 32), so it will have seen more data and cover more of the distribution. This should help on the hidden set. Let me now also sanity-check: does including val+public_test in training actually help generalization to a truly held-out set? I can simulate: train on train+val (132), evaluate on public_test (16) as held-out, vs train on train (100) eval public_test. If adding val helps public_test, then adding more data helps. Let me quickly check train+val → public_test. Actually, I already know more data helps (CV on 148 > CV on 132). Let me not belabor. Let me reconsider whether I should reduce the public_test errors by any means. The issue is specific hard brackets. Since the final model trains on ALL 148 including those, it won't have that exact problem on those samples (they're in training), but the hidden set may have similar hard cases. jitter helps robustness. I've already incorporated that. Let me also verify my earlier concern: the initial saved model (ep300 jit004, trained on train+val only, 132) gave public_test rel 0.297. Now with ep500 jit007 trained on all 148, public_test is in-sample so it'll be low, but that's not a valid estimate. For a valid estimate I rely on CV (0.229) and the train→val holdout (0.197). I'm fairly confident in the recipe. Let me now produce the FINAL model: - Config: iso, ep500, jit007, lr1e-3, wd1e-4, bs16, ema0.99, mse. - Data: all 148. - Seed selection: To hedge seed variance, let me train a few seeds and pick the most central one (closest to the mean prediction across seeds on the training data). Even though in-sample, the spread across seeds on in-sample points reflects model-to-model variation; picking the central one avoids outlier seeds. Actually, let me reconsider , a cleaner hedge: Let me reconsider the seed robustness more carefully with a valid method. I'll do this: Use the 148-pool, hold out a RANDOM 20% as a selection set, train the final config on the other 80% for each of several seeds, and pick the seed with best selection-set rel. Then RETRAIN that seed on all 148. But retraining changes the model (different data), so the "best seed" may not transfer. Seeds' relative quality isn't very transferable anyway. Given the low CV variance (±0.007-0.01) and that EMA stabilizes, I'll just train on all 148 with a fixed seed. To slightly de-risk, I'll pick the seed whose full-148 predictions are closest to the average of several seeds' predictions (central model). Let me implement that quickly , it's cheap and defensible, and at worst is neutral. Actually, simpler defensible approach that genuinely reduces variance: I'll train a few seeds on all 148 and select the model whose predictions on the 148 training points have the LOWEST disagreement with the ensemble mean (most central). This picks a repres …[truncated]

Strong independent check: trained on `train` only, the model reaches **val rel 0.197** (mae 0.133) , confirming the recipe genuinely generalizes. public_test (0.335) is a harder 16-sample subset (no heavy brackets, a couple of tricky light ones), consistent with its small-sample noise. The 148-pool CV (~0.229) sits between them and is my best estimate for the hidden set. Now I'll produce the final model on all 148 samples. To hedge seed variance (without an ensemble, since only one checkpoint ships), I'll train several seeds and select the most *central* model (closest to the mean prediction). Let me first verify seeds differ enough in-sample to make this meaningful.

bash

cd /app && python -c "
import torch, sys
sys.path.insert(0,'/app'); sys.path.insert(0,'/app/exp')
from solution_train import train_final
from common import load_cfg, load_split, metrics
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
X=torch.cat([tx,vx,px],0); Y=torch.cat([ty,vy,py],0)
hp=dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,loss='mse',epochs=500,jitter=0.007)
preds=[]
for s in range(3):
    m,fm,fs,lm,ls=train_final(hp,X,Y,cfg,seed=s,iso=True,recal=False)
    with torch.no_grad():
        p=torch.cat([m((X[i:i+64]-fm)/fs)*ls+lm for i in range(0,X.shape[0],64)],0)
    preds.append(p.reshape(-1))
    print('seed',s,'train-fit rel',metrics(p,Y)[0])
P=torch.stack(preds,0)
print('mean pairwise std across seeds (in-sample):', (P.std(0)).mean().item())
print('range of per-sample preds example (first 5):')
for i in range(5): print('  y=%.3f preds=%s'%(Y.reshape(-1)[i].item(), [round(P[j,i].item(),3) for j in range(3)]))
" 2>&1 | grep -v Warning

output

seed 0 train-fit rel 0.06244777515530586
seed 1 train-fit rel 0.052600450813770294
seed 2 train-fit rel 0.04749678075313568
mean pairwise std across seeds (in-sample): 0.018196849152445793
range of per-sample preds example (first 5):
  y=1.640 preds=[1.638, 1.649, 1.67]
  y=1.383 preds=[1.351, 1.396, 1.374]
  y=0.641 preds=[0.576, 0.576, 0.638]
  y=0.604 preds=[0.513, 0.536, 0.535]
  y=0.369 preds=[0.331, 0.327, 0.332]

The seeds fit the training data to rel ~0.05-0.06 (not perfectly , EMA + jitter prevent overfitting), and the in-sample prediction std across seeds is ~0.018. So seeds DO differ meaningfully in-sample. The "central model" selection is discriminative. Now let me implement the final production: 1. Train K=7 seeds on all 148 with ep500_jit007 iso. 2. Compute each model's predictions on all 148. 3. Compute ensemble mean prediction Pbar. 4. Select the seed whose predictions are closest to Pbar (most central) , this is the single model that best represents the consensus, hedging against outlier seeds. 5. Save that model. Wait, but is "closest to ensemble mean on training data" really selecting a better generalizer? The ensemble mean is a better predictor than individuals. The individual closest to the ensemble mean is the one most aligned with consensus. This is a reasonable proxy for robustness. However, it's measured in-sample. A model could be central in-sample but still vary out-of-sample. An alternative, more direct approach: use the CV/holdout structure to pick. But for the final all-148 model, I can't. Hmm, let me reconsider. Actually there's a cleaner idea that directly targets generalization: Instead of selecting a seed, produce the model via a method that inherently reduces variance. Since EMA within a run is already done, and cross-seed weight averaging fails for independent inits... Actually, one more legitimate variance-reduction technique for a SINGLE shipped model: knowledge distillation. Train an ensemble of K seed-models (teachers), compute their averaged predictions on the training inputs (a better target), then train a SINGLE student model (same architecture) to match these ensemble predictions (plus true labels). The student, trained to mimic the ensemble, inherits much of the ensemble's reduced variance in a single model! This is a well-known trick and fully compatible with shipping one state_dict. This could genuinely improve over a single seed. The student learns from the smoothed ensemble targets which are less noisy than individual seeds. Let me consider doing this: 1. Train K teacher models (seeds) on all 148. 2. Compute ensemble mean prediction on all 148 training points: T_i = mean_k pred_k(x_i). 3. Train a student (same arch) on all 148 with targets = blend of true labels and ensemble predictions (or just ensemble predictions, or true labels , since teachers fit training data well, T_i ≈ y_i on training; distillation mainly helps via soft targets on held-out, which we don't have here). Wait , the issue: on the TRAINING inputs, the ensemble predictions T_i are close to the true labels y_i (since teachers fit training data). So distilling on training inputs gives targets ≈ y_i, no new info. Distillation helps when you have unlabeled data where teacher ensemble provides soft targets. Here I could generate AUGMENTED inputs (jittered/noisy versions of training clouds) and use the ensemble's predictions on those as distillation targets. That provides new (input, soft-target) pairs capturing the ensemble's smooth behavior, which the student mimics → student approximates the ensemble function → lower variance. This is promising but complex and time-consuming. Given my budget, let me weigh it. The potential gain: ensemble of K models typically reduces rel L2 noticeably vs single model. If single ~0.229, ensemble might be ~0.21-0.22. A distilled student could capture part of that, maybe ~0.22. Actually, let me reconsider whether I can just ship the ensemble disguised as one model. No , the architecture is fixed to a single PointNetRegressor; I can't fit K models into it. Distillation into augmented inputs is the way to transfer ensemble benefits to one model. Let me estimate if it's worth it and feasible: - Generate a large set of jittered training clouds (e.g., 148 × 20 = 2960 augmented samples), get ensemble mean predictions as targets. - Train student on these. But the student is the same small mo …[truncated]

Seeds differ meaningfully in-sample (cross-seed std ≈ 0.018), so central-model selection is discriminative. Before finalizing, let me quantify the *ensemble ceiling* , how much a multi-seed ensemble would help , to decide whether distilling an ensemble into one model is worth it versus simply selecting the central seed.

bash

cd /app/exp && cat >> kfold.py << 'EOF'

def kfold_ensemble(hp, X, Y, cfg, k=6, seeds=(0,1,2,3,4,5), iso=True):
    # average predictions across seeds (ENSEMBLE) -> out-of-fold metrics
    N=X.shape[0]
    g=torch.Generator().manual_seed(123); perm=torch.randperm(N,generator=g)
    folds=[perm[i::k] for i in range(k)]
    allp=torch.zeros(N,1,device=X.device)
    for fi in range(k):
        vidx=folds[fi].to(X.device)
        tmask=torch.ones(N,dtype=torch.bool); tmask[folds[fi]]=False
        tidx=tmask.nonzero().squeeze(1).to(X.device)
        acc=torch.zeros(len(vidx),1,device=X.device)
        for s in seeds:
            pv,_=run_fold_recal(hp,X[tidx],Y[tidx],X[vidx],Y[vidx],cfg,seed=s,iso=iso)
            acc+=pv
        allp[vidx]=acc/len(seeds)
    return metrics(allp,Y)
EOF
cd /app/exp && cat > ens.py << 'EOF'
import torch
from common import *
from kfold import kfold_ensemble
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
X=torch.cat([tx,vx,px],0); Y=torch.cat([ty,vy,py],0)
hp=dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,loss='mse',epochs=500,jitter=0.007)
r=kfold_ensemble(hp,X,Y,cfg,k=6,seeds=(0,1,2,3,4,5),iso=True)
print('ENSEMBLE(6 seeds) CV rel=%.4f mae=%.4f max=%.4f'%r,flush=True)
print('DONE',flush=True)
EOF
rm -f ens.log && nohup python ens.py > ens.log 2>&1 &
echo "launched pid $!"

output

launched pid 969

This ensemble CV will take ~600s (6 seeds × 6 folds × ep500). While it runs, let me write the final production script with distillation capability, so I'm ready either way. Let me design the final production pipeline to support both: (A) central-seed selection, and (B) ensemble distillation. For distillation: 1. Train K teacher models on all 148 (seeds 0..K-1), store their EMA weights. 2. Build a distillation dataset: for each training cloud, create multiple jittered copies; teacher target = mean over teachers of prediction on that jittered cloud. Also include clean clouds with true labels. 3. Train student (same arch) with loss = MSE to targets. Possibly mix: on clean inputs use true labels; on jittered inputs use ensemble soft targets. Actually, a cleaner distillation that's known to work: train the student on the SAME inputs as teachers (with on-the-fly jitter), but with targets = a blend α*y_true + (1-α)*ensemble_pred_on_that_exact_jittered_input. Computing ensemble pred on each on-the-fly jittered batch requires running K teachers each step , expensive but K=5-7 small models is fine. Simpler and effective: Since on training inputs ensemble≈labels, the main benefit is matching the ensemble's function shape via augmented inputs. Let me do: student trained with jitter like teachers, but target = ensemble prediction on the clean input (precomputed once) , this is ≈ label but denoised. Actually that's nearly the same as labels. Honestly the ensemble CV result will tell me if it's worth it. Let me wait for it. If ensemble rel is, say, 0.205 vs single 0.229, that's a big gain worth distilling. If it's ~0.225, not worth it; I'll just central-select. Let me write the production script supporting central-seed selection (the safe default) now, and add distillation only if the ensemble shows large gains. Let me write final_produce.py with central-seed selection and K seeds.

While the ensemble CV runs, let me write the final production script supporting central-seed selection (safe default). I'll decide on distillation based on the ensemble ceiling.

bash

cd /app && cat > final_produce.py << 'EOF'
import torch, sys, argparse, json
sys.path.insert(0,'/app'); sys.path.insert(0,'/app/exp')
from solution_train import train_final, save_ckpt
from common import load_cfg, load_split, metrics

def main(K=7, epochs=500, jitter=0.007, out='/app/outputs/model.pt'):
    cfg=load_cfg()
    tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
    X=torch.cat([tx,vx,px],0); Y=torch.cat([ty,vy,py],0)
    hp=dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,loss='mse',epochs=epochs,jitter=jitter)
    models=[]; preds=[]
    for s in range(K):
        m,fm,fs,lm,ls=train_final(hp,X,Y,cfg,seed=s,iso=True,recal=False)
        with torch.no_grad():
            p=torch.cat([m((X[i:i+64]-fm)/fs)*ls+lm for i in range(0,X.shape[0],64)],0)
        models.append((m,fm,fs,lm,ls)); preds.append(p.reshape(-1))
        print(f'seed {s} trained, train-fit rel={metrics(p,Y)[0]:.4f}',flush=True)
    P=torch.stack(preds,0)               # [K,N]
    Pbar=P.mean(0)                        # ensemble mean (reference)
    dist=((P-Pbar[None])**2).mean(1)      # distance of each seed to consensus
    central=int(dist.argmin())
    print('per-seed dist to consensus:', [round(d.item(),5) for d in dist], '-> central seed', central, flush=True)
    m,fm,fs,lm,ls=models[central]
    save_ckpt(m,fm,fs,lm,ls,out)
    print('saved central model (seed %d)'%central, flush=True)

if __name__=='__main__':
    ap=argparse.ArgumentParser()
    ap.add_argument('--K',type=int,default=7)
    ap.add_argument('--epochs',type=int,default=500)
    ap.add_argument('--jitter',type=float,default=0.007)
    a=ap.parse_args()
    main(a.K,a.epochs,a.jitter)
EOF
echo "written final_produce.py"; sleep 180 && cat /app/exp/ens.log | grep -v Warning

output

written final_produce.py

Ensemble CV still running. Let me wait more.

bash

cd /app/exp && sleep 240 && cat ens.log | grep -v Warning

output

<system>Tool ran without output or errors</system>

bash

cd /app/exp && sleep 150 && cat ens.log | grep -v Warning

output

ENSEMBLE(6 seeds) CV rel=0.2204 mae=0.1395 max=0.6388
DONE

Ensemble (6 seeds) CV: rel 0.2204, mae 0.1395, max 0.6388. Compare to single model (6-seed avg of single-model CV): rel 0.2292, mae 0.1439, max 0.6768. So the ensemble improves: - rel: 0.2292 → 0.2204 (−0.009, ~4% relative improvement) - mae: 0.1439 → 0.1395 - max: 0.6768 → 0.6388 The ensemble is meaningfully better on ALL THREE metrics, especially worst-case (0.677→0.639). This makes sense , averaging reduces variance and tames outliers. So there IS real headroom (~0.009 rel, and notably better max) from ensembling. Since I can only ship ONE model, distillation to transfer the ensemble's behavior into a single model is worth attempting. If distillation captures even half the gain, I'd get rel ~0.225 and better max. Let me design the distillation carefully: Approach: 1. Train K teacher models (EMA) on all 148. (I'll use K=6 or 8.) 2. Create a distillation target function: the ensemble's prediction. 3. Train a student (same arch) to match the ensemble on a rich set of inputs. Key question: what inputs to distill on? On clean training inputs, ensemble≈labels. The ensemble's ADVANTAGE is its smoother generalization to nearby/unseen inputs. To transfer that, I should distill on AUGMENTED inputs (jittered versions of training clouds), using the ensemble's prediction on each augmented input as the target. This teaches the student the ensemble's behavior in the neighborhoods around training data (a denoised, lower-variance function), which should generalize better than training on hard labels alone. Concretely, per training step: - Sample a batch of training clouds. - Apply jitter (same as teacher training, σ=0.007) to get augmented inputs x'. - Compute teacher ensemble target: t = mean_k teacher_k(normalize(x')) [in physical units], with teachers in eval mode. (No grad.) - Also have true labels y for the clean clouds. - Student loss = MSE(student(normalize(x')), standardized target), where target = β*t + (1−β)*y_true. With β=1 (pure distillation on augmented inputs), the student learns the ensemble function. With β<1, blends with ground truth. Since ensemble on augmented inputs is a denoised target that already reflects labels, β=1 is reasonable, but a blend (e.g., β=0.5-0.7) keeps the student anchored to true labels and may be safer. Let me make β a hyperparameter and validate via CV. Validation of distillation via CV: This is trickier because distillation requires training teachers per fold. I can implement a fold function that: trains K teachers on fold-train, then trains a student on fold-train (with distillation), evaluates student on fold-val. Compare student CV rel to single-model CV (0.229) and ensemble CV (0.220). If student CV < 0.229, distillation helps; ideally approaching 0.220. This is expensive: per fold, train K teachers + 1 student. K=5 teachers → 6 trainings per fold × 6 folds = 36 trainings per seed-of-student. With a couple of student seeds, ~72 trainings × ep500 (~20s) = ~24 min per config. Doable for 1-2 configs. Let me implement distillation in a fold function and test a couple of β values. Let me be efficient: K=5 teachers, ep for teachers 500, student ep 500, jitter 0.007. Test β ∈ {0.5, 1.0} and maybe distill with MORE augmentation diversity. Actually, let me reconsider the compute. Teachers can be shared across β values within a fold (train teachers once, then train students with different β). Let me structure the CV to train teachers once per fold, cache their predictions strategy... but teacher predictions depend on the augmented inputs sampled during student training (on-the-fly). To keep it simple and correct, I'll run teachers live during student training (K forward passes per batch, cheap). To test multiple β efficiently in CV, I'll train teachers once per fold and then train a student per β reusing those teachers. Let me write a distill CV function. Let me implement and run a distillation CV for β=1.0 and β=0.6, K=5, to see if it beats 0.229. Given time, let me do k=6 folds, 1 stud …[truncated]

The 6-seed **ensemble** CV is clearly better on all three metrics: rel 0.2204 (vs single 0.2292), mae 0.1395, and notably max 0.6388 (vs 0.6768). Since I can only ship one model, it's worth trying to **distill the ensemble into a single model**. Let me implement and CV-test ensemble distillation (train teachers per fold, train a student to match the ensemble on jittered inputs).

bash

cd /app/exp && cat > distill.py << 'EOF'
import torch, math, time, sys
import torch.nn as nn, torch.nn.functional as F
from common import build_model, load_cfg, load_split, compute_norm, predict, metrics
from trainer import augment, EMA

def train_teacher(hp, tx, tyn, fm, fs, cfg, seed):
    torch.manual_seed(seed); torch.cuda.manual_seed_all(seed)
    model=build_model(cfg).cuda()
    opt=torch.optim.AdamW(model.parameters(),lr=hp['lr'],weight_decay=hp['wd'])
    epochs=hp['epochs']; bs=hp['bs']; N=tx.shape[0]
    steps_per=math.ceil(N/bs); total=steps_per*epochs; warmup=int(total*0.05)
    def lr_sc(s):
        if s<warmup: return s/max(warmup,1)
        p=(s-warmup)/max(total-warmup,1); return 0.5*(1+math.cos(math.pi*p))
    ema=EMA(model,hp.get('ema',0.99)); step=0
    for ep in range(epochs):
        model.train(); perm=torch.randperm(N,device=tx.device)
        for i in range(0,N,bs):
            idx=perm[i:i+bs]; xb=augment(tx[idx],hp); xb=(xb-fm)/fs; yb=tyn[idx]
            for g in opt.param_groups: g['lr']=hp['lr']*lr_sc(step)
            opt.zero_grad(set_to_none=True); loss=F.mse_loss(model(xb),yb); loss.backward(); opt.step(); ema.update(model); step+=1
    em=build_model(cfg).cuda(); em.load_state_dict(model.state_dict()); ema.copy_to(em); em.eval()
    return em

def train_student(hp, teachers, tx, ty, fm, fs, lm, ls, cfg, seed, beta):
    tyn=(ty-lm)/ls
    torch.manual_seed(seed+777); torch.cuda.manual_seed_all(seed+777)
    model=build_model(cfg).cuda()
    opt=torch.optim.AdamW(model.parameters(),lr=hp['lr'],weight_decay=hp['wd'])
    epochs=hp['epochs']; bs=hp['bs']; N=tx.shape[0]
    steps_per=math.ceil(N/bs); total=steps_per*epochs; warmup=int(total*0.05)
    def lr_sc(s):
        if s<warmup: return s/max(warmup,1)
        p=(s-warmup)/max(total-warmup,1); return 0.5*(1+math.cos(math.pi*p))
    ema=EMA(model,hp.get('ema',0.99)); step=0
    for ep in range(epochs):
        model.train(); perm=torch.randperm(N,device=tx.device)
        for i in range(0,N,bs):
            idx=perm[i:i+bs]; xraw=augment(tx[idx],hp); xb=(xraw-fm)/fs
            with torch.no_grad():
                t=torch.zeros(len(idx),1,device=tx.device)
                for tt in teachers: t+=tt(xb)
                t/=len(teachers)                      # ensemble target (standardized)
            target=beta*t+(1-beta)*tyn[idx]
            for g in opt.param_groups: g['lr']=hp['lr']*lr_sc(step)
            opt.zero_grad(set_to_none=True); loss=F.mse_loss(model(xb),target); loss.backward(); opt.step(); ema.update(model); step+=1
    em=build_model(cfg).cuda(); em.load_state_dict(model.state_dict()); ema.copy_to(em); em.eval()
    return em

def distill_cv(hp, X, Y, cfg, k=5, K=5, betas=(1.0,), iso=True):
    N=X.shape[0]
    g=torch.Generator().manual_seed(123); perm=torch.randperm(N,generator=g)
    folds=[perm[i::k] for i in range(k)]
    out={b:torch.zeros(N,1,device=X.device) for b in betas}
    ens=torch.zeros(N,1,device=X.device)
    for fi in range(k):
        vidx=folds[fi].to(X.device)
        tmask=torch.ones(N,dtype=torch.bool); tmask[folds[fi]]=False
        tidx=tmask.nonzero().squeeze(1).to(X.device)
        tx,ty=X[tidx],Y[tidx]; vx=X[vidx]
        fm,fs,lm,ls=compute_norm(tx,ty,iso=iso); tyn=(ty-lm)/ls
        teachers=[train_teacher(hp,tx,tyn,fm,fs,cfg,seed=s) for s in range(K)]
        # ensemble ref on val
        with torch.no_grad():
            ev=torch.zeros(len(vidx),1,device=X.device)
            for tt in teachers: ev+=predict(tt,vx,fm,fs,lm,ls)
            ens[vidx]=ev/K
        for b in betas:
            stu=train_student(hp,teachers,tx,ty,fm,fs,lm,ls,cfg,seed=0,beta=b)
            out[b][vidx]=predict(stu,vx,fm,fs,lm,ls)
    res={b:metrics(out[b],Y) for b in betas}
    return res, metrics(ens,Y)
EOF
cat > distill_run.py << 'EOF'
import torch, time
from common import *
from distill import distill_cv
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
X=torch.cat([tx
…[truncated]

output

launched pid 1015

This distillation CV will take a while: per fold, 5 teachers + 2 students = 7 trainings × ep500 (~20s, students slower due to teacher forward passes). 7 × ~25s = 175s/fold × 5 folds = ~875s ≈ 15 min. Let me wait. While waiting, let me reconsider: the key comparison will be student (beta=1.0 and 0.6) CV rel vs: - single-model CV ~0.229 (with this exact k=5? I used k=6 before; k=5 might differ slightly) - ensemble CV (will be printed, ~0.22) Note: this distill_cv uses k=5 (not 6) and K=5 teachers (not 6), so the ensemble ref here might be slightly different from the 0.2204 I got with k=6,K=6. That's fine; I'll compare student vs ensemble within the same run. If student beta rel is between single (0.229) and ensemble (0.22), distillation helps. If it's ≈ single, distillation doesn't transfer and I'll just ship the central seed. Let me wait and check. Meanwhile, let me think about the final decision tree: - If distillation student CV rel ≤ ~0.224 (clearly better than single 0.229) AND max is good → produce final via distillation: train K teachers on all 148, then train student on all 148 with best beta. Ship student. - Else → ship central-seed model (final_produce.py). Let me also make sure that if I go with distillation, the final student training is implemented to train on all 148 (no val split) , I have train_teacher and train_student that take tx,ty directly, so I can call them on all 148. Good. I'll write a final_distill_produce if needed. Let me wait for distill results.

The distillation CV will take ~15 min (5 teachers + 2 students per fold). Let me wait for it while it runs in the background.

bash

cd /app/exp && sleep 290 && cat distill_run.log | grep -v Warning

output

<system>Tool ran without output or errors</system>

bash

cd /app/exp && sleep 290 && cat distill_run.log | grep -v Warning

output

<system>Tool ran without output or errors</system>

Still running (no output until DONE since all prints are at the end). Let me check GPU activity and wait more.

bash

nvidia-smi | grep -E "%" | head -1; cd /app/exp && sleep 200 && cat distill_run.log | grep -v Warning

output

| N/A   53C    P0            410W /  700W |    4953MiB /  81559MiB |    100%      Default |
ensemble(K=5,k=5) CV rel=0.2248 mae=0.1413 max=0.7584
student beta=1.00 CV rel=0.2388 mae=0.1493 max=0.8858
student beta=0.60 CV rel=0.2380 mae=0.1488 max=0.8596
(607.6s) DONE

Results: - ensemble(K=5, k=5): rel 0.2248, mae 0.1413, max 0.7584 - student beta=1.0: rel 0.2388, mae 0.1493, max 0.8858 - student beta=0.6: rel 0.2380, mae 0.1488, max 0.8596 So the distilled students are WORSE than the single-model CV (~0.229-0.235 at k=5)! Student rel 0.238-0.239, worse than single model. And much worse max (0.86-0.88). Distillation did NOT help , it actually hurt. Why? A few reasons: 1. The student has a single seed (seed 0+777), so it's one model, not averaging. Distilling the ensemble's targets onto one model doesn't reduce variance the way true averaging does, because the student still has its own optimization noise and can't represent the ensemble (which is a richer function). The student just approximates a slightly smoothed target but adds its own variance. 2. The ensemble target on jittered inputs ≈ labels anyway (teachers fit training data), so the student is basically training on near-labels with extra noise from the jitter+teacher approximation , no real benefit, plus the student seed here might just be a poorer seed (the max 0.88 suggests an unlucky student). 3. Single-seed student is noisy; this CV used only 1 student seed, so it's a noisy estimate. Conclusion: Distillation into a single model does NOT transfer the ensemble benefit here. The ensemble benefit comes fundamentally from averaging multiple models, which I can't ship. So I'll abandon distillation and go with the robust single model via central-seed selection. The central-seed selection at least avoids unlucky seeds and is guaranteed not worse than a random single seed. Actually, wait , let me reconsider the central-seed selection value. The single-model CV over 6 seeds was rel 0.2292 (mean). The central seed should be near the middle, so ~0.229. That's my expected final. The ensemble would've been 0.220 but I can't ship it. Hold on , is there truly no way to ship an ensemble? The constraint: "your saved checkpoint has to load back into this same architecture." The grader builds ONE PointNetRegressor and loads my state_dict. So strictly one model. Confirmed no ensemble. Let me reconsider central-seed selection once more. Is it actually better than a fixed seed? It selects the model closest to the consensus. This avoids outlier seeds (both good-outlier and bad-outlier). Since bad outliers (high rel) are the risk, and they're likely also far from consensus, central selection should avoid the worst seeds. Expected to be slightly better than random single seed and lower variance. Good. I'll use it with a decent K (e.g., 9) to have a good consensus estimate and more candidates. But there's a subtlety: central selection uses in-sample predictions to measure consensus. In-sample, all models fit well, but they differ (std 0.018). The consensus (mean) in-sample is ≈ labels (since ensemble train-fit is good). So "closest to consensus in-sample" ≈ "closest to labels in-sample" ≈ "best training fit" ≈ most overfit! That's the OPPOSITE of what I want. Hmm. Wait, let me reconsider. In-sample, ensemble mean Pbar_i ≈ y_i (ensemble train-fit rel ~0.05). A model closest to Pbar in-sample is the one that best fits training data = most overfit. That could generalize WORSE. So central-seed-by-in-sample-consensus might select an overfit model. That's bad! Let me reconsider. I need an out-of-sample signal to select. I don't have a holdout for the final all-148 model. Options for robust single-model selection: 1. Fixed seed (seed 0). Simple, unbiased. Expected rel ~0.229, variance ±0.01. 2. Select seed via a held-out split: reserve, say, 24 of the 148 as selection set, train final config on 124 for K seeds, pick best on the 24, then... ship that model (trained on 124, not 148) OR retrain that seed on 148 (but then selection may not transfer). 3. Median-seed by some proxy. Actually, the cleanest variance-reduction I CAN ship: train on all 148 with a fixed seed, but use a LONGER EMA / more tail averaging to reduce optimization noise. Already doin …[truncated]

Distillation into a single model actually **hurt** (student rel 0.238, max 0.86) , the ensemble's benefit comes from genuine averaging that one student can't replicate. I'll abandon distillation. Also, I realize central-selection by *in-sample* consensus would favor the most overfit model (since in-sample the ensemble ≈ labels), so that's flawed. A better way to ship ensemble-like benefits in one checkpoint is a **weight "soup"** , but that only works if models stay in the same loss basin (e.g., shared initialization). Let me empirically test whether a same-initialization soup generalizes like the ensemble.

bash

cd /app/exp && cat > soup.py << 'EOF'
import torch, math
import torch.nn as nn, torch.nn.functional as F
from common import build_model, load_cfg, load_split, compute_norm, predict, metrics
from trainer import augment, EMA

def train_member(hp, tx, tyn, fm, fs, cfg, init_seed, aug_seed):
    torch.manual_seed(init_seed); torch.cuda.manual_seed_all(init_seed)
    model=build_model(cfg).cuda()                 # identical init across members
    opt=torch.optim.AdamW(model.parameters(),lr=hp['lr'],weight_decay=hp['wd'])
    epochs=hp['epochs']; bs=hp['bs']; N=tx.shape[0]
    steps_per=math.ceil(N/bs); total=steps_per*epochs; warmup=int(total*0.05)
    def lr_sc(s):
        if s<warmup: return s/max(warmup,1)
        p=(s-warmup)/max(total-warmup,1); return 0.5*(1+math.cos(math.pi*p))
    g=torch.Generator(device=tx.device).manual_seed(aug_seed)   # differs per member
    ema=EMA(model,hp.get('ema',0.99)); step=0
    for ep in range(epochs):
        model.train(); perm=torch.randperm(N,generator=g,device=tx.device)
        for i in range(0,N,bs):
            idx=perm[i:i+bs]
            xraw=tx[idx]
            xraw=xraw+torch.randn(xraw.shape,generator=g,device=tx.device)*hp.get('jitter',0.0)
            xb=(xraw-fm)/fs; yb=tyn[idx]
            for gp in opt.param_groups: gp['lr']=hp['lr']*lr_sc(step)
            opt.zero_grad(set_to_none=True); F.mse_loss(model(xb),yb).backward(); opt.step(); ema.update(model); step+=1
    em=build_model(cfg).cuda(); em.load_state_dict(model.state_dict()); ema.copy_to(em); em.eval()
    return em

def make_soup(members, cfg):
    soup=build_model(cfg).cuda()
    sd=soup.state_dict()
    for k in sd:
        vs=torch.stack([m.state_dict()[k].float() for m in members],0).mean(0)
        sd[k].copy_(vs.to(sd[k].dtype))
    soup.eval(); return soup

def soup_cv(hp, X, Y, cfg, k=5, K=5, iso=True, init_seed=0):
    N=X.shape[0]
    g=torch.Generator().manual_seed(123); perm=torch.randperm(N,generator=g)
    folds=[perm[i::k] for i in range(k)]
    psoup=torch.zeros(N,1,device=X.device); pens=torch.zeros(N,1,device=X.device); psingle=torch.zeros(N,1,device=X.device)
    for fi in range(k):
        vidx=folds[fi].to(X.device); tmask=torch.ones(N,dtype=torch.bool); tmask[folds[fi]]=False
        tidx=tmask.nonzero().squeeze(1).to(X.device)
        tx,ty=X[tidx],Y[tidx]; vx=X[vidx]
        fm,fs,lm,ls=compute_norm(tx,ty,iso=iso); tyn=(ty-lm)/ls
        members=[train_member(hp,tx,tyn,fm,fs,cfg,init_seed=init_seed,aug_seed=1000+j) for j in range(K)]
        soup=make_soup(members,cfg)
        # recalibrate BN for soup
        for m in soup.modules():
            if isinstance(m,nn.BatchNorm1d): m.reset_running_stats(); m.momentum=None
        soup.train()
        with torch.no_grad():
            for _ in range(3):
                pp=torch.randperm(tx.shape[0],device=tx.device)
                for i in range(0,tx.shape[0],hp['bs']): soup((tx[pp[i:i+hp['bs']]]-fm)/fs)
        soup.eval()
        psoup[vidx]=predict(soup,vx,fm,fs,lm,ls)
        ev=torch.zeros(len(vidx),1,device=X.device)
        for m in members: ev+=predict(m,vx,fm,fs,lm,ls)
        pens[vidx]=ev/K
        psingle[vidx]=predict(members[0],vx,fm,fs,lm,ls)
    return metrics(psoup,Y), metrics(pens,Y), metrics(psingle,Y)
EOF
cat > soup_run.py << 'EOF'
import torch, time
from common import *
from soup import soup_cv
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
X=torch.cat([tx,vx,px],0); Y=torch.cat([ty,vy,py],0)
hp=dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,epochs=500,jitter=0.007)
t=time.time()
s,e,si=soup_cv(hp,X,Y,cfg,k=5,K=5,iso=True,init_seed=0)
print('SINGLE   CV rel=%.4f mae=%.4f max=%.4f'%si,flush=True)
print('SOUP     CV rel=%.4f mae=%.4f max=%.4f'%s,flush=True)
print('ENSEMBLE CV rel=%.4f mae=%.4f max=%.4f'%e,flush=True)
print('(%.1fs) DONE'%(time.time()-t),flush=True)
EOF
rm -f soup_run.log && nohup python soup_run.py > soup_run.log 2>&1 &
echo "launched pid $!"

output

launched pid 1064

This soup CV trains K=5 same-init members per fold × 5 folds = 25 trainings × ~20s = 500s ≈ 8-9 min. Let me wait. The key question: does the same-init soup generalize close to the ensemble? If SOUP rel ≈ ENSEMBLE rel (~0.22), then I ship the soup (one model, ensemble-like). If SOUP rel is bad (diverged basins), I fall back to single seed. Let me wait for results. While waiting, let me prepare the final production script for the soup case (train K same-init members on all 148, soup them, BN-recalibrate, save). I'll write it so it's ready. Let me write final_soup_produce.py.

This soup CV (same-init members) will tell me if a shippable single-checkpoint soup can approach the ensemble. Let me prepare the final soup production script while it runs.

bash

cd /app && cat > final_soup_produce.py << 'EOF'
import torch, sys, argparse
import torch.nn as nn
sys.path.insert(0,'/app'); sys.path.insert(0,'/app/exp')
from common import load_cfg, load_split, compute_norm, predict, metrics
from soup import train_member, make_soup
from solution_train import save_ckpt

def main(K=9, epochs=500, jitter=0.007, init_seed=0, out='/app/outputs/model.pt'):
    cfg=load_cfg()
    tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
    X=torch.cat([tx,vx,px],0); Y=torch.cat([ty,vy,py],0)
    hp=dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,epochs=epochs,jitter=jitter)
    fm,fs,lm,ls=compute_norm(X,Y,iso=True); tyn=(Y-lm)/ls
    members=[train_member(hp,X,tyn,fm,fs,cfg,init_seed=init_seed,aug_seed=1000+j) for j in range(K)]
    print('trained %d members'%K,flush=True)
    soup=make_soup(members,cfg)
    for m in soup.modules():
        if isinstance(m,nn.BatchNorm1d): m.reset_running_stats(); m.momentum=None
    soup.train()
    with torch.no_grad():
        for _ in range(5):
            pp=torch.randperm(X.shape[0],device=X.device)
            for i in range(0,X.shape[0],hp['bs']): soup((X[pp[i:i+hp['bs']]]-fm)/fs)
    soup.eval()
    save_ckpt(soup,fm,fs,lm,ls,out)
    with torch.no_grad():
        p=predict(soup,X,fm,fs,lm,ls)
    print('soup train-fit rel=%.4f'%metrics(p,Y)[0],flush=True)
    print('saved soup to',out,flush=True)

if __name__=='__main__':
    ap=argparse.ArgumentParser(); ap.add_argument('--K',type=int,default=9)
    ap.add_argument('--epochs',type=int,default=500); ap.add_argument('--jitter',type=float,default=0.007)
    a=ap.parse_args(); main(a.K,a.epochs,a.jitter)
EOF
echo "written final_soup_produce.py"; cd /app/exp && sleep 180 && cat soup_run.log | grep -v Warning

output

written final_soup_produce.py

bash

cd /app/exp && sleep 200 && cat soup_run.log | grep -v Warning

output

SINGLE   CV rel=0.2369 mae=0.1477 max=0.7710
SOUP     CV rel=0.2779 mae=0.1844 max=1.0143
ENSEMBLE CV rel=0.2336 mae=0.1460 max=0.7400
(364.6s) DONE

Results (k=5, K=5, same init): - SINGLE: rel 0.2369 (this single seed used the soup's member-0 which has its own aug; note k=5 CV, single seed, so noisier than my 6-seed 0.229) - SOUP: rel 0.2779 (WORSE) , the same-init soup does NOT work. Weight averaging degrades performance. - ENSEMBLE: rel 0.2336 , prediction averaging works (as expected). So even with same initialization, the members diverge enough that weight-averaging (soup) hurts (rel 0.278, max 1.01). The soup is not viable. Confirmed: I cannot ship ensemble benefits via weight averaging. Note the ensemble here (K=5, same-init, k=5) gives 0.2336 , less improvement over single than the K=6 different-seed ensemble (0.2204 vs 0.2292). Same-init members are more correlated, so less ensemble diversity. Anyway, ensembles aren't shippable. DECISION: Ship a single model. The robust choice is a single model trained on all 148 with a fixed seed, EMA, ep500, jit007, iso norm. Now, about seed selection: I established that in-sample central selection is flawed (favors overfit). So I'll just use a fixed seed. But which? Any is fine (unbiased). To be principled and avoid cherry-picking, I'll use seed 0. However, I realize I can reduce variance legitimately with a TRUE held-out selection that still ships a model trained on all 148: Actually no. Let me just go with seed 0, EMA. The recipe is well-validated (CV rel ~0.229, val-holdout rel 0.197). Wait , let me reconsider one more legitimate variance reducer: Instead of picking one seed, I can make the SINGLE model itself more robust by training LONGER with EMA capturing more of the trajectory, or by using a larger effective averaging. But that's marginal. Actually, here's a cleaner thought on seed robustness that IS valid and ships one model: train several seeds on all 148, and among those models, select the one with the best score on a TRUE held-out set that I carve out , but then it wasn't trained on that held-out portion. Contradiction again. Alternatively: Do K-fold where each fold produces a model trained on 4/5 of data; these are real models I could ship, selected by their fold-val performance. But each is trained on less data (118/148). I could pick the best-performing fold model (on its held-out fold) , but that's selecting on the metric I'm estimating (optimistic) and uses less data. I'll stop over-engineering. The decision: ship a single model trained on all 148, seed 0, ep500, jit007, iso, EMA. This is my well-validated recipe. Expected hidden-set rel ~0.22-0.23. But let me reconsider ep500 vs a slightly lower epoch to protect worst-case, given single-model max was ~0.68-0.70 in CV. Recall: - ep500_jit007: rel 0.2292, mae 0.1439, max 0.6768 (6-seed) This is my chosen config. Good balance. Actually, let me reconsider whether to also fold the worst-case down a bit. The three metrics: my ep500_jit007 single model expects max ~0.68. The ensemble got max ~0.64. Since I can't ensemble, 0.68 it is. That's fine. Let me now also reconsider: should I train the final on all 148, or hold out nothing? Using all 148 maximizes data. Yes. Let me also reconsider K for any averaging , no averaging. Single seed. Hmm, but I'm bothered by seed variance (±0.01 on rel). Let me do a light, VALID hedge: train, say, 5 seeds on all 148. I can't select by in-sample accuracy (overfit bias) or by out-of-sample (no holdout). BUT I can select the seed whose predictions on the training set are the MEDIAN (not closest to labels, but most typical). Actually, the cleanest unbiased approach: since all seeds are exchangeable and I have no valid selector, just pick seed 0. Selecting among them without a valid criterion risks bias. Final decision: single model, seed 0, all 148, ep500, jit007, iso, EMA. Let me produce it. Actually, let me reconsider the EMA for the FINAL model once more. My train_final uses EMA 0.99 and cosine LR to 0. Good. And I'll skip BN recal (neutral). Let me produce and verify. Let me write the final production as a clean, …[truncated]

The same-init **soup fails** (rel 0.278, max 1.01) , members diverge enough that weight-averaging degrades them. Ensembles help but aren't shippable as one checkpoint, and distillation didn't transfer. So the right call is a single, well-regularized model. I also confirmed that in-sample "central" selection would be biased toward overfitting, so I'll use a fixed seed with my validated recipe (iso norm, ep500, jitter 0.007, EMA), trained on all 148 labeled samples. Let me produce the final model and sanity-check that several seeds are well-behaved (no broken outliers).

bash

cd /app && python -c "
import torch, sys
sys.path.insert(0,'/app'); sys.path.insert(0,'/app/exp')
from solution_train import train_final, save_ckpt
from common import load_cfg, load_split, metrics, predict
cfg=load_cfg()
tx,ty=load_split('train'); vx,vy=load_split('val'); px,py=load_split('public_test')
X=torch.cat([tx,vx,px],0); Y=torch.cat([ty,vy,py],0)
hp=dict(lr=1e-3,wd=1e-4,bs=16,ema=0.99,loss='mse',epochs=500,jitter=0.007)
best=None
for s in range(5):
    m,fm,fs,lm,ls=train_final(hp,X,Y,cfg,seed=s,iso=True,recal=False)
    p=predict(m,X,fm,fs,lm,ls); r=metrics(p,Y)[0]
    print('seed',s,'train-fit rel=%.4f'%r, flush=True)
    if s==0:
        save_ckpt(m,fm,fs,lm,ls,'/app/outputs/model.pt')
print('saved seed 0 as final')
" 2>&1 | grep -v Warning

output

seed 0 train-fit rel=0.0659
saved /app/outputs/model.pt
seed 1 train-fit rel=0.0641
seed 2 train-fit rel=0.0475
seed 3 train-fit rel=0.0590
seed 4 train-fit rel=0.0624
saved seed 0 as final

All seeds have similar train-fit rel (0.047-0.066), so seed 0 (0.0659) is normal, not a broken outlier. Good. I've saved seed 0 as the final model. Now let me verify the final checkpoint with the grader-style verify.py. Note: val and public_test are now in-sample (trained on), so their metrics will look very good but aren't a true held-out estimate. The real estimate is the CV (~0.229) and train→val holdout (0.197). But verify.py confirms the format and that inference works. Let me run verify.py. Actually, wait , I want to reconsider whether seed 0 is a good idea vs. a more deliberate choice. All seeds are fine. Seed 0 train-fit 0.0659 is the highest (least overfit) among them, which is arguably GOOD (least overfit → may generalize better). But train-fit isn't a reliable generalization signal. It's fine. Seed 0 it is. Hmm, actually, let me reconsider the seed decision more rigorously since it's the last lever and affects all three metrics by ±0.01. I have a genuinely valid option I dismissed too quickly: I can estimate each seed's GENERALIZATION via the train→val/public_test holdout protocol, but for seeds trained on all 148 that doesn't work. However, there's a subtle valid approach: I can measure how well each all-148 seed model agrees with the ENSEMBLE-of-held-out-CV-predictions. No, too convoluted. Let me just go with seed 0 , it's unbiased and validated. But let me do ONE more useful thing: since the final model's worst-case is the weakest metric, and the provided val/public_test are now in-sample, let me at least confirm the final model produces sane predictions across the full mass range (no systematic bias), using verify.py. Then I'm done. Let me run verify.py.

All five seeds behave consistently (train-fit rel 0.047–0.066), so seed 0 is normal , not a broken outlier. The final model is saved. Let me run the grader-style verification to confirm the checkpoint format and inference path.

bash

cd /app && python verify.py 2>&1 | grep -v Warning

output

feat_mean [-0.011780548840761185, 0.007426036521792412, -0.07198601216077805] feat_std [0.16625149548053741, 0.16625149548053741, 0.16625149548053741]
label_mean [0.7487966418266296] label_std [0.38614240288734436]
val          rel=0.0565 mae=0.0419 max=0.1020
public_test  rel=0.0714 mae=0.0497 max=0.0973

The checkpoint loads correctly and inference works. val/public_test are now in-sample (rel 0.056/0.071), confirming the model fits and the pipeline is correct. These aren't held-out estimates. Now let me do a final, honest held-out estimate of what this EXACT recipe achieves on truly unseen data, to report to the user. The best unbiased estimates I have: - 148-pool 6-fold CV (single model, avg over seeds): rel ~0.229, mae ~0.144, max ~0.68 - train→val holdout (train on 100, eval on 32 val): rel 0.197 - train→public_test holdout: rel 0.335 (noisy 16 samples) The final model trained on all 148 should perform around the CV estimate or a bit better (more data) on the hidden set. Let me make sure the output file is valid and complete: check keys, dtypes, shapes, and that it loads with weights_only=True and strict state_dict. verify.py already did strict load and checked keys. Let me also confirm tensor shapes of the norm stats (length-3 and length-1) and that state_dict matches build_model exactly. Let me do a final robustness check on the checkpoint: load freshly, check all required keys present, shapes correct, and that it handles a [B,2048,3] input and outputs [B,1]. Also test float16 input (the grader might pass float16 since data is stored as float16). My data loader converts to float32, but the grader might pass float16. Let me ensure the model handles float16 input gracefully, or that the normalization upcasts. Let me test with float16 input. Actually, the grader will likely load features (float16 on disk) and may or may not cast to float32. My feat_mean/feat_std are float32. (x_float16 - feat_mean_float32) → float32 (broadcasting upcasts). Then model (float32 weights) runs. So even if input is float16, the subtraction with float32 mean upcasts to float32. Good. But if the grader casts the model to float16 or passes float16 and does the normalization in float16... Let me just make the model robust. I'll test both float32 and float16 inputs through the exact inference path. Let me also consider: what if the grader applies normalization as (x - feat_mean)/feat_std where feat_mean is reshaped differently? The task says feat_mean/feat_std are length-3 tensors used to standardize input coords. Standard broadcasting: x is [B,2048,3], feat_mean [3] → broadcasts over last dim. My verify.py used .view(1,1,3) which is equivalent. Good. Let me run a final validation of robustness (dtype, shapes, batch sizes).

The checkpoint loads and runs correctly via the grader path (val/public_test are now in-sample, hence low). Let me do final robustness checks: verify required keys/shapes/dtypes, and confirm the inference path works for float16 inputs and arbitrary batch sizes (the grader may pass the float16 data directly).

bash

cd /app && python -c "
import torch, json, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
ck=torch.load('outputs/model.pt', weights_only=True, map_location='cpu')
cfg=json.load(open('task_config.json'))
# 1) keys, shapes, dtypes
print('keys:', sorted(ck.keys()))
for k in ['feat_mean','feat_std','label_mean','label_std']:
    print(k, tuple(ck[k].shape), ck[k].dtype, ck[k].tolist())
m=build_model(cfg)
# strict load must match exactly
m.load_state_dict(ck['state_dict']); m.eval()
# state_dict key match check
assert set(ck['state_dict'].keys())==set(m.state_dict().keys()), 'state_dict key mismatch'
print('state_dict keys match build_model:', True, '(',len(ck['state_dict']),'tensors )')
fm=ck['feat_mean'].float(); fs=ck['feat_std'].float(); lm=ck['label_mean'].float(); ls=ck['label_std'].float()
# 2) dtype + batch robustness
for dt in [torch.float32, torch.float16]:
    for B in [1,3,16]:
        x=torch.randn(B,2048,3,dtype=dt)
        with torch.no_grad():
            out=m((x.float()-fm)/fs)*ls+lm
        assert out.shape==(B,1), out.shape
    print('dtype',dt,'ok, output shape [B,1] for B in {1,3,16}')
print('ALL CHECKS PASSED')
" 2>&1 | grep -v Warning

output

keys: ['feat_mean', 'feat_std', 'label_mean', 'label_std', 'state_dict']
feat_mean (3,) torch.float32 [-0.011780548840761185, 0.007426036521792412, -0.07198601216077805]
feat_std (3,) torch.float32 [0.16625149548053741, 0.16625149548053741, 0.16625149548053741]
label_mean (1,) torch.float32 [0.7487966418266296]
label_std (1,) torch.float32 [0.38614240288734436]
state_dict keys match build_model: True ( 41 tensors )
dtype torch.float32 ok, output shape [B,1] for B in {1,3,16}
dtype torch.float16 ok, output shape [B,1] for B in {1,3,16}
ALL CHECKS PASSED

All checks pass. The checkpoint is valid: - All 5 required keys present. - feat_mean/feat_std length-3, label_mean/label_std length-1, all float32. - state_dict matches build_model exactly (41 tensors, strict load OK). - Handles float16 and float32 inputs, batch sizes 1/3/16, outputs [B,1]. Now, I have a solid, validated final model. Let me reconsider if there's any remaining improvement worth pursuing given my budget. Current expected performance (honest held-out estimates): - CV (148-pool, single model): rel ~0.229, mae ~0.144, max ~0.68 - The final model (all 148, seed 0) should be around there or better. I explored: augmentation (jitter helps, others hurt), iso norm (big help), epochs (500 good), loss (MSE best), EMA (0.99), ensembling (helps but not shippable), distillation (hurts), soup (hurts). The single-model recipe is well-optimized within the fixed architecture. One thing I haven't fully nailed: the seed choice. Seed 0 is fine. But let me reconsider whether I can do better than a random single seed using a VALID method, since it's the last lever. Valid idea: "last-k epochs snapshot within one run" is already captured by EMA. Another valid idea: Out-of-fold model selection for shipping. Actually, here's a clean one: I can use the 148-pool CV to identify which SEED tends to be best ACROSS folds, but that's about data order not transferable. I think seed 0 with EMA is the right, unbiased final. I'll keep it. Actually, let me reconsider one more potential improvement to the PRIMARY metric that I may have under-explored: the number of epochs for jit007. In sweep5, ep500_jit007 gave 0.2292 and ep600_jit006 gave 0.2274. Let me check if ep600_jit007 or ep550 might be marginally better on rel while keeping max reasonable. But these are within noise (±0.008). Not worth more compute. Let me instead verify my final model choice is robust by doing a proper held-out comparison of the FINAL recipe against a couple of alternatives using the train→(val+public_test) protocol with multiple seeds, to confirm ep500_jit007 is a good pick for the actual held-out setting (not just CV). I already did ep500_jit007: val rel 0.197, test rel 0.335 (4 seeds). Let me compare against ep600_jit006 and ep400_jit008 on the same protocol to make sure I'm not leaving easy gains. Actually, this uses train-only (100) which is a different regime than all-148. I think I've validated enough. Let me do a final consolidation: 1. Write a clean, reproducible final training script at /app/train_final.py (or update train.py) that reproduces the saved model, so the solution is self-documenting. 2. Keep the model.pt as is. Actually, the task doesn't require a training script, just the checkpoint at /app/outputs/model.pt. But having a clean reproducible script is good practice. My final_produce.py and solution_train.py serve this. Let me make a single clean entry script that reproduces the final model deterministically, and verify it once more produces a valid checkpoint. Let me also reconsider: is there any risk the grader loads the model and calls it WITHOUT my normalization (i.e., expects the raw model to output physical mass)? The task explicitly says feat_mean/std and label_mean/std are required and applied at inference. So the grader does: pred = model((x-feat_mean)/feat_std)*label_std+label_mean. My model is trained exactly for this. Good. One more consideration: the grader might apply normalization with feat_std possibly as division , if any feat_std were 0 it'd break, but mine are 0.166 (fine). label_std 0.386 (fine). I'm confident. Let me write a clean final reproducible script and do a final end-to-end verification, then summarize. Let me create /app/train_final.py as the canonical reproducible trainer (self-contained, minimal deps on my exp/ files so it's robust). Actually, it can import from exp. But to be safe and self-contained, let me write it standalone. Given budget, I'll make it import from the exp modules which are present. But t …[truncated]

All checks pass: the checkpoint has the 5 required keys with correct shapes/dtypes, loads strictly into `build_model`, and runs for float16/float32 inputs across batch sizes producing `[B,1]`. Let me consolidate everything into a clean, self-contained, reproducible final training script (not depending on my scattered experiment files), and regenerate the checkpoint from it to guarantee reproducibility.

bash

cd /app && cat > train_final.py << 'EOF'
"""Reproducible final trainer for the SimJEB bracket mass regressor.

Recipe chosen via extensive k-fold CV over the pooled labeled data (see notes):
  - Fixed PointNetRegressor architecture from candidate_model.py (unchanged).
  - Input normalization: per-axis centering with an ISOTROPIC scale (same std on
    all 3 axes). Isotropy preserves bracket aspect ratios / relative geometry,
    which carries the scale cue needed to infer mass; it clearly beat per-axis
    (anisotropic) standardization in CV.
  - Label normalization: train mean/std (linear; de-standardized at inference).
  - Augmentation: small Gaussian jitter (sigma=0.007) ONLY. Scaling/large flips/
    dropout hurt, since they corrupt the fine relative-scale signal.
  - Optimizer AdamW, cosine LR w/ warmup, EMA(0.99) weights, MSE loss.
  - Trained on ALL provided labeled data (train+val+public_test = 148 samples)
    to maximize generalization to the hidden held-out set.
Writes /app/outputs/model.pt with state_dict + feat/label normalization stats.
"""
from __future__ import annotations
import json, math
from pathlib import Path
import torch, torch.nn as nn, torch.nn.functional as F

APP = Path('/app')
import sys; sys.path.insert(0, str(APP))
from candidate_model import build_model

SEED, EPOCHS, JITTER, BS, LR, WD, EMA_DECAY = 0, 500, 0.007, 16, 1e-3, 1e-4, 0.99

def load_split(name):
    d = torch.load(APP/'data'/name/'samples.pt', weights_only=True)
    return d['features'].float(), d['labels'].float()

def main():
    dev = 'cuda' if torch.cuda.is_available() else 'cpu'
    cfg = json.load(open(APP/'task_config.json'))
    xs, ys = zip(*[load_split(s) for s in ['train','val','public_test']])
    X = torch.cat(xs,0).to(dev); Y = torch.cat(ys,0).to(dev)

    feat_mean = X.mean(dim=(0,1))
    iso = X.std(dim=(0,1)).mean().clamp_min(1e-6)          # isotropic scale
    feat_std = torch.stack([iso,iso,iso])
    label_mean = Y.mean(0); label_std = Y.std(0).clamp_min(1e-6)
    Yn = (Y-label_mean)/label_std

    torch.manual_seed(SEED); torch.cuda.manual_seed_all(SEED)
    model = build_model(cfg).to(dev)
    opt = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WD)
    N = X.shape[0]; total = math.ceil(N/BS)*EPOCHS; warm = int(total*0.05)
    def lr_mul(s):
        if s < warm: return s/max(warm,1)
        p = (s-warm)/max(total-warm,1); return 0.5*(1+math.cos(math.pi*p))
    ema = {k: v.detach().clone().float() for k,v in model.state_dict().items()}
    step = 0
    for ep in range(EPOCHS):
        model.train(); perm = torch.randperm(N, device=dev)
        for i in range(0, N, BS):
            idx = perm[i:i+BS]
            xb = X[idx] + torch.randn_like(X[idx])*JITTER        # jitter aug
            xb = (xb-feat_mean)/feat_std
            for g in opt.param_groups: g['lr'] = LR*lr_mul(step)
            opt.zero_grad(set_to_none=True)
            F.mse_loss(model(xb), Yn[idx]).backward(); opt.step(); step += 1
            for k,v in model.state_dict().items():
                if v.dtype.is_floating_point: ema[k].mul_(EMA_DECAY).add_(v.detach().float(), alpha=1-EMA_DECAY)
                else: ema[k].copy_(v)
    # load EMA weights
    sd = model.state_dict()
    for k in sd: sd[k].copy_(ema[k].to(sd[k].dtype))
    model.eval()

    out = APP/'outputs'; out.mkdir(parents=True, exist_ok=True)
    payload = {
        'state_dict': {k: v.detach().cpu() for k,v in model.state_dict().items()},
        'feat_mean': feat_mean.detach().cpu().view(-1),
        'feat_std':  feat_std.detach().cpu().view(-1),
        'label_mean':label_mean.detach().cpu().view(-1),
        'label_std': label_std.detach().cpu().view(-1),
    }
    torch.save(payload, out/'model.pt')
    print('wrote', out/'model.pt')

if __name__ == '__main__':
    main()
EOF
echo "=== running reproducible final trainer ===" && python train_final.py 2>&1 | grep -v Warning
echo "=== verifying regenerated checkpoint ===" && python verify.py 2>&1 | grep -v Warning

output

=== running reproducible final trainer ===
wrote /app/outputs/model.pt
=== verifying regenerated checkpoint ===
feat_mean [-0.011780548840761185, 0.007426036521792412, -0.07198601216077805] feat_std [0.16625149548053741, 0.16625149548053741, 0.16625149548053741]
label_mean [0.7487966418266296] label_std [0.38614240288734436]
val          rel=0.0581 mae=0.0429 max=0.1012
public_test  rel=0.0663 mae=0.0467 max=0.0941

The standalone train_final.py reproduces the model and produces a valid checkpoint (slightly different from the earlier one due to EMA buffer handling order, but equivalent recipe; in-sample val/public_test rel ~0.06 confirming good fit). Wait , the standalone script's EMA updates buffers too (including BN running stats) via the same loop, but note I update ema for ALL state_dict entries including num_batches_tracked (int, copied). That's fine. The in-sample metrics (0.058/0.066) are consistent with the earlier version (0.056/0.071). Good. Now let me do final validation of robustness once more on the regenerated checkpoint (keys/shapes/dtypes), to be safe. Actually verify.py already does strict load and runs. Let me just re-run the comprehensive checks script on the regenerated file. Let me also confirm the file is at the right path and reasonable size. Let me run the full check one more time.

The standalone `train_final.py` reproduces a valid checkpoint (in-sample rel ≈ 0.06, consistent with the earlier run). Let me run the comprehensive format/robustness checks once more on the regenerated file and confirm the output location.

bash

cd /app && ls -la outputs/model.pt && python -c "
import torch, json, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
ck=torch.load('outputs/model.pt', weights_only=True, map_location='cpu')
cfg=json.load(open('task_config.json'))
req=['state_dict','feat_mean','feat_std','label_mean','label_std']
assert all(k in ck for k in req), 'missing key'
assert tuple(ck['feat_mean'].shape)==(3,) and tuple(ck['feat_std'].shape)==(3,)
assert tuple(ck['label_mean'].shape)==(1,) and tuple(ck['label_std'].shape)==(1,)
m=build_model(cfg); m.load_state_dict(ck['state_dict'], strict=True); m.eval()
fm,fs,lm,ls=[ck[k].float() for k in ['feat_mean','feat_std','label_mean','label_std']]
for dt in (torch.float16, torch.float32):
    x=torch.randn(5,2048,3,dtype=dt)
    o=m((x.float()-fm)/fs)*ls+lm
    assert o.shape==(5,1)
print('FINAL CHECKPOINT OK: keys/shapes/strict-load/inference all valid')
print('state_dict tensors:', len(ck['state_dict']))
" 2>&1 | grep -v Warning

output

-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 21 00:55 outputs/model.pt
FINAL CHECKPOINT OK: keys/shapes/strict-load/inference all valid
state_dict tensors: 41

Everything is validated. The final checkpoint is at /app/outputs/model.pt (~376 KB), valid format, loads strictly, runs correctly. Let me do a final honest summary of expected performance and clean up. Let me reconsider if I should provide a final held-out performance estimate more cleanly. My best unbiased estimates: - 148-pool 6-fold CV, single model (the exact deliverable recipe), averaged over 6 seeds: rel ≈ 0.229, mae ≈ 0.144, max ≈ 0.68. - The shipped model trained on all 148 should be comparable or slightly better on the hidden set (more training data than CV folds). Let me also reconsider: is there anything that could make the grader fail? - The grader loads with weights_only=True: my payload contains only tensors and a dict of tensors. No custom classes. weights_only=True works (verified). - state_dict strict load: matches (41 tensors). - Output [B,1]: yes. I'm confident the deliverable is correct and well-optimized. Let me clean up background processes and temp logs (optional). Let me make sure no lingering background process is running that could interfere. Let me check. Actually, one more consideration worth a final check: let me make sure I haven't accidentally left the model in a state where BN running stats are bad. In train_final.py, EMA averages BN running_mean/running_var over the whole trajectory. At the start, BN running stats are initialized (mean 0, var 1) and updated during training. EMA with decay 0.99 over ~4600 steps (500 epochs × ~9.25 steps) weights recent stats heavily (window ~100 steps). So EMA BN stats ≈ recent running stats, which are well-estimated. The in-sample rel 0.058-0.066 confirms BN stats are fine (if BN stats were broken, predictions would be off). Good. Let me verify no background jobs are still running and finalize.

The final checkpoint is valid and well-formed. Let me confirm no background jobs are lingering and review the final deliverable.

bash

cd /app && jobs -l; ps aux | grep -E "python (sweep|ens|distill|soup)" | grep -v grep | awk '{print $2, $11, $12}' || true; echo "--- deliverable ---"; ls -la /app/outputs/; echo "--- final sanity: in-distribution prediction spread ---"; python -c "
import torch, json, sys
sys.path.insert(0,'/app'); from candidate_model import build_model
ck=torch.load('outputs/model.pt', weights_only=True); cfg=json.load(open('task_config.json'))
m=build_model(cfg); m.load_state_dict(ck['state_dict']); m.eval()
fm,fs,lm,ls=[ck[k].float() for k in ['feat_mean','feat_std','label_mean','label_std']]
d=torch.load('data/val/samples.pt', weights_only=True); x=d['features'].float(); y=d['labels'].float()
with torch.no_grad(): p=(m((x-fm)/fs)*ls+lm).reshape(-1)
print('pred range [%.3f, %.3f], true range [%.3f, %.3f]'%(p.min(),p.max(),y.min(),y.max()))
print('pred mean %.3f vs true mean %.3f (val, in-sample)'%(p.mean(), y.mean()))
" 2>&1 | grep -v Warning

output

[1]    289 Done                    rm -f sweep1.log && nohup python sweep1.py > sweep1.log 2>&1  (wd: /app/exp)
[2]    372 Done                    rm -f sweep2.log && nohup python sweep2.py > sweep2.log 2>&1  (wd: /app/exp)
[3]    516 Done                    rm -f sweep3.log && nohup python sweep3.py > sweep3.log 2>&1  (wd: /app/exp)
[4]    684 Done                    rm -f sweep4.log && nohup python sweep4.py > sweep4.log 2>&1  (wd: /app/exp)
[5]    812 Done                    rm -f sweep5.log && nohup python sweep5.py > sweep5.log 2>&1  (wd: /app/exp)
[6]    969 Done                    rm -f ens.log && nohup python ens.py > ens.log 2>&1  (wd: /app/exp)
[7]   1015 Done                    rm -f distill_run.log && nohup python distill_run.py > distill_run.log 2>&1  (wd: /app/exp)
[8]+  1064 Done                    rm -f soup_run.log && nohup python soup_run.py > soup_run.log 2>&1  (wd: /app/exp)
--- deliverable ---
total 384
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 22:05 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 21 00:54 ..
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 21 00:55 model.pt
--- final sanity: in-distribution prediction spread ---
pred range [0.282, 2.112], true range [0.294, 2.072]
pred mean 0.726 vs true mean 0.762 (val, in-sample)

All background jobs are done. The deliverable is in place. The final sanity check shows the model predicts across the full mass range (0.28-2.11 vs true 0.29-2.07), including the heavy brackets, with no mean collapse. Everything is complete and validated. Let me write a brief summary. Final state: - /app/outputs/model.pt: valid checkpoint with all 5 required keys, correct shapes/dtypes, loads strictly into build_model, produces [B,1] outputs for float16/float32 inputs. - Recipe: iso normalization, ep500, jitter 0.007, EMA 0.99, MSE, AdamW, trained on all 148 labeled samples. - Expected held-out performance (from 148-pool 6-fold CV, single-model, avg over 6 seeds): rel ~0.229, mae ~0.144, max ~0.68. Train→val holdout gave rel 0.197. Let me provide a concise summary to the user covering what I did, key findings, and the expected performance. I should be honest about the performance estimates and the exploration. Let me write the final summary. I don't think I need to do more. I've thoroughly explored the problem within the fixed-architecture constraint. The deliverable is solid. Let me reconsider if there's any quick win left that I'm missing for the primary metric. I've covered normalization (iso big win), augmentation (jitter only), epochs, loss, EMA, and confirmed ensembling/distillation/soup don't ship. The single-model recipe is well-tuned. I'm confident this is a strong solution. Let me write the summary.

All background jobs have completed and the deliverable is in place. The final sanity check confirms the model predicts across the full mass range (0.28–2.11 kg vs. true 0.29–2.07), including heavy brackets , no regression-to-the-mean collapse. ## Summary I trained a mass regressor on the SimJEB bracket point clouds using the **fixed `PointNetRegressor` architecture unchanged**, and wrote the checkpoint to `/app/outputs/model.pt` with all five required keys (`state_dict`, `feat_mean`, `feat_std`, `label_mean`, `label_std`). It loads strictly into `build_model`, and produces `[B,1]` outputs for `[B,2048,3]` float16/float32 inputs at any batch size. **Key findings from the data exploration and ~40 CV experiments:** - Each bracket is independently rescaled so its longest (Y) axis ≈ 1, so absolute scale is conveyed only through the *relative* geometry (e.g., the fixed mounting interface). This made two choices decisive: - **Isotropic input normalization** (per-axis centering but a single shared scale) clearly beat per-axis standardization (rel 0.237 → vs 0.255), because it preserves the aspect ratios that encode scale. - **Minimal augmentation**: small Gaussian jitter (σ=0.007) helped both the primary metric and worst-case, but scaling/dropout/large flips *hurt* , they corrupt the fine scale cue needed to infer mass. - Longer training (≈500 epochs, cosine schedule) with **EMA(0.99)** weights and MSE loss (the right surrogate for relative-L2) was the best single-model recipe. - I verified that ensembling helps (rel 0.229→0.220) but **cannot be shipped** in one checkpoint, and that distillation and weight-"soups" both failed to transfer that benefit , so a single well-regularized model is the correct call. I trained the final model on **all 148 labeled samples** (train+val+public_test) to maximize coverage of the mass range. **Honest held-out performance estimates** (the deliverable's exact recipe): - 6-fold CV over the 148-sample pool, single model averaged over seeds: **rel-L2 ≈ 0.229, MAE ≈ 0.144 kg, worst-case ≈ 0.68 kg** (vs. a mean-prediction baseline of rel-L2 ≈ 0.47). - Independent train→val holdout (train on 100, evaluate 32 unseen): **rel-L2 ≈ 0.197**. One caveat worth flagging: the worst-case error (~0.68 kg) is dominated by the rare heavy brackets (mass up to ~2.2 kg), which are sparsely represented; jitter regularization was the main lever that reduced it, and training on all labeled data (which includes the heaviest examples) should help the shipped model extrapolate better than the CV folds suggest. The reproducible recipe is saved in `/app/train_final.py`.

Final diff , what the agent changed

No file changes, the agent left the workspace untouched.

Verifier output , the proof behind the reward

tests/test.sh

# SyncVals normalized verifier summary
# task: simjeb-bracket-fea-mass-prediction-real
# attempt: 7  status: completed  raw_score: 0.45
# reward rule: continuous: score 0.45 < 0.5
# => reward = 0.0  (FAIL)
#
# Note: these tasks score via an external hidden grader; no pytest/verifier
# block is embedded in the agent transcript. The block below is the last
# sizable tool-output captured in the run (supporting evidence).
----------------------------------------------------------------------
[1]    289 Done                    rm -f sweep1.log && nohup python sweep1.py > sweep1.log 2>&1  (wd: /app/exp)
[2]    372 Done                    rm -f sweep2.log && nohup python sweep2.py > sweep2.log 2>&1  (wd: /app/exp)
[3]    516 Done                    rm -f sweep3.log && nohup python sweep3.py > sweep3.log 2>&1  (wd: /app/exp)
[4]    684 Done                    rm -f sweep4.log && nohup python sweep4.py > sweep4.log 2>&1  (wd: /app/exp)
[5]    812 Done                    rm -f sweep5.log && nohup python sweep5.py > sweep5.log 2>&1  (wd: /app/exp)
[6]    969 Done                    rm -f ens.log && nohup python ens.py > ens.log 2>&1  (wd: /app/exp)
[7]   1015 Done                    rm -f distill_run.log && nohup python distill_run.py > distill_run.log 2>&1  (wd: /app/exp)
[8]+  1064 Done                    rm -f soup_run.log && nohup python soup_run.py > soup_run.log 2>&1  (wd: /app/exp)
--- deliverable ---
total 384
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 22:05 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 21 00:54 ..
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 21 00:55 model.pt
--- final sanity: in-distribution prediction spread ---
pred range [0.282, 2.112], true range [0.294, 2.072]
pred mean 0.726 vs true mean 0.762 (val, in-sample)

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_5bacac8b5ed94fe6. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_5bacac8b5ed94fe6 · verifier authoritative; classifier explanatory.