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.

SubtypeComplexity Overwhelm

EvidenceAgent achieved rel-L2 ≈ 0.22 via 5-fold CV (beating handcrafted feature baseline of 0.23), but grader test achieved only 0.47 rel-L2 (reward=0.0). Target threshold is 0.359, baseline 0.41. Agent's checkpoint loads correctly into fixed PointNetRegressor, weights_only=True, all keys present, output shape correct. Verification shows in-sample val/public_test ~0.12-0.16 (expected since folded into training). The gap between CV (0.22) and grader (0.47) suggests either distribution shift in held-out test or inherent task difficulty on novel bracket designs.

Root causeThe agent implemented a rigorous, well-validated approach to a hard regression task on sparse 3D point clouds (2048 points predicting scalar mass). It achieved strong generalization estimates (~0.22 rel-L2) via proper CV, beating simpler baselines. However, the held-out grader test achieved 0.47, well below all metric thresholds. With only 148 labeled training samples and normalized geometry (removing absolute scale), this is genuinely difficult; the held-out test likely contains out-of-distribution bracket designs or is simply harder than the labeled splits.

RecommendationN/A - task is fine. This is a legitimately hard 3D shape-to-scalar prediction problem. The agent's methodology was sound. The failure reflects either (1) the inherent difficulty of generalizing from 148 examples to diverse held-out geometries, (2) genuine distribution shift between training and test (not communicated in instruction), or (3) high variance expected for small-sample learning problems. To improve: more training data, richer/multi-output representations of geometry, or explicit domain adaptation techniques would be needed , beyond what the current task allows.

Trajectory

Tool-by-tool agent trajectory

216 tool calls · 3 tool types · 216 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 "---" && cat task_config.json && echo "---" && 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 21:48 ..
-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_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"
    ]
  }
}
---
{
  "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 loop.

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

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,), 
…[truncated]

Now let me look at the data itself. Let me understand the shapes and distributions.

Now let me inspect the data files to understand their shapes and distributions.

bash

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

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
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(split, 'features', tuple(f.shape), f.dtype, 'labels', tuple(l.shape), l.dtype)
    lf = l.float()
    print('   label min/max/mean/std:', lf.min().item(), lf.max().item(), lf.mean().item(), lf.std().item())
    ff = f.float()
    print('   feat  min/max per-axis:', ff.reshape(-1,3).min(0).values.tolist(), ff.reshape(-1,3).max(0).values.tolist())
    print('   feat  mean per-axis:', ff.reshape(-1,3).mean(0).tolist(), 'std', ff.reshape(-1,3).std(0).tolist())
"

output

train features (100, 2048, 3) torch.float16 labels (100, 1) torch.float16
   label min/max/mean/std: 0.207763671875 2.21875 0.7475781440734863 0.3894648551940918
   feat  min/max per-axis: [-0.320556640625, -0.5, -0.2279052734375] [0.322998046875, 0.5, 0.22705078125]
   feat  mean per-axis: [-0.013504491187632084, 0.007462748326361179, -0.0709712877869606] std [0.14211857318878174, 0.2701786458492279, 0.0871192067861557]
val features (32, 2048, 3) torch.float16 labels (32, 1) torch.float16
   label min/max/mean/std: 0.293701171875 2.072265625 0.7621994018554688 0.41264674067497253
   feat  min/max per-axis: [-0.3095703125, -0.5, -0.2279052734375] [0.31005859375, 0.5, 0.2275390625]
   feat  mean per-axis: [-0.005372888408601284, 0.005196526646614075, -0.07424016296863556] std [0.1418035924434662, 0.2656756639480591, 0.08837021142244339]
public_test features (16, 2048, 3) torch.float16 labels (16, 1) torch.float16
   label min/max/mean/std: 0.1983642578125 1.44921875 0.7296066284179688 0.327779084444046
   feat  min/max per-axis: [-0.309326171875, -0.5, -0.1865234375] [0.309326171875, 0.5, 0.1864013671875]
   feat  mean per-axis: [-0.013821225613355637, 0.011655600741505623, -0.07381976395845413] std [0.14321814477443695, 0.27090150117874146, 0.08569176495075226]

So we have: - train: 100 samples - val: 32 samples - public_test: 16 samples The labels are mass in kg, ranging roughly 0.2 to 2.2. The point clouds are 2048 points in R^3. The data is small (100 training samples). This is a small-data regression problem. Let me look at the manifests to understand more about the data.

This is a small-data regression problem: only 100 training samples. Let me look at the manifests for more context.

bash

cd /app && echo "=== train manifest (head) ===" && head -n 3 data/train/manifest.jsonl && echo "=== val manifest (head) ===" && head -n 3 data/val/manifest.jsonl && echo "=== public_test manifest (head) ===" && head -n 3 data/public_test/manifest.jsonl && echo "--- counts ---" && for s in train val public_test; do echo -n "$s: "; wc -l < data/$s/manifest.jsonl; done

output

=== train manifest (head) ===
{"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 (head) ===
{"id": "val_0000", "source_bracket_id": 418, "n_points": 2048}
{"id": "val_0001", "source_bracket_id": 441, "n_points": 2048}
{"id": "val_0002", "source_bracket_id": 221, "n_points": 2048}
=== public_test manifest (head) ===
{"id": "public_test_0000", "source_bracket_id": 461, "n_points": 2048}
{"id": "public_test_0001", "source_bracket_id": 440, "n_points": 2048}
{"id": "public_test_0002", "source_bracket_id": 507, "n_points": 2048}
--- counts ---
train: 100
val: 32
public_test: 16

The data is from SimJEB (Simulated Jet Engine Bracket) dataset. Each bracket has a `source_bracket_id`. The brackets are unique per split (no overlap presumably). Let me check for overlap of source_bracket_id across splits.

Let me check whether bracket IDs overlap across splits, and understand the evaluation metrics better.

bash

cd /app && python -c "
import json
def ids(s):
    return set(json.loads(l)['source_bracket_id'] for l in open(f'data/{s}/manifest.jsonl'))
tr, va, te = ids('train'), ids('val'), ids('public_test')
print('train n=', len(tr), 'val n=', len(va), 'test n=', len(te))
print('train&val overlap:', len(tr & va))
print('train&test overlap:', len(tr & te))
print('val&test overlap:', len(va & te))
"

output

train n= 100 val n= 32 test n= 16
train&val overlap: 0
train&test overlap: 0
val&test overlap: 0

No overlap in bracket IDs, so this is a genuine generalization task. I have 100 training, 32 val, 16 public_test. The held-out test is separate (not given). The key insight: mass of a bracket. Mass = density * volume. Since all brackets are presumably the same material (density), mass is proportional to volume. But the point clouds are rescaled so bounding box fits in unit cube. Wait , but the rescaling would change the apparent volume! Let me think. The coordinates are "pre-centered and rescaled so the axis-aligned bounding box of each bracket fits inside the unit cube." Hmm, important question: is the rescaling the SAME for all brackets, or per-bracket? "each bracket mesh is centered at its bounding-box centroid and rescaled so the axis-aligned bounding box fits inside the unit cube." This says each bracket is rescaled so its bounding box fits inside the unit cube. If the scaling is per-bracket (each normalized independently), then absolute size information is lost, and mass would have to be inferred from shape alone. But if scaling is a global constant, then size is preserved. Let me check the bounding box sizes per sample. If per-bracket normalization, then each bracket's bounding box should exactly touch the unit cube boundaries (i.e., max extent = 1 along at least one axis, or the largest dimension spans [-0.5, 0.5]). Let me examine per-sample bounding boxes.

A crucial question for modeling mass: is the rescaling per-bracket (destroying absolute scale) or global? Let me examine per-sample bounding boxes.

bash

cd /app && python -c "
import torch
d = torch.load('data/train/samples.pt', weights_only=True)
f = d['features'].float()  # [100,2048,3]
l = d['labels'].float().squeeze(1)
mn = f.min(dim=1).values  # [100,3]
mx = f.max(dim=1).values
ext = mx - mn  # bbox extent per sample per axis
print('extent per-axis stats over samples:')
print(' min ', ext.min(0).values.tolist())
print(' max ', ext.max(0).values.tolist())
print(' mean', ext.mean(0).tolist())
maxext = ext.max(dim=1).values  # largest extent per sample
print('max-extent over axes per sample: min/max/mean', maxext.min().item(), maxext.max().item(), maxext.mean().item())
print('first 10 max-extent:', maxext[:10].tolist())
print('first 10 extents:')
for i in range(10):
    print('  ', [round(x,3) for x in ext[i].tolist()], 'mass', round(l[i].item(),3))
"

output

extent per-axis stats over samples:
 min  [0.502197265625, 0.9912109375, 0.2757568359375]
 max  [0.6435546875, 1.0, 0.4549560546875]
 mean [0.5887036323547363, 0.9991015791893005, 0.34989869594573975]
max-extent over axes per sample: min/max/mean 0.9912109375 1.0 0.9991015791893005
first 10 max-extent: [0.997314453125, 0.99951171875, 0.999267578125, 0.994140625, 0.999267578125, 0.999755859375, 0.999755859375, 0.999755859375, 0.999755859375, 0.99853515625]
first 10 extents:
   [0.59, 0.997, 0.347] mass 1.64
   [0.591, 1.0, 0.347] mass 1.383
   [0.587, 0.999, 0.358] mass 0.641
   [0.594, 0.994, 0.359] mass 0.604
   [0.595, 0.999, 0.366] mass 0.369
   [0.591, 1.0, 0.347] mass 0.778
   [0.591, 1.0, 0.347] mass 0.636
   [0.591, 1.0, 0.347] mass 0.677
   [0.557, 1.0, 0.345] mass 1.145
   [0.597, 0.999, 0.407] mass 0.853

Very important finding: The Y-axis extent is always ~1.0 (the largest dimension spans the full unit cube). This means the normalization is per-bracket: each bracket is scaled so its largest dimension (the Y axis, which is always the longest) spans the unit cube. So absolute scale is lost. The mass must be inferred from the SHAPE of the bracket , specifically, how much "material" (volume fraction) it occupies within its bounding box, combined with the aspect ratio. Wait, but actually, it's more subtle. The brackets all fit in the unit cube with Y spanning ~1.0. So the overall scale normalization is: scale = 1/max_extent. But the real-world brackets all have the same mounting interface (SimJEB brackets all connect to the same 4 bolts and a cylindrical interface). So their absolute sizes might actually be similar in real life, OR the normalization removes size. Actually, the key for mass: mass = density × volume. The volume of the actual bracket = (volume fraction within bounding box in normalized coords) × (real bounding box volume). But we've lost the real bounding box volume due to normalization. Hmm, but wait. Let me reconsider. SimJEB brackets: they all share the same mounting points and load application point. So they have roughly the same physical bounding box in reality (they must bolt onto the same engine mount). If that's the case, the normalization scale is roughly constant across brackets, and the normalized shape's "filled volume" correlates with mass. But regardless of whether absolute scale is recoverable, the model only sees the normalized point cloud. So it has to learn mass from normalized shape. The question is whether mass is learnable from the normalized point cloud. Given that the dataset was constructed this way for this task, presumably yes , the shape (including how "chunky" vs "thin" the bracket is) correlates with mass. The point cloud is a SURFACE sample. So denser/chunkier regions... actually a surface sample doesn't directly give volume. But the overall shape envelope (surface area, how spread out the surface points are, thickness of features) encodes volume. Let me think about what features correlate with mass. Mass ∝ volume of material. For a surface point cloud, we can't directly compute volume, but: - Surface area correlates somewhat - The "spread" / second moments of the points - The bounding box extents (X and Z vary since Y is fixed at 1) Let me check correlation of simple geometric features with mass. Things like: - bbox extents (X, Z, and the product = bbox volume in normalized coords) - mean pairwise distance / std of coordinates - nearest-neighbor spacing (denser sampling = more surface area for fixed point count? No, points are always 2048) Actually, since points are a fixed count (2048) sampled from the surface, the density of points in space relates inversely to surface area. If surface area is large, points are more spread out. Hmm, but that's sampling-dependent. Let me just compute some simple features and check correlations with mass. This will guide whether a simple model could do well, and will help me understand the problem. But ultimately I need to train the PointNet model (the fixed architecture) and save a checkpoint that loads into it. Actually wait , re-reading the task. "candidate_model.py - the fixed PointNet-style architecture. Keep it as is; your saved checkpoint has to load back into this same architecture." So I MUST use this architecture. I can't change it. But I can train it however I want (data augmentation, normalization, loss, epochs, etc.). The checkpoint must load into PointNetRegressor with the config sizes. So my job: train PointNetRegressor well. The architecture is fixed with feature_width=64, global_width=256, head_width=128. Let me first establish baselines and understand the data, then build a good training pipeline. Let me compute simple geometric feature correlations to understand the signal.

Key finding: the Y-axis extent is always ~1.0, confirming **per-bracket normalization** , each bracket is scaled so its longest dimension fills the unit cube. Absolute scale is largely removed, so mass must be inferred from normalized shape. Let me probe how well simple geometric features predict mass to understand the signal.

bash

cd /app && python -c "
import torch, numpy as np
def load(s):
    d=torch.load(f'data/{s}/samples.pt',weights_only=True)
    return d['features'].float(), d['labels'].float().squeeze(1)
fx,ly=load('train')
N=fx.shape[0]
# simple features
feats={}
mn=fx.min(1).values; mx=fx.max(1).values; ext=mx-mn
feats['ext_x']=ext[:,0]; feats['ext_z']=ext[:,2]
feats['bbox_vol']=ext[:,0]*ext[:,1]*ext[:,2]
feats['std_x']=fx[:,:,0].std(1); feats['std_y']=fx[:,:,1].std(1); feats['std_z']=fx[:,:,2].std(1)
feats['std_prod']=feats['std_x']*feats['std_y']*feats['std_z']
# mean distance from centroid
c=fx.mean(1,keepdim=True); r=(fx-c).norm(dim=2); feats['mean_r']=r.mean(1); feats['std_r']=r.std(1)
# mean nearest neighbor distance (subsample for speed)
import torch
nn=[]
for i in range(N):
    p=fx[i]
    d=torch.cdist(p,p); d.fill_diagonal_(1e9); nn.append(d.min(1).values.mean())
feats['mean_nn']=torch.stack(nn)
ly_np=ly.numpy()
for k,v in feats.items():
    v=v.numpy()
    c=np.corrcoef(v,ly_np)[0,1]
    print(f'{k:12s} corr={c:+.3f}')
"

output

ext_x        corr=+0.234
ext_z        corr=+0.177
bbox_vol     corr=+0.253
std_x        corr=+0.635
std_y        corr=+0.149
std_z        corr=+0.250
std_prod     corr=+0.667
mean_r       corr=+0.482
std_r        corr=-0.411
mean_nn      corr=+0.523

Some signal there. `std_prod` (product of per-axis stds) has 0.667 correlation, `mean_nn` (mean nearest neighbor distance) 0.523. The mean nearest-neighbor distance correlates positively , makes sense: bigger surface area → but fixed points → larger spacing. Actually larger mass → larger/chunkier object → more spread → larger NN distance. Let me think about a really informative feature: since points are sampled on the surface, the mean nearest-neighbor distance² × number of points ≈ surface area (roughly). And a convex-hull volume estimate could approximate the actual volume. But the real signal for mass is volume. Let me try to estimate volume via voxelization: voxelize the point cloud into a grid, count occupied voxels , but surface points only occupy a shell, not the filled interior. However, for closed surfaces, I could estimate filled volume by voxel flood-fill. That's complex. Let me instead check: can a gradient-boosted model or simple MLP on hand-crafted features get good relative L2? That gives me a sense of achievable performance. But remember, I must use the PointNet architecture for the final model. Still, understanding achievable error helps. Actually, let me reconsider the whole approach. The task requires the fixed PointNet. The PointNet can in principle learn these geometric features (it computes per-point features then max-pools). Max-pooling over per-point MLP features is decent for capturing extents and shape. But volume/density is harder for vanilla PointNet because max-pool loses count/density info. Key challenge: PointNet with max-pooling is invariant to point density and captures the "silhouette"/extent well but not volume directly. However, mass correlates with shape here, so it can work. Let me think about the most important modeling choices given only 100 training samples: 1. Heavy data augmentation to prevent overfitting: random rotations? NO , rotations would change the shape relative to axes, and since normalization is axis-aligned (bounding box), arbitrary 3D rotation would break the bbox normalization assumption. But small rotations, jitter, point dropout/resampling, and especially reflections along symmetry axes could help. Actually, important: SimJEB brackets are typically symmetric. Let me check symmetry. The bracket likely has mirror symmetry. Reflections across X and Z might be valid augmentations. Let me not over-assume. 2. Since mass is scale-related and the normalization fixes Y extent to 1, the absolute coordinate scale matters. Random scaling augmentation would change apparent size , but mass label wouldn't change, so scaling aug might hurt unless we think mass is scale-invariant in normalized space. Actually since all inputs are normalized the same way (Y spans 1), I should NOT apply random scaling that breaks that normalization, because test data will also have Y spanning 1. Better to keep augmentations consistent with the normalization used at test. Let me reconsider: at inference, the grader applies my feat_mean/feat_std normalization then runs the model. The input point clouds are already normalized (Y extent ~1). So augmentations during training should preserve the statistics of test inputs: point resampling/jitter/dropout, small rotations, and reflections consistent with symmetry. Random scaling that changes the bbox would create train/test mismatch, so avoid big scaling; maybe tiny anisotropic scaling as regularization is okay but risky. 3. Loss: primary metric is relative L2 over the full set = ||pred - true|| / ||true||. Also MAE and max abs error. To optimize relative L2, since labels range 0.2-2.2, MSE on raw would weight large masses more. Relative L2 weights by overall norm. Hmm. Let me think about what loss best targets these. Relative L2 = sqrt(sum (pred-true)^2) / sqrt(sum true^2). Minimizing sum (pred-true)^2 = MSE minimizes numerator → so plain MSE on raw mass targets directly minimizes the numerator of rel L2. Good. But MAE and max-abs also matter. But t …[truncated]

There's real but imperfect signal in simple features. Since mass ∝ material volume, let me test a voxel-based *filled-volume* estimate (flood-fill the outside, treat enclosed voxels as solid) and a richer feature set with a quick gradient-boosting model to gauge the achievable error ceiling. This informs how hard the PointNet must work.

bash

cd /app && python -c "
import torch, numpy as np
from scipy import ndimage
def load(s):
    d=torch.load(f'data/{s}/samples.pt',weights_only=True)
    return d['features'].float().numpy(), d['labels'].float().squeeze(1).numpy()
fx,ly=load('train')
N=fx.shape[0]
def filled_vol(p, R=32):
    # voxelize into RxRxR over [-0.5,0.5]
    idx=np.clip(((p+0.5)*R).astype(int),0,R-1)
    occ=np.zeros((R,R,R),bool)
    occ[idx[:,0],idx[:,1],idx[:,2]]=True
    # flood fill outside from border of complement
    free=~occ
    # label connected free space; the component touching border = outside
    lab,_=ndimage.label(free)
    border=set(np.unique(np.concatenate([lab[0,:,:].ravel(),lab[-1,:,:].ravel(),lab[:,0,:].ravel(),lab[:,-1,:].ravel(),lab[:,:,0].ravel(),lab[:,:,-1].ravel()])))
    outside=np.isin(lab,list(border))
    filled=~outside  # occupied + enclosed interior
    return filled.sum()/ (R**3)
vols=np.array([filled_vol(fx[i]) for i in range(N)])
print('filled-vol corr with mass:', np.corrcoef(vols,ly)[0,1])
# linear fit mass ~ vol
A=np.polyfit(vols,ly,1); pred=np.polyval(A,vols)
relL2=np.linalg.norm(pred-ly)/np.linalg.norm(ly)
print('linear vol->mass train relL2:', relL2, 'MAE', np.mean(np.abs(pred-ly)))
for R in [16,24,48]:
    vv=np.array([filled_vol(fx[i],R) for i in range(N)])
    print(f'R={R} corr', round(np.corrcoef(vv,ly)[0,1],3))
"

output

[stderr]
Traceback (most recent call last):
  File "<string>", line 3, in <module>
ModuleNotFoundError: No module named 'scipy'

bash

cd /app && pip install scipy scikit-learn 2>&1 | tail -n 2

output

ERROR: Could not find a version that satisfies the requirement scipy (from versions: none)
ERROR: No matching distribution found for scipy

No internet. Let me implement flood fill manually with a BFS/iterative approach using numpy and torch. Actually I can implement a simple 3D flood fill with a stack, or iterative dilation of the outside region. Let me implement flood fill via iterative propagation using convolution-like logic with numpy. Simpler: use a BFS from all border free voxels. Let me write a reasonably efficient flood fill with numpy using a queue (collections.deque). R=32 → 32768 voxels, fine. Actually, iterative dilation until convergence is simple with numpy roll operations. Let me do that: outside = border free voxels; repeatedly outside = outside OR (shifted outside AND free). Converges in ~R steps. For R=32, ~32 iterations each doing 6 rolls , fast enough for 100 samples? 100*32*6 = ~19k array ops on 32^3 , fine. Let me implement.

No internet for scipy. I'll implement the 3D flood-fill myself with numpy via iterative dilation.

bash

cd /app && python -c "
import torch, numpy as np
def load(s):
    d=torch.load(f'data/{s}/samples.pt',weights_only=True)
    return d['features'].float().numpy(), d['labels'].float().squeeze(1).numpy()
fx,ly=load('train')
N=fx.shape[0]
def outside_fill(free):
    # free: bool RxRxR. Return outside region connected to border.
    out=np.zeros_like(free)
    # seed borders
    out[0,:,:]|=free[0,:,:]; out[-1,:,:]|=free[-1,:,:]
    out[:,0,:]|=free[:,0,:]; out[:,-1,:]|=free[:,-1,:]
    out[:,:,0]|=free[:,:,0]; out[:,:,-1]|=free[:,:,-1]
    while True:
        nxt=out.copy()
        nxt[1:,:,:]|=out[:-1,:,:]; nxt[:-1,:,:]|=out[1:,:,:]
        nxt[:,1:,:]|=out[:,:-1,:]; nxt[:,:-1,:]|=out[:,1:,:]
        nxt[:,:,1:]|=out[:,:,:-1]; nxt[:,:,:-1]|=out[:,:,1:]
        nxt&=free
        if nxt.sum()==out.sum(): break
        out=nxt
    return out
def filled_vol(p, R=32):
    idx=np.clip(((p+0.5)*R).astype(int),0,R-1)
    occ=np.zeros((R,R,R),bool); occ[idx[:,0],idx[:,1],idx[:,2]]=True
    free=~occ
    outside=outside_fill(free)
    filled=~outside
    return filled.sum()/(R**3)
for R in [24,32,40]:
    vv=np.array([filled_vol(fx[i],R) for i in range(N)])
    A=np.polyfit(vv,ly,1); pred=np.polyval(A,vv)
    rel=np.linalg.norm(pred-ly)/np.linalg.norm(ly)
    print(f'R={R} corr={np.corrcoef(vv,ly)[0,1]:.3f} linear-relL2={rel:.3f} MAE={np.mean(np.abs(pred-ly)):.3f}')
"

output

R=24 corr=0.666 linear-relL2=0.343 MAE=0.205
R=32 corr=0.650 linear-relL2=0.350 MAE=0.212
R=40 corr=0.597 linear-relL2=0.369 MAE=0.230

Filled volume correlates ~0.66 with mass, similar to std_prod. A single feature gives relL2 ~0.34. Not great. The relationship is noisier than pure volume because the surface sampling and flood fill at coarse resolution is imperfect, plus mass isn't purely the enclosed volume (brackets have internal structure / varying thickness, and the flood-fill of a surface point cloud with only 2048 points is leaky , holes in the shell let the flood leak inside). 2048 points is quite sparse for a watertight voxel shell at R=32, so flood fill leaks. That's why correlation is modest. Let me combine multiple features with a proper regressor to see the ceiling. I'll implement a small gradient-boosting by hand? No. Let me just use a multi-feature linear/ridge regression and a small MLP in torch on handcrafted features, evaluated on val. But the real deliverable is the PointNet. Let me not overinvest. Let me get the achievable ceiling using many features + a torch MLP with cross-val. Actually, let me combine features and do ridge regression evaluated on val set to get a realistic generalization number. Let me build a handcrafted feature vector per sample and fit ridge, eval on val. This tells me what's achievable and whether PointNet should aim higher.

Filled-volume alone gives rel-L2 ≈ 0.34 (the sparse 2048-point shell leaks during flood-fill). Let me combine many handcrafted features with ridge regression, evaluated on the val split, to estimate a realistic generalization ceiling.

bash

cd /app && cat > /tmp/feat.py << 'EOF'
import torch, numpy as np
def load(s):
    d=torch.load(f'/app/data/{s}/samples.pt',weights_only=True)
    return d['features'].float(), d['labels'].float().squeeze(1)
def outside_fill(free):
    out=np.zeros_like(free)
    out[0,:,:]|=free[0,:,:]; out[-1,:,:]|=free[-1,:,:]
    out[:,0,:]|=free[:,0,:]; out[:,-1,:]|=free[:,-1,:]
    out[:,:,0]|=free[:,:,0]; out[:,:,-1]|=free[:,:,-1]
    while True:
        nxt=out.copy()
        nxt[1:,:,:]|=out[:-1,:,:]; nxt[:-1,:,:]|=out[1:,:,:]
        nxt[:,1:,:]|=out[:,:-1,:]; nxt[:,:-1,:]|=out[:,1:,:]
        nxt[:,:,1:]|=out[:,:,:-1]; nxt[:,:,:-1]|=out[:,:,1:]
        nxt&=free
        if nxt.sum()==out.sum(): break
        out=nxt
    return out
def fvol(p,R):
    idx=np.clip(((p+0.5)*R).astype(int),0,R-1)
    occ=np.zeros((R,R,R),bool); occ[idx[:,0],idx[:,1],idx[:,2]]=True
    free=~occ; outside=outside_fill(free); return (~outside).sum()/(R**3), occ.sum()/(R**3)
def features(fx):
    F=[]
    fxn=fx.numpy()
    for i in range(fx.shape[0]):
        p=fx[i]; pn=fxn[i]
        mn=p.min(0).values; mx=p.max(0).values; ext=mx-mn
        c=p.mean(0); s=p.std(0)
        r=(p-c).norm(dim=1)
        row=[]
        row+=ext.tolist()
        row+=[ext[0]*ext[1]*ext[2]]
        row+=s.tolist()
        row+=[s[0]*s[1]*s[2]]
        row+=[r.mean().item(), r.std().item(), r.max().item()]
        # occupancy/filled at couple resolutions
        for R in [20,32]:
            fv,ov=fvol(pn,R); row+=[fv,ov]
        # covariance eigenvalues
        cov=torch.cov(p.T); ev=torch.linalg.eigvalsh(cov); row+=ev.tolist()+[ev.prod().item()]
        F.append(row)
    return np.array(F)
if __name__=='__main__':
    import sys
    trx,trY=load('train'); vax,vaY=load('val')
    Xtr=features(trx); Xva=features(vax)
    np.savez('/tmp/feats.npz',Xtr=Xtr,ytr=trY.numpy(),Xva=Xva,yva=vaY.numpy())
    print('Xtr',Xtr.shape,'Xva',Xva.shape)
EOF
python /tmp/feat.py

output

Xtr (100, 19) Xva (32, 19)

bash

cd /app && python -c "
import numpy as np
d=np.load('/tmp/feats.npz')
Xtr,ytr,Xva,yva=d['Xtr'],d['ytr'],d['Xva'],d['yva']
mu=Xtr.mean(0); sd=Xtr.std(0)+1e-9
Xt=(Xtr-mu)/sd; Xv=(Xva-mu)/sd
def ridge(X,y,lam):
    n,p=X.shape; A=X.T@X+lam*np.eye(p); w=np.linalg.solve(A,X.T@(y-y.mean())); b=y.mean(); return w,b
def ev(w,b,X,y):
    pr=X@w+b; rel=np.linalg.norm(pr-y)/np.linalg.norm(y); return rel,np.mean(np.abs(pr-y)),np.max(np.abs(pr-y))
for lam in [0.1,1,3,10,30]:
    w,b=ridge(Xt,ytr,lam)
    rtr=ev(w,b,Xt,ytr); rva=ev(w,b,Xv,yva)
    print(f'lam={lam:5} train relL2={rtr[0]:.3f} | val relL2={rva[0]:.3f} MAE={rva[1]:.3f} max={rva[2]:.3f}')
"

output

lam=  0.1 train relL2=0.209 | val relL2=0.200 MAE=0.140 max=0.375
lam=    1 train relL2=0.214 | val relL2=0.210 MAE=0.147 max=0.385
lam=    3 train relL2=0.219 | val relL2=0.221 MAE=0.154 max=0.410
lam=   10 train relL2=0.226 | val relL2=0.232 MAE=0.163 max=0.474
lam=   30 train relL2=0.232 | val relL2=0.239 MAE=0.167 max=0.541

Ridge regression on handcrafted features gives val relL2 ≈ 0.20, MAE ≈ 0.14, max ≈ 0.375. This is a solid baseline and shows the signal is real. With a good PointNet and augmentation, I should be able to match or beat this. Let me also quickly check what the starter PointNet gives as-is (1 epoch) and then with a better training regime. But first, let me think about the best approach. Target: beat relL2 ~0.20 on val, ideally get to ~0.10-0.15. Let me see what the PointNet can do. Given 100 training points, a PointNet (which has ~ maybe 200k params) can overfit. I need: - Augmentation: per-epoch point resampling (subsample from 2048? they're already 2048; I can jitter + random subset with replacement to 2048, random dropout). Actually I can resample by randomly picking 2048 indices with replacement from the 2048 (bootstrap), plus jitter. Better: add Gaussian jitter to coordinates, random point dropout (drop some, duplicate others), small anisotropic scaling (careful), small rotation about the vertical (y) axis? Rotations about y would change x,z extents though , the bbox normalization is axis-aligned, so rotating would make the bbox no longer tight. Test data won't be rotated. So I should avoid rotations that break axis-alignment. Hmm, but jitter and point resampling are safe. Reflections: if brackets are symmetric about some plane, reflecting across x (x->-x) or z might produce valid-looking brackets with same mass. Let me check if data looks symmetric. Even if not perfectly symmetric, reflection augmentation produces plausible brackets with identical mass (mass is reflection-invariant). Since test brackets are real designs, a reflected bracket is also a valid bracket shape (mirror image) with the same mass. This is a safe, label-preserving augmentation that doubles/octuples data. Reflections across x and z axes (and combinations) are geometrically valid (mass invariant under reflection). Reflection across y too. So up to 8x augmentation via sign flips per axis. But wait , does reflecting break the normalization? No: reflection preserves the bounding box (just mirrored), Y extent still 1, centered at centroid still ~0. So reflections are perfectly consistent with the normalization. Great. This is the key augmentation. Also, does reflection preserve the "realism"? The mirror of a valid bracket is a valid bracket with same mass. Even if the original isn't symmetric, the mirrored shape is a legitimate different-but-valid geometry with the same mass. This effectively augments the dataset with plausible samples. Let me verify mass is truly reflection-invariant conceptually: yes, mass depends only on geometry volume & density, invariant to rigid transforms and reflections. So augmentations: 1. Random sign flip on x and z axes (and maybe y). 8 combinations. Safe, label-preserving, normalization-preserving. 2. Small Gaussian jitter on coordinates (e.g., std 0.005-0.01). Simulates sampling noise. 3. Random point resampling: pick a random subset/bootstrap of the 2048 points. Since PointNet max-pools, using a random 1024-2048 subset each time regularizes. But at test, full 2048 used. Using random subsets in training makes model robust to point identity. 4. Small global scaling jitter (e.g., 0.98-1.02) , risky re normalization but small; actually since normalization fixes the max extent, scaling up would push beyond [-0.5,0.5]. Test data strictly in [-0.5,0.5]. I'll apply only tiny scaling or skip. I'll skip global isotropic scaling to preserve the Y=1 invariant. Actually I could apply per-axis tiny scale then re-normalize to keep max extent 1, but that changes shape. Let me keep augmentation conservative: reflections + jitter + resampling + maybe tiny per-axis scale with renormalize. Let me start with reflections + jitter + resample. 5. Point dropout: randomly drop points (set to a duplicated point) , covered by resampling with replacement. Given max-pool architecture, jitter and resample give robustness. Training regime: - …[truncated]

Ridge on handcrafted features reaches **val rel-L2 ≈ 0.20, MAE ≈ 0.14**. That's a meaningful reference ceiling for simple methods; a well-trained PointNet with good augmentation should match or beat it. Key modeling insights so far: - Per-bracket normalization fixes the Y extent to ~1, so mass comes from *normalized shape* (how chunky/filled the geometry is). - **Reflections (axis sign flips) are label-preserving and normalization-preserving** , mass is invariant to mirroring, and the bounding box stays identical. This is a safe way to expand 100 samples up to 8×. - Only ~100 train samples, so overfitting is the main enemy. Let me check the GPU and confirm the starter's 1-epoch baseline before building a better trainer.

bash

cd /app && nvidia-smi --query-gpu=name,memory.total,memory.used --format=csv 2>/dev/null | head; echo "--- cpu count ---"; nproc

output

name, memory.total [MiB], memory.used [MiB]
NVIDIA H100 80GB HBM3, 81559 MiB, 4 MiB
--- cpu count ---
3

H100 available. Great. Let me write a robust trainer. I'll make a reusable evaluation function and a training function that I can call with different hyperparameters. Let me first check symmetry empirically to decide on reflection augmentation validity. Actually reflection is valid regardless of symmetry (mirror of a valid bracket is a valid bracket with same mass). So I'll use x and z reflections confidently; y reflection too. Let me just include all three sign flips. Actually, let me reconsider whether a reflected bracket is "in-distribution" for the test set. The test brackets are real SimJEB designs. A mirrored real design might not be an actual catalog design, but geometrically it's a perfectly valid bracket and the mass-from-shape mapping is identical (physics is mirror-symmetric). So training on mirrored versions teaches the same physical mapping and should generalize to real test brackets. This is standard and safe. Good. Let me write the trainer in a script that: - loads train & val - computes normalization from train (for dev) - builds model - trains with augmentation - evaluates rel L2, MAE, max on val (in physical units) - supports SWA Let me write it now. I'll keep data on GPU (small). For augmentation on GPU per batch: - reflect: multiply x[:, :, axis] by random ±1 per-sample per-axis. - jitter: add gaussian noise. - resample: random indices per sample. Since all have 2048 points, I can generate random permutation/choice per batch. To vary, pick random subset of size k (e.g., between 1024 and 2048) with replacement then... but model expects 2048? No , model accepts [B, n, 3] with any n because conv1d over points and max-pool over dim=2. Actually n can vary. But batching requires same n within a batch. I'll keep n=2048 but resample with replacement (bootstrap) to vary multiplicity. Or random dropout: choose 2048 indices from 2048 with replacement. This changes point density slightly. I'll do a random subset of size, say, 1536 unique then pad? Simpler: per-batch pick a single k and gather random indices of size 2048 with replacement. Let me do bootstrap resampling with prob, plus jitter. Actually the simplest effective: per sample, randomly permute and take all 2048 (no change) , permutation doesn't matter for PointNet (permutation invariant). So resampling to same 2048 with replacement = bootstrap, which drops ~37% and duplicates others. That's a meaningful augmentation for max-pool (some surface points removed). Combined with jitter, good. Let me implement and run a quick experiment (say 150 epochs) to see val performance, then tune. Let me write the dev trainer.

H100 available , plenty of compute. Let me write a flexible development trainer that supports augmentation (reflections, jitter, bootstrap resampling) and SWA, evaluating rel-L2 / MAE / max-abs on val in physical units.

bash

cd /app && cat > /tmp/trainer.py << 'EOF'
from __future__ import annotations
import json, os, sys, math, copy
from pathlib import Path
import torch, torch.nn as nn, torch.nn.functional as F
sys.path.insert(0,'/app')
from candidate_model import build_model

CFG=json.load(open('/app/task_config.json'))
DEV=torch.device('cuda')

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

def augment(x, refl=True, jit=0.006, boot=True):
    B,N,_=x.shape
    if refl:
        s=(torch.randint(0,2,(B,1,3),device=x.device).float()*2-1)
        x=x*s
    if boot:
        idx=torch.randint(0,N,(B,N),device=x.device)
        x=torch.gather(x,1,idx.unsqueeze(-1).expand(-1,-1,3))
    if jit>0:
        x=x+torch.randn_like(x)*jit
    return x

def evaluate(model, x, y, fm, fs, lm, ls):
    model.eval()
    with torch.no_grad():
        xn=(x-fm)/fs
        pr=model(xn)*ls+lm
    pr=pr.squeeze(1); yt=y.squeeze(1)
    rel=(torch.norm(pr-yt)/torch.norm(yt)).item()
    mae=(pr-yt).abs().mean().item()
    mx=(pr-yt).abs().max().item()
    return rel,mae,mx

def train_once(trx,trY,vax,vaY, epochs=200, bs=32, lr=1e-3, wd=1e-4,
               loss='mse', refl=True, jit=0.006, boot=True, swa_frac=0.25,
               seed=0, verbose=False):
    torch.manual_seed(seed)
    fm=trx.reshape(-1,3).mean(0).view(1,1,3).to(DEV)
    fs=trx.reshape(-1,3).std(0).clamp_min(1e-6).view(1,1,3).to(DEV)
    lm=trY.mean().view(1,1).to(DEV); ls=trY.std().clamp_min(1e-6).view(1,1).to(DEV)
    trx=trx.to(DEV); trY=trY.to(DEV); vax=vax.to(DEV); vaY=vaY.to(DEV)
    model=build_model(CFG).to(DEV)
    opt=torch.optim.AdamW(model.parameters(),lr=lr,weight_decay=wd)
    sched=torch.optim.lr_scheduler.CosineAnnealingLR(opt,T_max=epochs)
    N=trx.shape[0]
    swa_start=int(epochs*(1-swa_frac))
    swa_model=None; swa_n=0
    best=(1e9,None)
    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=augment(trx[idx],refl,jit,boot)
            yb=(trY[idx]-lm)/ls
            xn=(xb-fm)/fs
            opt.zero_grad(set_to_none=True)
            pr=model(xn)
            if loss=='mse': l=F.mse_loss(pr,yb)
            elif loss=='huber': l=F.smooth_l1_loss(pr,yb,beta=0.5)
            l.backward(); opt.step()
        sched.step()
        if ep>=swa_start:
            if swa_model is None: swa_model=copy.deepcopy(model)
            else:
                with torch.no_grad():
                    for ps,pm in zip(swa_model.parameters(),model.parameters()):
                        ps.mul_(swa_n/(swa_n+1)).add_(pm/(swa_n+1))
                    for bs_,bm in zip(swa_model.buffers(),model.buffers()):
                        if bs_.dtype.is_floating_point: bs_.mul_(swa_n/(swa_n+1)).add_(bm/(swa_n+1))
                        else: bs_.copy_(bm)
            swa_n+=1
        if verbose and (ep+1)%50==0:
            r=evaluate(model,vax,vaY,fm,fs,lm,ls); print(f'ep{ep+1} val rel={r[0]:.3f} mae={r[1]:.3f} mx={r[2]:.3f}')
    # finalize: update BN running stats for swa by a few forward passes
    final=swa_model if swa_model is not None else model
    # recompute BN stats on training data (no aug) for stability
    final.train()
    with torch.no_grad():
        for _ in range(10):
            perm=torch.randperm(N,device=DEV)
            for i in range(0,N,bs):
                idx=perm[i:i+bs]
                xb=augment(trx[idx],refl,jit,boot)
                final((xb-fm)/fs)
    r=evaluate(final,vax,vaY,fm,fs,lm,ls)
    return r, final, (fm,fs,lm,ls)

if __name__=='__main__':
    trx,trY=load('train'); vax,vaY=load('val')
    r,_,_=train_once(trx,trY,vax,vaY,epochs=200,verbose=True,seed=0)
    print('FINAL val relL2=%.4f MAE=%.4f max=%.4f'%r)
EOF
cd /app && python /tmp/trainer.py

output

ep50 val rel=0.338 mae=0.232 mx=0.828
ep100 val rel=0.320 mae=0.220 mx=0.798
ep150 val rel=0.299 mae=0.200 mx=0.678
ep200 val rel=0.290 mae=0.194 mx=0.687
FINAL val relL2=0.2914 MAE=0.1928 max=0.6518

PointNet gives val relL2 ≈ 0.29, worse than ridge (0.20). The PointNet is underfitting the signal or the augmentation/hparams aren't ideal. Let me investigate. Possibly too much augmentation (jitter/bootstrap) hurts, or needs more epochs, or the BN recompute with augmentation is adding noise. Let me do a small hyperparameter sweep: vary augmentation strength, epochs, loss, lr, wd. Let me also try no bootstrap, less jitter. Let me run a few configs quickly. I'll parallelize by running sequentially but with fewer epochs for the sweep, then refine. Actually, let me reconsider. The PointNet max-pool captures extents/shape silhouette. Ridge used filled-volume + covariance eigenvalues etc. PointNet should be able to learn comparable features. The gap suggests training issues. Let me sweep. Let me test several configs and print val metrics. I'll reduce verbosity.

The PointNet underfits (val rel-L2 ≈ 0.29 vs ridge's 0.20). Let me sweep augmentation strength, epochs, loss, and regularization to find what helps.

bash

cd /app && cat > /tmp/sweep.py << 'EOF'
import sys; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
from trainer import load, train_once
trx,trY=load('train'); vax,vaY=load('val')
cfgs=[
 dict(name='base',epochs=200),
 dict(name='nojit_noboot',epochs=200,jit=0.0,boot=False),
 dict(name='noboot',epochs=200,boot=False),
 dict(name='lessjit',epochs=200,jit=0.003,boot=False),
 dict(name='huber',epochs=200,loss='huber',boot=False,jit=0.003),
 dict(name='longer',epochs=400,boot=False,jit=0.003),
 dict(name='bs16',epochs=300,bs=16,boot=False,jit=0.003),
 dict(name='wd3e-3',epochs=300,wd=3e-3,boot=False,jit=0.003),
]
for c in cfgs:
    name=c.pop('name')
    r,_,_=train_once(trx,trY,vax,vaY,seed=0,**c)
    print(f'{name:16s} relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f}')
EOF
cd /app && python /tmp/sweep.py

output

base             relL2=0.2806 MAE=0.1879 max=0.5871
nojit_noboot     relL2=0.3073 MAE=0.2138 max=0.6125
noboot           relL2=0.3035 MAE=0.1981 max=0.7445
lessjit          relL2=0.2969 MAE=0.2054 max=0.7173
huber            relL2=0.3656 MAE=0.2208 max=0.9150
longer           relL2=0.2398 MAE=0.1568 max=0.5243
bs16             relL2=0.2342 MAE=0.1556 max=0.5622
wd3e-3           relL2=0.2692 MAE=0.1820 max=0.6304

Longer training (400 epochs) and smaller batch (16) help significantly → relL2 ~0.23-0.24. The model is underfitting, needs more training. Bootstrap + jitter (base) is decent too. Let me push further: more epochs, combine bootstrap + jitter + small batch, and try more epochs like 600-800. Note 'longer' (400 ep, jit=0.003, no boot) = 0.24, 'bs16' (300 ep, bs16, jit 0.003, no boot) = 0.234. Let me try combining: bs16 + 600 epochs + bootstrap + jitter. Also the 'base' with bootstrap at 200 was 0.28; longer with bootstrap might be better. Let me sweep epochs and batch size and augmentation combos more. The H100 is fast; 400 epochs took a few seconds. Let me try bigger sweeps. Let me also reconsider SWA fraction and lr. Let me try lr=2e-3, and more epochs. Let me run another sweep.

Longer training and smaller batches clearly help , the model was underfitting. Let me push epochs/batch further and combine augmentations.

bash

cd /app && cat > /tmp/sweep2.py << 'EOF'
import sys; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
from trainer import load, train_once
trx,trY=load('train'); vax,vaY=load('val')
cfgs=[
 dict(name='bs16_e600_boot',epochs=600,bs=16,jit=0.004,boot=True),
 dict(name='bs16_e600_nob',epochs=600,bs=16,jit=0.004,boot=False),
 dict(name='bs16_e800_boot',epochs=800,bs=16,jit=0.005,boot=True),
 dict(name='bs8_e600_boot',epochs=600,bs=8,jit=0.004,boot=True),
 dict(name='bs16_e600_lr2e3',epochs=600,bs=16,lr=2e-3,jit=0.004,boot=True),
 dict(name='bs16_e600_wd5e4',epochs=600,bs=16,wd=5e-4,jit=0.004,boot=True),
 dict(name='bs16_e600_swa4',epochs=600,bs=16,jit=0.004,boot=True,swa_frac=0.4),
 dict(name='bs32_e800_boot',epochs=800,bs=32,jit=0.004,boot=True),
]
for c in cfgs:
    name=c.pop('name')
    r,_,_=train_once(trx,trY,vax,vaY,seed=0,**c)
    print(f'{name:20s} relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f}')
EOF
cd /app && python /tmp/sweep2.py

output

bs16_e600_boot       relL2=0.2341 MAE=0.1422 max=0.6958
bs16_e600_nob        relL2=0.2158 MAE=0.1324 max=0.5789
bs16_e800_boot       relL2=0.2415 MAE=0.1353 max=0.7609
bs8_e600_boot        relL2=0.2784 MAE=0.1348 max=0.9162
bs16_e600_lr2e3      relL2=0.2403 MAE=0.1435 max=0.7517
bs16_e600_wd5e4      relL2=0.2337 MAE=0.1496 max=0.6530
bs16_e600_swa4       relL2=0.2253 MAE=0.1408 max=0.5608
bs32_e800_boot       relL2=0.2607 MAE=0.1832 max=0.5141

Best: bs16_e600_nob (no bootstrap, jit=0.004, 600 ep) → relL2=0.216, MAE=0.132, max=0.579. Close to ridge now. The bootstrap resampling seems to hurt rel L2 (adds noise). Jitter helps a bit. So the direction: bs16, ~600 epochs, jitter ~0.004, reflections, no bootstrap. relL2 ~0.216. Val set is only 32 samples , noisy. I should use cross-validation over combined data to get a more reliable estimate and avoid overfitting to this particular val split. But first, let me try to push the PointNet further. Ideas: 1. Ensemble via SWA already included. 2. Seed averaging: train multiple seeds and average predictions , but I can only save ONE model. However, I could average the WEIGHTS of multiple independently-trained models? That generally doesn't work for independently initialized nets (different basins). SWA works because same trajectory. So seed-ensemble weight averaging won't work. But I can do a "snapshot ensemble" within one run (cyclic LR) and average weights (like SWA) , that's fine and is one model. 3. The main limitation might be the architecture capacity / the max-pool bottleneck. I can't change architecture. But I can improve the INPUT representation! The model gets raw xyz. I could augment input features... no, the grader feeds [B,2048,3] and applies (x-feat_mean)/feat_std. So input is strictly the 3 coords. I cannot add features at inference. So input is fixed to 3D coords. Wait, can I? The feat_mean/feat_std are length-3. The model's first layer is Conv1d(3,...). So input must be 3 channels. I cannot expand input features. OK. 4. Better normalization: instead of per-axis standardization, maybe the raw coords are fine. feat_std per-axis ~ [0.14,0.27,0.087]. Standardizing makes each axis unit variance , this reshapes the geometry (anisotropic scaling), which might actually help or hurt. The starter uses per-axis std. Let me consider using a single global scale (isotropic) to preserve shape aspect ratio, which might be more physically meaningful. But feat_mean/feat_std must be length-3; I can set all three std equal to a single scalar (isotropic). Let me test isotropic normalization vs per-axis. Actually, the choice of feat_std affects the geometry the network sees. Per-axis standardization stretches z by 1/0.087 ≈ 11.5 and x by 1/0.14≈7 and y by 1/0.27≈3.7. This anisotropic stretching distorts shape a lot. Maybe isotropic (same scale all axes) preserves aspect ratios that correlate with mass. Let me test. 5. Loss targeting rel L2: Since rel L2 numerator = sum squared errors, MSE is right. But MAE and max also matter. Current MAE 0.13 is decent. Let me test normalization choice and a couple more things. Let me also properly implement cross-validation to get robust estimates. Given val is noisy (32 samples), let me combine train+val+public_test (148 samples) and do k-fold CV to choose hyperparameters robustly. Actually, let me step back on strategy and time budget. I have a reasonable pipeline at ~0.21 rel L2. Let me: (a) Test isotropic vs per-axis normalization and jitter levels (quick). (b) Set up 5-fold CV on the full 148 samples to robustly compare a few top configs. (c) Pick best, train final model on all 148 with SWA + multiple cyclic snapshots, save checkpoint. Let me do (a) first. Let me modify trainer to accept a normalization mode.

Best so far: bs16, ~600 epochs, jitter 0.004, reflections, no bootstrap → rel-L2 ≈ 0.216. Bootstrap resampling hurts rel-L2. The input is locked to 3 coords, so let me test something impactful: **isotropic vs per-axis normalization**. Per-axis standardization stretches z by ~11× and distorts aspect ratios that likely correlate with mass.

bash

cd /app && cat > /tmp/trainer2.py << 'EOF'
from __future__ import annotations
import json, sys, copy
import torch, torch.nn as nn, torch.nn.functional as F
sys.path.insert(0,'/app')
from candidate_model import build_model
CFG=json.load(open('/app/task_config.json')); DEV=torch.device('cuda')
def load(s):
    d=torch.load(f'/app/data/{s}/samples.pt',weights_only=True)
    return d['features'].float(), d['labels'].float()
def augment(x, refl=True, jit=0.004, boot=False):
    B,N,_=x.shape
    if refl:
        s=(torch.randint(0,2,(B,1,3),device=x.device).float()*2-1); x=x*s
    if boot:
        idx=torch.randint(0,N,(B,N),device=x.device); x=torch.gather(x,1,idx.unsqueeze(-1).expand(-1,-1,3))
    if jit>0: x=x+torch.randn_like(x)*jit
    return x
def get_norm(trx,trY,mode):
    flat=trx.reshape(-1,3)
    fm=flat.mean(0).view(1,1,3).to(DEV)
    if mode=='peraxis': fs=flat.std(0).clamp_min(1e-6).view(1,1,3).to(DEV)
    elif mode=='iso':
        s=flat.std(0).mean(); fs=torch.full((1,1,3),float(s),device=DEV).clamp_min(1e-6)
    elif mode=='none': fs=torch.ones(1,1,3,device=DEV)
    lm=trY.mean().view(1,1).to(DEV); ls=trY.std().clamp_min(1e-6).view(1,1).to(DEV)
    return fm,fs,lm,ls
def evaluate(model,x,y,fm,fs,lm,ls):
    model.eval()
    with torch.no_grad():
        pr=model((x-fm)/fs)*ls+lm
    pr=pr.squeeze(1); yt=y.squeeze(1)
    return (torch.norm(pr-yt)/torch.norm(yt)).item(),(pr-yt).abs().mean().item(),(pr-yt).abs().max().item()
def train_once(trx,trY,vax,vaY, epochs=600, bs=16, lr=1e-3, wd=1e-4, loss='mse',
               refl=True, jit=0.004, boot=False, swa_frac=0.3, norm='peraxis', seed=0, ret_pred=False):
    torch.manual_seed(seed)
    fm,fs,lm,ls=get_norm(trx,trY,norm)
    trx=trx.to(DEV); trY=trY.to(DEV); vax=vax.to(DEV); vaY=vaY.to(DEV)
    model=build_model(CFG).to(DEV)
    opt=torch.optim.AdamW(model.parameters(),lr=lr,weight_decay=wd)
    sched=torch.optim.lr_scheduler.CosineAnnealingLR(opt,T_max=epochs)
    N=trx.shape[0]; swa_start=int(epochs*(1-swa_frac)); swa=None; swa_n=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=augment(trx[idx],refl,jit,boot); yb=(trY[idx]-lm)/ls
            opt.zero_grad(set_to_none=True); pr=model((xb-fm)/fs)
            l=F.mse_loss(pr,yb) if loss=='mse' else F.smooth_l1_loss(pr,yb,beta=0.5)
            l.backward(); opt.step()
        sched.step()
        if ep>=swa_start:
            if swa is None: swa=copy.deepcopy(model)
            else:
                with torch.no_grad():
                    for ps,pm in zip(swa.parameters(),model.parameters()): ps.mul_(swa_n/(swa_n+1)).add_(pm/(swa_n+1))
                    for bsf,bm in zip(swa.buffers(),model.buffers()):
                        if bsf.dtype.is_floating_point: bsf.mul_(swa_n/(swa_n+1)).add_(bm/(swa_n+1))
                        else: bsf.copy_(bm)
            swa_n+=1
    final=swa if swa is not None else model
    final.train()
    with torch.no_grad():
        for _ in range(12):
            perm=torch.randperm(N,device=DEV)
            for i in range(0,N,bs):
                idx=perm[i:i+bs]; final((augment(trx[idx],refl,jit,boot)-fm)/fs)
    r=evaluate(final,vax,vaY,fm,fs,lm,ls)
    if ret_pred:
        final.eval()
        with torch.no_grad(): pr=(final((vax-fm)/fs)*ls+lm).squeeze(1).cpu()
        return r, final,(fm,fs,lm,ls), pr
    return r, final,(fm,fs,lm,ls)
EOF
cat > /tmp/sweep3.py << 'EOF'
import sys; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
from trainer2 import load, train_once
trx,trY=load('train'); vax,vaY=load('val')
for norm in ['peraxis','iso','none']:
    for jit in [0.002,0.004,0.008]:
        r,_,_=train_once(trx,trY,vax,vaY,epochs=600,bs=16,jit=jit,norm=norm,seed=0)
        print(f'norm={norm:8s} jit={jit:.3f} relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f}')
EOF
cd /app && python /tmp/sweep3.py

output

norm=peraxis  jit=0.002 relL2=0.2431 MAE=0.1461 max=0.6783
norm=peraxis  jit=0.004 relL2=0.2148 MAE=0.1255 max=0.5694
norm=peraxis  jit=0.008 relL2=0.2376 MAE=0.1572 max=0.4738
norm=iso      jit=0.002 relL2=0.2391 MAE=0.1414 max=0.7163
norm=iso      jit=0.004 relL2=0.2513 MAE=0.1445 max=0.7972
norm=iso      jit=0.008 relL2=0.2463 MAE=0.1429 max=0.7591
norm=none     jit=0.002 relL2=0.2785 MAE=0.1444 max=0.9594
norm=none     jit=0.004 relL2=0.2413 MAE=0.1368 max=0.7125
norm=none     jit=0.008 relL2=0.2374 MAE=0.1414 max=0.6117

per-axis norm with jit=0.004 is best (0.2148). Isotropic didn't help. So keep per-axis standardization. Now I'm plateauing around 0.21. Val is noisy (32 samples). Let me set up proper k-fold cross-validation on the full labeled set (train+val+public_test = 148) to (a) get a robust estimate and (b) ultimately train the final model on all data. Also, I realize seed variance could be large with 32 val samples. Let me measure seed variance for the best config. And importantly , I should think about whether averaging predictions from multiple seeds (ensemble) would help a lot. But I can only save one model. Idea: snapshot ensemble within a single cosine-cycle SWA gives one model. Another idea: I could train a single model longer / with more data. Actually , the biggest lever might be MORE DATA. I have train(100)+val(32)+public_test(16)=148 labeled. For the FINAL model, train on all 148. That ~48% more data than 100. Should reduce error. But I need a validation signal to choose hyperparams and avoid overfitting. Let me do k-fold CV on the 148. Let me also reconsider: is there test-time augmentation? The grader does a single forward. But I can bake TTA-like robustness via training augmentation. Alternatively... hmm, can I make the model itself average over reflections internally? No, architecture fixed. Let me measure seed variance and set up CV. Let me combine all data and do 5-fold CV with the best config, across a few seeds. I'll report mean rel L2. One concern: with CV, each fold trains on ~118 samples. The final model trains on 148, so CV slightly underestimates final performance. Let me build a CV harness.

Per-axis normalization with jitter ≈ 0.004 is best. I'm plateauing near 0.21 on a noisy 32-sample val set. Let me (1) measure seed variance and (2) set up proper k-fold cross-validation on the **combined 148 labeled samples** (train+val+public_test) for robust hyperparameter decisions , and I'll use all 148 for the final model.

bash

cd /app && cat > /tmp/cv.py << 'EOF'
import sys; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
import torch, numpy as np
from trainer2 import load, train_once
# combined data
fx=torch.cat([load('train')[0],load('val')[0],load('public_test')[0]],0)
fy=torch.cat([load('train')[1],load('val')[1],load('public_test')[1]],0)
Ntot=fx.shape[0]; print('total',Ntot)
g=torch.Generator().manual_seed(12345); order=torch.randperm(Ntot,generator=g)
K=5; folds=[order[i::K] for i in range(K)]
def run_cv(seed=0, **kw):
    allpr=torch.zeros(Ntot); ally=fy.squeeze(1).clone()
    for k in range(K):
        va=folds[k]; tr=torch.cat([folds[j] for j in range(K) if j!=k])
        r,_,_,pr=train_once(fx[tr],fy[tr],fx[va],fy[va],ret_pred=True,seed=seed,**kw)
        allpr[va]=pr
    rel=(torch.norm(allpr-ally)/torch.norm(ally)).item()
    mae=(allpr-ally).abs().mean().item(); mx=(allpr-ally).abs().max().item()
    return rel,mae,mx
if __name__=='__main__':
    import itertools
    base=dict(epochs=600,bs=16,jit=0.004,norm='peraxis')
    # seed variance
    for seed in [0,1,2]:
        r=run_cv(seed=seed,**base); print(f'seed{seed} CV relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f}')
EOF
cd /app && python /tmp/cv.py

output

total 148
seed0 CV relL2=0.2694 MAE=0.1708 max=0.7138
seed1 CV relL2=0.2514 MAE=0.1526 max=0.9232
seed2 CV relL2=0.2543 MAE=0.1549 max=0.7723

CV rel L2 ≈ 0.25-0.27, worse than the single val split estimate (0.21). This is a more honest estimate. The val split earlier (0.21) was optimistic. So true generalization is around 0.25. Max abs error is large (~0.7-0.9), meaning some outliers. I need to improve. Let me think harder about what would make the PointNet better. Key ideas to try: 1. **Ensembling via prediction averaging** , but one model only. However, I can average predictions of reflected inputs at... no, single forward by grader. Wait, actually let me reconsider. Can I do something clever: the grader calls model(normalized_x). What if my model internally, in forward(), is just the PointNet , I can't add TTA. Confirmed single forward. 2. **More aggressive but valid augmentation.** Reflections give 8x. What about small rotations about the y-axis (vertical)? That changes x-z extents and breaks axis-aligned bbox tightness. Test data is axis-aligned though. But maybe small rotations (±5°) act as regularization without too much distribution shift. Let me test. Actually, a better invariance-preserving augmentation: since the bracket's bounding box is axis aligned and tight, I shouldn't rotate. 3. **Mixup / manifold mixup**: interpolate point clouds? Point clouds can't be linearly mixed (unordered). But I could mix at the label level with... no. 4. **Better loss for rel L2 and max.** The max abs error is big. Maybe use a loss that penalizes large errors more, or train on log-mass? Mass spans 0.2-2.2 (~10x). Relative errors matter. Predicting log(mass) might help because it equalizes relative scale and the metric is relative L2. Let me reconsider: rel L2 = ||pred-true||/||true||. If I predict in log space and exponentiate, small masses get relatively more weight. Let me test predicting log-mass then exp. This could help MAE on small masses and overall rel L2 if errors are multiplicative. Actually the relative L2 aggregate is dominated by large-mass samples (since ||true|| and numerator dominated by big values). So to reduce rel L2, focus on large-mass samples' absolute accuracy. MSE already does that. Hmm. But worst-case abs error and MAE care about all. Let me analyze where errors are. 5. **Capacity**: maybe the model underfits because max-pool of a 256-dim global vector isn't enough, but I can't change it. 6. **Input: center per-sample?** The data is already centered at bbox centroid. feat_mean subtracts global mean (~0). Fine. Let me analyze the CV predictions vs true to see error structure (are large masses underpredicted? outliers?). Let me get per-sample errors from one CV run. Let me also try: log-target, huber in log space, rotations, and longer training with larger effective augmentation. And try averaging the per-fold model predictions isn't relevant for final. Let me first analyze error structure.

CV gives a more honest rel-L2 ≈ 0.25–0.27 (the single val split was optimistic), with large worst-case errors (~0.7–0.9). Let me analyze the error structure , are large-mass brackets underpredicted? Are there consistent outliers?

bash

cd /app && cat > /tmp/analyze.py << 'EOF'
import sys; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
import torch, numpy as np
from trainer2 import load, train_once
fx=torch.cat([load('train')[0],load('val')[0],load('public_test')[0]],0)
fy=torch.cat([load('train')[1],load('val')[1],load('public_test')[1]],0)
Ntot=fx.shape[0]
g=torch.Generator().manual_seed(12345); order=torch.randperm(Ntot,generator=g)
K=5; folds=[order[i::K] for i in range(K)]
allpr=torch.zeros(Ntot); ally=fy.squeeze(1).clone()
for k in range(K):
    va=folds[k]; tr=torch.cat([folds[j] for j in range(K) if j!=k])
    _,_,_,pr=train_once(fx[tr],fy[tr],fx[va],fy[va],ret_pred=True,seed=0,epochs=600,bs=16,jit=0.004)
    allpr[va]=pr
err=(allpr-ally)
print('bias (mean pred-true):',err.mean().item())
# sort by true mass, show binned errors
idx=torch.argsort(ally)
for frac,lab in [((0,0.33),'low'),((0.33,0.66),'mid'),((0.66,1.0),'high')]:
    s=idx[int(frac[0]*Ntot):int(frac[1]*Ntot)]
    print(f'{lab:5s} mass[{ally[s].min():.2f},{ally[s].max():.2f}] mean_err={err[s].mean():+.3f} mean|err|={err[s].abs().mean():.3f}')
# worst 8
w=torch.argsort(err.abs(),descending=True)[:8]
for i in w.tolist():
    print(f'  true={ally[i]:.3f} pred={allpr[i]:.3f} err={err[i]:+.3f}')
EOF
cd /app && python /tmp/analyze.py

output

bias (mean pred-true): -0.04517599195241928
low   mass[0.20,0.52] mean_err=+0.087 mean|err|=0.120
mid   mass[0.52,0.80] mean_err=-0.009 mean|err|=0.159
high  mass[0.80,2.22] mean_err=-0.204 mean|err|=0.232
  true=0.596 pred=1.399 err=+0.803
  true=1.584 pred=0.870 err=-0.714
  true=1.584 pred=0.920 err=-0.664
  true=0.515 pred=1.131 err=+0.616
  true=0.521 pred=1.119 err=+0.599
  true=1.619 pred=1.087 err=-0.532
  true=0.529 pred=1.006 err=+0.477
  true=1.584 pred=1.129 err=-0.455

Clear pattern: the model UNDERPREDICTS high-mass brackets (mean err -0.20 for high) and OVERPREDICTS low-mass (mean err +0.087). This is classic regression-to-the-mean / underfitting. The high-mass samples (up to 2.2) are underpredicted heavily , these dominate rel L2 (big absolute errors on big values). Reducing high-mass underprediction is key. Why underprediction of extremes? Regression to mean due to: - Limited capacity / underfitting - The high-mass examples are rare (few samples above 1.2) - MSE on standardized target still regresses to mean when features are noisy The worst errors: true=0.596 predicted 1.399 (big over), and true=1.584 predicted ~0.9 (big under). These could be genuinely hard / mislabeled-looking, or shapes that are ambiguous. Interesting: there are multiple samples with true=1.584 , likely the same bracket id appearing? No, ids unique. Maybe coincidence. They're all underpredicted (0.87-1.13). So mass 1.584 brackets look "lighter" than they are to the model. To reduce regression-to-mean on high masses, I can: 1. Train longer / more capacity (underfitting). 2. Weight high-mass samples more in the loss. But the metric rel L2 already weights high mass (since errors there are large in absolute terms). MSE on standardized target treats all equally in standardized units. Since std=0.39, an error of 0.2 kg = 0.5 std. 3. Use a loss that doesn't regress to mean as much. Actually, the key issue is likely the tiny number of high-mass training examples. Let me check the mass distribution. The high tail (1.2-2.2) is sparse. With few examples, the model can't learn them well and regresses toward the mean. A targeted fix: sample weighting or oversampling high-mass examples. Or a loss weighting by mass. Let me test weighting the loss by (mass)^p or by inverse frequency. But careful: overweighting high mass could hurt low/mid. The metric: rel L2 dominated by high mass, MAE uniform, max is the single worst. Let me try weighting. Also, predicting in a way that corrects the shrinkage bias: I could post-hoc calibrate (linear rescale predictions to undo regression-to-mean). Since I control label_mean/label_std and the model output, but a linear calibration pred' = a*pred + b can be folded into label_mean/label_std! Because inference computes out*label_std+label_mean. If I find that predictions are shrunk toward mean, I can set label_std slightly larger to expand. But that's a global linear fix; the bias is roughly linear (under-predict high, over-predict low = slope <1). A linear calibration could help rel L2 and MAE. Let me quantify: fit true ≈ a*pred + b on CV out-of-fold predictions, see if correcting helps. If slope>1 needed, expanding predictions around mean helps. This is a cheap, robust post-hoc fix that I can bake into label_mean/label_std OR into the final by adjusting. But wait , I must keep the model output mapping as out*label_std+label_mean. If I want pred_final = a*(raw_out*ls+lm)+b, that's = raw_out*(a*ls) + (a*lm+b). So I can fold a and b into new label_std'=a*ls and label_mean'=a*lm+b. So a linear calibration IS expressible via label_mean/label_std! Great. So I can calibrate freely. So plan: after training, fit a 1D linear calibration on held-out (CV) predictions to de-shrink, and fold into label stats. But for the FINAL model trained on all data, I won't have held-out preds. I can estimate the calibration slope from CV (it's a property of the method) and apply the same slope to the final model. That's reasonable. Let me test how much linear calibration helps on the CV predictions. Actually, better: address root cause (shrinkage) via training, AND calibrate. Let me first measure calibration benefit. Let me compute: given allpr, ally from CV, fit linear, report rel L2 before/after.

Clear bias: the model **underpredicts high-mass brackets** (mean err −0.20) and overpredicts low-mass ones (+0.09) , classic regression-to-the-mean shrinkage that inflates rel-L2 (dominated by large masses). A key realization: a linear calibration `a·pred + b` folds exactly into `label_mean`/`label_std` (since inference computes `out·label_std + label_mean`), so I can de-shrink for free. Let me quantify how much it helps on the CV predictions, and also test loss-weighting to fix the root cause.

bash

cd /app && cat > /tmp/calib.py << 'EOF'
import sys; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
import torch, numpy as np
from trainer2 import load, train_once
fx=torch.cat([load('train')[0],load('val')[0],load('public_test')[0]],0)
fy=torch.cat([load('train')[1],load('val')[1],load('public_test')[1]],0)
Ntot=fx.shape[0]
g=torch.Generator().manual_seed(12345); order=torch.randperm(Ntot,generator=g)
K=5; folds=[order[i::K] for i in range(K)]
def cv_preds(seed=0,**kw):
    allpr=torch.zeros(Ntot)
    for k in range(K):
        va=folds[k]; tr=torch.cat([folds[j] for j in range(K) if j!=k])
        _,_,_,pr=train_once(fx[tr],fy[tr],fx[va],fy[va],ret_pred=True,seed=seed,**kw)
        allpr[va]=pr
    return allpr
ally=fy.squeeze(1).clone()
def metrics(pr):
    return (torch.norm(pr-ally)/torch.norm(ally)).item(),(pr-ally).abs().mean().item(),(pr-ally).abs().max().item()
base=dict(epochs=600,bs=16,jit=0.004)
# average predictions over seeds to reduce variance, then calibrate
prs=[cv_preds(seed=s,**base) for s in [0,1,2]]
pr=torch.stack(prs).mean(0)
print('raw (3-seed avg)     relL2=%.4f MAE=%.4f max=%.4f'%metrics(pr))
# linear calibration a*pr+b via least squares (this is what folding into label stats does)
A=torch.stack([pr,torch.ones_like(pr)],1)
sol=torch.linalg.lstsq(A,ally).solution
prc=A@sol
print('global-cal a=%.3f b=%.3f'%(sol[0],sol[1]),'relL2=%.4f MAE=%.4f max=%.4f'%metrics(prc))
# how consistent is slope across seeds individually?
for s,p in zip([0,1,2],prs):
    Ai=torch.stack([p,torch.ones_like(p)],1); so=torch.linalg.lstsq(Ai,ally).solution
    print(f'  seed{s} a={so[0]:.3f} b={so[1]:.3f}')
EOF
cd /app && python /tmp/calib.py

output

raw (3-seed avg)     relL2=0.2506 MAE=0.1558 max=0.7272
global-cal a=1.004 b=0.028 relL2=0.2479 MAE=0.1512 max=0.7600
  seed0 a=1.040 b=0.019
  seed1 a=0.959 b=0.051
  seed2 a=0.954 b=0.058

Interesting , a global linear calibration barely helps (slope ≈ 1.0). That's because the shrinkage is NOT a simple global linear effect; it's more that the model fails to identify the extreme cases at all (predicts them near mean because it can't distinguish them). A linear fix can't recover that. The slope being ~1 means on average predictions track truth, but the extremes have high variance/error. Also 3-seed averaging of CV predictions only modestly helped (0.251 vs 0.25-0.27 single). Ensembling helps a bit but I can only ship one model. The real problem is the model can't distinguish high-mass brackets well. This is fundamentally a capacity/signal issue within the fixed architecture. Let me think about what else I can do. Key lever: The input is just xyz coordinates. The PointNet computes per-point features via shared MLP then max-pools. Max-pool captures extremes of features (like bounding extents, presence of certain local configs) but is fundamentally density/count-blind. Mass ∝ volume, which correlates with how much the surface encloses. Max-pooling can't count points or integrate volume well. BUT , I can't change the architecture. However, I CAN change what coordinates I feed, via feat_mean/feat_std? No, that's just affine per-axis; can't add features. Hmm wait, is there any transformation of the input I can bake in? The grader does (x - feat_mean)/feat_std then model.forward. feat_mean, feat_std are length-3. So only per-axis affine. Can't add channels. So I'm stuck with raw coords → PointNet. Let me maximize what this can do. Let me reconsider the training. Maybe the model needs MORE capacity usage , e.g., train longer with lower final lr, or the BN is an issue. Let me look at train vs CV error to see if it's overfitting or underfitting. Earlier longer training kept helping, suggesting underfitting. Let me check training error at the chosen config. Let me also try: - Higher capacity utilization: more epochs (1000-1500). - Different optimizer schedule (warmup). - Remove SWA (maybe SWA oversmooths and increases shrinkage?). Let me test SWA vs best-epoch. But best-epoch needs val; in CV I can measure. - Larger jitter reduces overfit but may increase shrinkage. Let me check train error to diagnose under/overfitting. Actually, let me reconsider the whole framing. The rel-L2 of ~0.25 , is that actually "bad"? Let me reconsider the scoring. "relative L2 over the full set." The grader likely converts to a score. Lower is better. We want as low as possible. Ridge got 0.20 on val. The theoretical best given the info (normalized shape → mass) might be limited because normalization removed absolute scale. Wait , reconsider: maybe absolute scale ISN'T fully removed. The brackets all share mounting interfaces; in SimJEB all designs fit the same design envelope (228mm x 160mm bounding or similar). If the real bounding boxes differ, normalization removes that. But if real bounding boxes are ~identical (same envelope), then normalized shape fully determines mass (given same material density). Let me check: if normalization scale were constant, then the filled volume in normalized coords × constant = real volume ∝ mass. We found filled-volume correlation only 0.66, suggesting either leaky flood-fill (sparse points) or variable scale. Let me reconsider. The relationship might genuinely be noisy from 2048-point sampling. The ceiling might be ~0.2. Ridge got 0.20 on val (but CV might be worse). Let me get ridge CV for a fair comparison to PointNet CV. Let me compute ridge CV on the same folds with the handcrafted features. If ridge CV ≈ 0.20 and PointNet CV ≈ 0.25, then there's room to improve PointNet. If ridge CV also ≈ 0.25, then 0.25 is near the ceiling. Let me compute handcrafted features for ALL 148 samples and do ridge CV on the same folds.

A global linear calibration barely helps (slope ≈ 1.0) , the issue isn't uniform shrinkage but that the model can't *distinguish* extreme brackets. Let me establish whether ~0.25 is near the ceiling by running ridge (on handcrafted features) on the **same CV folds** for an apples-to-apples comparison.

bash

cd /app && cat > /tmp/featall.py << 'EOF'
import sys; sys.path.insert(0,'/tmp')
import torch, numpy as np
sys.path.insert(0,'/app')
exec(open('/tmp/feat.py').read().split("if __name__")[0])
def loadt(s):
    d=torch.load(f'/app/data/{s}/samples.pt',weights_only=True)
    return d['features'].float(), d['labels'].float().squeeze(1)
fx=torch.cat([loadt('train')[0],loadt('val')[0],loadt('public_test')[0]],0)
fy=torch.cat([loadt('train')[1],loadt('val')[1],loadt('public_test')[1]],0)
X=features(fx); y=fy.numpy()
np.savez('/tmp/featall.npz',X=X,y=y)
print('done',X.shape)
EOF
python /tmp/featall.py
python -c "
import torch, numpy as np
d=np.load('/tmp/featall.npz'); X,y=d['X'],d['y']
Ntot=len(y)
g=torch.Generator().manual_seed(12345); order=torch.randperm(Ntot,generator=g).numpy()
K=5; folds=[order[i::K] for i in range(K)]
def ridge_cv(lam):
    pr=np.zeros(Ntot)
    for k in range(K):
        va=folds[k]; tr=np.concatenate([folds[j] for j in range(K) if j!=k])
        mu=X[tr].mean(0); sd=X[tr].std(0)+1e-9
        Xt=(X[tr]-mu)/sd; Xv=(X[va]-mu)/sd
        A=Xt.T@Xt+lam*np.eye(X.shape[1]); w=np.linalg.solve(A,Xt.T@(y[tr]-y[tr].mean()))
        pr[va]=Xv@w+y[tr].mean()
    rel=np.linalg.norm(pr-y)/np.linalg.norm(y); return rel,np.mean(np.abs(pr-y)),np.max(np.abs(pr-y))
for lam in [0.3,1,3,10]:
    r=ridge_cv(lam); print(f'ridge lam={lam:5} CV relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f}')
"

output

done (148, 19)
ridge lam=  0.3 CV relL2=0.2315 MAE=0.1492 max=0.6236
ridge lam=    1 CV relL2=0.2308 MAE=0.1491 max=0.6154
ridge lam=    3 CV relL2=0.2348 MAE=0.1498 max=0.6066
ridge lam=   10 CV relL2=0.2414 MAE=0.1509 max=0.6013

Ridge CV ≈ 0.231, PointNet CV ≈ 0.25. So ridge (with engineered volume/covariance features) is a bit better than the PointNet, and represents roughly the achievable signal ceiling from this 2048-point data (~0.23). The PointNet has ~0.25, so there's ~0.02 to recover to match ridge, and the ceiling may be a bit below 0.23 with better features. So the information ceiling is around 0.22-0.23 rel L2. The data is inherently noisy (sparse surface sampling + scale normalization). Now, can I combine the strengths? The engineered features capture volume via flood-fill that PointNet can't. But I can't feed extra features to the model. Hmm, but wait: Can I bias the PointNet toward learning volume-like features? Max pooling can't count, but it CAN capture bounding extents and the spatial extent of the surface. The engineered features that matter most: let me check which features drive ridge. If it's covariance eigenvalues / std products (shape spread), PointNet can learn those. If it's flood-fill volume, PointNet struggles. Let me check ridge feature importance (standardized weights). Actually, let me reconsider the goal. The PointNet at 0.25 CV and ridge at 0.23. To do best on the held-out test, I want the single PointNet model as good as possible. Options to close the gap and push below 0.23: 1. Make PointNet match ridge by improving training (ensemble-in-one via SWA, better aug, more epochs). 2. Knowledge distillation: train PointNet to predict not just mass but to match a strong teacher? No benefit, same labels. Actually the cleanest high-impact idea: since I can only ship one PointNet, but ensembles help, I can use SWA aggressively (already do) and also train with more data (all 148 for final). CV trains on 118; final on 148 → should improve maybe 0.01-0.02. Also, I could blend the PointNet with the engineered-feature model... but I can only ship the PointNet (state_dict into PointNetRegressor). I cannot ship ridge. Unless... I bake ridge into the PointNet? No. Wait, is there any way to incorporate the engineered volume signal into the PointNet's weights? No, the input is only coordinates. So the ceiling for my deliverable is basically "best PointNet." Let me focus on making the PointNet as good as possible, targeting ~0.23 or below. Let me reconsider: maybe the PointNet can exceed ridge if trained really well, because it can learn richer features than my 19 hand-crafted ones. The fact it's at 0.25 suggests optimization/regularization headroom. Let me diagnose train vs val error for the PointNet. Let me also try: - More epochs (1000). - Two-stage: no SWA, pick by... can't pick by val in final. SWA is good for one-shot. - Try feature standardization variant: maybe not standardizing labels or using robust scaling. - Try deeper augmentation: combine reflection + small y-rotation + jitter. - Ensemble within single model via multiple cosine cycles (snapshot) averaged (SWA across cycles). Let me check under/overfitting first by reporting train rel L2 alongside CV. Let me add a quick diagnostic. Actually, let me just try a broader sweep focusing on epochs and augmentation, using the 3-seed CV average (more stable) for the top configs. But 3-seed × 5-fold × 600ep is 15 trainings per config , each ~5-10s on H100? Let me check timing. The earlier 3-seed calib run did 45 trainings (3 seeds×5 folds×3 configs = 45) in reasonable time. Let me time one CV. Let me be efficient. Let me run a focused sweep over epochs {800,1200}, swa_frac {0.3,0.5}, jit {0.004,0.006}, and rotation aug, using single-seed CV first to rank, then confirm top-2 with 3-seed. Let me also add small y-axis rotation augmentation to trainer2 and a "scale" aug option. Let me extend. Actually, let me reconsider rotation: rotating about y changes the x-z bounding box, making it non-tight and possibly exceeding [-0.5,0.5]? Rotating about the vertical y-axis keeps y the same; x,z get rotated. Since x extent ~0.59 and z ~0.35, rotating could push x beyond 0.5? x max …[truncated]

PointNet CV ≈ 0.25 vs ridge CV ≈ 0.23 , so ridge (with flood-fill volume + covariance features) is the rough signal ceiling (~0.22–0.23), and the PointNet has ~0.02 of optimization headroom. Since I can only ship the PointNet, let me focus on closing that gap. First, diagnose under/overfitting and test more epochs, SWA fraction, and a small y-rotation augmentation.

bash

cd /app && sed -n '1,30p' /tmp/trainer2.py | head -1 >/dev/null; python - << 'EOF'
# patch trainer2 to add y-rotation + scale aug
import re
src=open('/tmp/trainer2.py').read()
src=src.replace(
"def augment(x, refl=True, jit=0.004, boot=False):\n    B,N,_=x.shape\n    if refl:\n        s=(torch.randint(0,2,(B,1,3),device=x.device).float()*2-1); x=x*s\n    if boot:\n        idx=torch.randint(0,N,(B,N),device=x.device); x=torch.gather(x,1,idx.unsqueeze(-1).expand(-1,-1,3))\n    if jit>0: x=x+torch.randn_like(x)*jit\n    return x",
"""def augment(x, refl=True, jit=0.004, boot=False, rot=0.0, scl=0.0):
    B,N,_=x.shape
    if refl:
        s=(torch.randint(0,2,(B,1,3),device=x.device).float()*2-1); x=x*s
    if rot>0:
        th=(torch.rand(B,device=x.device)*2-1)*rot
        c=torch.cos(th); s=torch.sin(th)
        xx=x[:,:,0]*c.unsqueeze(1)-x[:,:,2]*s.unsqueeze(1)
        zz=x[:,:,0]*s.unsqueeze(1)+x[:,:,2]*c.unsqueeze(1)
        x=torch.stack([xx,x[:,:,1],zz],2)
    if scl>0:
        f=1.0+(torch.rand(B,1,3,device=x.device)*2-1)*scl; x=x*f
    if boot:
        idx=torch.randint(0,N,(B,N),device=x.device); x=torch.gather(x,1,idx.unsqueeze(-1).expand(-1,-1,3))
    if jit>0: x=x+torch.randn_like(x)*jit
    return x""")
# thread rot,scl through train_once augment calls
src=src.replace("refl=True, jit=0.004, boot=False, swa_frac=0.3, norm='peraxis', seed=0, ret_pred=False):",
                "refl=True, jit=0.004, boot=False, swa_frac=0.3, norm='peraxis', seed=0, ret_pred=False, rot=0.0, scl=0.0, report_train=False):")
src=src.replace("xb=augment(trx[idx],refl,jit,boot); yb=(trY[idx]-lm)/ls",
                "xb=augment(trx[idx],refl,jit,boot,rot,scl); yb=(trY[idx]-lm)/ls")
src=src.replace("idx=perm[i:i+bs]; final((augment(trx[idx],refl,jit,boot)-fm)/fs)",
                "idx=perm[i:i+bs]; final((augment(trx[idx],refl,jit,boot,rot,scl)-fm)/fs)")
src=src.replace("    r=evaluate(final,vax,vaY,fm,fs,lm,ls)\n    if ret_pred:",
                "    r=evaluate(final,vax,vaY,fm,fs,lm,ls)\n    if report_train:\n        rt=evaluate(final,trx,trY,fm,fs,lm,ls); print('   train rel=%.3f mae=%.3f'%(rt[0],rt[1]))\n    if ret_pred:")
open('/tmp/trainer2.py','w').write(src)
print('patched')
EOF
python -c "import sys;sys.path.insert(0,'/tmp');import trainer2;print('import ok')"

output

patched
import ok

bash

cd /app && cat > /tmp/cvfast.py << 'EOF'
import sys; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
import torch, time
from trainer2 import load, train_once
fx=torch.cat([load('train')[0],load('val')[0],load('public_test')[0]],0)
fy=torch.cat([load('train')[1],load('val')[1],load('public_test')[1]],0)
Ntot=fx.shape[0]
g=torch.Generator().manual_seed(12345); order=torch.randperm(Ntot,generator=g)
K=5; folds=[order[i::K] for i in range(K)]
ally=fy.squeeze(1).clone()
def cv(seeds=(0,),**kw):
    prs=[]
    for s in seeds:
        allpr=torch.zeros(Ntot)
        for k in range(K):
            va=folds[k]; tr=torch.cat([folds[j] for j in range(K) if j!=k])
            _,_,_,pr=train_once(fx[tr],fy[tr],fx[va],fy[va],ret_pred=True,seed=s,**kw)
            allpr[va]=pr
        prs.append(allpr)
    pr=torch.stack(prs).mean(0)
    return (torch.norm(pr-ally)/torch.norm(ally)).item(),(pr-ally).abs().mean().item(),(pr-ally).abs().max().item()
if __name__=='__main__':
    import itertools
    t=time.time()
    cfgs=[
      ('e600 base',dict(epochs=600,bs=16,jit=0.004)),
      ('e1000',dict(epochs=1000,bs=16,jit=0.004)),
      ('e1000 swa5',dict(epochs=1000,bs=16,jit=0.004,swa_frac=0.5)),
      ('e1000 rot10',dict(epochs=1000,bs=16,jit=0.004,rot=0.17)),
      ('e1000 scl05',dict(epochs=1000,bs=16,jit=0.004,scl=0.05)),
      ('e1000 jit006',dict(epochs=1000,bs=16,jit=0.006)),
    ]
    for name,kw in cfgs:
        r=cv(seeds=(0,),**kw)
        print(f'{name:14s} relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f}  [{time.time()-t:.0f}s]')
EOF
cd /app && python /tmp/cvfast.py

output

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

The 1000-epoch runs are too slow for 5 folds × 6 configs in 300s. Let me restart and run in background, writing results to a file. Let me time a single CV run first.

The longer runs exceeded the 300s limit. Let me restart the shell and run experiments in the background, writing results to a file so I don't block on them.

bash

restart: true

output

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

bash

cd /app && cat > /tmp/time1.py << 'EOF'
import sys,time; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
import torch
from trainer2 import load, train_once
trx,trY=load('train'); vax,vaY=load('val')
t=time.time(); r,_,_=train_once(trx,trY,vax,vaY,epochs=600,bs=16,jit=0.004,seed=0)
print('600ep one train: %.1fs rel=%.3f'%(time.time()-t,r[0]))
EOF
python /tmp/time1.py

output

600ep one train: 15.9s rel=0.236

600 epochs = 16s. So 5-fold = 80s, 1 config single-seed CV = 80s. 1000 ep = ~27s each, 5-fold=135s. For multiple configs I must run in background. Let me run the sweep in background and poll. Let me launch the cvfast sweep in background with nohup, writing to a log.

16s per 600-epoch train, so one single-seed CV (5 folds) ≈ 80s. I'll run sweeps in the background and poll the log.

bash

cd /app && nohup python /tmp/cvfast.py > /tmp/sweep_cvfast.log 2>&1 &
echo "launched pid $!"

output

launched pid 390

bash

sleep 280 && cat /tmp/sweep_cvfast.log

output

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

Empty log after 280s? That's odd. Maybe stdout buffering. Let me check if process is still running and check the log.

bash

sleep 60; echo "--- log ---"; cat /tmp/sweep_cvfast.log; echo "--- procs ---"; ps aux | grep -c "[c]vfast"

output

--- log ---
--- procs ---
1

The log is still empty but the process is running. Python is buffering stdout when redirected. Let me wait more and it should flush at the end, or I should have used -u. The first config (e600) should have printed after ~80s. It's been 340s. Something's buffered. Let me wait for more completions; output flushes on newline normally but when redirected to file, Python uses block buffering (4KB), so prints accumulate. The print statements are small, so they buffer. I'll just wait for the whole thing and rely on the fact it writes eventually, OR kill and relaunch with python -u. Let me relaunch with -u for unbuffered, and also reduce to fewer epochs to get results faster. Actually the process is running fine; let me just wait. But I can't see intermediate. Let me kill and relaunch with -u so I can monitor. Actually, let me leave this one running (it will produce results) and not waste the compute. But I want visibility. Let me relaunch a fresh one with -u to a different log, and kill the old to avoid GPU contention. GPU contention would slow both. Let me kill old and relaunch unbuffered.

Python is block-buffering to the file. Let me relaunch unbuffered (`-u`) so I can monitor progress, killing the old run to avoid GPU contention.

bash

pkill -f cvfast; sleep 3; cd /app && nohup python -u /tmp/cvfast.py > /tmp/sweep_cvfast.log 2>&1 & echo "launched pid $!"; sleep 200; echo "--- log @200s ---"; cat /tmp/sweep_cvfast.log

output

launched pid 426
--- log @200s ---
e600 base      relL2=0.2640 MAE=0.1693 max=0.7138  [75s]
e1000          relL2=0.2102 MAE=0.1390 max=0.5567  [195s]

1000 epochs dramatically helps: relL2 0.210 vs 0.264 at 600! So the model was underfitting. More epochs → better. Let me keep waiting for the rest (swa5, rot10, scl05, jit006). Let me wait for more.

1000 epochs helps a lot (0.210 vs 0.264) , the model was underfitting. Let me wait for the remaining configs.

bash

sleep 240; cat /tmp/sweep_cvfast.log

output

e600 base      relL2=0.2640 MAE=0.1693 max=0.7138  [75s]
e1000          relL2=0.2102 MAE=0.1390 max=0.5567  [195s]
e1000 swa5     relL2=0.2226 MAE=0.1428 max=0.6826  [316s]

bash

sleep 290; cat /tmp/sweep_cvfast.log

output

e600 base      relL2=0.2640 MAE=0.1693 max=0.7138  [75s]
e1000          relL2=0.2102 MAE=0.1390 max=0.5567  [195s]
e1000 swa5     relL2=0.2226 MAE=0.1428 max=0.6826  [316s]
e1000 rot10    relL2=0.2278 MAE=0.1419 max=0.7826  [445s]
e1000 scl05    relL2=0.2467 MAE=0.1441 max=0.8361  [565s]
e1000 jit006   relL2=0.2393 MAE=0.1525 max=0.7171  [697s]

Results: - e1000 (swa_frac=0.3, jit=0.004): relL2=0.2102 ← best - e1000 swa5 (swa_frac=0.5): 0.2226 - e1000 rot10: 0.2278 (rotation hurts slightly) - e1000 scl05: 0.2467 (scaling hurts) - e1000 jit006: 0.2393 (more jitter hurts) So best config: 1000 epochs, bs=16, jit=0.004, swa_frac=0.3, reflections only, per-axis norm. relL2=0.210, beating ridge (0.231)! Now let me push further. Since more epochs helped, let me try 1500 and 2000 epochs. Also try swa_frac=0.2. And test 3-seed averaging for stability on the best config. Let me also reconsider: the improvement from 600→1000 epochs was big. Let me check 1500, 2000. There may be more gains. Let me run these in background. Also consider: the best single-seed CV is 0.210. With 3-seed averaging it might drop to ~0.20. But final model is single. However, for the final model trained on ALL data (no held-out), I can still use SWA (weight averaging within the run) which is a single model. And I could train the final longer. Let me now sweep epochs {1000,1500,2000} and swa_frac {0.2,0.3} and lr {1e-3, maybe with a warmup}. Let me launch in background. Also, I want to verify the finding is robust across seeds (not a lucky fold/seed). Let me include 3-seed CV for the top config. Let me structure a second sweep.

1000 epochs with reflections + jitter 0.004 + SWA(0.3) gives **CV rel-L2 = 0.210**, already beating ridge (0.231). Rotation/scaling augmentations hurt. Since more epochs kept helping, let me test even longer training and confirm robustness across seeds.

bash

cd /app && cat > /tmp/cvfast2.py << 'EOF'
import sys,time; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
import torch
from cvfast import cv
t=time.time()
cfgs=[
  ('e1500 swa3',dict(epochs=1500,bs=16,jit=0.004,swa_frac=0.3)),
  ('e2000 swa3',dict(epochs=2000,bs=16,jit=0.004,swa_frac=0.3)),
  ('e1500 swa2',dict(epochs=1500,bs=16,jit=0.004,swa_frac=0.2)),
  ('e1500 swa25 3seed',dict(epochs=1500,bs=16,jit=0.004,swa_frac=0.25)),
]
for name,kw in cfgs[:3]:
    r=cv(seeds=(0,),**kw); print(f'{name:18s} relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f} [{time.time()-t:.0f}s]',flush=True)
# 3-seed on best-ish
r=cv(seeds=(0,1,2),**cfgs[3][1]); print(f'{cfgs[3][0]:18s} relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f} [{time.time()-t:.0f}s]',flush=True)
EOF
pkill -f cvfast; sleep 2
cd /app && nohup python -u /tmp/cvfast2.py > /tmp/sweep2b.log 2>&1 & echo "launched $!"; sleep 250; cat /tmp/sweep2b.log

output

launched 462
e1500 swa3         relL2=0.2117 MAE=0.1345 max=0.7345 [192s]

1500 epochs swa3 = 0.2117, about same as 1000 (0.2102). So diminishing returns beyond 1000. Let me wait for 2000 and swa2 and the 3-seed result.

1500 epochs (0.2117) ≈ 1000 epochs (0.2102) , diminishing returns. Let me see the 2000-epoch, swa-fraction, and 3-seed results.

bash

sleep 260; cat /tmp/sweep2b.log

output

e1500 swa3         relL2=0.2117 MAE=0.1345 max=0.7345 [192s]
e2000 swa3         relL2=0.2108 MAE=0.1268 max=0.8420 [446s]

bash

sleep 240; cat /tmp/sweep2b.log

output

e1500 swa3         relL2=0.2117 MAE=0.1345 max=0.7345 [192s]
e2000 swa3         relL2=0.2108 MAE=0.1268 max=0.8420 [446s]
e1500 swa2         relL2=0.2130 MAE=0.1347 max=0.7261 [636s]

All around 0.21. The 3-seed result is still pending (it takes 3x longer). Let me wait. Meanwhile, let me reflect. We're at ~0.21 rel L2, MAE ~0.13, max ~0.7-0.84. The max abs error is concerning (worst-case ~0.7-0.84). The 2000-epoch run got MAE down to 0.127 but max up to 0.84 (overfitting extremes?). The three metrics: rel L2 ~0.21 (good, beats ridge), MAE ~0.13, max ~0.73. The worst-case error is dominated by a few hard samples (the true=0.596→pred 1.4 type). These may be intrinsically ambiguous shapes or outliers. Hard to fix for one sample. Let me now think about the best FINAL training recipe and whether 3-seed helps. The 3-seed averaging of CV predictions tests whether ensembling reduces error , but I can't ship an ensemble. However, it tells me the "variance" component. Let me wait for it. Let me also consider: for the final single model, I could train with a longer SWA tail and more epochs to approximate an ensemble (SWA averages many points along the trajectory = implicit ensemble → reduces variance → should approach the 3-seed number). So a single SWA model trained well might get close to the multi-seed average. Let me wait for the 3-seed number.

bash

sleep 250; cat /tmp/sweep2b.log

output

e1500 swa3         relL2=0.2117 MAE=0.1345 max=0.7345 [192s]
e2000 swa3         relL2=0.2108 MAE=0.1268 max=0.8420 [446s]
e1500 swa2         relL2=0.2130 MAE=0.1347 max=0.7261 [636s]

bash

sleep 200; cat /tmp/sweep2b.log; echo "---run?---"; ps aux|grep -c "[c]vfast2"

output

e1500 swa3         relL2=0.2117 MAE=0.1345 max=0.7345 [192s]
e2000 swa3         relL2=0.2108 MAE=0.1268 max=0.8420 [446s]
e1500 swa2         relL2=0.2130 MAE=0.1347 max=0.7261 [636s]
e1500 swa25 3seed  relL2=0.2080 MAE=0.1281 max=0.6501 [1202s]
---run?---
0

3-seed averaging (e1500 swa25) → relL2=0.2080, MAE=0.1281, max=0.6501. So ensembling 3 seeds only marginally improves rel L2 (0.208 vs 0.211) but notably reduces max error (0.65 vs 0.73) and MAE a bit. The variance reduction mainly helps worst-case. Since I can ship only ONE model, let me think about how to get ensemble-like benefits in a single checkpoint: Option A: SWA within a single long run (already doing). This reduces variance along the trajectory. Option B: Train one model but with a longer SWA collection and more epochs. Option C: Since the architecture is fixed and I ship one state_dict, I genuinely can only have one network. SWA is the way to get ensemble-like robustness in one network. The marginal gains from 3-seed suggest the single SWA model is already near the achievable. The bigger win was epochs (600→1000). Now, the key decision: the FINAL model must be trained on ALL 148 labeled samples (to maximize data), with no held-out for picking. I'll use the best config: epochs~1000-1500, bs=16, jit=0.004, reflections, per-axis norm, swa_frac~0.25-0.3. But wait , when I train the final on all 148, my CV estimate (trained on 118) suggests ~0.21. Training on 148 should be a bit better. But I have no val to confirm. That's fine; CV validated the recipe. Concern: The held-out TEST set for grading , is its mass distribution similar to train? The public_test had lower mean mass (0.73) and max only 1.45. The true held-out could differ. My model should be robust across the range. The CV over all 148 covers the range 0.2-2.2, so the recipe is validated across the full range. Let me also reconsider whether I can squeeze more by: 1. Better handling of the high-mass underprediction. Let me try a loss that weights samples by mass (so high-mass errors matter more) , this could reduce the high-mass underprediction that dominates rel L2. But earlier the shrinkage wasn't fixable by linear calib. Let me try sample weighting in training: weight ∝ mass or ∝ mass^2. Since rel L2 numerator = sum of squared errors, and big masses have big errors, weighting high-mass more in MSE should directly target rel L2. Actually, plain MSE already weights by squared error magnitude, but the model still underpredicts high mass due to few samples. Weighting could push it to fit them better (at cost of low-mass). Let me test weighting by mass. 2. Alternative: train on all data with a few different seeds and pick... no held-out. Let me test a couple of loss/weighting ideas via CV to see if rel L2 improves. Let me add sample weighting to trainer2. Let me think about what directly optimizes rel L2. rel L2 = sqrt(Σ(pred_i - y_i)^2) / sqrt(Σ y_i^2). For training, minimizing Σ(pred_i-y_i)^2 (unweighted SSE = MSE) is the exact numerator. So plain MSE on RAW mass (not standardized) is the theoretically aligned loss for rel L2. Currently I standardize labels (divide by std) and use MSE , that's MSE on (y-mean)/std, i.e., Σ((pred-y)/std)^2 = (1/std²)Σ(pred-y)², same minimizer as raw MSE. And the mean subtraction is just an offset the network learns. So standardized MSE = raw MSE minimizer. Good, already aligned. So for rel L2, plain MSE is right. The underprediction is an optimization/capacity artifact, not a loss-misalignment. Weighting by mass would trade rel L2 numerator differently , might help or hurt. Let me just test empirically: weight = mass^1 and mass^2 (normalized). Actually, weighting by y² in MSE means minimizing Σ y²(pred-y)² , that over-focuses on high mass, likely reduces high-mass error but could worsen overall SSE (rel L2). Let me test weight ∝ y (mild). Also I could try: predict and measure if Huber helps max error. Earlier huber at 200ep was bad, but with 1000 ep might differ. Let me test huber with beta tuned. Let me run a focused CV sweep (single seed, 1000 ep) over: weighting {none, mass, mass2}, loss {mse, huber(beta=1.0 in std units)}. Let me add weighting to trainer2. Given time budget (each single-seed CV ~195s for 100 …[truncated]

Three-seed averaging only marginally improves rel-L2 (0.208 vs 0.211) but cuts worst-case error (0.65 vs 0.73) , so variance reduction mainly helps the max metric, and a single well-collected SWA model is near-optimal. The main structural error is high-mass underprediction. Since plain MSE already matches the rel-L2 numerator, let me test whether mild mass-weighting or Huber reduces the high-mass/worst-case errors. I'll add sample weighting to the trainer.

bash

cd /app && python - << 'EOF'
src=open('/tmp/trainer2.py').read()
src=src.replace("ret_pred=False, rot=0.0, scl=0.0, report_train=False):",
                "ret_pred=False, rot=0.0, scl=0.0, report_train=False, wpow=0.0, beta=0.0):")
src=src.replace(
"            opt.zero_grad(set_to_none=True); pr=model((xb-fm)/fs)\n            l=F.mse_loss(pr,yb) if loss=='mse' else F.smooth_l1_loss(pr,yb,beta=0.5)\n            l.backward(); opt.step()",
"""            opt.zero_grad(set_to_none=True); pr=model((xb-fm)/fs)
            if beta>0: perr=F.smooth_l1_loss(pr,yb,beta=beta,reduction='none')
            else: perr=(pr-yb)**2
            if wpow>0:
                w=(trY[idx]/lm).pow(wpow); w=w/w.mean(); perr=perr*w
            l=perr.mean(); l.backward(); opt.step()""")
open('/tmp/trainer2.py','w').write(src)
print('patched wpow/beta')
EOF
cat > /tmp/cvw.py << 'EOF'
import sys,time; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
from cvfast import cv
t=time.time()
cfgs=[
  ('w0 mse (ref)',dict(epochs=1000,bs=16,jit=0.004)),
  ('wpow1',dict(epochs=1000,bs=16,jit=0.004,wpow=1.0)),
  ('wpow2',dict(epochs=1000,bs=16,jit=0.004,wpow=2.0)),
  ('huber b1',dict(epochs=1000,bs=16,jit=0.004,beta=1.0)),
]
for name,kw in cfgs:
    r=cv(seeds=(0,),**kw); print(f'{name:14s} relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f} [{time.time()-t:.0f}s]',flush=True)
EOF
pkill -f cvfast; sleep 2
cd /app && nohup python -u /tmp/cvw.py > /tmp/cvw.log 2>&1 & echo "launched $!"; sleep 200; cat /tmp/cvw.log

output

patched wpow/beta
launched 517
w0 mse (ref)   relL2=0.2173 MAE=0.1397 max=0.6327 [137s]

Reference reproduces ~0.217 (slightly different from 0.210 due to the patch possibly reordering RNG, but fine). Let me wait for the weighted variants. Note: the patch changed loss computation from F.mse_loss to (pr-yb)**2 mean , same thing but RNG/order identical. The ref got 0.2173 now vs 0.2102 before; small variation due to... actually the reduction is identical. Hmm, the difference might be because previously `l=F.mse_loss` and now `perr=(pr-yb)**2; l=perr.mean()` , identical. But the earlier e1000 run gave 0.2102. Why 0.2173 now? Possibly because I changed the code path and there's a subtle RNG difference (e.g., the `w` computation not triggered). Actually no RNG difference. Wait , maybe GPU nondeterminism. train runs aren't seeded for cudnn determinism. There's inherent run-to-run variation ~±0.01. So 0.210 vs 0.217 is within noise. This reinforces that I shouldn't over-tune to 0.001 differences. Let me wait for weighted results.

The reference reproduces at 0.217 (run-to-run GPU nondeterminism is ~±0.01, so I won't over-tune tiny differences). Let me see the weighting/Huber results.

bash

sleep 290; cat /tmp/cvw.log

output

w0 mse (ref)   relL2=0.2173 MAE=0.1397 max=0.6327 [137s]
wpow1          relL2=0.2286 MAE=0.1414 max=0.6309 [279s]
wpow2          relL2=0.2654 MAE=0.1523 max=0.8278 [413s]

Mass weighting hurts (wpow1: 0.229, wpow2: 0.265). So plain unweighted MSE is best for rel L2, confirming theory. Let me wait for huber.

Mass-weighting hurts rel-L2, confirming plain MSE is right. Let me check Huber, then finalize the recipe.

bash

sleep 60; cat /tmp/cvw.log

output

w0 mse (ref)   relL2=0.2173 MAE=0.1397 max=0.6327 [137s]
wpow1          relL2=0.2286 MAE=0.1414 max=0.6309 [279s]
wpow2          relL2=0.2654 MAE=0.1523 max=0.8278 [413s]
huber b1       relL2=0.2104 MAE=0.1305 max=0.5521 [541s]

Huber with beta=1.0 (in standardized units): relL2=0.2104, MAE=0.1305, max=0.5521. This is slightly better than plain MSE on ALL THREE metrics (rel 0.210 vs 0.217, MAE 0.131 vs 0.140, max 0.552 vs 0.633)! Huber is more robust to the outlier samples, reducing max error notably while keeping rel L2 competitive. Wait, but beta=1.0 in standardized units means the L1/L2 transition at 1.0 std ≈ 0.39 kg. Errors below 0.39 kg are treated as L2 (squared), above as L1. Since most errors are <0.39 kg, it's mostly L2 but caps the influence of big outliers → lower max. That aligns well with our goals. But run-to-run noise is ±0.01, so huber 0.210 vs mse 0.217 , the MAE and max improvements (0.131 vs 0.140, 0.552 vs 0.633) are more consistently better and make sense theoretically. Huber reduces worst-case by not over-penalizing... wait, Huber penalizes large errors LESS than MSE (linear vs quadratic beyond beta), so it's less influenced by outliers during training, leading to... hmm, that would mean it fits outliers less, potentially larger max error. But empirically max is lower. The mechanism: by not letting a few huge-error training samples dominate gradients, the model generalizes better overall, reducing test-time max error. Let me verify Huber's benefit is robust with a 3-seed CV and also test beta variations (0.5, 2.0). Let me run a focused comparison: huber beta {0.5, 1.0, 2.0} vs mse, each 3-seed for stability. That's 4 configs × 3 seeds × 5 folds × 1000ep ≈ 4×3×80s... way too long (2880s). Let me do single-seed for beta sweep, then 3-seed confirm on the winner. Actually, given noise, let me do beta {0.5, 1.5} single seed to bracket, plus re-confirm b1 with different seed. Let me run 3 configs single-seed (~420s). Let me be mindful of token budget too. I've used a lot. Let me be decisive: Huber beta≈1.0 looks good on all three metrics and is theoretically sound (robust). Let me do one more quick confirm sweep of beta, then lock the recipe and build the final training script + checkpoint. Let me run beta in {0.5, 0.75, 1.0, 1.5} single seed, 1000 ep. ~4×137s ≈ 550s. Run in background. Actually, I realize I should also reconcile: the "ref" MSE here (0.2173) vs earlier e1000 (0.2102) differ by 0.007 purely from nondeterminism. So Huber's 0.2104 vs ref 0.2173 could be partly noise for rel L2. But MAE/max improvements are solid. I'll adopt Huber with beta~1.0. Let me quickly confirm beta choice with a short sweep and multi-seed, then finalize. Let me run beta sweep with 2 seeds each to reduce noise, beta in {0.5,1.0,1.5}. 3 configs × 2 seeds × 5 folds × ~27s(1000ep/5... wait each fold train is 1000ep on ~118 samples). Let me estimate: one 5-fold single-seed CV took 137s. ×2 seeds ×3 betas = 822s. Too long for one call; background it. Let me launch and poll.

Huber (β=1.0 in standardized units) improves **all three** metrics (rel-L2 0.210, MAE 0.131, max 0.552) , it limits the gradient influence of a few outlier brackets, improving generalization. Let me confirm the β choice with a short multi-seed sweep before locking the recipe.

bash

cd /app && cat > /tmp/cvbeta.py << 'EOF'
import sys,time; sys.path.insert(0,'/tmp'); sys.path.insert(0,'/app')
from cvfast import cv
t=time.time()
for beta in [0.5,0.75,1.0,1.5]:
    r=cv(seeds=(0,1),epochs=1000,bs=16,jit=0.004,beta=beta)
    print(f'huber b={beta:<4} (2seed) relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f} [{time.time()-t:.0f}s]',flush=True)
r=cv(seeds=(0,1),epochs=1000,bs=16,jit=0.004)
print(f'mse       (2seed) relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f} [{time.time()-t:.0f}s]',flush=True)
EOF
pkill -f cvw; sleep 2
cd /app && nohup python -u /tmp/cvbeta.py > /tmp/cvbeta.log 2>&1 & echo "launched $!"; sleep 270; cat /tmp/cvbeta.log

output

launched 553
huber b=0.5  (2seed) relL2=0.2232 MAE=0.1367 max=0.6877 [254s]

bash

sleep 290; cat /tmp/cvbeta.log

output

huber b=0.5  (2seed) relL2=0.2232 MAE=0.1367 max=0.6877 [254s]
huber b=0.75 (2seed) relL2=0.2246 MAE=0.1368 max=0.7608 [505s]

bash

sleep 290; cat /tmp/cvbeta.log

output

huber b=0.5  (2seed) relL2=0.2232 MAE=0.1367 max=0.6877 [254s]
huber b=0.75 (2seed) relL2=0.2246 MAE=0.1368 max=0.7608 [505s]
huber b=1.0  (2seed) relL2=0.2216 MAE=0.1375 max=0.6496 [753s]

2-seed results: - huber b=0.5: rel 0.2232, MAE 0.1367, max 0.6877 - huber b=0.75: rel 0.2246, MAE 0.1368, max 0.7608 - huber b=1.0: rel 0.2216, MAE 0.1375, max 0.6496 These are all ~0.22, a bit higher than the single-seed 0.210 earlier. The 2-seed averaging here averages two noisy CV runs , wait, `cv(seeds=(0,1))` averages predictions across seeds 0 and 1, which should REDUCE error, not increase. But it's showing 0.22, higher than single-seed huber b1 (0.2104). That's odd. Oh I see , averaging predictions across seeds should reduce variance and give LOWER or equal rel L2 than the average of individual seeds' rel L2, but not necessarily lower than the BEST single seed. The earlier 0.2104 was a single seed (seed 0) which might have been lucky. The 2-seed avg of 0.2216 is more reliable. So the true performance is ~0.22, and the 0.210 was optimistic noise. Let me wait for the mse 2-seed to compare fairly. If mse 2-seed ≈ huber 2-seed for rel L2 but huber has lower max, I'll pick huber b=1.0. The differences between betas are within noise (~0.22 all). b=1.0 has best rel and max. Let me confirm with mse baseline.

bash

sleep 220; cat /tmp/cvbeta.log

output

huber b=0.5  (2seed) relL2=0.2232 MAE=0.1367 max=0.6877 [254s]
huber b=0.75 (2seed) relL2=0.2246 MAE=0.1368 max=0.7608 [505s]
huber b=1.0  (2seed) relL2=0.2216 MAE=0.1375 max=0.6496 [753s]
huber b=1.5  (2seed) relL2=0.2258 MAE=0.1370 max=0.6942 [998s]

bash

sleep 170; cat /tmp/cvbeta.log

output

huber b=0.5  (2seed) relL2=0.2232 MAE=0.1367 max=0.6877 [254s]
huber b=0.75 (2seed) relL2=0.2246 MAE=0.1368 max=0.7608 [505s]
huber b=1.0  (2seed) relL2=0.2216 MAE=0.1375 max=0.6496 [753s]
huber b=1.5  (2seed) relL2=0.2258 MAE=0.1370 max=0.6942 [998s]
mse       (2seed) relL2=0.2129 MAE=0.1346 max=0.6616 [1247s]

Interesting! The 2-seed MSE (0.2129) is actually BETTER than 2-seed Huber (0.2216) for rel L2, and similar MAE/max. So MSE reclaims the lead when we reduce noise via 2-seed averaging. The earlier single-seed comparison (huber 0.210 vs mse 0.217) was just noise. So the more reliable 2-seed picture: - MSE: rel 0.2129, MAE 0.1346, max 0.6616 - Huber b1: rel 0.2216, MAE 0.1375, max 0.6496 MSE wins on rel L2 (the primary) and MAE, ties on max. So MSE is the better choice. Good , and it's the theoretically aligned loss for rel L2. OK so final recipe: MSE loss, per-axis norm, reflections, jitter=0.004, bs=16, epochs~1000-1500, swa_frac~0.25-0.3. 2-seed CV rel L2 ~0.213. Now, the differences among all these are within ~0.01 noise. I should lock the recipe and not over-optimize. Key robust findings: 1. ~1000+ epochs (not 600) , big effect. 2. Reflections + light jitter(0.004) , helps. 3. MSE loss, per-axis norm, SWA. 4. No rotation/scaling/bootstrap/weighting. Now the FINAL model. Two considerations: A. Train on all 148 labeled samples (no held-out) to maximize data. B. To get ensemble-like robustness in ONE shipped network, rely on SWA with a good collection phase. Optionally, I could make the SWA collection longer (swa_frac 0.3-0.4) and/or use a cyclic LR in the SWA phase to collect more diverse snapshots. But swa_frac=0.5 was slightly worse (0.2226). swa_frac 0.25-0.3 best. Let me also reconsider: should the final model train on all 148, or should I hold out a small val to enable picking the best checkpoint / early stopping? With SWA I don't need early stopping (SWA averages tail). And more data is better. I'll train final on all 148. But there's risk: training on all data with a fixed epoch count chosen from CV (where models trained on 118) , the optimal epoch count might differ slightly with 148 samples, but SWA makes it robust. 1000-1500 epochs with cosine to ~0 and SWA over last 25-30% is robust. One more idea to improve the single shipped model: Instead of pure SWA on one trajectory, I can run the cosine schedule and collect SWA, which is what I do. Good. Let me also double check: the grader loads state_dict into PointNetRegressor and runs model.eval() presumably. With BatchNorm, eval uses running stats. My SWA model's BN running buffers: I recompute them via forward passes in train mode at the end (with augmentation). That gives stable BN stats matching the averaged weights. Good. But I recompute with augmentation (reflections+jitter) , at inference there's no augmentation. The BN running stats should reflect the inference distribution (no aug) ideally. Hmm. Let me reconsider: I recompute BN stats using augmented inputs. At test, inputs are un-augmented. Reflections don't change per-axis distribution statistics much (reflection flips sign but BN stats are over the batch of points across many samples; reflecting x flips x sign per-sample, but aggregated over random ± it's symmetric , mean stays ~0, var same). Jitter adds tiny variance. So BN stats computed with aug ≈ those without, basically. But to be safe and correct, I should recompute BN running stats on UN-augmented training data (the actual inference-like distribution). Actually, reflections keep the point distribution statistically identical (the cloud is roughly symmetric and we feed normalized coords). Let me recompute BN stats WITHOUT augmentation to match inference exactly. That's more correct. Actually, there's subtlety: if I recompute BN stats without aug, they match test-time input distribution (no aug). That's what BN running stats are for. So let me recompute BN with NO augmentation. Let me update the final training to do a BN-recalc pass without augmentation. Let me verify this choice via CV quickly? It's a minor effect. Let me just do the principled thing: recompute BN stats on clean (un-augmented) training data. Actually, wait: during SWA, standard practice (torch SWA) updates BN by running forward passes over the training data loader (which may i …[truncated]

The cleaner 2-seed comparison flips the verdict: **MSE** (rel-L2 0.213, MAE 0.135, max 0.662) beats Huber on the primary metric and ties elsewhere , the earlier Huber win was noise. MSE is also the theoretically aligned loss for rel-L2. My recipe is now locked: MSE, per-axis normalization, reflections + jitter 0.004, bs=16, ~1000–1500 epochs, SWA over the last ~30%. Differences beyond this are within ±0.01 run-to-run noise, so I'll stop tuning and build the final model. Let me write the final training script that trains on **all 148 labeled samples**.

str_replace_based_edit_tool

command: create
path: /app/train_final.py
file_text: """Final training: PointNetRegressor on all labeled bracket data.

Recipe chosen via 5-fold cross-validation on the combined
train+val+public_test set (148 samples):
  * per-axis input standardization, label standardization
  * label-preserving augmentation: random axis sign-flips (reflections) +
    small Gaussian coordinate jitter
  * MSE loss (aligned with the primary relative-L2 metric)
  * long training with cosine LR decay + SWA (tail weight averaging)
The saved checkpoint loads into the fixed candidate_model.PointNetRegressor.
"""
from __future__ import annotations
import json, os, sys, copy
from pathlib import Path
import torch, torch.nn.functional as F

APP = Path(os.environ.get("APP_DIR", "/app"))
sys.path.insert(0, str(APP))
from candidate_model import build_model  # noqa: E402

CFG = json.load(open(APP / "task_config.json"))
DEV = torch.device("cuda" if torch.cuda.is_available() else "cpu")


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


def augment(x, jit=0.004):
    # reflections across each axis (mass is invariant to mirroring and the
    # axis-aligned bounding box is preserved) + tiny coordinate jitter.
    s = (torch.randint(0, 2, (x.shape[0], 1, 3), device=x.device).float() * 2 - 1)
    x = x * s
    if jit > 0:
        x = x + torch.randn_like(x) * jit
    return x


def main():
    seed = int(os.environ.get("FINAL_SEED", "0"))
    epochs = int(os.environ.get("FINAL_EPOCHS", "1200"))
    bs = 16
    jit = 0.004
    swa_frac = 0.30
    torch.manual_seed(seed)

    feats, labels = [], []
    for sp in ["train", "val", "public_test"]:
        fx, fy = load(sp)
        feats.append(fx); labels.append(fy)
    X = torch.cat(feats, 0).to(DEV)
    Y = torch.cat(labels, 0).to(DEV)
    N = X.shape[0]
    print(f"[final] device={DEV} samples={N} epochs={epochs} bs={bs} seed={seed}", flush=True)

    flat = X.reshape(-1, 3)
    fm = flat.mean(0).view(1, 1, 3)
    fs = flat.std(0).clamp_min(1e-6).view(1, 1, 3)
    lm = Y.mean().view(1, 1)
    ls = Y.std().clamp_min(1e-6).view(1, 1)

    model = build_model(CFG).to(DEV)
    opt = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)
    sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=epochs)
    swa_start = int(epochs * (1 - swa_frac))
    swa, swa_n = None, 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 = augment(X[idx], jit)
            yb = (Y[idx] - lm) / ls
            opt.zero_grad(set_to_none=True)
            loss = F.mse_loss(model((xb - fm) / fs), yb)
            loss.backward()
            opt.step()
        sched.step()
        if ep >= swa_start:
            if swa is None:
                swa = copy.deepcopy(model)
            else:
                with torch.no_grad():
                    for ps, pm in zip(swa.parameters(), model.parameters()):
                        ps.mul_(swa_n / (swa_n + 1)).add_(pm / (swa_n + 1))
                    for bsf, bm in zip(swa.buffers(), model.buffers()):
                        if bsf.dtype.is_floating_point:
                            bsf.mul_(swa_n / (swa_n + 1)).add_(bm / (swa_n + 1))
                        else:
                            bsf.copy_(bm)
            swa_n += 1
        if (ep + 1) % 200 == 0:
            print(f"[final] epoch {ep+1}/{epochs} loss={loss.item():.4f}", flush=True)

    final = swa if swa is not None else model
    # Recompute BatchNorm running stats on clean (un-augmented) data so the
    # buffers match the inference-time input distribution.
    for m in final.modules():
        if isinstance(m, torch.nn.BatchNorm1d):
            m.reset_running_stats(); m.momentum = None
    final.train()
    with torch.no_grad():
        for _ in range(30):
            perm = torch.randperm(
…[truncated]

output

New file created successfully at /app/train_final.py

Before running the final (which trains on all 148 including val/public_test), let me first validate this EXACT final-script recipe (including the BN reset with momentum=None recompute) via a quick CV to make sure the BN-reset change doesn't hurt. The trainer2 used manual running-stat averaging during SWA and a 12-pass recompute WITH augmentation. The final script resets BN and recomputes with momentum=None (cumulative average) over clean data. Let me make sure this produces good results. Actually, let me just validate the final recipe end-to-end with a holdout: train on (train+public_test) = 116 samples using train_final's exact logic, evaluate on val (32). That directly tests the shipped recipe including BN reset. Let me do a quick version: I'll refactor to allow passing train/eval splits, or just do a one-off inline test. Let me write a quick validation that mimics train_final but on a train/eval split, to confirm ~0.21 and that BN reset with momentum=None works well. I'll use fewer epochs maybe 1000 to save time, single run. Actually simpler: let me just run train_final.py as-is (trains on all 148, ~1200 epochs ≈ 32s? no, 148 samples, 10 batches/epoch... earlier 100 samples 600 epochs=16s, so 148 samples 1200 epochs ≈ 16*(148/100)*(1200/600) ≈ 47s plus BN recompute). That's quick. But it trains on all data so in-sample rel L2 isn't a generalization measure. I need a generalization check of the FINAL recipe. Let me do a proper holdout test of the exact recipe (with BN reset momentum=None + clean recompute) using a function. Let me add a validation mode to train_final via env var that holds out val. Actually cleaner: write a small test script that imports the pieces. But train_final's main() is monolithic. Let me just copy the logic into a quick CV test using trainer2 but with the BN-reset-clean-recompute variant to confirm it matches. Let me modify trainer2's finalize to optionally use the "reset BN + momentum=None + clean recompute" path and compare against its current "augmented 12-pass" path, via CV. This checks whether my final BN handling is as good. Given time/token budget, let me just do ONE holdout run of the exact final recipe: train on train+public_test (116), eval on val (32). If rel L2 ≈ 0.21-0.23, good. Let me write it quickly by importing train_final functions. train_final has load(), augment(), and main(). I can import load and augment. Let me write a validation harness replicating the train loop but with holdout. To avoid code duplication risk, I'll just parametrize: create a function train_eval(Xtr,Ytr,Xva,Yva,...) in a test file copying the loop. Let me do that. Actually I realize I can refactor train_final to have a reusable train_model() function and call it from both main() and a validation script. That's cleaner and ensures the validated code == shipped code. Let me refactor.

Before running the final model (which consumes val/public_test, leaving no holdout), I want to validate the **exact** final recipe , including the BatchNorm reset + clean-data recompute , on a real holdout. Let me refactor the core into a reusable function so the validated code is identical to the shipped code.

str_replace_based_edit_tool

command: str_replace
path: /app/train_final.py
old_str: def main():
    seed = int(os.environ.get("FINAL_SEED", "0"))
    epochs = int(os.environ.get("FINAL_EPOCHS", "1200"))
    bs = 16
    jit = 0.004
    swa_frac = 0.30
    torch.manual_seed(seed)

    feats, labels = [], []
    for sp in ["train", "val", "public_test"]:
        fx, fy = load(sp)
        feats.append(fx); labels.append(fy)
    X = torch.cat(feats, 0).to(DEV)
    Y = torch.cat(labels, 0).to(DEV)
    N = X.shape[0]
    print(f"[final] device={DEV} samples={N} epochs={epochs} bs={bs} seed={seed}", flush=True)

    flat = X.reshape(-1, 3)
new_str: def train_model(X, Y, epochs=1200, bs=16, jit=0.004, swa_frac=0.30, seed=0):
    """Train PointNetRegressor; returns (model, (fm, fs, lm, ls)). This is the
    exact routine used for the shipped checkpoint (validated via holdout)."""
    torch.manual_seed(seed)
    X = X.to(DEV); Y = Y.to(DEV)
    N = X.shape[0]
    flat = X.reshape(-1, 3)

output

The file /app/train_final.py has been edited successfully.

str_replace_based_edit_tool

command: str_replace
path: /app/train_final.py
old_str:             for i in range(0, N, bs):
                idx = perm[i:i + bs]
                final((X[idx] - fm) / fs)

    final.eval()
    with torch.no_grad():
        pr = (final((X - fm) / fs) * ls + lm).squeeze(1)
    yt = Y.squeeze(1)
    rel = (torch.norm(pr - yt) / torch.norm(yt)).item()
    print(f"[final] in-sample relL2={rel:.4f} MAE={(pr-yt).abs().mean():.4f} "
          f"max={(pr-yt).abs().max():.4f}", flush=True)

    out = APP / "outputs"; out.mkdir(parents=True, exist_ok=True)
new_str:             for i in range(0, N, bs):
                idx = perm[i:i + bs]
                final((X[idx] - fm) / fs)
    final.eval()
    return final, (fm, fs, lm, ls)


def main():
    seed = int(os.environ.get("FINAL_SEED", "0"))
    epochs = int(os.environ.get("FINAL_EPOCHS", "1200"))
    feats, labels = [], []
    for sp in ["train", "val", "public_test"]:
        fx, fy = load(sp)
        feats.append(fx); labels.append(fy)
    X = torch.cat(feats, 0)
    Y = torch.cat(labels, 0)
    print(f"[final] device={DEV} samples={X.shape[0]} epochs={epochs} seed={seed}", flush=True)

    final, (fm, fs, lm, ls) = train_model(X, Y, epochs=epochs, seed=seed)

    with torch.no_grad():
        Xd = X.to(DEV)
        pr = (final((Xd - fm) / fs) * ls + lm).squeeze(1).cpu()
    yt = Y.squeeze(1)
    rel = (torch.norm(pr - yt) / torch.norm(yt)).item()
    print(f"[final] in-sample relL2={rel:.4f} MAE={(pr-yt).abs().mean():.4f} "
          f"max={(pr-yt).abs().max():.4f}", flush=True)

    out = APP / "outputs"; out.mkdir(parents=True, exist_ok=True)

output

The file /app/train_final.py has been edited successfully.

I removed the original main()'s beginning but need to check the middle part (model/opt/training loop) is still intact and that the `print epoch` and final save use the right variables. Let me view the full file to ensure coherence.

Let me view the refactored file to ensure it's coherent.

bash

cd /app && cat -n train_final.py

output

1	"""Final training: PointNetRegressor on all labeled bracket data.
     2	
     3	Recipe chosen via 5-fold cross-validation on the combined
     4	train+val+public_test set (148 samples):
     5	  * per-axis input standardization, label standardization
     6	  * label-preserving augmentation: random axis sign-flips (reflections) +
     7	    small Gaussian coordinate jitter
     8	  * MSE loss (aligned with the primary relative-L2 metric)
     9	  * long training with cosine LR decay + SWA (tail weight averaging)
    10	The saved checkpoint loads into the fixed candidate_model.PointNetRegressor.
    11	"""
    12	from __future__ import annotations
    13	import json, os, sys, copy
    14	from pathlib import Path
    15	import torch, torch.nn.functional as F
    16	
    17	APP = Path(os.environ.get("APP_DIR", "/app"))
    18	sys.path.insert(0, str(APP))
    19	from candidate_model import build_model  # noqa: E402
    20	
    21	CFG = json.load(open(APP / "task_config.json"))
    22	DEV = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    23	
    24	
    25	def load(split):
    26	    d = torch.load(APP / "data" / split / "samples.pt", weights_only=True)
    27	    return d["features"].float(), d["labels"].float()
    28	
    29	
    30	def augment(x, jit=0.004):
    31	    # reflections across each axis (mass is invariant to mirroring and the
    32	    # axis-aligned bounding box is preserved) + tiny coordinate jitter.
    33	    s = (torch.randint(0, 2, (x.shape[0], 1, 3), device=x.device).float() * 2 - 1)
    34	    x = x * s
    35	    if jit > 0:
    36	        x = x + torch.randn_like(x) * jit
    37	    return x
    38	
    39	
    40	def train_model(X, Y, epochs=1200, bs=16, jit=0.004, swa_frac=0.30, seed=0):
    41	    """Train PointNetRegressor; returns (model, (fm, fs, lm, ls)). This is the
    42	    exact routine used for the shipped checkpoint (validated via holdout)."""
    43	    torch.manual_seed(seed)
    44	    X = X.to(DEV); Y = Y.to(DEV)
    45	    N = X.shape[0]
    46	    flat = X.reshape(-1, 3)
    47	    fm = flat.mean(0).view(1, 1, 3)
    48	    fs = flat.std(0).clamp_min(1e-6).view(1, 1, 3)
    49	    lm = Y.mean().view(1, 1)
    50	    ls = Y.std().clamp_min(1e-6).view(1, 1)
    51	
    52	    model = build_model(CFG).to(DEV)
    53	    opt = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)
    54	    sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=epochs)
    55	    swa_start = int(epochs * (1 - swa_frac))
    56	    swa, swa_n = None, 0
    57	
    58	    for ep in range(epochs):
    59	        model.train()
    60	        perm = torch.randperm(N, device=DEV)
    61	        for i in range(0, N, bs):
    62	            idx = perm[i:i + bs]
    63	            xb = augment(X[idx], jit)
    64	            yb = (Y[idx] - lm) / ls
    65	            opt.zero_grad(set_to_none=True)
    66	            loss = F.mse_loss(model((xb - fm) / fs), yb)
    67	            loss.backward()
    68	            opt.step()
    69	        sched.step()
    70	        if ep >= swa_start:
    71	            if swa is None:
    72	                swa = copy.deepcopy(model)
    73	            else:
    74	                with torch.no_grad():
    75	                    for ps, pm in zip(swa.parameters(), model.parameters()):
    76	                        ps.mul_(swa_n / (swa_n + 1)).add_(pm / (swa_n + 1))
    77	                    for bsf, bm in zip(swa.buffers(), model.buffers()):
    78	                        if bsf.dtype.is_floating_point:
    79	                            bsf.mul_(swa_n / (swa_n + 1)).add_(bm / (swa_n + 1))
    80	                        else:
    81	                            bsf.copy_(bm)
    82	            swa_n += 1
    83	        if (ep + 1) % 200 == 0:
    84	            print(f"[final] epoch {ep+1}/{epochs} loss={loss.item():.4f}", flush=True)
    85	
    86	    final = swa if swa is not None else model
    87	    # Recompute BatchNorm running sta
…[truncated]

The file is coherent. Now let me validate the exact recipe (via train_model) on a holdout: train on train+public_test (116), eval on val (32). And also do a proper 5-fold CV using this exact train_model to confirm the generalization estimate with the BN-reset path. Let me run a holdout first (fast, single), then if good, a 5-fold CV in background. Let me write a validation script using train_model.

The refactor is clean. Now let me validate the **exact** shipped routine (`train_model`, including BN reset + clean recompute) with 5-fold CV on the 148 samples, to confirm the generalization estimate holds.

bash

cd /app && cat > /tmp/val_final.py << 'EOF'
import sys,time; sys.path.insert(0,'/app')
import torch
from train_final import load, train_model, DEV
fx=torch.cat([load('train')[0],load('val')[0],load('public_test')[0]],0)
fy=torch.cat([load('train')[1],load('val')[1],load('public_test')[1]],0)
N=fx.shape[0]
g=torch.Generator().manual_seed(12345); order=torch.randperm(N,generator=g)
K=5; folds=[order[i::K] for i in range(K)]
ally=fy.squeeze(1).clone(); allpr=torch.zeros(N)
t=time.time()
for k in range(K):
    va=folds[k]; tr=torch.cat([folds[j] for j in range(K) if j!=k])
    m,(fm,fs,lm,ls)=train_model(fx[tr],fy[tr],epochs=1200,seed=0)
    with torch.no_grad():
        pr=(m((fx[va].to(DEV)-fm)/fs)*ls+lm).squeeze(1).cpu()
    allpr[va]=pr
    print(f' fold{k} done [{time.time()-t:.0f}s]',flush=True)
rel=(torch.norm(allpr-ally)/torch.norm(ally)).item()
print('FINAL-RECIPE CV relL2=%.4f MAE=%.4f max=%.4f'%(rel,(allpr-ally).abs().mean(),(allpr-ally).abs().max()),flush=True)
EOF
pkill -f cvbeta 2>/dev/null; sleep 2
nohup python -u /tmp/val_final.py > /tmp/val_final.log 2>&1 & echo "launched $!"; sleep 260; cat /tmp/val_final.log

output

launched 645
[final] epoch 200/1200 loss=0.5311
[final] epoch 400/1200 loss=0.7315
[final] epoch 600/1200 loss=0.2288
[final] epoch 800/1200 loss=0.1235
[final] epoch 1000/1200 loss=0.0824
[final] epoch 1200/1200 loss=0.1493
 fold0 done [34s]
[final] epoch 200/1200 loss=0.4653
[final] epoch 400/1200 loss=0.2070
[final] epoch 600/1200 loss=0.3304
[final] epoch 800/1200 loss=0.1233
[final] epoch 1000/1200 loss=0.0971
[final] epoch 1200/1200 loss=0.1232
 fold1 done [65s]
[final] epoch 200/1200 loss=0.8854
[final] epoch 400/1200 loss=0.1830
[final] epoch 600/1200 loss=0.1608
[final] epoch 800/1200 loss=0.1125
[final] epoch 1000/1200 loss=0.0624
[final] epoch 1200/1200 loss=0.0491
 fold2 done [95s]
[final] epoch 200/1200 loss=0.0991
[final] epoch 400/1200 loss=0.3108
[final] epoch 600/1200 loss=0.3379
[final] epoch 800/1200 loss=0.2562
[final] epoch 1000/1200 loss=0.0925
[final] epoch 1200/1200 loss=0.1402
 fold3 done [125s]
[final] epoch 200/1200 loss=0.2385
[final] epoch 400/1200 loss=0.1728
[final] epoch 600/1200 loss=0.1012
[final] epoch 800/1200 loss=0.5003
[final] epoch 1000/1200 loss=0.0109
[final] epoch 1200/1200 loss=0.0894
 fold4 done [154s]
FINAL-RECIPE CV relL2=0.3378 MAE=0.2025 max=1.1438

The final-recipe CV gives relL2=0.338, much WORSE than the trainer2 recipe (0.21)! Something in the refactor/BN handling hurt. The difference between train_final.train_model and trainer2.train_once: 1. BN handling: train_final resets BN stats and sets momentum=None then does 30 passes on CLEAN data. trainer2 did 12 passes WITH augmentation and kept the SWA-averaged buffers (didn't reset). Wait, trainer2's finalize: it does NOT reset BN; it runs forward in train mode with augmentation for 12 epochs, which updates running stats with default momentum=0.1 (so exponential moving average, mostly recent). train_final resets and uses momentum=None (cumulative average over all 30 passes). 2. The big difference could be momentum=None with reset: cumulative average over 30×10=300 batches. That should give accurate dataset stats. Hmm. Actually, wait , a bigger issue. In train_final I reset BN and recompute on CLEAN data (no augmentation). But the model was trained with reflection augmentation. With reflections, the sign of x and z per sample is random ±. After per-axis standardization the network's first BN sees the conv output of reflected coords. At inference (clean, no reflection), the distribution of conv outputs differs from training (where reflections were applied)! Specifically: the first conv is Conv1d(3→64) applied to standardized coords. With reflection augmentation, each coordinate axis is randomly sign-flipped during training. So the conv weights adapt to inputs where x,z signs are random. At BN recompute time: - trainer2 recomputes WITH augmentation (reflections) → BN stats match training distribution. - train_final recomputes WITHOUT augmentation (clean) → BN stats match the clean distribution. At INFERENCE, the grader feeds clean (un-reflected) data. So the clean BN stats SHOULD be correct for inference. But the result is much worse (0.338). Hmm, that's counterintuitive. Unless the model relies on the augmentation-induced symmetry. Let me think: with reflections, the model effectively must be invariant to sign flips of x and z. A model trained this way should give the same output regardless of reflection. So clean vs reflected BN stats should be similar IF the model learned reflection invariance. But maybe it didn't fully, and the BN running stats computed over reflected (symmetric) data differ from clean data. Actually the more likely culprit: momentum=None + reset gives cumulative average, which over 300 batches is fine. But maybe the issue is that resetting BN and recomputing changes the stats a lot from what SWA collected, and the SWA-averaged weights were calibrated to the SWA-averaged BN buffers (which trainer2 kept and refined with aug). Let me just test: which part causes the regression? Let me replicate trainer2's exact finalize in train_model (no reset, 12 passes WITH augmentation) and re-run CV. That's the known-good 0.21 path. Let me revert the BN handling to match trainer2 exactly. The known-good trainer2 finalize: ``` final.train() with torch.no_grad(): for _ in range(12): perm=torch.randperm(N) for i in range(0,N,bs): idx=perm[i:i+bs]; final((augment(trx[idx],...)-fm)/fs) ``` No reset, momentum default 0.1, WITH augmentation. Let me change train_final to match exactly: no reset, augmentation in recompute, 12 passes. Let me also double check there's not another difference (e.g., AdamW vs the trainer2 also AdamW wd=1e-4, same; cosine T_max=epochs, same; swa_frac 0.3 vs trainer2 default 0.3, same; jit 0.004 same; bs 16 same). The main diff is BN finalize. Let me fix it. Actually, let me reconsider which is more correct for inference. The grader feeds clean data. If I keep BN stats from augmented recompute, they're computed on reflected data. Reflection flips sign of coordinates, but after the conv+... the BN is on conv outputs. For BN running mean of conv output channel: conv output = sum_j W_j * coord_j. Under random reflection, coord_j → ±coord_j randomly per sam …[truncated]

The CV jumped to 0.338 , much worse than trainer2's validated 0.21. The only change is the BatchNorm finalize: train_final resets BN and recomputes on *clean* data, whereas the validated path recomputes **with augmentation** and without resetting. Since the model is trained to be reflection-invariant, its BN buffers must reflect the augmented distribution. Let me revert to the validated BN handling and re-confirm.

str_replace_based_edit_tool

command: str_replace
path: /app/train_final.py
old_str:     final = swa if swa is not None else model
    # Recompute BatchNorm running stats on clean (un-augmented) data so the
    # buffers match the inference-time input distribution.
    for m in final.modules():
        if isinstance(m, torch.nn.BatchNorm1d):
            m.reset_running_stats(); m.momentum = None
    final.train()
    with torch.no_grad():
        for _ in range(30):
            perm = torch.randperm(N, device=DEV)
            for i in range(0, N, bs):
                idx = perm[i:i + bs]
                final((X[idx] - fm) / fs)
    final.eval()
    return final, (fm, fs, lm, ls)
new_str:     final = swa if swa is not None else model
    # Refresh BatchNorm running stats for the SWA-averaged weights. The model
    # is trained to be reflection-invariant, so the buffers must reflect the
    # augmented input distribution -> recompute WITH augmentation (validated to
    # generalize markedly better than recomputing on un-augmented data).
    final.train()
    with torch.no_grad():
        for _ in range(20):
            perm = torch.randperm(N, device=DEV)
            for i in range(0, N, bs):
                idx = perm[i:i + bs]
                final((augment(X[idx], jit) - fm) / fs)
    final.eval()
    return final, (fm, fs, lm, ls)

output

The file /app/train_final.py has been edited successfully.

bash

cd /app && pkill -f val_final 2>/dev/null; sleep 2
nohup python -u /tmp/val_final.py > /tmp/val_final2.log 2>&1 & echo "launched $!"; sleep 175; grep -E "fold|FINAL" /tmp/val_final2.log

output

launched 675
 fold0 done [33s]
 fold1 done [61s]
 fold2 done [90s]
 fold3 done [119s]
 fold4 done [147s]
FINAL-RECIPE CV relL2=0.2178 MAE=0.1375 max=0.6789

Now the final recipe (with aug-recompute BN) gives CV relL2=0.2178, MAE=0.1375, max=0.6789, matching the validated trainer2 result (~0.21). So the BN finalize was the culprit, and it's fixed. The shipped `train_model` is now validated. So my honest generalization estimate for the shipped recipe is ~0.218 rel L2 (trained on 118 per fold). The final model trains on 148, likely slightly better. Now I should consider: seed variation. Let me pick a good seed or just use seed 0 (validated). The final trains on all 148. Let me also consider doing a small seed-robustness: train final with seed 0 and ship. But I worry about an unlucky single final run. To be safe, I could train a few seeds on all-data, but I have no holdout to pick among them. The CV showed seed variation ~±0.01. Since I can't measure the all-data model's generalization, I'll just use the validated recipe with seed 0. Actually, there's a subtle improvement available: I could make the final model more robust by training LONGER SWA collection. But CV validated 1200/0.3. Keep it. Let me reconsider whether I can do better on the primary metric. Current ~0.218 CV. Ridge was 0.231. I'm beating ridge. Could a hybrid help? I can only ship the PointNet. One more idea that could genuinely help the shipped single model: ensembling in weight space won't work across seeds, but I already use SWA. Let me also reconsider: the max abs error ~0.68 is the worst single sample. That's intrinsic to a hard sample. Probably can't fix much. Let me now run the FINAL training on all 148 and produce the checkpoint. Then I'll write a standalone inference verifier that mimics the grader: load checkpoint into fresh PointNetRegressor, apply normalization, predict on public_test (and val), and report metrics , to ensure the saved artifact works and is sensible. Let me run train_final.py now. Actually, before that , let me reconsider the epoch count for the final model on 148 samples. With 148 vs 118 samples, 10 batches/epoch vs ~8. The LR schedule over 1200 epochs is fine. SWA over last 360 epochs. Good. Let me also think about whether I should include MORE augmentation diversity in SWA snapshots for the final. No, keep validated. Let me run the final training.

Fixed , the validated BN handling restores CV rel-L2 = 0.218 (MAE 0.137, max 0.679), matching the tuned pipeline. The shipped `train_model` is now confirmed to generalize. Let me train the final model on all 148 labeled samples and write the checkpoint.

bash

cd /app && pkill -f val_final 2>/dev/null; sleep 2; rm -f outputs/model.pt
nohup python -u train_final.py > /tmp/final_train.log 2>&1 & echo "launched $!"; sleep 70; cat /tmp/final_train.log; echo "---"; ls -la outputs/ 2>/dev/null

output

launched 694
[final] device=cuda samples=148 epochs=1200 seed=0
[final] epoch 200/1200 loss=0.2814
[final] epoch 400/1200 loss=1.0196
[final] epoch 600/1200 loss=0.0708
[final] epoch 800/1200 loss=0.0865
[final] epoch 1000/1200 loss=0.0440
[final] epoch 1200/1200 loss=0.0308
[final] in-sample relL2=0.1177 MAE=0.0739 max=0.3894
[final] wrote /app/outputs/model.pt
---
total 384
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 23:34 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 23:34 ..
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 23:34 model.pt

The final model trained. In-sample relL2=0.1177 (MAE 0.074, max 0.389). In-sample is better than CV (0.218) as expected since it includes training data, but not near-zero, indicating it's regularized (not overfit). Good sign. Now let me write a standalone verification script that mimics the grader exactly: 1. Load candidate_model.PointNetRegressor via build_model(config). 2. Load checkpoint, load_state_dict. 3. Load feat_mean/std, label_mean/std. 4. For a given [B,2048,3] input: pred = model((x - feat_mean)/feat_std) * label_std + label_mean. 5. Verify output shape [B,1], dtype, and compute metrics on public_test and val. This confirms the artifact is valid and loads cleanly into the fixed architecture. Let me also verify the keys and tensor shapes match the spec (feat_mean/std length 3, label_mean/std length 1). Let me write and run the verifier.

The final checkpoint is written (in-sample rel-L2 = 0.118, well-regularized rather than overfit to ~0). Now let me write a standalone verifier that **mimics the grader exactly** , load the checkpoint into a fresh `PointNetRegressor`, apply the saved normalization, and check output shape plus metrics.

bash

cd /app && cat > /tmp/verify.py << 'EOF'
import json, sys, torch
sys.path.insert(0,'/app')
from candidate_model import build_model

cfg=json.load(open('/app/task_config.json'))
ckpt=torch.load('/app/outputs/model.pt', weights_only=True)
print('keys:', sorted(ckpt.keys()))
for k in ['feat_mean','feat_std','label_mean','label_std']:
    v=ckpt[k]; print(f'  {k}: shape={tuple(v.shape)} dtype={v.dtype} val={v.tolist()}')

# Build fresh architecture and load (strict)
model=build_model(cfg)
missing=model.load_state_dict(ckpt['state_dict'], strict=True)
print('load_state_dict:', missing)
model.eval()

fm=ckpt['feat_mean'].float(); fs=ckpt['feat_std'].float()
lm=ckpt['label_mean'].float(); ls=ckpt['label_std'].float()

def predict(x):  # x: [B,2048,3] raw coords, exactly as grader would feed
    with torch.no_grad():
        out=model((x-fm)/fs)*ls+lm
    return out

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

# shape/dtype check on a dummy batch
dummy=torch.randn(5,2048,3)
o=predict(dummy); print('dummy output shape:', tuple(o.shape), 'dtype', o.dtype)
assert o.shape==(5,1), 'output must be [B,1]'

for s in ['val','public_test']:
    x,y=load(s); pr=predict(x).squeeze(1); yt=y.squeeze(1)
    rel=(torch.norm(pr-yt)/torch.norm(yt)).item()
    mae=(pr-yt).abs().mean().item(); mx=(pr-yt).abs().max().item()
    print(f'{s:12s} relL2={rel:.4f} MAE={mae:.4f} max={mx:.4f}  (n={len(yt)})')
print('OK: checkpoint loads into fixed architecture and runs.')
EOF
python /tmp/verify.py

output

keys: ['feat_mean', 'feat_std', 'label_mean', 'label_std', 'state_dict']
  feat_mean: shape=(3,) dtype=torch.float32 val=[-0.011780548840761185, 0.007426036521792412, -0.07198601216077805]
  feat_std: shape=(3,) dtype=torch.float32 val=[0.1422092616558075, 0.2692946195602417, 0.08725058287382126]
  label_mean: shape=(1,) dtype=torch.float32 val=[0.7487966418266296]
  label_std: shape=(1,) dtype=torch.float32 val=[0.38614240288734436]
load_state_dict: <All keys matched successfully>
dummy output shape: (5, 1) dtype torch.float32
val          relL2=0.1205 MAE=0.0786 max=0.2774  (n=32)
public_test  relL2=0.1615 MAE=0.0917 max=0.3407  (n=16)
OK: checkpoint loads into fixed architecture and runs.

The checkpoint: - Has all required keys. - feat_mean/std shape (3,), label_mean/std shape (1,). Correct. - Loads into the fixed architecture with strict=True, all keys matched. - Produces [B,1] output. - val relL2=0.1205, public_test relL2=0.1615. But these are IN-SAMPLE now (the final model was trained on train+val+public_test), so these aren't generalization estimates. The honest generalization estimate is the CV ~0.218. Everything works. The artifact is valid. Now, should I try to improve further? Current honest CV estimate ~0.218 rel L2. Let me think about whether there's a meaningful improvement I'm missing, given remaining budget. Ideas not yet tried that could help the PRIMARY metric (rel L2): 1. **Larger/deeper effective training via more SWA diversity** - marginal. 2. **Better input normalization / centering per-sample** - the data is already centered. Could try per-sample recentering to exact bbox center. Minor. 3. **Test-time**: can't. 4. **Different architecture tricks within fixed arch**: The architecture max-pools. I can't change it. 5. **Ensreg**: The 3-seed CV average gave 0.208 (vs single ~0.218). If I could ship an ensemble... I can't. BUT , here's a thought: I could train the final model and then, within the SINGLE model's weights, there's no way to embed an ensemble. Actually, wait. There might be a clever trick: I could train ONE model but feed it augmented copies... no, grader does single forward. 6. **Could I make the single model effectively ensemble over reflections at inference?** The grader feeds x once. But if my model were invariant to reflections (which I train for), then a single forward already captures that. No extra gain. 7. Let me reconsider whether more data augmentation types help now that I train longer. Earlier rotation/scaling hurt at 1000 ep. 8. **Point count**: I use all 2048. Fine. I think ~0.218 CV is solid and beats the ridge baseline (0.231). Given the information ceiling appears to be ~0.22 (ridge with explicit volume features got 0.231, PointNet 0.218), I'm near the achievable limit. Let me reconsider the information ceiling more carefully though , maybe I can improve the CEILING by reconsidering the problem. The key difficulty is that mass ∝ real volume, but normalization removed absolute scale. However, maybe the combination of (normalized shape) + (the actual scale factor) could be recovered if scale correlates with shape. We don't have scale. But actually , reconsider: is the normalization truly per-bracket, or could the dataset have applied a SINGLE global scale? We saw Y-extent always ≈1.0 (0.991-1.0). If it were a single global scale, Y-extents would vary (different brackets have different heights). The fact that ALL have Y-extent ≈ exactly 1.0 means per-bracket normalization (each scaled so its max dimension =1). Actually it means the LONGEST axis is always Y and it's scaled to 1. So yes per-bracket. Absolute scale is gone. So the ceiling is what it is. Given that, mass must be predicted from normalized shape. Two brackets with identical normalized shape but different real sizes would have different mass but identical input → irreducible error. How much irreducible error is there? If the SimJEB brackets all share the same mounting envelope (same real bounding box along the longest dimension), then the normalization scale is ~constant and there's little irreducible error from scale. The ~0.22 floor likely comes from: (a) sparse 2048-point sampling noise, (b) the shape→volume mapping being genuinely complex, (c) small dataset. I believe ~0.218 is a strong result. Let me do a couple of final robustness things: A. Confirm the final shipped model isn't a "bad seed." Since I can't measure generalization of the all-data model directly, let me at least verify across a couple of seeds that the RECIPE's all-data in-sample behavior is stable, and more importantly rely on the CV (which used seed 0 and gave 0.218). The CV used the same seed=0 and same recipe, so the sh …[truncated]

The checkpoint is valid and grader-compatible: all required keys, correct shapes (`feat_*` length-3, `label_*` length-1), loads into the fixed architecture with `strict=True`, and outputs `[B,1]`. The honest generalization estimate remains the CV value (~0.218); the val/public_test numbers above are now in-sample since the final model trained on them. Earlier, 3-seed prediction averaging reliably reached ~0.208, so variance reduction is the main remaining lever for a single shipped model. Let me test the most principled version of that , a classic-SWA constant-LR tail (more diverse snapshots averaged into one weight set) , and adopt it only if it clearly wins.

bash

cd /app && cat > /tmp/trainer_swa.py << 'EOF'
import sys; sys.path.insert(0,'/app')
import json, copy, torch, torch.nn.functional as F
from candidate_model import build_model
from train_final import load, augment, DEV
CFG=json.load(open('/app/task_config.json'))
def train_model2(X,Y,epochs=1400,bs=16,jit=0.004,swa_frac=0.4,seed=0,swa_lr=3e-4,cyclic=True):
    torch.manual_seed(seed); X=X.to(DEV);Y=Y.to(DEV);N=X.shape[0]
    flat=X.reshape(-1,3); fm=flat.mean(0).view(1,1,3); fs=flat.std(0).clamp_min(1e-6).view(1,1,3)
    lm=Y.mean().view(1,1); ls=Y.std().clamp_min(1e-6).view(1,1)
    model=build_model(CFG).to(DEV)
    opt=torch.optim.AdamW(model.parameters(),lr=1e-3,weight_decay=1e-4)
    swa_start=int(epochs*(1-swa_frac))
    cos=torch.optim.lr_scheduler.CosineAnnealingLR(opt,T_max=swa_start)
    swa=None;swa_n=0; cyc_len=50
    for ep in range(epochs):
        model.train(); perm=torch.randperm(N,device=DEV)
        if ep<swa_start: lr=None # cosine handles
        else:
            if cyclic:
                t=((ep-swa_start)%cyc_len)/cyc_len; lr=swa_lr*(1-0.9*t)
            else: lr=swa_lr
            for g in opt.param_groups: g['lr']=lr
        for i in range(0,N,bs):
            idx=perm[i:i+bs]; xb=augment(X[idx],jit); yb=(Y[idx]-lm)/ls
            opt.zero_grad(set_to_none=True); F.mse_loss(model((xb-fm)/fs),yb).backward(); opt.step()
        if ep<swa_start: cos.step()
        take = ep>=swa_start and ((not cyclic) or ((ep-swa_start)%cyc_len==cyc_len-1))
        if take:
            if swa is None: swa=copy.deepcopy(model)
            else:
                with torch.no_grad():
                    for ps,pm in zip(swa.parameters(),model.parameters()): ps.mul_(swa_n/(swa_n+1)).add_(pm/(swa_n+1))
                    for bsf,bm in zip(swa.buffers(),model.buffers()):
                        if bsf.dtype.is_floating_point: bsf.mul_(swa_n/(swa_n+1)).add_(bm/(swa_n+1))
                        else: bsf.copy_(bm)
            swa_n+=1
    final=swa if swa is not None else model
    final.train()
    with torch.no_grad():
        for _ in range(20):
            perm=torch.randperm(N,device=DEV)
            for i in range(0,N,bs):
                idx=perm[i:i+bs]; final((augment(X[idx],jit)-fm)/fs)
    final.eval(); return final,(fm,fs,lm,ls),swa_n
EOF
cat > /tmp/cv_swa.py << 'EOF'
import sys,time; sys.path.insert(0,'/app'); sys.path.insert(0,'/tmp')
import torch
from train_final import load, DEV
from trainer_swa import train_model2
fx=torch.cat([load('train')[0],load('val')[0],load('public_test')[0]],0)
fy=torch.cat([load('train')[1],load('val')[1],load('public_test')[1]],0)
N=fx.shape[0]; g=torch.Generator().manual_seed(12345); order=torch.randperm(N,generator=g)
K=5; folds=[order[i::K] for i in range(K)]; ally=fy.squeeze(1).clone()
def run(tag,**kw):
    allpr=torch.zeros(N); t=time.time()
    for k in range(K):
        va=folds[k]; tr=torch.cat([folds[j] for j in range(K) if j!=k])
        m,(fm,fs,lm,ls),sn=train_model2(fx[tr],fy[tr],seed=0,**kw)
        with torch.no_grad(): allpr[va]=(m((fx[va].to(DEV)-fm)/fs)*ls+lm).squeeze(1).cpu()
    rel=(torch.norm(allpr-ally)/torch.norm(ally)).item()
    print(f'{tag:26s} relL2={rel:.4f} MAE={(allpr-ally).abs().mean():.4f} max={(allpr-ally).abs().max():.4f} snaps={sn} [{time.time()-t:.0f}s]',flush=True)
run('cyclic swa_lr3e4 f0.4',epochs=1400,swa_frac=0.4,swa_lr=3e-4,cyclic=True)
run('const swa_lr2e4 f0.4',epochs=1400,swa_frac=0.4,swa_lr=2e-4,cyclic=False)
run('cyclic swa_lr5e4 f0.5',epochs=1600,swa_frac=0.5,swa_lr=5e-4,cyclic=True)
EOF
pkill -f val_final 2>/dev/null; sleep 2
nohup python -u /tmp/cv_swa.py > /tmp/cv_swa.log 2>&1 & echo "launched $!"; sleep 240; cat /tmp/cv_swa.log

output

launched 727
cyclic swa_lr3e4 f0.4      relL2=0.2240 MAE=0.1410 max=0.7576 snaps=11 [177s]

First classic-SWA variant (cyclic, swa_lr=3e-4, f=0.4): relL2=0.2240, slightly worse than current 0.218. Let me wait for the other two.

bash

sleep 230; cat /tmp/cv_swa.log

output

cyclic swa_lr3e4 f0.4      relL2=0.2240 MAE=0.1410 max=0.7576 snaps=11 [177s]
const swa_lr2e4 f0.4       relL2=0.2331 MAE=0.1434 max=0.7962 snaps=560 [183s]

bash

sleep 200; cat /tmp/cv_swa.log

output

cyclic swa_lr3e4 f0.4      relL2=0.2240 MAE=0.1410 max=0.7576 snaps=11 [177s]
const swa_lr2e4 f0.4       relL2=0.2331 MAE=0.1434 max=0.7962 snaps=560 [183s]
cyclic swa_lr5e4 f0.5      relL2=0.2238 MAE=0.1342 max=0.8071 snaps=16 [210s]

All classic-SWA variants (0.224, 0.233, 0.224) are WORSE than my current cosine-tail averaging (0.218). So the constant/cyclic-LR SWA doesn't help here; my cosine-to-zero tail averaging is better. Good , I'll keep the current recipe. So my shipped model (cosine-tail SWA, 1200 epochs, seed 0) at CV ~0.218 is the best validated configuration. I've now tested: - epochs (600 bad, 1000-2000 good ~0.21) - loss (MSE best for rel L2; Huber similar, slightly worse rel L2) - augmentation (reflections+jitter0.004 best; rotation/scale/bootstrap/weighting all worse) - normalization (per-axis best) - SWA (cosine-tail 0.25-0.3 best; classic constant/cyclic worse; frac 0.5 slightly worse) I'm confident the current checkpoint is near-optimal for this fixed architecture and data. Let me reconsider one thing: seed robustness of the FINAL all-data model. The CV used seed 0 and gave 0.218. But the final model is a single training run on all 148 data with seed 0. Different seeds give ±0.01 variation on held-out. There's a small chance seed 0 on all-data is slightly unlucky. Can I reduce this risk? One option: I can't measure generalization of all-data models. But I CAN reduce seed-variance by making the single model an average over multiple independent runs IF they're compatible , they're not (independent inits). Alternatively, I could pick the final seed by a proxy: train final on all-data with several seeds, and for each, measure something correlated with generalization. But I have no holdout. Actually, a cleaner approach to reduce final-model variance: train on all 148 but hold out nothing, and trust SWA (which already reduces variance within a run). The CV (seed 0) validated this exact procedure and got 0.218. The shipped model IS this procedure with seed 0 on all data. So it's as validated as possible. Good. Hmm, but one more consideration: I could make the final model more robust by using the knowledge that 3-seed prediction averaging gave ~0.208. Since I can't ensemble predictions, but I wonder , would averaging the WEIGHTS of the SWA tails from multiple seeds started from the SAME init help? They diverge due to different augmentation/shuffle RNG but same init and same data. Weight averaging across seeds from same init sometimes works if they stay in the same basin (like SWA across a wider exploration). Risky and likely to just smear. Not worth it. I'm confident. Let me finalize. Let me do final housekeeping: 1. Verify the outputs/model.pt once more (already did, works). 2. Make sure the file is at the exact required path /app/outputs/model.pt. Yes. 3. Clean up , the task says don't write to /tmp/out. I haven't. Good. 4. Double check float16 loading in grader: grader likely loads features as float16 then converts? The spec says features float16 on disk. My feat_mean/std are float32. At inference, grader does (x - feat_mean)/feat_std. If x is float16 and feat_mean float32, broadcasting promotes to float32. Should be fine. But to be safe, maybe the grader passes x as float32. Either way works. Let me make sure my model weights are float32 (they are). Let me also consider: the grader might feed the raw float16 features directly to my normalization. Let me test that my predict path works with float16 input too (promotion). Let me verify quickly. Also, let me double-check: does the grader apply model.eval()? If it doesn't and leaves model in train mode, BatchNorm would use batch stats → different results. Most graders call eval(). But to be safe, I can't control that. However, standard practice is eval(). The checkpoint's BN running stats are set. If grader forgets eval(), with a large batch the batch stats ≈ running stats anyway (since input distribution matches). With small batch could differ. I'll assume eval(). This is standard. Let me test float16 input and a single-sample batch to be safe. Let me also reconsider whether the grader loads state_dict into build_model(config) with the SAME config. Yes, task_config.json model siz …[truncated]

All classic/cyclic-SWA variants (0.224–0.233) are worse than my cosine-tail averaging (0.218), so the current recipe stands as the best validated configuration. I've now swept epochs, loss, augmentation, normalization, and SWA strategy , the shipped model is near-optimal for this fixed architecture and data. Let me run a few final robustness checks on the saved artifact: float16 input (as stored on disk), batch-size-1, and determinism in eval mode.

bash

cd /app && python -c "
import json, torch, sys
sys.path.insert(0,'/app'); from candidate_model import build_model
cfg=json.load(open('task_config.json')); ck=torch.load('outputs/model.pt',weights_only=True)
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'])
def pred(x): 
    with torch.no_grad(): return m((x.float()-fm)/fs)*ls+lm
d=torch.load('data/public_test/samples.pt',weights_only=True)
xf16=d['features']  # float16 as on disk
o1=pred(xf16); print('float16 input -> shape',tuple(o1.shape),'dtype',o1.dtype)
# batch size 1
o_b1=pred(xf16[:1]); print('bs=1 ok, pred0=',round(o_b1.item(),4))
# determinism
o2=pred(xf16); print('determinism max diff:', (o1-o2).abs().max().item())
# single-sample vs batched consistency (BN eval => should match)
idx=7; single=pred(xf16[idx:idx+1]).item(); batched=o1[idx].item()
print(f'single vs batched sample {idx}: {single:.5f} vs {batched:.5f} diff={abs(single-batched):.2e}')
print('pred range over public_test:', round(o1.min().item(),3), round(o1.max().item(),3))
"

output

float16 input -> shape (16, 1) dtype torch.float32
bs=1 ok, pred0= 0.3854
determinism max diff: 0.0
single vs batched sample 7: 0.65547 vs 0.65547 diff=2.38e-07
pred range over public_test: 0.189 1.469

All robustness checks pass: - float16 input works (promotes to float32), output [16,1] float32. - batch size 1 works. - Deterministic (diff 0) in eval mode. - Single vs batched consistent (BN in eval uses running stats, so batch-size-independent). diff ~2e-7. - Prediction range 0.189-1.469, matching the label range (public_test labels 0.198-1.449). Sensible. The model is robust to batch size (important in case grader uses different batching) and input dtype. Now let me write a short README/summary in outputs? Not required. The task only requires /app/outputs/model.pt. Let me reconsider if there's anything I can do to further improve the primary metric meaningfully. I've hit ~0.218 CV, beating ridge (0.231). The information ceiling seems near. Let me reconsider the ceiling one more time by trying a stronger engineered-feature model (e.g., gradient boosting-like or a small MLP on features) via CV, to see if the true ceiling is notably below 0.22. If a much better feature model exists (say 0.15), that would suggest the PointNet is leaving signal on the table and motivate more work. If the feature ceiling is also ~0.2, then ~0.218 is essentially optimal. I don't have sklearn. I can do a small MLP on the 19 features in torch with CV, or ridge with polynomial/interaction features. Let me quickly try ridge with expanded features (squares + interactions) and a small torch MLP, CV'd, to probe the ceiling. This is cheap (features already computed in /tmp/featall.npz) and informative. Let me do that quickly.

Robustness confirmed: works with float16 input, batch-size-1, is deterministic in eval mode, batch-size-independent (BN uses running stats), and predicts a sensible mass range (0.19–1.47 kg). Let me probe whether the ~0.22 signal ceiling is real by testing stronger feature models (expanded ridge + a small MLP) under the same CV , if they also plateau near 0.2, my PointNet is essentially optimal for this data.

bash

cd /app && python -c "
import torch, numpy as np
d=np.load('/tmp/featall.npz'); X,y=d['X'],d['y']; N=len(y)
g=torch.Generator().manual_seed(12345); order=torch.randperm(N,generator=g).numpy()
K=5; folds=[order[i::K] for i in range(K)]
# expanded features: original + squares + log
Xe=np.concatenate([X, X**2, np.log(np.abs(X)+1e-6)],1)
def cv_ridge(Xf,lam):
    pr=np.zeros(N)
    for k in range(K):
        va=folds[k]; tr=np.concatenate([folds[j] for j in range(K) if j!=k])
        mu=Xf[tr].mean(0); sd=Xf[tr].std(0)+1e-9
        Xt=(Xf[tr]-mu)/sd; Xv=(Xf[va]-mu)/sd
        A=Xt.T@Xt+lam*np.eye(Xf.shape[1]); w=np.linalg.solve(A,Xt.T@(y[tr]-y[tr].mean()))
        pr[va]=Xv@w+y[tr].mean()
    return np.linalg.norm(pr-y)/np.linalg.norm(y), np.mean(np.abs(pr-y)), np.max(np.abs(pr-y))
for lam in [1,3,10,30]:
    r=cv_ridge(Xe,lam); print(f'ridge-expanded lam={lam:4} relL2={r[0]:.4f} MAE={r[1]:.4f} max={r[2]:.4f}')
" 2>&1
echo "--- small MLP on features (torch) ---"
python -c "
import torch, numpy as np, torch.nn as nn
d=np.load('/tmp/featall.npz'); X=torch.tensor(d['X']).float(); y=torch.tensor(d['y']).float(); N=len(y)
g=torch.Generator().manual_seed(12345); order=torch.randperm(N,generator=g)
K=5; folds=[order[i::K] for i in range(K)]
pr=torch.zeros(N)
for k in range(K):
    va=folds[k]; tr=torch.cat([folds[j] for j in range(K) if j!=k])
    mu=X[tr].mean(0); sd=X[tr].std(0)+1e-9; Xt=(X[tr]-mu)/sd; Xv=(X[va]-mu)/sd
    ym=y[tr].mean(); ys=y[tr].std()
    torch.manual_seed(0)
    net=nn.Sequential(nn.Linear(X.shape[1],64),nn.GELU(),nn.Dropout(0.2),nn.Linear(64,32),nn.GELU(),nn.Linear(32,1))
    opt=torch.optim.AdamW(net.parameters(),lr=3e-3,weight_decay=1e-3)
    yt=((y[tr]-ym)/ys).view(-1,1)
    for ep in range(400):
        net.train(); opt.zero_grad(); loss=((net(Xt)-yt)**2).mean(); loss.backward(); opt.step()
    net.eval()
    with torch.no_grad(): pr[va]=(net(Xv).squeeze(1)*ys+ym)
rel=(torch.norm(pr-y)/torch.norm(y)).item()
print(f'feature-MLP CV relL2={rel:.4f} MAE={(pr-y).abs().mean():.4f} max={(pr-y).abs().max():.4f}')
"

output

ridge-expanded lam=   1 relL2=0.2492 MAE=0.1530 max=0.8321
ridge-expanded lam=   3 relL2=0.2399 MAE=0.1519 max=0.6522
ridge-expanded lam=  10 relL2=0.2376 MAE=0.1522 max=0.5944
ridge-expanded lam=  30 relL2=0.2416 MAE=0.1526 max=0.6133
--- small MLP on features (torch) ---
feature-MLP CV relL2=0.2272 MAE=0.1393 max=0.6490

The feature-based ceiling probes: - ridge-expanded: 0.238 (worse than plain ridge 0.231; overfit) - feature-MLP: 0.2272 Both are WORSE than my PointNet's 0.218. So the PointNet is actually the best model so far, and the information ceiling is around 0.21-0.23. My PointNet at 0.218 is essentially at/above the ceiling of what my engineered features achieve. This strongly confirms the PointNet is near-optimal and well-trained. So I'm confident: my shipped model at CV ~0.218 is excellent for this task. Further gains would require more data or a different (non-allowed) architecture. Let me reconsider whether combining the PointNet with features would help , but I can only ship the PointNet. The PointNet already beats the feature models, so no point. Final decision: keep the current checkpoint. Let me do final cleanup and a last confirmation: 1. Confirm /app/outputs/model.pt exists and is valid (done). 2. Remove any stray temp files from /app (I created /app/train_final.py which is fine to keep; it documents the method). I did not write to /tmp/out. 3. Summarize. Wait , I should double check one potential grader concern: the grader might call build_model with the config and then load_state_dict with strict=True (default). My state_dict keys must match exactly. I verified "All keys matched successfully." Good. Also the grader might use weights_only=True for torch.load , my payload contains only tensors and dicts of tensors, which is fine for weights_only=True (verified I loaded it with weights_only=True in verify.py). Actually, let me double-check that torch.load(weights_only=True) works on my saved file , yes, verify.py used weights_only=True and it loaded. Good. Let me also make sure feat_mean etc. are plain tensors (not requiring grad). They come from computations on data; they might carry grad? No, they're computed under no autograd context? Actually fm = flat.mean(0)... flat is from X which is a leaf with requires_grad=False (data tensor). So fm has requires_grad=False. Good. And state_dict values are detached params (state_dict returns tensors without grad tracking for saving; I also .cpu() them). Fine. Let me verify the saved tensors have requires_grad False and are contiguous, just to be safe, and re-run the final verify once more to be 100% sure the on-disk artifact is good. Already did. Let me also save a small metadata/readme in outputs documenting the approach and expected metrics (optional, harmless). The task says write checkpoint to /app/outputs/model.pt. I'll add a brief outputs/README note , but that might not be wanted. I'll keep outputs clean with just model.pt to avoid confusion. Actually a README is harmless and can document. I'll add a short one. Actually, let me not clutter. The deliverable is model.pt. I'll leave just that. But train_final.py in /app documents the method. Let me reconsider the seed robustness issue once more, because it's the one real risk. The shipped model is a single seed-0 run on all 148 data. CV (seed 0) = 0.218. Let me quickly check how much the FINAL in-sample metrics vary across seeds , not generalization, but if seed-0's behavior is anomalous. Better: let me retrain final on all-data with seed 1 and seed 2 and compare their predictions to seed-0 on public_test+val (in-sample). If all three agree closely, seed-0 is representative. If seed-0 is an outlier, I'd reconsider. This is a cheap sanity check (~35s each). Actually, more useful: I could ENSEMBLE-via-weight-averaging across seeds won't work. But I could pick the final model as the one whose predictions best agree with the ensemble mean (most "central" / representative seed), reducing the chance of shipping an outlier. Let me train seeds 0,1,2,3 on all data (each ~35s, total ~150s), compute pairwise agreement, and ship the medoid seed (closest to the average prediction). This reduces variance risk without needing a holdout. The average of several seeds' predictions is a better estimator; the medoid single model is the …[truncated]

The feature-based models (expanded ridge 0.238, feature-MLP 0.227) are both **worse** than my PointNet (0.218), confirming the signal ceiling is ~0.21–0.23 and my model is essentially optimal for this fixed architecture and data. The one remaining risk is shipping a single unlucky seed. Since I have no holdout for the all-data model, I'll train several seeds and ship the **medoid** , the model whose predictions are most central (closest to the multi-seed consensus). This avoids outlier seeds in a principled, label-free way.

bash

cd /app && cat > /tmp/medoid.py << 'EOF'
import sys,time; sys.path.insert(0,'/app')
import torch
from train_final import load, train_model, DEV
feats=[load(s) for s in ['train','val','public_test']]
X=torch.cat([f[0] for f in feats],0); Y=torch.cat([f[1] for f in feats],0)
seeds=[0,1,2,3,4]; preds=[]; payloads=[]
t=time.time()
for sd in seeds:
    m,(fm,fs,lm,ls)=train_model(X,Y,epochs=1200,seed=sd)
    with torch.no_grad(): p=(m((X.to(DEV)-fm)/fs)*ls+lm).squeeze(1).cpu()
    preds.append(p)
    payloads.append({'state_dict':{k:v.cpu() for k,v in m.state_dict().items()},
        'feat_mean':fm.reshape(3).cpu(),'feat_std':fs.reshape(3).cpu(),
        'label_mean':lm.reshape(1).cpu(),'label_std':ls.reshape(1).cpu()})
    yt=Y.squeeze(1); rel=(torch.norm(p-yt)/torch.norm(yt)).item()
    print(f'seed {sd} in-sample rel={rel:.4f} [{time.time()-t:.0f}s]',flush=True)
P=torch.stack(preds)             # [S, N]
mean_pred=P.mean(0)
# medoid: seed whose preds are closest to the consensus mean
dist=[ (P[i]-mean_pred).pow(2).sum().item() for i in range(len(seeds))]
medoid=int(torch.tensor(dist).argmin())
print('dists to consensus:',[round(d,4) for d in dist],'-> medoid seed',seeds[medoid],flush=True)
# pairwise agreement (std across seeds)
print('mean inter-seed std on 148 inputs:',P.std(0).mean().item())
torch.save(payloads[medoid],'/app/outputs/model.pt')
print('saved medoid (seed %d) to /app/outputs/model.pt'%seeds[medoid],flush=True)
EOF
pkill -f cv_swa 2>/dev/null; sleep 2
nohup python -u /tmp/medoid.py > /tmp/medoid.log 2>&1 & echo "launched $!"; sleep 210; cat /tmp/medoid.log

output

launched 789
[final] epoch 200/1200 loss=0.3591
[final] epoch 400/1200 loss=1.0637
[final] epoch 600/1200 loss=0.0865
[final] epoch 800/1200 loss=0.1205
[final] epoch 1000/1200 loss=0.0390
[final] epoch 1200/1200 loss=0.0160
seed 0 in-sample rel=0.1127 [46s]
[final] epoch 200/1200 loss=0.3403
[final] epoch 400/1200 loss=0.7514
[final] epoch 600/1200 loss=0.3737
[final] epoch 800/1200 loss=0.0900
[final] epoch 1000/1200 loss=0.0712
[final] epoch 1200/1200 loss=0.3636
seed 1 in-sample rel=0.0998 [86s]
[final] epoch 200/1200 loss=0.2465
[final] epoch 400/1200 loss=0.0967
[final] epoch 600/1200 loss=0.2056
[final] epoch 800/1200 loss=0.1461
[final] epoch 1000/1200 loss=0.0059
[final] epoch 1200/1200 loss=0.0627
seed 2 in-sample rel=0.1138 [125s]
[final] epoch 200/1200 loss=0.1647
[final] epoch 400/1200 loss=0.0684
[final] epoch 600/1200 loss=0.0403
[final] epoch 800/1200 loss=0.0818
[final] epoch 1000/1200 loss=0.3937
[final] epoch 1200/1200 loss=0.0620
seed 3 in-sample rel=0.1256 [165s]
[final] epoch 200/1200 loss=0.9050
[final] epoch 400/1200 loss=0.4553
[final] epoch 600/1200 loss=0.2070
[final] epoch 800/1200 loss=0.0634
[final] epoch 1000/1200 loss=0.0281
[final] epoch 1200/1200 loss=0.0395
seed 4 in-sample rel=0.1101 [205s]
dists to consensus: [0.2752, 0.3668, 0.4365, 0.3237, 0.2888] -> medoid seed 0
mean inter-seed std on 148 inputs: 0.04596555605530739
saved medoid (seed 0) to /app/outputs/model.pt

The medoid analysis: - 5 seeds trained on all 148 data. - Inter-seed std on predictions ~0.046 (fairly consistent across seeds , good, low variance). - Medoid = seed 0 (closest to consensus mean, dist 0.2752). Seed 4 was close second (0.2888). - Saved the medoid (seed 0) checkpoint. So seed 0 happens to be the most central/representative. The inter-seed std of 0.046 is modest relative to typical errors (~0.13 MAE), confirming the model is reasonably stable across seeds. Shipping the medoid minimizes the risk of an unlucky run. Now the shipped /app/outputs/model.pt is the medoid (seed 0) trained on all 148. Let me re-verify this final artifact (it was overwritten) with the grader-mimicking verifier to confirm it's valid. Actually, wait , I want to reconsider whether shipping the medoid is truly better than a prediction-averaged ensemble. I can't ship an ensemble. The medoid is the best single-model proxy for the ensemble mean. Good. But actually, there's an even better idea I should consider: since inter-seed std is low (0.046) and the consensus (mean of seeds) is a better predictor than any single seed, and I can only ship ONE network... the medoid is the closest single network to that consensus. That's the right choice. However, let me reconsider: could I reduce variance further by averaging the predictions and then... no. Medoid it is. Let me verify the final saved artifact once more and compute its in-sample metrics (just to confirm it loads and runs; generalization ~0.218 per CV). Let me run the verifier. Actually, one concern: is seed 0 on all-data genuinely good on a true holdout? My CV with seed 0 (on 118-sample folds) gave 0.218. The all-data seed-0 model should be at least as good (more data). And it's the medoid, so representative. Let me run the final verification on the saved artifact.

Five seeds trained on all 148 samples show low inter-seed prediction std (~0.046), confirming the model is stable. The **medoid is seed 0** (most central to the consensus), and it's now saved. Let me run the grader-mimicking verifier one final time on the saved artifact to confirm it's valid.

bash

cd /app && python /tmp/verify.py

output

keys: ['feat_mean', 'feat_std', 'label_mean', 'label_std', 'state_dict']
  feat_mean: shape=(3,) dtype=torch.float32 val=[-0.011780548840761185, 0.007426036521792412, -0.07198601216077805]
  feat_std: shape=(3,) dtype=torch.float32 val=[0.1422092616558075, 0.2692946195602417, 0.08725058287382126]
  label_mean: shape=(1,) dtype=torch.float32 val=[0.7487966418266296]
  label_std: shape=(1,) dtype=torch.float32 val=[0.38614240288734436]
load_state_dict: <All keys matched successfully>
dummy output shape: (5, 1) dtype torch.float32
val          relL2=0.1240 MAE=0.0771 max=0.2759  (n=32)
public_test  relL2=0.1325 MAE=0.0821 max=0.2918  (n=16)
OK: checkpoint loads into fixed architecture and runs.

The final saved artifact (medoid, seed 0, all-data): - All required keys present, correct shapes/dtypes. - Loads into fixed architecture with strict matching. - Output [B,1]. - In-sample (these splits were in training): val 0.124, public_test 0.133. These are in-sample so optimistic; the honest held-out estimate is the CV ~0.218. Everything is in order. Let me do final housekeeping: - Confirm final file location and integrity. - Clean up /tmp logs (optional). - I did NOT write to /tmp/out. Good. Let me verify the output directory contents and that model.pt is the only deliverable needed. Let me also reconsider: the grader's "held-out set" , my honest estimate is ~0.218 rel L2, ~0.137 MAE, ~0.68 max. That's solid. Actually, let me reconsider whether I should present expected held-out metrics more carefully. The CV estimate (0.218) trained on 118 per fold. The final model trained on 148 (all). More data → likely slightly better than 0.218. And it's the medoid. So I expect held-out rel L2 around 0.20-0.22, MAE ~0.13, max ~0.5-0.7 depending on whether a hard sample appears. I'm confident this is a strong, well-validated submission. Let me finalize. Let me do a final listing and a clean summary. Let me also make sure there are no leftover large temp files in /app that could interfere. train_final.py is a legit deliverable/documentation. Let me check /app/outputs. Let me also remove the __pycache__ if created. Not necessary. Let me confirm the final state.

The final artifact is valid and grader-compatible. Let me do a final confirmation of the deliverable location and a clean summary of what's saved.

bash

cd /app && echo "=== /app/outputs ===" && ls -la outputs/ && echo && echo "=== integrity re-load (weights_only=True) ===" && python -c "
import torch
ck=torch.load('/app/outputs/model.pt', weights_only=True)
assert set(ck)>= {'state_dict','feat_mean','feat_std','label_mean','label_std'}
assert ck['feat_mean'].shape==(3,) and ck['feat_std'].shape==(3,)
assert ck['label_mean'].shape==(1,) and ck['label_std'].shape==(1,)
assert len(ck['state_dict'])>0 and all(not v.requires_grad for v in ck['state_dict'].values())
print('size on disk:', round(__import__('os').path.getsize('/app/outputs/model.pt')/1024,1),'KB')
print('all required keys present, shapes correct, tensors detached -> OK')
"

output

=== /app/outputs ===
total 384
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 23:34 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 23:34 ..
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 23:52 model.pt

=== integrity re-load (weights_only=True) ===
size on disk: 375.7 KB
all required keys present, shapes correct, tensors detached -> OK

I've completed the task. Here's a summary of what I did and delivered. ## Deliverable `/app/outputs/model.pt` , a checkpoint that loads cleanly (`strict=True`) into the fixed `candidate_model.PointNetRegressor`, with all required keys: `state_dict`, `feat_mean`/`feat_std` (length‑3), `label_mean`/`label_std` (length‑1). It consumes `[B, 2048, 3]` and outputs `[B, 1]`. The training code is saved in `/app/train_final.py`. ## Key findings that shaped the approach - **Data analysis:** Each bracket is normalized *per‑bracket* (the longest axis, Y, always fills the unit cube to extent ≈1.0), so absolute scale is removed and mass must be inferred from **normalized shape**. Only 100 train samples (132 with val, 148 with public_test) , overfitting is the main risk, and bracket IDs don't overlap across splits. - **Reflections are a free, valid augmentation:** mass is invariant to mirroring and the axis‑aligned bounding box is preserved. Random per‑axis sign flips expand the data up to 8× without distribution shift. - **Honest evaluation:** I relied on 5‑fold CV over the combined 148 samples rather than the noisy 32‑sample val split (which was optimistic at 0.21 vs. the true ~0.22). ## What I swept (all via CV) - **Epochs:** the biggest lever , 600 underfits (0.26); 1000–2000 plateau near **0.21**. - **Loss:** plain **MSE** is best for the primary relative‑L2 (it *is* that metric's numerator); mass‑weighting and Huber were equal‑or‑worse once seed noise was averaged out. - **Augmentation:** reflections + small jitter (0.004) help; rotation, scaling, bootstrap resampling all hurt. - **Normalization:** per‑axis standardization beats isotropic/none. - **SWA:** cosine‑tail weight averaging (~last 30%) beats classic constant/cyclic‑LR SWA here. - **A critical implementation detail:** because the model is trained to be reflection‑invariant, its BatchNorm running stats must be refreshed **with augmentation** , recomputing on un‑augmented data silently wrecked generalization (0.34 vs 0.22). I caught this by validating the exact shipped routine end‑to‑end. ## Results and context - PointNet CV: **rel‑L2 ≈ 0.218, MAE ≈ 0.137, max‑abs ≈ 0.68**, beating ridge/MLP on hand‑crafted features (including a flood‑fill volume estimate), which plateaued at 0.23–0.24. This indicates my model is near the information ceiling for this fixed architecture and sparse 2048‑point data. - To avoid shipping an unlucky seed, I trained 5 seeds on all 148 samples (low inter‑seed std ≈ 0.046) and shipped the **medoid** , the single network closest to the multi‑seed consensus. I verified the saved artifact mimicking the grader: it reloads with `weights_only=True`, handles float16 inputs, batch‑size‑1, is deterministic in eval mode, batch‑size‑independent, and predicts a sensible mass range (0.19–1.47 kg). Note the val/public_test numbers printed by the verifier are now in‑sample (those splits were folded into final training); the honest held‑out expectation is the CV figure (~0.20–0.22 rel‑L2), and I optimized for all three metrics jointly rather than trading one off. One caveat worth stating plainly: since the final model is trained on all labeled data, I can't directly measure its generalization , I'm relying on the CV estimate of the identical procedure plus the medoid selection to stand in for that.

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: 8  status: completed  raw_score: 0.47
# reward rule: continuous: score 0.47 < 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).
----------------------------------------------------------------------
=== /app/outputs ===
total 384
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 23:34 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 23:34 ..
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 23:52 model.pt

=== integrity re-load (weights_only=True) ===
size on disk: 375.7 KB
all required keys present, shapes correct, tensors detached -> OK

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_6dfcc6d7e6994393. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_6dfcc6d7e6994393 · verifier authoritative; classifier explanatory.