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 CV relL2≈0.24-0.26 with comprehensive optimization (jitter augmentation, SWA, distillation, hyperparameter sweeps), but hidden test raw score was 0.45 (threshold <0.204 required for pass, corresponding to relL2<0.204). The agent's own analysis showed ridge regression on geometric features achieves relL2≈0.17, but PointNet's fixed architecture with max-pooling cannot compute volume/occupancy features (corr 0.85 with mass) that ridge relies on. Agent correctly identified the ceiling: 'PointNet's max-pooling fundamentally struggles to compute...per-cell thickness', 'PointNet ceiling is lower', and 'the representational ceiling (~0.25 CV) is the binding constraint'. The ~0.04-0.05 relL2 gap (0.26 CV to required 0.204) proved unbridgeable.

Root causeThe PointNet architecture is fundamentally limited for this geometry-to-mass prediction task because it uses global max-pooling, which loses per-point spatial information needed to compute volume proxies (voxel occupancy, per-pillar thickness) that correlate 0.85 with mass. Even with perfect training, the architecture ceiling (~relL2 0.22-0.26) exceeds the required threshold (< 0.204). The agent's 5-fold CV and iterative optimization were technically sound but couldn't overcome the architectural mismatch to the problem requirements.

RecommendationN/A - task is fine. The instruction and test are appropriate; they require solving a hard geometry problem. The baseline model architecture (PointNet) is undersized for the problem. This tests whether agents can recognize architectural limitations and either: (1) request architectural modifications (not possible in this case), or (2) engineer alternative solutions within constraints (agent attempted distillation and augmentation but still bottlenecked by max-pooling). The agent's failure to reach 0.204 is not a task specification issue but a legitimate demonstration that this benchmark requires substantial geometric feature extraction capability.

Trajectory

Tool-by-tool agent trajectory

237 tool calls · 3 tool types · 237 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 task, data, and provided code.

bash

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

output

total 14
drwxrws--- 3 h2tagent h2tagent  138 Jun 20 18:13 .
drwxr-xr-x 1 root     root     4096 Jun 20 20:55 ..
-rw-rw---- 1 h2tagent h2tagent 1639 Jun 20 18:10 candidate_model.py
drwxrws--- 5 h2tagent h2tagent   58 Jun 20 18:10 data
-rw-rw---- 1 h2tagent h2tagent 1183 Jun 20 18:10 dataset.py
-rw-rw---- 1 h2tagent h2tagent  499 Jun 20 18:10 feature_schema.json
-rw-rw---- 1 h2tagent h2tagent  581 Jun 20 18:10 task_config.json
-rw-rw---- 1 h2tagent h2tagent 3117 Jun 20 18:10 train.py
---TASK CONFIG---
{
  "task_id": "simjeb-bracket-fea-mass-prediction-real",
  "dataset_name": "simjeb-bracket-fea-mass-pilot",
  "n_points": 2048,
  "point_dim": 3,
  "label_dim": 1,
  "label_per_point": false,
  "seed": 20260518,
  "model": {
    "feature_width": 64,
    "global_width": 256,
    "head_width": 128
  },
  "training": {
    "epochs": 1,
    "batch_size": 16,
    "lr": 0.001,
    "weight_decay": 0.0001,
    "loss": "MSE"
  },
  "evaluation": {
    "primary_metric": "mass_rel_l2_score",
    "secondary_metrics": [
      "mass_mae_score",
      "mass_max_abs_err_score"
    ]
  }
}
---FEATURE SCHEMA---
{
  "version": "simjeb-bracket-fea-mass-pilot-v1",
  "input": {
    "type": "point_cloud",
    "n_points": 2048,
    "dim": 3,
    "coord_range": [
      -0.5,
      0.5
    ],
    "description": "Unordered surface point sample of a jet-engine bracket; each bracket mesh is centered at its bounding-box centroid and rescaled so the axis-aligned bounding box fits inside the unit cube."
  },
  "label": {
    "type": "scalar",
    "dim": 1,
    "description": "FEA-derived bracket mass in kg."
  }
}

Let me look at the model, dataset, and train files.

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

bash

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

output

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

from typing import Mapping

import torch
import torch.nn as nn


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

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


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

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


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

from pathlib import Path
from typing import Iterable

import torch
from torch.utils.data import Dataset


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

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

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


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

import json
import os
import sys
from pathlib import Path

import torch
import torch.nn.functional as F

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

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


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


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


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

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

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

Now let me look at the data. Let me understand the sizes, distributions, and check the environment (GPU availability, library versions).

Now let me inspect the data and the compute environment available to me.

bash

cd /app && ls -la data && echo "---" && for d in train val public_test; do echo "== $d =="; ls -la data/$d; done && echo "---PYTHON/TORCH---" && python -c "import torch,sys; print('py',sys.version); print('torch',torch.__version__); print('cuda avail',torch.cuda.is_available()); print('mps',torch.backends.mps.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
---
== 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
== 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
== 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
---PYTHON/TORCH---
py 3.11.10 | packaged by conda-forge | (main, Oct 16 2024, 01:27:36) [GCC 13.3.0]
torch 2.5.1+cu124
cuda avail True
mps False

We have CUDA available. Let me examine the data shapes, label distributions, and the manifests.

bash

cd /app && python -c "
import torch
for d in ['train','val','public_test']:
    p=torch.load(f'data/{d}/samples.pt', weights_only=True)
    f=p['features']; l=p['labels']
    print(f'== {d} ==')
    print('features', f.shape, f.dtype, 'labels', l.shape, l.dtype)
    lf=l.float()
    print('label min/max/mean/std', float(lf.min()), float(lf.max()), float(lf.mean()), float(lf.std()))
    ff=f.float()
    print('feat per-axis min', ff.amin(dim=(0,1)).tolist())
    print('feat per-axis max', ff.amax(dim=(0,1)).tolist())
    print('feat per-axis mean', ff.mean(dim=(0,1)).tolist())
    print('feat per-axis std', ff.std(dim=(0,1)).tolist())
"

output

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

So we have: - train: 100 samples - val: 32 samples - public_test: 16 samples This is a small dataset. The labels are mass in kg, ranging from ~0.2 to ~2.2. Let me look at the manifests to understand more.

This is a small dataset (100 train, 32 val, 16 public_test). Let me examine the manifests for more context.

bash

cd /app && echo "=== train manifest (first 3) ===" && head -n 3 data/train/manifest.jsonl && echo "=== val manifest (first 2) ===" && head -n 2 data/val/manifest.jsonl && echo "=== public_test manifest (first 2) ===" && head -n 2 data/public_test/manifest.jsonl && echo "=== counts ===" && wc -l data/*/manifest.jsonl

output

=== train manifest (first 3) ===
{"id": "train_0000", "source_bracket_id": 214, "n_points": 2048}
{"id": "train_0001", "source_bracket_id": 538, "n_points": 2048}
{"id": "train_0002", "source_bracket_id": 546, "n_points": 2048}
=== val manifest (first 2) ===
{"id": "val_0000", "source_bracket_id": 418, "n_points": 2048}
{"id": "val_0001", "source_bracket_id": 441, "n_points": 2048}
=== public_test manifest (first 2) ===
{"id": "public_test_0000", "source_bracket_id": 461, "n_points": 2048}
{"id": "public_test_0001", "source_bracket_id": 440, "n_points": 2048}
=== counts ===
  16 data/public_test/manifest.jsonl
 100 data/train/manifest.jsonl
  32 data/val/manifest.jsonl
 148 total

The data is derived from the SimJEB dataset (Simulated Jet Engine Bracket). Each sample has a source_bracket_id. Let me check if there's overlap in source_bracket_id between splits (there shouldn't be, but good to know). Also I want to understand the relationship between geometry and mass. Key insight: mass is proportional to volume times density. If all brackets are the same material (density), then mass ∝ volume. The point cloud is a surface sample. So we need to infer volume from surface points. Actually, a key physics insight: mass = density × volume. The brackets are rescaled so bounding box fits in unit cube. Wait - but the rescaling is applied to the point cloud coordinates. So the actual physical size information might be lost due to rescaling... but the mass label is the true FEA mass. Wait, let me think. Each bracket mesh is centered and rescaled so the bounding box fits inside the unit cube. This means the scale factor varies per bracket. So two brackets with the same shape but different physical sizes would look identical after rescaling but have different masses. Hmm, but that would make the problem ill-posed unless the scaling is consistent. Actually wait - let me reconsider. The bounding box fits inside the unit cube. Looking at the data, the y-axis (axis 1) spans exactly [-0.5, 0.5], so the y dimension is the largest dimension and it's normalized to exactly 1.0. The x and z are smaller. So the normalization is: take the bracket, center it, and divide by the largest dimension (so the largest spans 1.0). This means absolute scale information is removed. So how can we predict mass? Hmm. Unless... the brackets all come from the SimJEB dataset where they fit in a fixed design envelope. In SimJEB, all brackets share the same bounding box / mounting interface (they're all designed to fit the same bolt pattern and load conditions). So the physical scale is actually roughly constant across brackets! The design envelope is fixed. So after normalizing by bounding box, the scale is approximately consistent because all brackets have approximately the same real-world bounding box. Actually in SimJEB, the brackets all have the same 4 bolt holes and 1 load point in a fixed position, so they share a common coordinate frame and scale. The normalization here (fit bounding box in unit cube) might slightly vary but the shapes are genuinely different designs with different amounts of material. So mass ∝ volume of material. We need to estimate the volume enclosed by the surface point cloud. This is the core of the problem. The PointNet architecture is fixed. We can't change it. But we can: 1. Train it well (many epochs, good LR schedule). 2. Use data augmentation. 3. Normalize inputs/outputs well. The config says epochs=1, which is clearly just a default. We should train much longer. Let me think about what matters: - We have a fixed architecture (PointNetRegressor). We must keep it as-is so the checkpoint loads. - We can control: normalization, training procedure, augmentation, loss function, number of epochs, LR schedule, weight decay, etc. Wait - can we change the architecture? "Keep it as is; your saved checkpoint has to load back into this same architecture." So we must use exactly this architecture. The state_dict must load into `build_model(config)` with the config's model sizes (feature_width=64, global_width=256, head_width=128). So I need to train this exact PointNet well. Let me think about the challenges: - Small training set (100 samples). Overfitting is a risk. - PointNet with BatchNorm. With batch_size=16 and 100 samples. - Need good generalization. Strategy: 1. Data augmentation that preserves mass: - Random rotation? NO - rotation would change the bounding box normalization and the shape orientation. Actually, the brackets are in a canonical orientation (they share a common frame). Rotating would break the learned relationship... but small rotations might help. Actually, for mass prediction, ro …[truncated]

This is the SimJEB (Simulated Jet Engine Bracket) dataset. The data is small, so careful training and regularization will matter. Let me check the GPU and do some exploratory analysis on the geometry→mass relationship.

bash

cd /app && nvidia-smi --query-gpu=name,memory.total,memory.used --format=csv && echo "---overlap check---" && python -c "
import json
def ids(d):
    s=set()
    for line in open(f'data/{d}/manifest.jsonl'):
        s.add(json.loads(line)['source_bracket_id'])
    return s
tr=ids('train'); va=ids('val'); te=ids('public_test')
print('train brackets', len(tr), 'val', len(va), 'test', len(te))
print('train∩val', tr&va)
print('train∩test', tr&te)
print('val∩test', va&te)
"

output

name, memory.total [MiB], memory.used [MiB]
NVIDIA H100 80GB HBM3, 81559 MiB, 19607 MiB
---overlap check---
train brackets 100 val 32 test 16
train∩val set()
train∩test set()
val∩test set()

No overlap between splits - good, proper generalization test. We have an H100 GPU with plenty of memory. The datasets are tiny so training will be fast. Let me do EDA to understand the geometry→mass relationship. Let me compute some hand-crafted geometric features and see their correlation with mass.

No data leakage between splits, and we have a powerful H100 GPU. Let me explore how geometry relates to mass with some hand-crafted features to gauge the signal.

bash

cd /app && python -c "
import torch, numpy as np
p=torch.load('data/train/samples.pt', weights_only=True)
f=p['features'].float().numpy(); l=p['labels'].float().numpy().ravel()
N=f.shape[0]
feats={}
# per-axis extent
ext=f.max(1)-f.min(1)  # [N,3]
feats['ext_x']=ext[:,0]; feats['ext_y']=ext[:,1]; feats['ext_z']=ext[:,2]
feats['bbox_vol']=ext.prod(1)
# std of coords
sd=f.std(1)
feats['std_x']=sd[:,0]; feats['std_y']=sd[:,1]; feats['std_z']=sd[:,2]
# mean distance from centroid
c=f.mean(1,keepdims=True)
r=np.linalg.norm(f-c,axis=2)
feats['mean_r']=r.mean(1); feats['max_r']=r.max(1)
# covariance determinant (spread volume)
for name,v in feats.items():
    cc=np.corrcoef(v,l)[0,1]
    print(f'{name:10s} corr={cc:+.3f}')
print('label mean',l.mean(),'std',l.std())
"

output

ext_x      corr=+0.234
ext_y      corr=+0.143
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
mean_r     corr=+0.482
max_r      corr=-0.587
label mean 0.74757814 std 0.38751265

Interesting. Some hand-crafted features have moderate correlation (std_x 0.635, max_r -0.587). These aren't super strong on their own, but combined they could give good prediction. The relationship is nonlinear and the PointNet should be able to learn it. Let me think about a "volume estimation" feature more carefully. One powerful physics-based feature for mass: the volume of the solid. For a surface point cloud, we can estimate volume via a voxel occupancy approach. Let me try: voxelize the points into a grid, count occupied voxels as a proxy for surface area; but volume needs filling. Actually, let me try a better volume estimate: divergence theorem. Volume = (1/3) ∮ r · n dA. But we don't have normals. We could estimate normals from local PCA, but orientation ambiguity is an issue. Alternatively, a voxel-based solid volume: voxelize surface points, then for each (x,y) column or using flood fill from outside to determine inside/outside. This is complex. Let me instead check: a simple multi-feature linear/MLP regression on hand-crafted features - how well does it do? This gives me a baseline sense. But ultimately I must use the PointNet architecture for the submission. Still, EDA helps me understand the ceiling. Actually, let me reconsider the task. I MUST use the PointNet architecture. So let me focus on training it well. The EDA tells me the signal is there (multiple features correlate), so a well-trained PointNet should capture it. Let me establish the evaluation metrics precisely: - relative L2 over full set: ||pred - true||_2 / ||true||_2 (primary) - MAE: mean |pred - true| - max abs err: max |pred - true| Let me write an evaluation function and a baseline. First, let me run the starter train.py to see the baseline, then improve. Actually, the starter only trains 1 epoch. Let me build a better training pipeline. Key design decisions: 1. **Normalization**: - feat_mean/feat_std: standardize coordinates. The starter uses global mean/std across all points and samples. That's fine. Per-axis std is [0.14, 0.27, 0.09]. Standardizing makes each axis unit variance. Alternatively, we could keep isotropic scaling to preserve shape. Hmm. Standardizing per-axis distorts aspect ratios. But since all samples share the same normalization, it's a fixed linear transform that the network can handle. The requirement is just that feat_mean/feat_std are length-3 and applied as (x - mean)/std. I'll use the training global per-axis mean/std. Actually, anisotropic standardization could distort geometry in a way that loses volume info. But it's applied consistently. The network's first layer is a 1x1 conv (linear) on the 3 coords, which can undo any linear scaling. So feat normalization choice doesn't fundamentally limit the model - it just affects optimization conditioning. I'll use per-axis standardization for good conditioning. Wait, but there's subtlety: the first layer Conv1d(3, 64) followed by BatchNorm. BatchNorm after the conv normalizes anyway. So input scaling mostly affects the very first linear map's effective learning rate. Standardizing is good practice. Fine. 2. **Label normalization**: standardize labels to zero mean unit std. Good for MSE training. label_mean≈0.75, label_std≈0.39. But the primary metric is relative L2, which weights errors relative to the total norm. Relative L2 = sqrt(sum (pred-true)^2) / sqrt(sum true^2). This is like normalized RMSE. MSE training aligns with minimizing sum of squared errors, which aligns with relative L2 numerator. So MSE is a reasonable training loss. But relative L2 doesn't weight by individual true values; it's a global ratio. MAE and max-abs are also important. Consider: should I train on standardized-label MSE, or directly optimize something closer to the metrics? Since relative L2 ∝ sqrt(sum squared errors), MSE on raw labels is the right proxy for the numerator. Training on standardized labels with MSE = MSE on raw scaled by 1/std^2, same optimum. Good. Fo …[truncated]

No leakage, H100 available, and several hand-crafted features correlate with mass (e.g. `std_x` +0.64, `max_r` −0.59), so there's clear learnable signal. Since I must use the fixed PointNet, my levers are normalization, training length/schedule, augmentation, and weight-averaging. Let me set up a solid experimentation harness first.

bash

cd /app && python -c "
from candidate_model import build_model
import json
cfg=json.load(open('task_config.json'))
m=build_model(cfg)
n=sum(p.numel() for p in m.parameters())
print('total params', n)
for name,p in m.named_parameters():
    print(f'{name:28s} {tuple(p.shape)}')
"

output

total params 92289
feature.net.0.weight         (64, 3, 1)
feature.net.0.bias           (64,)
feature.net.1.weight         (64,)
feature.net.1.bias           (64,)
feature.net.3.weight         (64, 64, 1)
feature.net.3.bias           (64,)
feature.net.4.weight         (64,)
feature.net.4.bias           (64,)
feature.net.6.weight         (64, 64, 1)
feature.net.6.bias           (64,)
feature.net.7.weight         (64,)
feature.net.7.bias           (64,)
lift.net.0.weight            (128, 64, 1)
lift.net.0.bias              (128,)
lift.net.1.weight            (128,)
lift.net.1.bias              (128,)
lift.net.3.weight            (256, 128, 1)
lift.net.3.bias              (256,)
lift.net.4.weight            (256,)
lift.net.4.bias              (256,)
head.0.weight                (128, 256)
head.0.bias                  (128,)
head.2.weight                (64, 128)
head.2.bias                  (64,)
head.4.weight                (1, 64)
head.4.bias                  (1,)

92k parameters, 100 training samples. Overfitting risk is real. Regularization via augmentation, weight decay, and weight averaging is key. Let me build a harness. I'll create a file `exp.py` under /app (not outputs) for experiments. Let me write it to: - Load data once. - Provide augmentation functions. - Train with config. - Compute val/test metrics. - Support SWA/EMA. Let me write a metrics helper and a baseline training to measure. I'll make everything GPU-based and fast. Let me establish the baseline with the provided approach first (1 epoch) then longer. Actually, let me write a comprehensive experiment script that I can call with different settings.

Only 92k params but just 100 training samples, so overfitting control is the central challenge. Let me build an experiment harness with proper metrics and augmentation, keeping the model architecture untouched.

str_replace_based_edit_tool

command: create
path: /app/exp.py
file_text: from __future__ import annotations
import json, os, sys, copy, math, time
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F

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

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


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


def metrics(pred, true):
    pred = pred.reshape(-1).double()
    true = true.reshape(-1).double()
    err = pred - true
    rel_l2 = (err.norm() / true.norm()).item()
    mae = err.abs().mean().item()
    maxabs = err.abs().max().item()
    return rel_l2, mae, maxabs


def augment(x, cfg):
    # x: [B,2048,3] on device
    B, N, _ = x.shape
    out = x
    if cfg.get('reflect_x'):
        m = (torch.rand(B, 1, 1, device=x.device) < 0.5).float() * (-2) + 1  # +1 or -1
        out = out * torch.cat([m, torch.ones_like(m), torch.ones_like(m)], dim=2)
    if cfg.get('reflect_z'):
        m = (torch.rand(B, 1, 1, device=x.device) < 0.5).float() * (-2) + 1
        out = out * torch.cat([torch.ones_like(m), torch.ones_like(m), m], dim=2)
    rot = cfg.get('rot_y', 0.0)
    if rot:
        ang = (torch.rand(B, device=x.device) * 2 - 1) * rot
        c, s = torch.cos(ang), torch.sin(ang)
        R = torch.zeros(B, 3, 3, device=x.device)
        R[:, 0, 0] = c; R[:, 0, 2] = s; R[:, 1, 1] = 1; R[:, 2, 0] = -s; R[:, 2, 2] = c
        out = torch.bmm(out, R.transpose(1, 2))
    sc = cfg.get('scale', 0.0)
    if sc:
        s = 1 + (torch.rand(B, 1, 3, device=x.device) * 2 - 1) * sc
        out = out * s
    jit = cfg.get('jitter', 0.0)
    if jit:
        out = out + torch.randn_like(out) * jit
    if cfg.get('resample'):
        idx = torch.randint(0, N, (B, N), device=x.device)
        out = torch.gather(out, 1, idx.unsqueeze(-1).expand(-1, -1, 3))
    return out


def run(cfg, verbose=True, return_model=False, extra_train=None):
    torch.manual_seed(cfg.get('seed', 0))
    tx, ty = load('train')
    vx, vy = load('val')
    if extra_train:
        exs = [load(s) for s in extra_train]
        tx = torch.cat([tx] + [e[0] for e in exs], 0)
        ty = torch.cat([ty] + [e[1] for e in exs], 0)
    fmean = tx.mean((0, 1))
    fstd = tx.std((0, 1)).clamp_min(1e-6)
    lmean = ty.mean(0)
    lstd = ty.std(0).clamp_min(1e-6)

    def nx(x):
        return (x - fmean) / fstd

    txn = nx(tx).to(DEV); tyn = ((ty - lmean) / lstd).to(DEV)
    vxn = nx(vx).to(DEV)
    model = build_model(CFG).to(DEV)
    opt = torch.optim.AdamW(model.parameters(), lr=cfg['lr'], weight_decay=cfg['wd'])
    epochs = cfg['epochs']; bs = cfg['bs']
    n = txn.shape[0]
    warmup = cfg.get('warmup', 0)
    sched = []
    for e in range(epochs):
        if e < warmup:
            sched.append(cfg['lr'] * (e + 1) / max(1, warmup))
        else:
            t = (e - warmup) / max(1, epochs - warmup)
            sched.append(cfg.get('min_lr', 0.0) + 0.5 * (cfg['lr'] - cfg.get('min_lr', 0.0)) * (1 + math.cos(math.pi * t)))

    swa_model = None; swa_n = 0
    swa_start = cfg.get('swa_start', epochs + 1)
    best = (1e9, None)
    for e in range(epochs):
        model.train()
        for g in opt.param_groups:
            g['lr'] = sched[e]
        perm = torch.randperm(n, device=DEV)
        for i in range(0, n, bs):
            idx = perm[i:i + bs]
            xb = augment(txn[idx], cfg)
            yb = tyn[idx]
            opt.zero_grad(set_to_none=True)
            pred = model(xb)
            loss = F.mse_loss(pred, yb)
            loss.backward()
            opt.step()
        if e + 1 >= swa_start:
            if swa_model is None:
                swa_model = copy.deepcopy(model.state_dict())
                swa_n = 1
            else:
                swa_n += 1
                sd = model.state_dict()
         
…[truncated]

output

New file created successfully at /app/exp.py

bash

cd /app && time python exp.py

output

baseline (no aug):
  plain: relL2=0.2389 mae=0.1609 max=0.4800
[stderr]
real	0m11.082s
user	0m9.090s
sys	0m1.700s

Baseline with 150 epochs, no augmentation: relL2=0.2389, mae=0.1609, max=0.4800. Training is fast (11s). Let me now experiment with augmentation and other settings. Let me create a quick sweep script that tests different augmentation configs and reports val metrics. I'll run several seeds to reduce noise since val is small (32 samples). Let me test: 1. reflect_x 2. jitter 3. resample 4. scale 5. rot_y 6. combinations 7. SWA Let me write a sweep. I'll average over 3 seeds.

Baseline (150 epochs, no augmentation) gives val relL2≈0.24. Training is fast (~11s), so I can sweep augmentations and settings. Since val has only 32 samples, I'll average over multiple seeds to reduce noise.

bash

cd /app && cat > sweep.py << 'EOF'
from exp import run
import numpy as np

def avg(cfgbase, seeds=(0,1,2,3)):
    accP=[]; accS=[]
    for s in seeds:
        c=dict(cfgbase); c['seed']=s
        r=run(c, verbose=False)
        accP.append(r['plain'])
        if 'swa' in r: accS.append(r['swa'])
    P=np.array(accP).mean(0)
    out=f"plain relL2={P[0]:.4f} mae={P[1]:.4f} max={P[2]:.4f}"
    if accS:
        S=np.array(accS).mean(0)
        out+=f" || swa relL2={S[0]:.4f} mae={S[1]:.4f} max={S[2]:.4f}"
    return out

base=dict(lr=2e-3, wd=1e-4, epochs=150, bs=16, warmup=10, min_lr=1e-5)
tests={
 'no_aug': {},
 'jitter0.01': {'jitter':0.01},
 'jitter0.02': {'jitter':0.02},
 'resample': {'resample':True},
 'reflect_x': {'reflect_x':True},
 'reflect_xz': {'reflect_x':True,'reflect_z':True},
 'scale0.05': {'scale':0.05},
 'roty0.1': {'rot_y':0.1},
 'jit+resample': {'jitter':0.01,'resample':True},
 'jit+refx': {'jitter':0.01,'reflect_x':True},
 'jit+resamp+refx': {'jitter':0.01,'resample':True,'reflect_x':True},
}
for name,aug in tests.items():
    c=dict(base); c.update(aug)
    print(f"{name:22s} {avg(c)}")
EOF
time python sweep.py

output

no_aug                 plain relL2=0.2342 mae=0.1604 max=0.4711
jitter0.01             plain relL2=0.2157 mae=0.1478 max=0.4156
jitter0.02             plain relL2=0.2188 mae=0.1444 max=0.4837
resample               plain relL2=0.2237 mae=0.1507 max=0.4966
reflect_x              plain relL2=0.2484 mae=0.1665 max=0.5782
reflect_xz             plain relL2=0.2765 mae=0.1986 max=0.5283
scale0.05              plain relL2=0.2296 mae=0.1532 max=0.4922
roty0.1                plain relL2=0.2270 mae=0.1514 max=0.4709
jit+resample           plain relL2=0.2197 mae=0.1488 max=0.4491
jit+refx               plain relL2=0.2435 mae=0.1678 max=0.4819
jit+resamp+refx        plain relL2=0.2472 mae=0.1682 max=0.5087
[stderr]
real	2m31.021s
user	2m24.370s
sys	0m2.490s

Observations: - jitter 0.01 is the best single augmentation: relL2=0.2157, mae=0.1478, max=0.4156. - reflect_x HURTS - so the brackets are NOT symmetric in x (or the orientation matters). Interesting. So reflection is not a valid augmentation here. Good to know. - resample slightly helps or neutral. - scale, rot_y don't help much. So jitter is the key augmentation. Let me focus there. Let me now test: - SWA with jitter. - Different epoch counts, LR. - Weight decay values. The reflect hurting is surprising but tells me the coordinate frame is meaningful (canonical pose). So no reflection/rotation. Let me now test SWA and longer training with jitter. Let me also test bigger models... no, architecture fixed. Let me tune: epochs, lr, wd, jitter level, and SWA. Let me set swa_start and see. Let me also consider that val is only 32 samples so differences of 0.01 in relL2 may be noise. I'll use more seeds for final decisions. Let me test SWA configs and training length.

Key findings: **jitter (σ≈0.01) is the most effective augmentation**, while reflection actually *hurts* , meaning the brackets are in a meaningful canonical pose (no symmetry to exploit). Let me now tune training length, learning rate, weight decay, and test SWA weight-averaging on top of jitter.

bash

cd /app && cat > sweep2.py << 'EOF'
from exp import run
import numpy as np
def avg(cfgbase, seeds=(0,1,2,3,4)):
    accP=[]; accS=[]
    for s in seeds:
        c=dict(cfgbase); c['seed']=s
        r=run(c, verbose=False)
        accP.append(r['plain'])
        if 'swa' in r: accS.append(r['swa'])
    P=np.array(accP).mean(0)
    out=f"plain relL2={P[0]:.4f} mae={P[1]:.4f} max={P[2]:.4f}"
    if accS:
        S=np.array(accS).mean(0)
        out+=f" || SWA relL2={S[0]:.4f} mae={S[1]:.4f} max={S[2]:.4f}"
    return out
base=dict(lr=2e-3, wd=1e-4, epochs=150, bs=16, warmup=10, min_lr=1e-5, jitter=0.01)
tests={
 'base150': dict(base),
 'ep250': {**base,'epochs':250},
 'ep400': {**base,'epochs':400},
 'lr3e-3': {**base,'lr':3e-3},
 'lr1e-3': {**base,'lr':1e-3},
 'wd5e-4': {**base,'wd':5e-4},
 'wd1e-3': {**base,'wd':1e-3},
 'bs8': {**base,'bs':8},
 'bs32': {**base,'bs':32},
 'swa_ep250': {**base,'epochs':250,'swa_start':150,'bn_passes':5},
 'swa_ep400': {**base,'epochs':400,'swa_start':200,'bn_passes':5},
}
for name,c in tests.items():
    print(f"{name:18s} {avg(c)}")
EOF
time python sweep2.py

output

base150            plain relL2=0.2128 mae=0.1439 max=0.4744
ep250              plain relL2=0.2283 mae=0.1504 max=0.4840
ep400              plain relL2=0.2271 mae=0.1466 max=0.5158
lr3e-3             plain relL2=0.2345 mae=0.1617 max=0.4602
lr1e-3             plain relL2=0.2152 mae=0.1452 max=0.4259
wd5e-4             plain relL2=0.2332 mae=0.1530 max=0.4590
wd1e-3             plain relL2=0.2193 mae=0.1455 max=0.4686
bs8                plain relL2=0.2254 mae=0.1486 max=0.4899
bs32               plain relL2=0.2515 mae=0.1733 max=0.5041
swa_ep250          plain relL2=0.2218 mae=0.1490 max=0.4403 || SWA relL2=0.2227 mae=0.1492 max=0.4520
swa_ep400          plain relL2=0.2190 mae=0.1453 max=0.4938 || SWA relL2=0.2182 mae=0.1432 max=0.4927
[stderr]
real	4m19.940s
user	4m10.810s
sys	0m3.220s

Results are noisy and hovering around 0.21-0.23 relL2. SWA doesn't dramatically help. The model seems to plateau around 0.21-0.22 relL2. Longer training doesn't help (overfitting). lr1e-3 and base150 are among the best. The val set is small (32), so these differences are noisy. Let me think about whether I can do fundamentally better. Current best ~0.21 relL2. Is that good? The label std/mean ratio: std=0.39, mean=0.75. A constant predictor (predict mean) would have relL2 = std-ish / rms. RMS of true = sqrt(mean^2+std^2)=sqrt(0.56+0.15)=sqrt(0.71)=0.84. Relative L2 of predicting the mean = ||true-mean||/||true|| = std*sqrt(N)/ (rms*sqrt(N)) = std/rms = 0.39/0.84 = 0.46. Wait that's for predicting the global mean. Hmm, actually for val, predicting constant mean gives relL2 = std_val/rms_val. Let me compute: val std=0.41, mean=0.76, rms=sqrt(0.58+0.17)=0.866, so 0.41/0.866=0.47. So our 0.21 is much better than constant (0.47). Good, but can we get lower? Let me think about the ceiling. The relationship geometry->mass should be quite learnable (mass is basically volume × density). With a surface point cloud of 2048 points, estimating volume precisely is hard but doable. A relL2 of ~0.1 or below might be achievable with a better approach. But we're constrained to this PointNet. Let me think about what limits PointNet here: 1. PointNet uses max-pooling over per-point features. It captures the "silhouette"/extent well but not volume directly. For volume estimation it's limited. 2. The input is just raw xyz. Adding engineered per-point features could help a lot, BUT the architecture's first layer is Conv1d(3,...), so input must be [B,2048,3]. We can't add extra channels. And the task says input is [2048,3]. So we're limited to 3D coords. Wait - but we CAN transform the 3 input coordinates. The feat_mean/feat_std normalization is required and applied at inference. But it's a fixed per-axis affine. We can't add channels. However, I realize there might be a subtle but powerful idea: the feat_mean/feat_std are applied at inference by the grader. So the model always receives standardized coords. That's fixed. Let me reconsider the normalization. The current approach standardizes each axis independently: x/0.14, y/0.27, z/0.09. This blows up the z axis relative to others (divides by smaller number => z gets larger scale). Actually dividing by std makes each axis unit std. This distorts aspect ratios which carry volume info. But the first conv is linear so it can re-weight. Probably fine. Let me reconsider: maybe isotropic normalization (divide all axes by the same scalar) preserves shape better and the model can learn volume better? Let me test feat normalization variants. But the harness currently hardcodes per-axis std. Let me add an option. Actually, more importantly: let me reconsider the core difficulty. The val metric is noisy with 32 samples. Let me evaluate on train+val+public_test combined via cross-validation to get a more robust estimate of generalization, and to pick hyperparameters more reliably. Actually, a cleaner approach: pool all 148 labeled samples and do k-fold CV to estimate generalization robustly. Then train final model on all 148. This uses data much better. Let me restructure. But wait, the splits are given presumably because the grader trains... no, the grader just loads my checkpoint and evaluates on a hidden set. I control training entirely. So I can use all 148 labeled samples however I want. k-fold CV for hyperparameter selection, then train final on everything. Let me implement a pooled k-fold CV harness. This gives more reliable metric estimates (148 samples, e.g., 5-fold => test on ~30 each, all samples used as test once). Let me refactor to pool all data and do CV. This is more robust than the tiny val set. Let me rewrite exp to support pooled CV. Actually, let me reconsider the whole approach to push quality: Idea: Since mass ≈ density × volume, and we have only surface points, perhaps I s …[truncated]

Results plateau around relL2≈0.21. The 32-sample val set is noisy, so hyperparameter differences are hard to trust. To get **robust** estimates and use data better, I'll pool all 148 labeled samples and do k-fold cross-validation. This also mirrors the final plan: train on all available data for the hidden test. Let me also make normalization and loss configurable.

str_replace_based_edit_tool

command: create
path: /app/cv.py
file_text: from __future__ import annotations
import json, sys, copy, math
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F

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

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


def load_all():
    xs, ys = [], []
    for s in ['train', 'val', 'public_test']:
        p = torch.load(APP / 'data' / s / 'samples.pt', weights_only=True)
        xs.append(p['features'].float()); ys.append(p['labels'].float())
    return torch.cat(xs, 0), torch.cat(ys, 0)


ALLX, ALLY = load_all()


def make_folds(n, k, seed=0):
    g = torch.Generator().manual_seed(seed)
    perm = torch.randperm(n, generator=g)
    return [perm[i::k] for i in range(k)]


def train_one(txn, tyn, cfg, lstd, lmean):
    torch.manual_seed(cfg.get('seed', 0))
    model = build_model(CFG).to(DEV)
    opt = torch.optim.AdamW(model.parameters(), lr=cfg['lr'], weight_decay=cfg['wd'])
    epochs = cfg['epochs']; bs = cfg['bs']; n = txn.shape[0]
    warmup = cfg.get('warmup', 0)
    sched = []
    for e in range(epochs):
        if e < warmup:
            sched.append(cfg['lr'] * (e + 1) / max(1, warmup))
        else:
            t = (e - warmup) / max(1, epochs - warmup)
            sched.append(cfg.get('min_lr', 0.0) + 0.5 * (cfg['lr'] - cfg.get('min_lr', 0.0)) * (1 + math.cos(math.pi * t)))
    loss_type = cfg.get('loss', 'mse')
    huber_d = cfg.get('huber_delta', 1.0)
    swa_sd = None; swa_n = 0
    swa_start = cfg.get('swa_start', epochs + 1)
    for e in range(epochs):
        model.train()
        for g in opt.param_groups:
            g['lr'] = sched[e]
        perm = torch.randperm(n, device=DEV)
        for i in range(0, n, bs):
            idx = perm[i:i + bs]
            xb = augment(txn[idx], cfg); yb = tyn[idx]
            opt.zero_grad(set_to_none=True)
            pred = model(xb)
            if loss_type == 'mse':
                loss = F.mse_loss(pred, yb)
            elif loss_type == 'huber':
                loss = F.huber_loss(pred, yb, delta=huber_d)
            elif loss_type == 'mse+mae':
                loss = F.mse_loss(pred, yb) + cfg.get('mae_w', 0.1) * F.l1_loss(pred, yb)
            loss.backward(); opt.step()
        if e + 1 >= swa_start:
            sd = model.state_dict()
            if swa_sd is None:
                swa_sd = copy.deepcopy(sd); swa_n = 1
            else:
                swa_n += 1
                for k in swa_sd:
                    if swa_sd[k].dtype.is_floating_point:
                        swa_sd[k].mul_(1 - 1 / swa_n).add_(sd[k], alpha=1 / swa_n)
                    else:
                        swa_sd[k] = sd[k]
    if swa_sd is not None:
        model.load_state_dict(swa_sd)
        model.train()
        with torch.no_grad():
            for _ in range(cfg.get('bn_passes', 4)):
                perm = torch.randperm(n, device=DEV)
                for i in range(0, n, bs):
                    model(augment(txn[perm[i:i + bs]], cfg))
    model.eval()
    return model


def cv(cfg, k=5, seeds=(0,), norm='peraxis', verbose=False):
    n = ALLX.shape[0]
    all_pred = []; all_true = []
    per_fold = []
    for seed in seeds:
        folds = make_folds(n, k, seed=seed)
        for fi in range(k):
            te_idx = folds[fi]
            tr_idx = torch.cat([folds[j] for j in range(k) if j != fi])
            tx = ALLX[tr_idx]; ty = ALLY[tr_idx]
            ex = ALLX[te_idx]; ey = ALLY[te_idx]
            if norm == 'peraxis':
                fmean = tx.mean((0, 1)); fstd = tx.std((0, 1)).clamp_min(1e-6)
            elif norm == 'iso':
                fmean = tx.mean((0, 1)); fstd = tx.std((0, 1)).mean().repeat(3).clamp_min(1e-6)
            elif norm == 'none':
                fmean = torch.zeros(3); fstd = torch.ones(3)
            lmean = ty.mean(0); lstd = ty.s
…[truncated]

output

New file created successfully at /app/cv.py

bash

cd /app && time python cv.py

output

CV pooled estimate (k=5, 2 seeds):
  pooled: relL2=0.2609 mae=0.1664 max=0.9039
per-fold max range: 0.4678337574005127 0.9038679599761963
[stderr]
real	0m42.703s
user	0m39.570s
sys	0m1.910s

The pooled CV gives relL2=0.26, max=0.90. The max abs error is large (0.90)! This means there are some outlier samples that are very hard to predict. The worst-case metric is concerning. The pooled CV (0.26) is worse than the val-only estimate (0.21) , likely because CV tests on all samples including hard ones, giving a more honest (pessimistic) estimate. Also the pooled relL2 now includes harder folds. The max abs error of 0.90 is a big concern for the worst-case metric. Let me investigate which samples are hard. Possibly the highest-mass sample (2.2 kg) is an outlier that the model always underpredicts. Let me look at the label distribution more carefully and identify outliers.

Pooled CV gives a more honest (and worse) picture: relL2≈0.26 and a **worst-case error of 0.90** , some samples are very hard. The worst-case metric is one of my objectives, so let me investigate which samples drive it, especially at the extremes of the mass range.

bash

cd /app && python -c "
import torch, numpy as np
from cv import ALLY
y=ALLY.numpy().ravel()
print('n',len(y))
print('sorted top10:', np.sort(y)[-10:])
print('sorted bot10:', np.sort(y)[:10])
import numpy as np
print('quantiles', np.percentile(y,[0,5,25,50,75,95,100]))
# how many above 1.5
print('frac>1.5', (y>1.5).mean(), 'count', (y>1.5).sum())
print('frac>1.2', (y>1.2).mean(), 'count', (y>1.2).sum())
"

output

n 148
sorted top10: [1.4492188 1.5839844 1.5839844 1.5839844 1.5917969 1.6191406 1.6337891
 1.6396484 2.0722656 2.21875  ]
sorted bot10: [0.19836426 0.20776367 0.2446289  0.2553711  0.26733398 0.2861328
 0.28710938 0.29370117 0.30322266 0.3046875 ]
quantiles [0.19836426 0.29703369 0.46813965 0.65307617 0.94067383 1.58398438
 2.21875   ]
frac>1.5 0.060810810810810814 count 9
frac>1.2 0.12837837837837837 count 19

The distribution is right-skewed. There are a few high-mass samples (2.2, 2.07) that are rare (only 9 samples > 1.5, and the top two are 2.07 and 2.22 which are well above the rest). These outliers are hard to predict and drive the max-abs error and the relL2 (since relL2 is dominated by large true values). This is the classic challenge: rare high-mass samples. The model underpredicts them because training data has few examples. Strategies to handle the skew: 1. Weighted loss: weight samples by mass (or mass^2) so large-mass samples contribute more. Since relL2 is dominated by large true values, weighting by true^2 aligns the training loss with relL2. Actually relL2^2 = sum(err^2)/sum(true^2). The numerator sum(err^2) is plain MSE (unweighted). So to minimize relL2, we minimize sum of squared errors , unweighted MSE is already correct for relL2! The large-mass samples naturally have larger errors in absolute terms, but MSE treats all equally. To reduce relL2, reducing error on any sample helps proportionally to that sample's squared error. Since high-mass samples tend to have the largest errors, focusing on them helps most. But for MAE and max-abs, we also want to nail the extremes. Weighting high-mass samples more would help max-abs and the relL2 but might increase error on low-mass samples (raising MAE slightly). 2. The fundamental issue: with only ~140 training samples and a few extreme outliers, the model can't learn the extremes well. Let me think differently. Let me check: is the model systematically underpredicting high masses (regression to mean)? That's typical. Let me look at predictions vs true in CV to see the pattern. Let me add a diagnostic to cv.py to return predictions and plot residuals vs true.

The mass distribution is right-skewed with rare high-mass outliers (top values 2.07, 2.22 well above the rest). These likely drive both the worst-case error and relL2. Let me check whether the model systematically underpredicts high-mass brackets (regression-to-mean), which is the typical failure mode.

bash

cd /app && python -c "
import torch, numpy as np
from cv import cv, ALLX, ALLY, make_folds
# get pooled predictions aligned to indices
import cv as CV
base = dict(lr=2e-3, wd=1e-4, epochs=150, bs=16, warmup=10, min_lr=1e-5, jitter=0.01)
# monkey: replicate cv but keep indices
n=ALLX.shape[0]; k=5
from candidate_model import build_model
preds=np.zeros(n); trues=ALLY.numpy().ravel()
folds=make_folds(n,k,seed=0)
for fi in range(k):
    te=folds[fi]; tr=torch.cat([folds[j] for j in range(k) if j!=fi])
    tx,ty=ALLX[tr],ALLY[tr]; ex,ey=ALLX[te],ALLY[te]
    fmean=tx.mean((0,1)); fstd=tx.std((0,1)).clamp_min(1e-6)
    lmean=ty.mean(0); lstd=ty.std(0).clamp_min(1e-6)
    txn=((tx-fmean)/fstd).to(CV.DEV); tyn=((ty-lmean)/lstd).to(CV.DEV); exn=((ex-fmean)/fstd).to(CV.DEV)
    c=dict(base); c['seed']=fi
    m=CV.train_one(txn,tyn,c,lstd,lmean)
    with torch.no_grad():
        p=m(exn).cpu()*lstd+lmean
    preds[te.numpy()]=p.numpy().ravel()
res=preds-trues
order=np.argsort(trues)
print('true  pred  err  (sorted by true, showing extremes)')
for i in list(order[:6])+list(order[-10:]):
    print(f'{trues[i]:.3f} {preds[i]:.3f} {res[i]:+.3f}')
print('corr(true,pred)', np.corrcoef(trues,preds)[0,1])
# bias in high region
hi=trues>1.2
print('mean err high(>1.2):', res[hi].mean(), 'mean err low:', res[~hi].mean())
"

output

true  pred  err  (sorted by true, showing extremes)
0.198 0.308 +0.109
0.208 0.299 +0.091
0.245 0.297 +0.052
0.255 0.385 +0.130
0.267 0.469 +0.201
0.286 0.273 -0.013
1.449 1.391 -0.058
1.584 1.622 +0.038
1.584 1.688 +0.104
1.584 1.618 +0.034
1.592 1.225 -0.367
1.619 1.277 -0.342
1.634 1.359 -0.275
1.640 1.187 -0.453
2.072 2.175 +0.102
2.219 1.751 -0.468
corr(true,pred) 0.8317904243963107
mean err high(>1.2): -0.15206616175802132 mean err low: 0.034735054008720456

Clear pattern: - High-mass samples are systematically UNDER-predicted (mean err high = -0.15). - Low-mass samples are slightly OVER-predicted (mean err low = +0.035). - This is classic regression-to-the-mean / shrinkage. The worst errors are on high-mass samples (e.g., true=2.219 predicted 1.751, err -0.468; true=1.640 predicted 1.187, err -0.453). Correlation is 0.83, so the model captures the trend but shrinks toward the mean. How to fix regression-to-mean / shrinkage: 1. **Reduce shrinkage from weight decay + MSE**: Shrinkage partly comes from the model not being confident at extremes. 2. **Weighted loss**: upweight high-mass samples so the model doesn't shrink them. This directly addresses the bias. I can weight samples by true mass (or a function). Let me try weighting by mass or by mass^2. 3. **Bias correction / calibration**: Fit a linear calibration pred_cal = a*pred + b on held-out (or in-sample) to correct the slope shrinkage. Since corr is 0.83 but slope <1, a linear rescale could significantly reduce error. But I must embed this in the single model. I could fold the calibration into... hmm, the output is head(x). I could adjust label_mean/label_std? No, calibration is a post-hoc affine on predictions: pred_phys = model_out * lstd + lmean. If I want pred_cal = a*pred_phys + b, I can set label_std' = a*lstd and label_mean' = a*lmean + b. Because pred_cal = a*(out*lstd+lmean)+b = out*(a*lstd) + (a*lmean+b). So I can absorb a linear calibration into label_mean/label_std! That's a clean trick. But wait , calibration to undo shrinkage: if the model underpredicts high and overpredicts low (slope < 1 in pred vs true), then to correct I need to EXPAND predictions around the mean, i.e., a>1. I can fit a,b by regressing true on pred (pred as x). Let me see: we want pred_cal ≈ true. Regress true = a*pred + b. Given corr 0.83 and the shrinkage, a>1 likely. However, this calibration is a global linear fit. It should help relL2 and MAE and especially the extremes. Let me test this in CV (fit calibration on training-fold predictions, apply to test fold). Actually, better: the model trained on train fold, then I get predictions on train fold (in-sample, but biased since model fits train). Hmm, in-sample train predictions would show less shrinkage than test. Better to fit calibration using a proper held-out or using the same shrinkage present at test time. Actually the cleanest: use out-of-fold predictions (CV) to fit ONE global calibration (a,b), which I then apply to the final model. The final model is trained on all data; its shrinkage behavior should be similar to CV models. The calibration estimated from OOF predictions should transfer. Let me test. 4. **Better: weighted training to reduce shrinkage at the source**, combined with calibration. Let me test weighting first. Let me think about which is more robust. Weighting changes training; calibration is post-hoc. Both can help. Let me try weighting by mass in the loss. For weighting, the loss becomes sum w_i (pred_i - true_i)^2. To reduce high-mass underprediction, set w_i increasing with true_i. E.g., w_i = true_i or true_i^2. But this could overfit to the few high samples. Let me test a few. Let me also reconsider: the systematic low-end overprediction (true 0.267 -> pred 0.469, err +0.20) is also bad for max-abs and MAE. So both ends are shrunk. A calibration expanding around the mean fixes both ends simultaneously. This seems very promising. Let me test calibration in CV. I'll add an option to cv() to fit a linear calibration on OOF predictions and report metrics before/after. But careful to fit calibration without leaking test labels. Approach: nested , for each fold, fit calibration on the OTHER folds' OOF predictions? That's complex. Simpler and valid: do the full CV to get OOF preds for all samples, fit a,b on all OOF preds vs true (this is one global transform), and report metrics with calibration. Since a,b are just 2 parameters fit on 148 points …[truncated]

Clear diagnosis: the model suffers from **regression-to-the-mean shrinkage** , high-mass brackets are systematically under-predicted (mean error −0.15) and low-mass ones over-predicted (+0.03), with correlation 0.83 but slope < 1. This drives all three error metrics. Two fixes I can apply within the fixed-architecture constraint: 1. **Loss weighting** to reduce shrinkage at the source. 2. **Linear calibration** `pred→a·pred+b` to undo shrinkage , and elegantly, I can fold it into the required `label_mean`/`label_std` since `out·(a·lstd)+(a·lmean+b)` is an exact re-parameterization. Let me test both in CV.

bash

cd /app && cat > calib.py << 'EOF'
import torch, numpy as np, sys
sys.path.insert(0,'/app')
import cv as CV
from cv import ALLX, ALLY, make_folds, train_one, DEV
from exp import metrics

def oof_preds(base, k=5, seed=0, weight=None):
    n=ALLX.shape[0]; preds=np.zeros(n); trues=ALLY.numpy().ravel()
    folds=make_folds(n,k,seed=seed)
    for fi in range(k):
        te=folds[fi]; tr=torch.cat([folds[j] for j in range(k) if j!=fi])
        tx,ty=ALLX[tr],ALLY[tr]; ex,ey=ALLX[te],ALLY[te]
        fmean=tx.mean((0,1)); fstd=tx.std((0,1)).clamp_min(1e-6)
        lmean=ty.mean(0); lstd=ty.std(0).clamp_min(1e-6)
        txn=((tx-fmean)/fstd).to(DEV); tyn=((ty-lmean)/lstd).to(DEV); exn=((ex-fmean)/fstd).to(DEV)
        c=dict(base); c['seed']=seed*100+fi
        if weight is not None:
            c['sample_w']=weight(ty).to(DEV)
        m=train_one(txn,tyn,c,lstd,lmean)
        with torch.no_grad():
            p=m(exn).cpu()*lstd+lmean
        preds[te.numpy()]=p.numpy().ravel()
    return preds, trues

def fit_calib(pred, true):
    # least squares true = a*pred + b
    A=np.vstack([pred, np.ones_like(pred)]).T
    a,b=np.linalg.lstsq(A, true, rcond=None)[0]
    return a,b

if __name__=='__main__':
    base = dict(lr=2e-3, wd=1e-4, epochs=150, bs=16, warmup=10, min_lr=1e-5, jitter=0.01)
    # seed 0 for fit, seed 1 for eval to avoid optimism
    p0,t0=oof_preds(base,seed=0)
    p1,t1=oof_preds(base,seed=1)
    print('raw seed0:', metrics(torch.tensor(p0),torch.tensor(t0)))
    a,b=fit_calib(p0,t0)
    print('calib a,b=',a,b)
    # apply calib fit on seed0 to seed1 preds (honest)
    pc1=a*p1+b
    print('raw seed1:', metrics(torch.tensor(p1),torch.tensor(t1)))
    print('calib(seed0 params) on seed1:', metrics(torch.tensor(pc1),torch.tensor(t1)))
    # also self-calib seed0
    pc0=a*p0+b
    print('calib on seed0(self):', metrics(torch.tensor(pc0),torch.tensor(t0)))
EOF
time python calib.py

output

raw seed0: (0.26531928250339437, 0.1714589690437188, 0.7539219856262207)
calib a,b= 0.9073864639410161 0.059109049165287
raw seed1: (0.260809830284241, 0.16657257905683, 0.8223996162414551)
calib(seed0 params) on seed1: (0.2598447438883666, 0.16622640488140003, 0.7571372598311179)
calib on seed0(self): (0.2622102692001261, 0.17169020494801512, 0.6879922594892622)
[stderr]
real	0m43.397s
user	0m40.700s
sys	0m1.770s

Interesting. The global linear calibration gives a=0.907, b=0.059. That's a SHRINKAGE (a<1), not expansion! That contradicts my expectation. Wait , the least-squares fit of true = a*pred+b minimizes error; since pred has noise, the regression of true on pred gives a slope that can be <1 (attenuation). But the residual pattern showed underprediction at high end... Let me reconsider. The mean error at high end was -0.15 (underpredict), low end +0.03 (overpredict). A fit true=a*pred+b: if model underpredicts high and overpredicts low, then pred range is compressed relative to true, so to map pred->true we need a>1 (expand). But lstsq gave a=0.907 (<1). The discrepancy: lstsq minimizes squared error treating pred as the regressor. When pred has lots of noise (not just shrinkage), the optimal a can be <1 even if there's compression, because it's balancing. Actually the noise in predictions causes attenuation toward a<1. Hmm. But the calibration barely helped (0.2608 -> 0.2598 on seed1). So global linear calibration is NOT the win I hoped. The improvement is marginal (relL2 0.2608->0.2598, max 0.822->0.757). The max-abs improved a bit. But overall small. So the shrinkage isn't simply linear-correctable because predictions are noisy. The real issue is prediction noise/variance, not just a global slope. The model's predictions have substantial noise (corr 0.83). So to really improve, I need to reduce prediction variance / improve the model's accuracy. That means: 1. Better training (more stable, ensemble-like via SWA). 2. Reducing variance via ensembling , but we can only submit one model. 3. Better features / representation. Wait , reducing prediction variance. One model is noisy. SWA/weight averaging reduces variance. But earlier SWA didn't help much. Let me reconsider. Actually, the biggest lever might be to reduce the noise in predictions. Since the val set and CV show high variance, and the model is small, maybe the issue is that PointNet with max-pooling loses too much info. Let me reconsider the input normalization. Let me test 'iso' (isotropic) vs 'peraxis' vs 'none' in CV. Isotropic preserves shape aspect ratios which might matter for volume. Also let me reconsider: maybe the model needs MORE capacity in how it uses points, but we can't change architecture. Let me step back and think about the theoretical best. The hand-crafted features had correlations up to 0.63. A good nonlinear model on geometry should beat 0.83 correlation. Let me check what correlation a gradient-boosted model on hand-crafted features achieves , this tells me the ceiling and whether PointNet is underperforming. Let me quickly test: extract a rich set of geometric features (extents, std, percentiles, moments, PCA eigenvalues, pairwise distance stats, voxel occupancy counts) and fit a simple model (e.g., sklearn GBM or ridge) with CV. If that gets relL2 ~0.1, then PointNet is underperforming and I should think harder. If it also gets ~0.25, then the problem is just hard with this data. Let me do that EDA. Check if sklearn is available.

The global linear calibration barely helps (relL2 0.261→0.260) , the fitted slope is actually 0.91, meaning the dominant issue is **prediction noise/variance**, not a simple correctable slope. So I need to genuinely improve model accuracy and reduce variance. Let me first establish a reference ceiling: how well can a classic model on rich hand-crafted geometric features do under the same CV? This tells me whether PointNet is underperforming or the task is just inherently hard with this data.

bash

cd /app && python -c "import sklearn; print('sklearn', sklearn.__version__)" 2>&1 | head -1

output

Traceback (most recent call last):

sklearn not available. Let me check what's available and possibly do ridge/GBM manually or with numpy. Let me just build a feature extractor and use a simple numpy ridge regression and a random forest via a quick implementation... Actually, let me check available packages.

bash

cd /app && pip list 2>/dev/null | grep -iE "sklearn|scikit|xgboost|lightgbm|scipy|numpy|pandas"

output

numpy                     2.1.2

Only numpy. No internet to install. Let me build a rich feature extractor and use a numpy-based model. I can implement a simple gradient boosting or kernel ridge. Actually, let me implement a quick k-NN regressor and ridge regression with polynomial features using numpy. That gives a ceiling estimate. Let me extract features: - extents (3), bbox vol - std per axis (3) - PCA eigenvalues of covariance (3) -> shape descriptors - mean/max/min radial distance from centroid - percentiles of each coordinate (e.g., 10,25,50,75,90) - voxel occupancy: voxelize into e.g. 16^3 grid, count occupied voxels (proxy for surface area), and also a "filled volume" estimate via counting occupied columns. - fraction of points near bbox faces etc. Then fit ridge regression on standardized features with CV, and also k-NN. Let me see the achievable relL2. Let me build this. The voxel-based solid volume estimate is probably the strongest feature for mass. Let me implement: voxelize at resolution R. For solid volume, I can do: for each z-slice, fill the interior? That's complex. Simpler: occupancy count at high res ~ surface area; at low res ~ volume-ish. Let me include multiple resolutions. Actually, a cleaner solid-volume proxy: for a convex-ish shape, voxel occupancy at coarse resolution approximates volume because coarse voxels get filled if any surface point is inside; interior coarse voxels have no surface points though (surface only). Hmm, surface points only lie on the boundary, so interior voxels are empty. So occupancy count ≈ surface area (number of boundary voxels), not volume. To estimate enclosed volume from surface points, I'd need to fill. One approach: for each (x,y) pillar, the z-extent between min and max surface point z approximates solid thickness IF convex in z. Sum over pillars ≈ volume. Let me compute: discretize (x,y) into grid, for each cell with points, volume contribution = (max_z - min_z)*cell_area. This is a "z-convex" volume estimate. Do similarly for x and y directions and average. This is a decent volume proxy. Let me include these. Let me build the feature extractor and test ridge + kNN.

Only numpy is available, so I'll implement a feature-based reference myself. A key physics-motivated feature for mass is **enclosed volume**, which I can approximate from surface points via per-pillar thickness (max−min along an axis, summed over an (x,y) grid). Let me build a rich feature set and test ridge + kNN regressors under the same pooled CV to estimate the achievable ceiling.

bash

cd /app && cat > feats.py << 'EOF'
import numpy as np, torch, sys
sys.path.insert(0,'/app')
from cv import ALLX, ALLY, make_folds
from exp import metrics

X=ALLX.numpy(); Y=ALLY.numpy().ravel()

def pillar_vol(pc, axis, R):
    # project onto plane perpendicular to axis, grid RxR, sum (max-min) along axis
    other=[i for i in range(3) if i!=axis]
    a=pc[:,other[0]]; b=pc[:,other[1]]; c=pc[:,axis]
    lo=-0.5; hi=0.5; cell=(hi-lo)/R
    ia=np.clip(((a-lo)/cell).astype(int),0,R-1)
    ib=np.clip(((b-lo)/cell).astype(int),0,R-1)
    key=ia*R+ib
    vol=0.0
    order=np.argsort(key)
    key_s=key[order]; c_s=c[order]
    uniq,start=np.unique(key_s,return_index=True)
    ends=np.append(start[1:],len(key_s))
    for s,e in zip(start,ends):
        seg=c_s[s:e]
        vol+=(seg.max()-seg.min())
    return vol*cell*cell

def occ(pc,R):
    lo=-0.5;cell=1.0/R
    idx=np.clip(((pc-lo)/cell).astype(int),0,R-1)
    k=idx[:,0]*R*R+idx[:,1]*R+idx[:,2]
    return len(np.unique(k))

def features(pc):
    f=[]
    ext=pc.max(0)-pc.min(0); f+=list(ext); f.append(ext.prod())
    sd=pc.std(0); f+=list(sd)
    c=pc.mean(0); r=np.linalg.norm(pc-c,axis=1)
    f+=[r.mean(),r.max(),r.min(),r.std()]
    # percentiles per axis
    for ax in range(3):
        f+=list(np.percentile(pc[:,ax],[5,25,50,75,95]))
    # covariance eigenvalues
    cov=np.cov(pc.T); ev=np.linalg.eigvalsh(cov); f+=list(ev)
    # pillar volumes at multiple res
    for R in [8,12,16,20]:
        for ax in range(3):
            f.append(pillar_vol(pc,ax,R))
    # occupancy at multiple res
    for R in [8,12,16,24,32]:
        f.append(occ(pc,R))
    return np.array(f)

def build():
    F=np.array([features(X[i]) for i in range(len(X))])
    return F

if __name__=='__main__':
    F=build()
    print('feature dim', F.shape)
    # standardize, correlations
    for j in range(F.shape[1]):
        pass
    cors=[abs(np.corrcoef(F[:,j],Y)[0,1]) for j in range(F.shape[1])]
    print('top corr feats (idx,corr):', sorted([(round(c,3),j) for j,c in enumerate(cors)],reverse=True)[:8])
    np.save('/app/_feats.npy',F)

    # Ridge regression CV
    def ridge_cv(F,Y,lam=1.0,k=5,seed=0):
        n=len(Y); folds=make_folds(n,k,seed=seed); preds=np.zeros(n)
        for fi in range(k):
            te=folds[fi].numpy(); tr=torch.cat([folds[j] for j in range(k) if j!=fi]).numpy()
            mu=F[tr].mean(0); sd=F[tr].std(0)+1e-8
            Ftr=(F[tr]-mu)/sd; Fte=(F[te]-mu)/sd
            ym=Y[tr].mean()
            A=Ftr.T@Ftr+lam*np.eye(Ftr.shape[1])
            w=np.linalg.solve(A,Ftr.T@(Y[tr]-ym))
            preds[te]=Fte@w+ym
        return metrics(torch.tensor(preds),torch.tensor(Y))
    for lam in [1.0,3.0,10.0,30.0]:
        print('ridge lam',lam, ridge_cv(F,Y,lam))

    # kNN CV
    def knn_cv(F,Y,kk=5,k=5,seed=0):
        n=len(Y); folds=make_folds(n,k,seed=seed); preds=np.zeros(n)
        mu=F.mean(0); sd=F.std(0)+1e-8; Fs=(F-mu)/sd
        for fi in range(k):
            te=folds[fi].numpy(); tr=torch.cat([folds[j] for j in range(k) if j!=fi]).numpy()
            for i in te:
                d=np.linalg.norm(Fs[tr]-Fs[i],axis=1)
                nn=tr[np.argsort(d)[:kk]]
                preds[i]=Y[nn].mean()
        return metrics(torch.tensor(preds),torch.tensor(Y))
    for kk in [3,5,8]:
        print('knn k',kk, knn_cv(F,Y,kk))
EOF
time python feats.py

output

feature dim (148, 46)
top corr feats (idx,corr): [(np.float64(0.85), 40), (np.float64(0.832), 37), (np.float64(0.791), 34), (np.float64(0.754), 35), (np.float64(0.746), 43), (np.float64(0.739), 38), (np.float64(0.729), 31), (np.float64(0.71), 32)]
ridge lam 1.0 (0.17240317060264013, 0.10771520231898041, 0.5708204604882579)
ridge lam 3.0 (0.1754652381323475, 0.10752636080079987, 0.6010243687963488)
ridge lam 10.0 (0.18446993009915005, 0.11386815132737484, 0.597926853285391)
ridge lam 30.0 (0.19584839546223373, 0.12189332024482111, 0.5549633387507449)
knn k 3 (0.26986364675521696, 0.17745421766429334, 0.724853515625)
knn k 5 (0.25731315343437616, 0.17077686154359095, 0.6800781488418579)
knn k 8 (0.26411738714544464, 0.16607439195787585, 0.64306640625)
[stderr]
real	0m4.323s
user	0m3.580s
sys	0m0.720s

Very informative! - The best single features are the occupancy/pillar-volume features (idx 40 = occ at some resolution, corr 0.85; idx 37 = pillar vol, corr 0.83). - Ridge regression on hand-crafted features gets relL2=0.172, mae=0.108, max=0.57 (lam=1). That's notably better than PointNet's 0.26! So PointNet at relL2 0.26 is UNDERPERFORMING compared to a simple ridge on good geometric features (0.172). This means PointNet isn't extracting the volume/occupancy information well. The pillar-volume and occupancy features are powerful and PointNet's max-pooling architecture can't easily compute them (it can't count occupied voxels or integrate thickness). This is a key insight. The question is: can I make PointNet learn better? The architecture is fixed. But I control the INPUT normalization (feat_mean, feat_std) , which is just affine, can't add features. Hmm wait. Let me re-read the constraints. "feat_mean, feat_std - length-3 tensors used to standardize the input coordinates before the forward pass." So the input to the model is exactly the standardized [B,2048,3] coords. I cannot inject extra features. The model is fixed PointNet. So I'm limited by PointNet's representational ability. But ridge on features gets 0.172. Can PointNet approach that? PointNet CAN in principle approximate many functions, but the volume/occupancy features require counting/integration that max-pooling struggles with. Max-pooling captures "is there a point with feature value > threshold" type info (extents, presence), good for bounding-box-like features but not volume integration. However, the pillar-volume feature is essentially sum over grid cells of (max_z - min_z). PointNet can compute per-point features and max-pool, giving max and min over ALL points (global), not per-cell. So global extent yes, per-cell thickness no. So PointNet fundamentally can't compute pillar volume. Hence its ceiling is lower. So PointNet is limited. The best I can do is optimize it. Current ~0.21-0.26. Let me see if I can push it. But wait , maybe I should reconsider. The grader applies MY model (the PointNet loaded from checkpoint). I cannot replace the architecture. So I'm stuck with PointNet's ceiling. Let me reconsider the ridge result though , it shows the DATA contains enough signal for 0.17. PointNet just can't capture it as well. Let me focus on squeezing PointNet. Ideas to improve PointNet: 1. Input normalization that helps it. Let me test iso vs peraxis vs none in CV. 2. Training improvements: LR, schedule, longer with better regularization, EMA. 3. The key bottleneck: max-pooling loses volume info. Can augmentation help it generalize better? Jitter helps. Let me also reconsider: maybe training multiple models and averaging their WEIGHTS won't work well (different minima). But I could train with a good schedule and use the final model. Actually, let me reconsider the variance issue. Individual PointNet predictions are noisy (corr 0.83). If I could ensemble 10 PointNets, predictions would be much better , but I can only submit ONE state_dict. BUT: here's a thought. What if I train ONE PointNet but with a lot of augmentation and long training so it's well-regularized, AND use SWA to average weights along the trajectory (reduces variance of the single model)? SWA gives one state_dict. Earlier SWA barely helped, but let me tune it better (longer SWA phase, constant LR during SWA). Another idea: knowledge distillation. Train a strong ensemble/ridge teacher, then distill into the PointNet by training it to match teacher predictions on augmented point clouds. But PointNet still can't represent the function well... though distillation might still push it to a better optimum than direct regression on 148 noisy targets. The teacher provides smoother targets. Actually, distillation on augmented inputs generates effectively unlimited training data with smooth labels, which could significantly help the data-starved PointNet generalize. The teacher (ridge on feat …[truncated]

**Major insight:** A simple ridge regression on hand-crafted geometric features achieves relL2≈0.17 (vs PointNet's 0.26) under the same CV. The strongest features are **voxel-occupancy and per-pillar volume** (corr up to 0.85) , quantities PointNet's global max-pooling fundamentally struggles to compute. So the data supports much better accuracy; my PointNet is leaving signal on the table. This motivates two directions: (1) squeeze PointNet via normalization/training tweaks, and (2) **knowledge distillation** from a strong feature-based teacher using augmented point clouds as an unlimited, smoothly-labeled training signal. Let me first quickly test normalization variants and a better SWA setup.

bash

cd /app && cat > sweep3.py << 'EOF'
from cv import cv
import numpy as np
base = dict(lr=2e-3, wd=1e-4, epochs=150, bs=16, warmup=10, min_lr=1e-5, jitter=0.01)
def show(name,cfg,**kw):
    m,pf=cv(cfg,k=5,seeds=(0,1),**kw)
    print(f"{name:26s} relL2={m[0]:.4f} mae={m[1]:.4f} max={m[2]:.4f}")
show('peraxis',base,norm='peraxis')
show('iso',base,norm='iso')
show('none',base,norm='none')
show('iso+jit0.02',{**base,'jitter':0.02},norm='iso')
show('iso+lr1e-3',{**base,'lr':1e-3},norm='iso')
show('iso+ep250',{**base,'epochs':250},norm='iso')
show('iso+swa',{**base,'epochs':250,'swa_start':150,'bn_passes':5},norm='iso')
show('iso+wd3e-4',{**base,'wd':3e-4},norm='iso')
EOF
time python sweep3.py

output

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

bash

restart: true

output

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

The CV with 2 seeds * 5 folds = 10 trainings per config, 8 configs = 80 trainings, each ~5s plus overhead... actually each training is 150 epochs. Earlier single cv (10 trainings) took ~43s. 8 configs * 43s = 344s > 300s timeout. Let me run in background and poll, or reduce to 1 seed. Let me run things in background with nohup and log to file.

The sweep exceeded the 300s limit. I'll run experiments in the background and poll the log instead.

bash

cd /app && nohup python sweep3.py > sweep3.log 2>&1 &
echo "started pid $!"

output

started pid 332

bash

cd /app && sleep 120 && cat sweep3.log

output

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

bash

cd /app && sleep 150 && cat sweep3.log

output

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

Still no output after 270s? The log is empty. Maybe output buffering. Let me check if the process is running and whether python buffers stdout. I should use python -u or flush. Let me check.

bash

cd /app && ps aux | grep sweep3 | grep -v grep | head; echo "---log---"; cat sweep3.log; echo "---size---"; ls -la sweep3.log

output

h2tagent   334 97.5 15.9 19714300 5014656 ?    Sl   21:14   4:29 python sweep3.py
---log---
---size---
-rw-r--r-- 1 h2tagent h2tagent 0 Jun 20 21:14 sweep3.log

It's running (4:29 CPU time) but output is buffered because stdout is a pipe (not a tty), and print flushes only at program end or buffer fill. I should have used python -u. Let me wait for it to finish or kill and restart with -u. Since it's been running ~4.5 min and each config ~43s * 8 = ~344s, it's probably near done. Let me wait a bit more.

The process is running but Python is buffering stdout to the file. Let me wait for completion (it prints at the end).

bash

cd /app && sleep 90 && cat sweep3.log; echo "==="; ps aux|grep sweep3|grep -v grep|wc -l

output

peraxis                    relL2=0.2612 mae=0.1673 max=0.8476
iso                        relL2=0.2724 mae=0.1732 max=0.8477
none                       relL2=0.2839 mae=0.1880 max=0.7337
iso+jit0.02                relL2=0.2742 mae=0.1773 max=0.7954
iso+lr1e-3                 relL2=0.2568 mae=0.1604 max=0.7856
iso+ep250                  relL2=0.2538 mae=0.1574 max=0.8959
iso+swa                    relL2=0.2608 mae=0.1625 max=0.9539
iso+wd3e-4                 relL2=0.2801 mae=0.1791 max=0.8729
===
0

Results: - peraxis norm is best among norms (0.2612). iso and none are worse. - iso+lr1e-3 helps a bit (0.2568). - iso+ep250 helps (0.2538) but max gets worse. - SWA doesn't help much. Everything is stuck around 0.25-0.28 relL2 and max ~0.8. The PointNet just can't get below ~0.25 on this CV. The feature-ridge gets 0.17. So distillation is the promising path: use the ridge teacher (0.17) to provide smooth targets, and train PointNet on heavily augmented point clouds to match teacher. The hope: PointNet learns a smoother, better-generalizing function than direct fit to 148 noisy labels. Even if it can't fully match the teacher, it might improve from 0.26 toward maybe 0.20. But wait , there's a subtlety. The teacher uses features like pillar-volume and occupancy that PointNet can't compute. So PointNet distilling the teacher will have irreducible error where those features matter. But distillation still might help because: 1. Teacher targets are less noisy than FEA labels (teacher is a smooth function of geometry). 2. Augmentation (jitter) creates many training points with consistent teacher labels. Actually, here's a cleaner idea that's guaranteed not to hurt: **blend** the training target between true label and teacher prediction, OR better, use an ENSEMBLE of PointNet + implicit... no, single model only. Hold on. Let me reconsider the whole problem. The deliverable is a checkpoint that loads into PointNetRegressor and maps [B,2048,3] -> [B,1]. The grader applies feat normalization then the model then label de-normalization. Is there ANY way to get the powerful features into the model? The model's forward is fixed: it transposes, applies feature MLP (conv1d on 3 channels), etc. The input is 3 channels only. I cannot add channels. So the model genuinely only sees xyz coordinates through 1x1 convs + max pool. Its ceiling is PointNet's ceiling. Given that, my best bet to improve generalization of this specific architecture: 1. Distillation from teacher (smooth targets + augmentation). 2. Best hyperparameters found: peraxis norm, lr~1e-3 to 2e-3, ep~150-250, jitter 0.01. Let me think about whether distillation can actually help PointNet beat 0.26. Alternative framing: The problem with direct training is 148 noisy labels → overfitting/high variance. If I generate a large synthetic dataset by augmenting each training cloud many times (jitter, small perturbations) with the SAME label, that's standard augmentation which I already do on the fly. Distillation replaces the label with teacher(augmented cloud), which for jitter is ≈ same label. So distillation ≈ augmentation unless the teacher meaningfully varies. Marginal. Hmm. Let me reconsider. Maybe the bigger win is simply a better teacher and then distill, OR maybe I'm overthinking and should accept ~0.22 and make sure all three metrics are optimized, particularly worst-case. Wait, actually , let me reconsider the achievable PointNet performance. Let me reconsider variance reduction via a DIFFERENT mechanism: train with many augmented views and LOTS of epochs but strong weight decay to reduce variance. Or , the single best lever for variance reduction with one model is to train longer with SWA over a wide window with cyclical/constant LR. My SWA impl averaged the last 100 epochs of a cosine schedule (LR decaying to ~0), so the weights didn't move much → SWA≈final. For SWA to reduce variance, I should use a HIGHER constant LR during the SWA phase so snapshots are diverse. Let me fix SWA: after warmup+cosine to some mid LR, switch to constant moderate LR and average periodically. Actually, let me reconsider the value of all this. Let me test distillation quickly since it's the most principled path to beat 0.26. Let me build: teacher = ridge on features (already have features in _feats.npy). For CV honesty, teacher must be trained only on training folds. Distillation plan in CV: - For each fold: train ridge teacher on train fold features. Train PointNet on train fold po …[truncated]

PointNet is firmly stuck around relL2≈0.25–0.27 across all these variants, while the feature-ridge teacher reaches 0.17. The architecture genuinely can't compute volume-like features, but I can try **distillation**: train PointNet against the smoother, more accurate teacher targets (denoised labels) instead of the noisy FEA labels. Let me test whether fitting teacher targets improves PointNet's generalization. I'll use `-u` for unbuffered logging and run in the background.

bash

cd /app && cat > distill.py << 'EOF'
import numpy as np, torch, sys, math, copy
sys.path.insert(0,'/app')
import cv as CV
from cv import ALLX, ALLY, make_folds, train_one, DEV
from exp import metrics

F=np.load('/app/_feats.npy')
Y=ALLY.numpy().ravel()

def ridge_fit(Ftr,ytr,lam=1.0):
    mu=Ftr.mean(0); sd=Ftr.std(0)+1e-8
    Z=(Ftr-mu)/sd; ym=ytr.mean()
    A=Z.T@Z+lam*np.eye(Z.shape[1])
    w=np.linalg.solve(A,Z.T@(ytr-ym))
    return (mu,sd,ym,w)
def ridge_pred(model,Fx):
    mu,sd,ym,w=model
    return ((Fx-mu)/sd)@w+ym

def distill_cv(cfg, blend=0.0, lam=1.0, k=5, seed=0):
    n=ALLX.shape[0]; folds=make_folds(n,k,seed=seed)
    preds=np.zeros(n)
    for fi in range(k):
        te=folds[fi]; tr=torch.cat([folds[j] for j in range(k) if j!=fi])
        tri=tr.numpy()
        tm=ridge_fit(F[tri],Y[tri],lam)
        teach_tr=ridge_pred(tm,F[tri])
        target=blend*Y[tri]+(1-blend)*teach_tr
        tx=ALLX[tr]; ex=ALLX[te]; ey=ALLY[te]
        fmean=tx.mean((0,1)); fstd=tx.std((0,1)).clamp_min(1e-6)
        ty=torch.tensor(target,dtype=torch.float32).unsqueeze(1)
        lmean=ty.mean(0); lstd=ty.std(0).clamp_min(1e-6)
        txn=((tx-fmean)/fstd).to(DEV); tyn=((ty-lmean)/lstd).to(DEV); exn=((ex-fmean)/fstd).to(DEV)
        c=dict(cfg); c['seed']=seed*100+fi
        m=train_one(txn,tyn,c,lstd,lmean)
        with torch.no_grad():
            p=m(exn).cpu()*lstd+lmean
        preds[te.numpy()]=p.numpy().ravel()
    return metrics(torch.tensor(preds),torch.tensor(Y))

if __name__=='__main__':
    base=dict(lr=2e-3,wd=1e-4,epochs=150,bs=16,warmup=10,min_lr=1e-5,jitter=0.01)
    for blend in [1.0,0.5,0.25,0.0]:
        m=distill_cv(base,blend=blend,lam=1.0,seed=0)
        print(f'blend(true w)={blend}  relL2={m[0]:.4f} mae={m[1]:.4f} max={m[2]:.4f}',flush=True)
EOF
nohup python -u distill.py > distill.log 2>&1 &
echo "pid $!"

output

pid 388

bash

cd /app && sleep 150 && cat distill.log

output

blend(true w)=1.0  relL2=0.2629 mae=0.1704 max=0.6677
blend(true w)=0.5  relL2=0.2528 mae=0.1616 max=0.7128
blend(true w)=0.25  relL2=0.2581 mae=0.1645 max=0.6756
blend(true w)=0.0  relL2=0.2546 mae=0.1652 max=0.6191

Distillation helps modestly: - blend=1.0 (pure true): 0.2629, max 0.668 - blend=0.5: 0.2528, max 0.713 - blend=0.0 (pure teacher): 0.2546, max 0.619 (best max!) Pure teacher (blend=0) gives the best worst-case (0.619 vs 0.668) and relL2 0.2546. Blend 0.5 gives best relL2 0.2528. So distillation modestly improves both relL2 and especially max-abs (from ~0.85 down to ~0.62). That's a meaningful worst-case improvement. But we're still around 0.25, far from teacher's 0.17. PointNet can't represent the teacher function well. Hmm. Let me reconsider. The improvement is modest. The worst-case improved notably (0.62-0.67 vs 0.85). Let me think about the biggest possible win. Since the feature-based model is much better, and I can only submit a PointNet... Wait. Let me reconsider what functions PointNet CAN compute and whether I can design the training so it computes something close to occupancy/volume. Key realization: PointNet computes per-point features φ(x_i) then max-pools: g = max_i φ(x_i), then head(g). The max over points of each feature channel. This captures the "extent" of the point set in learned nonlinear directions. For a convex shape, the support function (max over points of <x, direction>) fully characterizes it. The max-pooling of linear features gives exactly support function samples. With nonlinear per-point features, it captures more. Volume of a convex body can be approximated from its support function. But brackets aren't convex (they have holes). Still, PointNet can get a reasonable volume proxy from support-function-like features (the overall silhouette). That's probably why it reaches 0.26 but not better , it misses the internal holes/thickness. The occupancy feature captures surface area (which correlates with the shape complexity). PointNet can't count points per voxel. I think ~0.22-0.25 is near PointNet's ceiling here. Let me make sure I get the best within that and optimize all three metrics, especially worst-case. Actually, wait. Let me reconsider the variance angle more aggressively. The predictions are noisy (corr 0.83). What drives the noise? Random init + small data → each trained model lands in a different place. If I could average MANY models' predictions, I'd reduce this. I can't ensemble at inference. BUT, distillation from a SELF-ensemble could transfer ensemble benefits into one model! Idea: 1. Train an ensemble of N PointNets (and/or use the ridge teacher). 2. Create a combined teacher = average of ensemble predictions (+ ridge). This ensemble is more accurate and less noisy than any single PointNet. 3. Distill this strong teacher into a single PointNet. But again, the student PointNet has the same representational limits. Distilling a PointNet-ensemble into one PointNet: the ensemble average is a smoother function but still within "things PointNets can represent on average". A single PointNet might approximate the ensemble mean better than it fits raw labels, reducing variance. This is the classic "distill ensemble into single net" which often captures most ensemble gains. Worth trying. Actually, combining teachers: ridge (0.17, feature-based, captures volume) + PointNet-ensemble (captures silhouette). Their average might be even better than ridge alone. Then distill into PointNet. But the student can't represent ridge's volume features... so distilling ridge into PointNet is limited (we saw blend=0 gives 0.25, not 0.17). The student is the bottleneck. So the student PointNet ceiling (~0.22-0.25) is the binding constraint regardless of teacher quality. Distillation from a better teacher won't break past the student's representational ceiling. Therefore: I should find the student PointNet's best achievable and lock it in. Let me estimate the ceiling by training on ALL 148 and testing on... I don't have held-out beyond CV. The CV estimate ~0.25 is my honest number. Hmm, but the val-only number was 0.21. Let me reconcile: when training on train(100) and testing on val(32), go …[truncated]

Distillation helps modestly , blend 0.5 gives the best relL2 (0.253) and pure-teacher gives the best worst-case (0.62 vs ~0.85 for direct). The student PointNet's representational ceiling (~0.25 CV) is the binding constraint, but distillation denoises targets and notably improves worst-case error. Note the max-abs metric is very noisy (one unlucky sample), so I shouldn't over-optimize it. Let me run a more robust 3-seed CV comparison of my top candidate configs in the background to make a reliable final choice.

bash

cd /app && cat > decide.py << 'EOF'
import numpy as np, torch, sys
sys.path.insert(0,'/app')
from distill import distill_cv
import cv as CV

def multi(cfg, blend, seeds=(0,1,2)):
    rows=[distill_cv(cfg,blend=blend,seed=s) for s in seeds]
    a=np.array(rows)
    return a.mean(0), a.std(0)

cfgs={
 'direct_lr1.5_ep180': (dict(lr=1.5e-3,wd=1e-4,epochs=180,bs=16,warmup=10,min_lr=1e-5,jitter=0.01),1.0),
 'distill0.5_lr1.5_ep180': (dict(lr=1.5e-3,wd=1e-4,epochs=180,bs=16,warmup=10,min_lr=1e-5,jitter=0.01),0.5),
 'distill0.5_swa': (dict(lr=1.5e-3,wd=1e-4,epochs=220,bs=16,warmup=10,min_lr=3e-4,jitter=0.01,swa_start=140,bn_passes=6),0.5),
 'distill0.3_ep180': (dict(lr=1.5e-3,wd=1e-4,epochs=180,bs=16,warmup=10,min_lr=1e-5,jitter=0.01),0.3),
}
for name,(cfg,bl) in cfgs.items():
    mean,std=multi(cfg,bl)
    print(f"{name:26s} relL2={mean[0]:.4f}±{std[0]:.3f} mae={mean[1]:.4f} max={mean[2]:.4f}±{std[2]:.3f}",flush=True)
EOF
nohup python -u decide.py > decide.log 2>&1 &
echo "pid $!"

output

pid 411

This will take a while (4 configs * 3 seeds * 5 folds * 180-220 epochs). Each training ~5-6s, so 4*3*5=60 trainings * ~6s = 360s. Let me wait and meanwhile think/prepare the final training script. While waiting, let me reconsider the teacher quality. Let me improve the teacher features with a proper solid-volume estimate and maybe a small MLP teacher. Actually ridge at 0.17 is decent. Let me also consider an ensemble teacher (ridge + kNN) which might lower variance. Actually, let me reconsider the most important thing: the FINAL model must be trained on all 148 and generalize to hidden test. My CV uses the same distribution. Good. Let me also reconsider: should I fold calibration in? Let me decide based on CV after. Let me prepare the final training script that: - Loads all 148 samples (train+val+public_test). - Computes peraxis feat_mean/std on all. - Builds teacher (ridge on features) trained on all 148, produces teacher preds for all 148. - target = blend*true + (1-blend)*teacher. - Normalizes target by its mean/std. - Trains PointNet with jitter, cosine schedule. - Optionally SWA. - Saves checkpoint with state_dict, feat_mean, feat_std, label_mean, label_std. For the label_mean/label_std to de-normalize correctly: pred_phys = out*lstd + lmean where lstd,lmean are the mean/std of the TRAINING TARGET (the blended target). Since the blended target has mean≈mass mean and std slightly less than mass std (teacher shrinks), using the target's own mean/std is consistent: the model learns normalized target, de-norm gives back target-scale which ≈ mass. But there's a subtlety: the model is trained to predict the blended target, which is a shrunk version of true mass. At inference we want to predict true mass, not the shrunk target! Hmm. This is important. If target = 0.5*true + 0.5*teacher, and teacher ≈ shrunk true, then the target is a shrunk version of true. The model learns to predict this shrunk target. De-normalizing with target mean/std recovers the shrunk target scale, which UNDERESTIMATES extremes. That would hurt! Wait, but in distill_cv I evaluated against TRUE Y and got good results (0.2528). Let me check how: in distill_cv, lmean/lstd are the target's mean/std, pred = out*lstd+lmean (target scale), then compared to true Y. It worked (0.2528 vs direct 0.2629). So empirically the shrinkage wasn't a problem , because the teacher isn't that shrunk (ridge teacher has slope near 1, relL2 0.17, so teacher ≈ true + noise, not heavily shrunk). The teacher reduces NOISE without much bias. So blended target ≈ true with less noise. Good. That's why distillation helps. But to be safe about extremes, let me reconsider. The ridge teacher predictions: do they preserve the range of true masses? ridge relL2 0.17 with slope... in-sample fit preserves range well. For the final model, teacher is fit on all 148 and predicts all 148 in-sample (low bias). So target ≈ true with reduced variance. Blending further anchors to true. Good. Actually, I realize for the FINAL model I could even distill from OUT-OF-FOLD teacher preds to avoid teacher overfitting... but in-sample ridge with lam=1 is regularized, fine. Let me also reconsider: maybe skip distillation for simplicity/robustness and just do direct training with best hyperparams + calibration. The distillation gain is ~0.26->0.253, modest and within noise. But the worst-case improvement seemed more real. Let me wait for decide.log to see the 3-seed averages with std to judge if distillation is reliably better. Let me wait.

While that runs, let me reconsider a subtle risk in distillation: if the teacher shrinks extremes, training on blended targets (and de-normalizing with the target's own mean/std) could systematically underestimate high-mass brackets. Let me verify the ridge teacher preserves the mass range rather than shrinking it, since that determines whether distillation is safe.

bash

cd /app && python -c "
import numpy as np, torch
from distill import ridge_fit, ridge_pred, F, Y
# in-sample teacher on all 148
m=ridge_fit(F,Y,lam=1.0)
p=ridge_pred(m,F)
print('teacher in-sample relL2', np.linalg.norm(p-Y)/np.linalg.norm(Y))
# slope of true vs teacher
A=np.vstack([p,np.ones_like(p)]).T
a,b=np.linalg.lstsq(A,Y,rcond=None)[0]
print('fit true=a*teacher+b a,b', round(a,3), round(b,3))
# range preservation
print('true range', Y.min(), Y.max(), 'teacher range', round(p.min(),3), round(p.max(),3))
hi=Y>1.2
print('teacher mean err high(>1.2)', round((p[hi]-Y[hi]).mean(),3), 'low', round((p[~hi]-Y[~hi]).mean(),3))
"

output

teacher in-sample relL2 0.1258633278143386
fit true=a*teacher+b a,b 1.017 -0.012
true range 0.19836426 2.21875 teacher range 0.181 1.899
teacher mean err high(>1.2) -0.073 low 0.011

The teacher (ridge on all 148, in-sample) has relL2 0.126, slope 1.017 (essentially unbiased), and only mild shrinkage at the high end (-0.073, much less than PointNet's -0.15). Teacher range 0.18-1.90 vs true 0.20-2.22 , it shrinks the very top (2.22 -> ~1.9) but overall good. Blending with true (blend 0.5) recovers the extremes further. So distillation targets are ≈ true with less noise and mild high-end shrink. Safe enough, especially with blend toward true. Good. Let me wait for decide.log.

The teacher is nearly unbiased (slope 1.017, in-sample relL2 0.126) with only mild high-end shrinkage , blending with true labels compensates. Distillation is safe. Let me check the robust CV comparison.

bash

cd /app && sleep 180 && cat decide.log

output

direct_lr1.5_ep180         relL2=0.2830±0.019 mae=0.1748 max=0.9233±0.057
distill0.5_lr1.5_ep180     relL2=0.2722±0.029 mae=0.1672 max=0.8523±0.083

Hmm, now with 3 seeds the numbers are worse and noisier: - direct: relL2 0.283±0.019, max 0.923±0.057 - distill0.5: relL2 0.272±0.029, max 0.852±0.083 So distillation helps a little (0.283 -> 0.272) but the variance is large. And these are worse than earlier single-seed runs (0.25). The variance across seeds is ±0.02-0.03, which is large relative to the differences between methods. This tells me the PointNet is quite unstable on this tiny dataset. The max-abs is ~0.85-0.92, large. Let me wait for the other two configs (swa, distill0.3). Meanwhile, let me reconsider. The high variance across seeds is the core problem. To reduce it with a single model, I really want weight averaging over multiple independent runs , but averaging weights of independently-initialized networks doesn't work (permutation symmetry). However! There's a technique: I can average the weights of models that share the same initialization and training but differ only in the data ordering/augmentation noise... still they diverge. Alternatively, SWA within one run reduces variance along the trajectory. Let me see the swa result. Actually, the most reliable variance reducer available to me for a SINGLE output model is: train one model but make it robust. OR , reconsider: I can DISTILL an ensemble into one model. The ensemble of K PointNets (different seeds) has much lower variance. Distilling into one student transfers much of that. The student's predictions would approximate the ensemble mean, which is more accurate. This is the key technique to capture ensemble benefits in one model. Let me pursue this: it directly attacks the variance problem. Plan for ensemble distillation: 1. Train K=10 PointNets on all data (different seeds), each with jitter. Also include the ridge teacher. 2. Teacher target for each training sample = average of the K PointNet OOF predictions + ridge. But for training the student on TRAINING samples, I need teacher predictions on those same samples. If teachers are trained on all 148, their in-sample preds are overfit (low bias, not representative). Better: teacher predictions should be the ENSEMBLE MEAN evaluated on each sample. For the student to learn the smooth ensemble function, I want teacher targets = ensemble mean prediction on each training cloud. Using in-sample ensemble preds is okay because the student learns to reproduce the ensemble's function (the student will also "overfit" similarly). Actually what we want: the student mimics the ensemble's input->output mapping. Evaluate ensemble on training clouds (in-sample for teachers) → these are the ensemble's actual outputs. Student regresses them. Student generalizes like... hmm. This is getting subtle. Let me think about what actually reduces test error. The ensemble mean f_ens(x) = (1/K) Σ f_k(x) has lower variance than single f_k. On TEST points, f_ens is better. If I train a student to match f_ens on TRAINING points, the student learns f_ens's behavior on training points but may not generalize to test points better than a single model (student is itself a single PointNet with its own variance). Actually, distilling an ensemble into a single model of the SAME architecture: empirically the student often performs between single model and ensemble. The student benefits because the ensemble provides smooth, consistent targets that regularize training. But the student's own capacity/variance limits it. Given the large seed variance (±0.02-0.03), even capturing half the ensemble gain would help. But it's complex and my token budget is limited. Let me reconsider a simpler, robust approach: Since individual models vary a lot, and I must pick ONE, the safest is to reduce variance via: (a) SWA over a long constant-LR tail (averages many snapshots along one trajectory , cheap, one run). (b) Strong augmentation + moderate epochs. (c) Distillation from ridge (denoised targets) , gives small but consistent gain. And crucially, I should EVALUATE the final chosen model on hel …[truncated]

Variance across seeds is large (±0.02–0.03) , instability on this tiny dataset is the core issue. Distillation helps a bit. The principled fix for variance is ensembling, but I can only submit one model. Before investing in ensemble-distillation, let me measure the **potential**: how much does averaging predictions of K models actually help in CV? That sets the ceiling for variance reduction. Let me check the remaining decide results first, then test ensembling.

bash

cd /app && cat decide.log; echo "=== (waiting on last 2 configs) ==="; ps aux|grep decide|grep -v grep|wc -l

output

direct_lr1.5_ep180         relL2=0.2830±0.019 mae=0.1748 max=0.9233±0.057
distill0.5_lr1.5_ep180     relL2=0.2722±0.029 mae=0.1672 max=0.8523±0.083
distill0.5_swa             relL2=0.2668±0.030 mae=0.1660 max=0.8329±0.090
=== (waiting on last 2 configs) ===
1

distill0.5_swa: relL2 0.2668±0.030, max 0.833 , slightly better than non-swa (0.272). So SWA helps a touch. Let me wait for distill0.3. Meanwhile, let me set up the ensemble potential test. Let me write it to run after decide finishes (to not compete for GPU, though H100 can handle concurrent). Actually let me just run it now in background; H100 has plenty of memory and these are tiny. Let me write ensemble_test.py: for K in {1,4,8}, CV where each fold trains K models and averages test predictions.

SWA gives a small additional gain (0.267 vs 0.272). Let me now measure the ensemble potential , if averaging K models' predictions substantially beats a single model, ensemble-distillation into one student is worth pursuing.

bash

cd /app && cat > ens_test.py << 'EOF'
import numpy as np, torch, sys
sys.path.insert(0,'/app')
from cv import ALLX, ALLY, make_folds, train_one, DEV
from exp import metrics
from distill import ridge_fit, ridge_pred, F, Y

def ens_cv(cfg, K=8, blend=0.5, lam=1.0, k=5, seed=0, add_ridge=False):
    n=ALLX.shape[0]; folds=make_folds(n,k,seed=seed)
    preds=np.zeros(n)
    for fi in range(k):
        te=folds[fi]; tr=torch.cat([folds[j] for j in range(k) if j!=fi]); tri=tr.numpy()
        tm=ridge_fit(F[tri],Y[tri],lam); teach=ridge_pred(tm,F[tri])
        target=blend*Y[tri]+(1-blend)*teach
        tx=ALLX[tr]; ex=ALLX[te]
        fmean=tx.mean((0,1)); fstd=tx.std((0,1)).clamp_min(1e-6)
        ty=torch.tensor(target,dtype=torch.float32).unsqueeze(1)
        lmean=ty.mean(0); lstd=ty.std(0).clamp_min(1e-6)
        txn=((tx-fmean)/fstd).to(DEV); tyn=((ty-lmean)/lstd).to(DEV); exn=((ex-fmean)/fstd).to(DEV)
        acc=torch.zeros(len(te))
        for kk in range(K):
            c=dict(cfg); c['seed']=seed*1000+fi*50+kk
            m=train_one(txn,tyn,c,lstd,lmean)
            with torch.no_grad():
                acc+=(m(exn).cpu()*lstd+lmean).squeeze(1)
        p=(acc/K).numpy()
        if add_ridge:
            rp=ridge_pred(tm,F[te.numpy()])
            p=0.5*p+0.5*rp
        preds[te.numpy()]=p
    return metrics(torch.tensor(preds),torch.tensor(Y))

if __name__=='__main__':
    cfg=dict(lr=1.5e-3,wd=1e-4,epochs=180,bs=16,warmup=10,min_lr=1e-5,jitter=0.01)
    for K in [1,4,8,16]:
        rows=[ens_cv(cfg,K=K,seed=s) for s in (0,1)]
        a=np.array(rows).mean(0)
        print(f'K={K:2d} blend0.5     relL2={a[0]:.4f} mae={a[1]:.4f} max={a[2]:.4f}',flush=True)
    # ensemble + ridge blended at inference
    a=np.array([ens_cv(cfg,K=8,seed=s,add_ridge=True) for s in (0,1)]).mean(0)
    print(f'K=8 +ridge@infer relL2={a[0]:.4f} mae={a[1]:.4f} max={a[2]:.4f}',flush=True)
EOF
nohup python -u ens_test.py > ens_test.log 2>&1 &
echo "pid $!"

output

pid 457

This trains K models per fold: K in {1,4,8,16}, 2 seeds, 5 folds. Total models = (1+4+8+16)*2*5 = 290 models + the add_ridge one (8*2*5=80). ~370 models * ~6s = 2200s. That's too long (37 min). Let me let it run in background and check periodically, but I may not wait for all. The K=1,4,8 results will come first and are most informative. Actually each training at 180 epochs on ~118 samples: earlier 150 epochs * 10 models took 43s → ~4.3s each. 180 epochs ~5s each. 370*5 = 1850s ~31 min. Let me check after the K=1,4,8 are done (first seed loop). Actually the loop structure: for K in [1,4,8,16]: rows=[ens_cv for s in (0,1)]. So K=1 does 2 seeds*5 folds*1 model=10 models (~50s), K=4: 40 models (~200s), K=8: 80 (~400s), K=16: 160 (~800s). So K=1 at ~50s, K=4 at ~250s cumulative, K=8 at ~650s, K=16 at ~1450s. Let me check at 250s for K=1,4. While waiting, let me also get the distill0.3 result from decide.log. Let me wait ~200s and check both logs.

That ensemble test is lengthy, so I'll poll it. Let me wait and check both the remaining decide result and the early ensemble (K=1 vs K=4) numbers.

bash

cd /app && sleep 220 && echo "=== decide ===" && cat decide.log && echo "=== ens_test ===" && cat ens_test.log

output

=== decide ===
direct_lr1.5_ep180         relL2=0.2830±0.019 mae=0.1748 max=0.9233±0.057
distill0.5_lr1.5_ep180     relL2=0.2722±0.029 mae=0.1672 max=0.8523±0.083
distill0.5_swa             relL2=0.2668±0.030 mae=0.1660 max=0.8329±0.090
distill0.3_ep180           relL2=0.2749±0.022 mae=0.1714 max=0.8026±0.046
=== ens_test ===
K= 1 blend0.5     relL2=0.2535 mae=0.1607 max=0.8385
K= 4 blend0.5     relL2=0.2457 mae=0.1549 max=0.8157

Interesting: - decide: distill0.5_swa is best (0.2668), distill0.3 (0.2749, but best max 0.803). - ens_test: K=1 → 0.2535, K=4 → 0.2457. So ensembling helps but only modestly (0.2535 → 0.2457 for K=4). Let me see K=8, K=16. Wait, ens_test K=1 gives 0.2535 but decide distill0.5 gives 0.2722 , different because ens_test uses seeds (0,1) with different seed offsets and the distill_cv vs ens_cv have slightly different seed handling. Also ens_test K=1 seed handling: c['seed']=seed*1000+fi*50+kk. Anyway, within ens_test, K=1→0.2535, K=4→0.2457. The ensemble gain is modest (~0.008). So even a 4-model ensemble only improves relL2 by ~0.008. The variance reduction potential is limited because the models share the same systematic bias (they all can't compute volume features, so they're biased similarly on the same samples). Ensembling reduces variance but not the shared bias. So ensemble distillation won't help dramatically. Let me see K=8, K=16, and +ridge. The +ridge@infer is interesting , but I CAN'T use ridge at inference (only the PointNet). So that's just informational (shows if combining with features would help). Let me wait for those. Given the modest ensemble gain, and that I can't ensemble at inference anyway, the realistic best single-model CV is ~0.26-0.27. Let me reconsider the whole thing. The numbers are noisy and all hovering 0.25-0.28. The differences between my configs are within noise. Let me just pick a robust config and finalize. The key reliable choices: - distillation blend 0.5 + SWA: best relL2 (0.2668) in decide. - jitter 0.01, peraxis, lr 1.5e-3. For the FINAL model, I'll train on all 148 with these settings. But since a single model is noisy (±0.02-0.03 depending on seed), the specific final model I train could be lucky or unlucky. To reduce this risk for the single submitted model, I should: 1. Use SWA (averages along trajectory → more stable than final-epoch weights). 2. Train a few candidate final models with different seeds and pick the one with best performance on a held-out check... but I'd be selecting on public_test/val which is small. Actually, here's a cleaner idea for the SINGLE submitted model that reduces variance WITHOUT ensembling at inference: train K models and average their predictions is out. But I can do "moving-target" training or longer SWA. Alternatively , weight averaging of models from DIFFERENT seeds fails due to permutation. But what if all K models start from the SAME initialization and see the same data in the same order, differing only by augmentation randomness? They'd stay in the same basin and their weights could be averaged meaningfully (like SWA across parallel runs). This is "fast geometric ensembling" style. Could work. But complex. Simplest robust approach: SWA within one long run. Let me verify SWA reduces seed-variance by checking std across seeds for the SWA config , decide showed distill0.5_swa std 0.030, not lower than non-swa 0.029. So SWA didn't reduce seed variance here. Hmm. OK, I think I'm in diminishing returns. Let me make a decision and build the final model, then validate it on val and public_test as sanity checks, and compare a couple of seeds to pick a good final checkpoint. Let me reconsider the selection concern: if I train the final model on all 148, I have no held-out. If I train on train+val (132) I can check public_test (16). But public_test is tiny (16) so noisy. Best to train on all 148 for max data and trust CV. But then to avoid an unlucky single model, I'll generate a few (e.g., 5) final candidates with different seeds using the chosen config, and select based on a blend of val+public_test+train-fit... but val and public_test are IN the training set if I train on all 148 (can't evaluate honestly). Resolution: Train final candidates on train+val (132 samples), evaluate on public_test (16) AND use CV-style estimate, pick best seed, then... but the chosen model is trained on 132 not 148. Using 16 more samples (public_test) helps. Tr …[truncated]

Ensembling yields only a small gain (K=1→0.2535, K=4→0.2457), because all PointNets share the same representational bias , averaging cuts variance but not the common bias. Since I also can't ensemble at inference, ensemble-distillation isn't worth the complexity. Let me confirm the K=8/16 trend, then finalize with the best reliable recipe (distill blend 0.5 + jitter + SWA + per-axis norm).

bash

cd /app && sleep 180 && cat ens_test.log

output

K= 1 blend0.5     relL2=0.2535 mae=0.1607 max=0.8385
K= 4 blend0.5     relL2=0.2457 mae=0.1549 max=0.8157

bash

cd /app && sleep 150 && cat ens_test.log; echo "running: $(ps aux|grep ens_test|grep -v grep|wc -l)"

output

K= 1 blend0.5     relL2=0.2535 mae=0.1607 max=0.8385
K= 4 blend0.5     relL2=0.2457 mae=0.1549 max=0.8157
K= 8 blend0.5     relL2=0.2435 mae=0.1530 max=0.7889
running: 1

K=8 → 0.2435. Diminishing returns (K=4: 0.2457, K=8: 0.2435). So ensemble ceiling ~0.24. Single model ~0.25. The gain from ensembling is small (~0.01). Confirmed: not worth ensemble distillation. Let me wait for K=16 and +ridge to complete, but I can start building the final pipeline now. Let me kill the ens_test since K=16 won't change my decision much, to free GPU. Actually let me let it finish the +ridge one which tells me if feature-blending at inference would help a lot (informational - if +ridge gives like 0.18, it confirms features are key, but I can't use it). Let me just check once more then proceed regardless. Actually, let me be efficient with tokens. I have enough info. Decision: FINAL RECIPE: - Train on all 148 samples. - peraxis feat normalization (from all 148). - Ridge teacher on all 148 features; target = 0.5*true + 0.5*teacher. - label_mean/std from target. - jitter 0.01 augmentation. - AdamW lr 1.5e-3, wd 1e-4, warmup 10, cosine to 3e-4 by epoch 140, then constant 3e-4 with SWA averaging epochs 140-200, bn_passes 6. - Save checkpoint. But wait , there's a dependency problem for the deliverable! The final model uses a ridge teacher built from hand-crafted features (feats.py). But the teacher is only used to create TRAINING TARGETS. At inference, the grader only uses the PointNet + feat/label normalization. The ridge teacher is NOT needed at inference. So the deliverable is self-contained (just the PointNet checkpoint). Good. The feature extraction is only used offline during training. However, I want the final training script to be reproducible and self-contained. Let me write final_train.py that: 1. Loads all 148 samples. 2. Extracts features, fits ridge teacher, computes blended targets. 3. Trains PointNet with the recipe. 4. Saves /app/outputs/model.pt. Let me also verify the checkpoint loads and produces correct shapes and reasonable predictions. Let me reconsider the seed-variance issue for the single final model once more. To get a reliable (not unlucky) final model, I'll train a FEW seeds on all 148 and select the best by an honest proxy. But I have no held-out if I train on all 148. Compromise: I'll train the final candidates on all 148, and ALSO run the exact same recipe through CV (OOF) with the SAME seeds to estimate each recipe's quality. But individual final-model luck isn't captured by CV. Alternative: Use SWA heavily (averages ~60 epochs of snapshots) , this inherently reduces the "unlucky final weights" problem by averaging many points on the trajectory. That's my main variance control for the single model. Good enough. Additional robustness: I can train the final model and sanity-check its predictions on the full 148 (in-sample). In-sample error will be lower than test, but if a particular seed gives pathologically bad in-sample fit, I'd catch it. Mainly a sanity check. Let me also reconsider: should I blend in the ridge predictions via the label normalization trick? No , ridge needs features at inference, not available. Can't. OK let me also reconsider: to reduce variance of the single submitted model, I could train with a LARGER effective dataset via more aggressive but safe augmentation. Jitter 0.01 is safe. What about combining jitter + resample (bootstrap points)? Earlier jit+resample was ~0.2197 on val (similar to jitter alone 0.2157). Neutral. I'll keep jitter only, maybe add light resample. Let me keep it simple: jitter 0.01. Let me now write final_train.py and run it. I'll make SWA robust. Let me also compute and print in-sample metrics and load-check. Let me kill ens_test to free resources (K=16 not needed). Actually, let me keep ens_test; H100 handles it. But to be clean, let me check +ridge result quickly then kill. Let me just proceed to write final_train.py. I'll incorporate a modest SWA. Let me write it carefully. One more consideration: BatchNorm in eval mode uses running stats. With SWA I recompute BN stats via forward passes in train mode. I must ensure the …[truncated]

Ensembling plateaus at ~0.24 (K=8), only ~0.01 better than a single model , confirming the bias is shared and ensemble-distillation isn't worthwhile. I now have a clear, reliable recipe. Let me write the self-contained final training script. Key point: the ridge teacher is used **only to denoise training targets offline** , inference uses only the PointNet + the required normalizations, so the checkpoint is fully self-contained.

str_replace_based_edit_tool

command: create
path: /app/final_train.py
file_text: from __future__ import annotations
import json, sys, math, copy
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F

APP = Path('/app')
sys.path.insert(0, str(APP))
from candidate_model import build_model
from exp import augment, metrics
from feats import features  # offline feature extractor for the teacher

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


def load_all():
    xs, ys = [], []
    for s in ['train', 'val', 'public_test']:
        p = torch.load(APP / 'data' / s / 'samples.pt', weights_only=True)
        xs.append(p['features'].float()); ys.append(p['labels'].float())
    return torch.cat(xs, 0), torch.cat(ys, 0)


def ridge_teacher(X, y, lam=1.0):
    Fm = np.array([features(X[i].numpy()) for i in range(len(X))])
    mu = Fm.mean(0); sd = Fm.std(0) + 1e-8
    Z = (Fm - mu) / sd; ym = y.mean()
    A = Z.T @ Z + lam * np.eye(Z.shape[1])
    w = np.linalg.solve(A, Z.T @ (y - ym))
    return (Z @ w + ym).astype(np.float32)


def train_final(cfg, seed):
    torch.manual_seed(seed); np.random.seed(seed)
    X, Ymass = load_all()
    y = Ymass.numpy().ravel()
    teach = ridge_teacher(X, y, lam=cfg['ridge_lam'])
    blend = cfg['blend']
    target = blend * y + (1 - blend) * teach
    target = torch.tensor(target, dtype=torch.float32).unsqueeze(1)

    fmean = X.mean((0, 1)); fstd = X.std((0, 1)).clamp_min(1e-6)
    lmean = target.mean(0); lstd = target.std(0).clamp_min(1e-6)
    txn = ((X - fmean) / fstd).to(DEV)
    tyn = ((target - lmean) / lstd).to(DEV)

    model = build_model(CFG).to(DEV)
    opt = torch.optim.AdamW(model.parameters(), lr=cfg['lr'], weight_decay=cfg['wd'])
    epochs = cfg['epochs']; bs = cfg['bs']; n = txn.shape[0]
    warmup = cfg['warmup']; swa_start = cfg['swa_start']
    swa_lr = cfg['swa_lr']; min_lr = cfg['min_lr']

    def lr_at(e):
        if e < warmup:
            return cfg['lr'] * (e + 1) / warmup
        if e < swa_start:
            t = (e - warmup) / max(1, swa_start - warmup)
            return swa_lr + 0.5 * (cfg['lr'] - swa_lr) * (1 + math.cos(math.pi * t))
        return swa_lr  # constant during SWA phase

    swa_sd = None; swa_n = 0
    for e in range(epochs):
        model.train()
        for g in opt.param_groups:
            g['lr'] = lr_at(e)
        perm = torch.randperm(n, device=DEV)
        for i in range(0, n, bs):
            idx = perm[i:i + bs]
            xb = augment(txn[idx], cfg); yb = tyn[idx]
            opt.zero_grad(set_to_none=True)
            loss = F.mse_loss(model(xb), yb)
            loss.backward(); opt.step()
        if e + 1 > swa_start:
            sd = model.state_dict()
            if swa_sd is None:
                swa_sd = copy.deepcopy(sd); swa_n = 1
            else:
                swa_n += 1
                for k in swa_sd:
                    if swa_sd[k].dtype.is_floating_point:
                        swa_sd[k].mul_(1 - 1 / swa_n).add_(sd[k], alpha=1 / swa_n)
                    else:
                        swa_sd[k] = sd[k]

    if swa_sd is not None:
        model.load_state_dict(swa_sd)
        model.train()  # recompute BN running stats for averaged weights
        with torch.no_grad():
            for _ in range(cfg['bn_passes']):
                perm = torch.randperm(n, device=DEV)
                for i in range(0, n, bs):
                    model(augment(txn[perm[i:i + bs]], cfg))
    model.eval()
    # in-sample sanity metrics (vs TRUE mass)
    with torch.no_grad():
        pred = (model(txn).cpu() * lstd + lmean)
    ins = metrics(pred, Ymass)
    return model, (fmean, fstd, lmean, lstd), ins, swa_n


def save_ckpt(model, stats, path):
    fmean, fstd, lmean, lstd = stats
    payload = {
        'state_dict': {k: v.cpu() for k, v in model.state_dict().items()},
        'feat_mean': fmean.cpu().float(),
        'feat_std': fstd.cpu().float(),
        'label_mean': lm
…[truncated]

output

New file created successfully at /app/final_train.py

Before running final, I need an honest evaluation of this exact final recipe (with the SWA constant-LR-tail schedule) via CV, to confirm it's at least as good as what I measured. Let me also build a proper inference/eval harness that mimics the grader: load model.pt, apply feat normalization, forward, de-normalize with label stats, compute metrics on a held-out set. But the final model trains on all 148, so I can't evaluate it on held-out honestly. I'll instead: 1. Run the final recipe through CV (OOF) to confirm expected performance. 2. Train the final model on all 148. 3. Build a grader-style inference script and at least verify it loads and runs, and check in-sample metrics are sensible (should be better than CV ~0.26, maybe ~0.15-0.20 in-sample). Actually, let me first validate the final recipe's schedule via CV. Let me add a CV wrapper that uses the same constant-LR-tail SWA schedule. I'll quickly adapt by running cv.train_one but its schedule differs (cosine to min_lr, SWA averages during decaying LR). My final uses constant swa_lr tail. Let me just test the final recipe in CV by writing a small CV using train_final-like logic but with folds. Given tokens, let me write one combined validation that runs the EXACT final training function on CV folds (train on 4 folds, test on 5th). I'll refactor train_final to accept explicit train/eval data. Actually simpler: let me write a CV that reuses the scheduling logic inline. Let me just test via a quick script that imports and adapts. Let me refactor: make a core function train_core(txn, tyn, cfg, n) -> model that does the training+SWA+BN, used by both final and CV. Let me edit final_train.py to expose train_core, then write a CV test. Actually, train_final already does everything given all data. For CV I need to hold out. Let me add a function train_core that takes normalized train tensors and returns a trained model, and have train_final call it. Then CV calls train_core on folds. Let me refactor minimally.

Before training the final model, I must validate this exact recipe (with the constant-LR SWA tail) under CV. Let me refactor the training core so both the final run and a CV check share identical logic, then run the CV check.

str_replace_based_edit_tool

command: str_replace
path: /app/final_train.py
old_str: def train_final(cfg, seed):
    torch.manual_seed(seed); np.random.seed(seed)
    X, Ymass = load_all()
    y = Ymass.numpy().ravel()
    teach = ridge_teacher(X, y, lam=cfg['ridge_lam'])
    blend = cfg['blend']
    target = blend * y + (1 - blend) * teach
    target = torch.tensor(target, dtype=torch.float32).unsqueeze(1)

    fmean = X.mean((0, 1)); fstd = X.std((0, 1)).clamp_min(1e-6)
    lmean = target.mean(0); lstd = target.std(0).clamp_min(1e-6)
    txn = ((X - fmean) / fstd).to(DEV)
    tyn = ((target - lmean) / lstd).to(DEV)

    model = build_model(CFG).to(DEV)
new_str: def train_core(txn, tyn, cfg, seed):
    torch.manual_seed(seed)
    n = txn.shape[0]
    model = build_model(CFG).to(DEV)

output

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

str_replace_based_edit_tool

command: str_replace
path: /app/final_train.py
old_str:     opt = torch.optim.AdamW(model.parameters(), lr=cfg['lr'], weight_decay=cfg['wd'])
    epochs = cfg['epochs']; bs = cfg['bs']; n = txn.shape[0]
    warmup = cfg['warmup']; swa_start = cfg['swa_start']
new_str:     opt = torch.optim.AdamW(model.parameters(), lr=cfg['lr'], weight_decay=cfg['wd'])
    epochs = cfg['epochs']; bs = cfg['bs']
    warmup = cfg['warmup']; swa_start = cfg['swa_start']

output

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

str_replace_based_edit_tool

command: str_replace
path: /app/final_train.py
old_str:                 for i in range(0, n, bs):
                    model(augment(txn[perm[i:i + bs]], cfg))
    model.eval()
    # in-sample sanity metrics (vs TRUE mass)
    with torch.no_grad():
        pred = (model(txn).cpu() * lstd + lmean)
    ins = metrics(pred, Ymass)
    return model, (fmean, fstd, lmean, lstd), ins, swa_n
new_str:                 for i in range(0, n, bs):
                    model(augment(txn[perm[i:i + bs]], cfg))
    model.eval()
    return model, swa_n


def prep_targets(X, y, cfg):
    teach = ridge_teacher(X, y, lam=cfg['ridge_lam'])
    blend = cfg['blend']
    target = blend * y + (1 - blend) * teach
    return torch.tensor(target, dtype=torch.float32).unsqueeze(1)


def train_final(cfg, seed):
    torch.manual_seed(seed); np.random.seed(seed)
    X, Ymass = load_all()
    y = Ymass.numpy().ravel()
    target = prep_targets(X, y, cfg)
    fmean = X.mean((0, 1)); fstd = X.std((0, 1)).clamp_min(1e-6)
    lmean = target.mean(0); lstd = target.std(0).clamp_min(1e-6)
    txn = ((X - fmean) / fstd).to(DEV)
    tyn = ((target - lmean) / lstd).to(DEV)
    model, swa_n = train_core(txn, tyn, cfg, seed)
    with torch.no_grad():
        pred = (model(txn).cpu() * lstd + lmean)
    ins = metrics(pred, Ymass)
    return model, (fmean, fstd, lmean, lstd), ins, swa_n

output

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

Now let me write a CV validation of this exact final recipe, where the teacher is fit only on training folds (no leakage):

bash

cd /app && cat > final_cv.py << 'EOF'
import numpy as np, torch, sys
sys.path.insert(0,'/app')
import final_train as FT
from final_train import DEV, train_core, ridge_teacher, metrics
from cv import make_folds

def cv_final(cfg, seeds=(0,1,2), k=5):
    X, Ymass = FT.load_all(); y=Ymass.numpy().ravel(); n=len(y)
    out=[]
    for seed in seeds:
        folds=make_folds(n,k,seed=seed); preds=np.zeros(n)
        for fi in range(k):
            te=folds[fi]; tr=torch.cat([folds[j] for j in range(k) if j!=fi]); tri=tr.numpy()
            Xtr=X[tr]; ytr=y[tri]
            teach=ridge_teacher(Xtr,ytr,lam=cfg['ridge_lam'])
            target=cfg['blend']*ytr+(1-cfg['blend'])*teach
            target=torch.tensor(target,dtype=torch.float32).unsqueeze(1)
            fmean=Xtr.mean((0,1)); fstd=Xtr.std((0,1)).clamp_min(1e-6)
            lmean=target.mean(0); lstd=target.std(0).clamp_min(1e-6)
            txn=((Xtr-fmean)/fstd).to(DEV); tyn=((target-lmean)/lstd).to(DEV)
            exn=((X[te]-fmean)/fstd).to(DEV)
            m,_=train_core(txn,tyn,cfg,seed*100+fi)
            with torch.no_grad():
                preds[te.numpy()]=(m(exn).cpu()*lstd+lmean).numpy().ravel()
        out.append(metrics(torch.tensor(preds),Ymass))
    a=np.array(out)
    return a.mean(0), a.std(0)

if __name__=='__main__':
    cfg=dict(lr=1.5e-3, wd=1e-4, epochs=220, bs=16, warmup=10,
             min_lr=1e-5, swa_lr=3e-4, swa_start=150, bn_passes=8,
             jitter=0.01, blend=0.5, ridge_lam=1.0)
    mean,std=cv_final(cfg, seeds=(0,1,2))
    print(f'FINAL recipe CV: relL2={mean[0]:.4f}±{std[0]:.3f} mae={mean[1]:.4f}±{std[1]:.3f} max={mean[2]:.4f}±{std[2]:.3f}',flush=True)
EOF
nohup python -u final_cv.py > final_cv.log 2>&1 &
echo "pid $!"

output

pid 547

While that runs (3 seeds * 5 folds * 220 epochs + feature extraction per fold ~ maybe 150s), let me verify the final_train.py works end to end by doing a quick smoke test with tiny epochs, and build the inference/grader-style script to validate the checkpoint format. Let me wait for final_cv first, then decide. Meanwhile, let me write the inference harness that mimics the grader exactly, so I can validate the saved checkpoint loads and produces correct outputs. Let me write infer.py: loads /app/outputs/model.pt, builds model from candidate_model.build_model(cfg), loads state_dict, applies (x-feat_mean)/feat_std, forward, *label_std+label_mean. Evaluate on a given split. I'll use this to check the final model on... well, everything is in training. But I can at least confirm format correctness and in-sample behavior. And I can run it on each split to see per-split numbers (in-sample). Let me write it.

That CV runs in the background. Meanwhile let me write a grader-style inference harness to validate the checkpoint format (load into the untouched architecture, apply the exact normalization contract) and smoke-test the pipeline.

bash

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

CFG=json.load(open('/app/task_config.json'))

def load_model(path='/app/outputs/model.pt'):
    ck=torch.load(path, weights_only=True, map_location='cpu')
    m=build_model(CFG)
    m.load_state_dict(ck['state_dict'])
    m.eval()
    return m, ck

@torch.no_grad()
def predict(m, ck, x):
    fm=ck['feat_mean'].view(1,1,3); fs=ck['feat_std'].view(1,1,3)
    xn=(x-fm)/fs
    out=m(xn)
    return out*ck['label_std'].view(1,1)+ck['label_mean'].view(1,1)

def eval_split(split):
    p=torch.load(f'/app/data/{split}/samples.pt', weights_only=True)
    x=p['features'].float(); y=p['labels'].float()
    m,ck=load_model()
    pred=predict(m,ck,x)
    assert pred.shape==(x.shape[0],1), pred.shape
    return metrics(pred,y)

if __name__=='__main__':
    for s in ['train','val','public_test']:
        r=eval_split(s)
        print(f'{s:12s} relL2={r[0]:.4f} mae={r[1]:.4f} max={r[2]:.4f} (in-sample if trained on all)')
EOF
echo "infer.py written"

output

infer.py written

bash

cd /app && sleep 60 && cat final_cv.log; echo "running: $(ps aux|grep final_cv|grep -v grep|wc -l)"

output

running: 1

Still running. Let me wait more. Each fold also extracts features for ~118 samples (feature extraction is ~4s for 148 in feats.py, so ~3s per fold * 15 folds = 45s extra). Let me wait ~120s more.

bash

cd /app && sleep 140 && cat final_cv.log; echo "running: $(ps aux|grep final_cv|grep -v grep|wc -l)"

output

FINAL recipe CV: relL2=0.2694±0.025 mae=0.1663±0.015 max=0.8564±0.045
running: 0

Final recipe CV: relL2=0.2694±0.025, mae=0.1663, max=0.856. This is consistent with earlier distill0.5 numbers (~0.267-0.272). The constant-LR SWA tail didn't notably beat the earlier cosine SWA. It's in the expected range. So my final recipe gives ~0.27 CV relL2. The seed std is 0.025, so a single final model could be anywhere in ~0.24-0.29. Let me reconsider if there's a way to reliably reduce variance for the single submitted model. Idea: The biggest issue is single-model variance. What if I reduce it by averaging predictions across AUGMENTATIONS at inference? No , inference is fixed by grader (single forward on the given cloud). Can't. Idea: Pick the best of several seeds using CV-style OOF selection. But that selects a recipe, not a specific final model. Idea: For the single final model, use MORE SWA snapshots (longer tail, lower LR variance) to stabilize. Already doing epochs 150-220 = 70 snapshots. Idea: Train the final model and verify it's not an outlier by checking it predicts the known 148 labels with in-sample relL2 in a sane range (expect in-sample < CV, maybe 0.15-0.22 given SWA smoothing). If a seed gives bad in-sample, reject. Actually, a cleaner variance-reduction that gives a SINGLE model: train on all 148 with a LONGER run and heavier SWA. But gains are marginal. Let me reconsider the ceiling question differently. The feature-ridge gets 0.17. Is there ANY legitimate way to get features into the PointNet? The model input is [B,2048,3]. What if I could make the per-point features richer by... no, I can only provide xyz. Wait , what about the feat_mean/feat_std? They're length-3. Applied as (x-mean)/std per-axis. That's it. No channel expansion. OK, PointNet ceiling it is. ~0.25-0.27. Hmm, let me reconsider one thing: maybe I can do better than 0.27 by reducing the systematic bias (regression to mean at extremes) more aggressively, which helps relL2, MAE, AND max-abs simultaneously. The distillation blend helps a bit. What about a loss that penalizes under-prediction of high-mass samples more? Or sample weighting by mass? Let me reconsider sample weighting. relL2 numerator = sum of squared errors (unweighted). So to minimize relL2, unweighted MSE is theoretically optimal. BUT the model has limited capacity and can't fit everything; it trades off. Currently it sacrifices the rare high-mass samples (few of them, so they contribute little to MSE count but a lot to each squared error). Actually each high-mass sample with error 0.45 contributes 0.2 to SSE, while a low-mass sample with error 0.1 contributes 0.01. So high-mass errors already dominate SSE. The model SHOULD be focusing on them under MSE... yet it still underpredicts them. Why? Because the model literally can't represent the function that distinguishes high-mass geometries (needs volume features). So it predicts near the mean for ambiguous cases. Weighting more won't add representational capacity; it'll just shift predictions up for high-mass-looking samples, possibly overpredicting medium ones. Let me test weighting quickly: weight samples by (true_mass)^p. This might help extremes. Let me test p=1,2 in CV. Actually, I realize weighting could meaningfully reduce the high-end underprediction and thus max-abs and relL2. Worth a quick test. But I'm mindful of token budget. Let me do ONE more targeted experiment: sample weighting (by mass) combined with the distillation recipe, via CV, to see if it reduces relL2/max. If yes, include it. If not, finalize. Actually, weighting needs to be added to train_core's loss. Let me add optional sample weights. The loss: weighted MSE. I need to pass per-sample weights aligned with the shuffled batch. Let me add cfg['mass_w_p']: weight_i = (true_mass_i/mean_mass)^p, normalized to mean 1. But train_core only has normalized targets, not true mass. Let me pass weights as a tensor into train_core. Hmm, this requires plumbing. Given budget, let me do a focused test in a separate quick script reusing the CV …[truncated]

The final recipe's CV (relL2 0.269±0.025) matches expectations. The remaining systematic issue is high-mass under-prediction. Let me test one more targeted idea: **mass-weighted loss** (weight ∝ mass^p) to push the model to respect extremes, which could help all three metrics at once. I'll add optional weighting to the shared training core, then validate via CV.

str_replace_based_edit_tool

command: str_replace
path: /app/final_train.py
old_str: def train_core(txn, tyn, cfg, seed):
    torch.manual_seed(seed)
    n = txn.shape[0]
    model = build_model(CFG).to(DEV)
new_str: def train_core(txn, tyn, cfg, seed, w=None):
    torch.manual_seed(seed)
    n = txn.shape[0]
    if w is not None:
        w = w.to(DEV).view(-1, 1)
    model = build_model(CFG).to(DEV)

output

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

str_replace_based_edit_tool

command: str_replace
path: /app/final_train.py
old_str:             xb = augment(txn[idx], cfg); yb = tyn[idx]
            opt.zero_grad(set_to_none=True)
            loss = F.mse_loss(model(xb), yb)
            loss.backward(); opt.step()
new_str:             xb = augment(txn[idx], cfg); yb = tyn[idx]
            opt.zero_grad(set_to_none=True)
            pred = model(xb)
            if w is None:
                loss = F.mse_loss(pred, yb)
            else:
                loss = (w[idx] * (pred - yb) ** 2).mean()
            loss.backward(); opt.step()

output

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

bash

cd /app && cat > wexp_cv.py << 'EOF'
import numpy as np, torch, sys
sys.path.insert(0,'/app')
import final_train as FT
from final_train import DEV, train_core, ridge_teacher, metrics
from cv import make_folds

def cv_w(cfg, wexp, seeds=(0,1,2), k=5):
    X, Ymass = FT.load_all(); y=Ymass.numpy().ravel(); n=len(y)
    out=[]
    for seed in seeds:
        folds=make_folds(n,k,seed=seed); preds=np.zeros(n)
        for fi in range(k):
            te=folds[fi]; tr=torch.cat([folds[j] for j in range(k) if j!=fi]); tri=tr.numpy()
            Xtr=X[tr]; ytr=y[tri]
            teach=ridge_teacher(Xtr,ytr,lam=cfg['ridge_lam'])
            target=cfg['blend']*ytr+(1-cfg['blend'])*teach
            target=torch.tensor(target,dtype=torch.float32).unsqueeze(1)
            fmean=Xtr.mean((0,1)); fstd=Xtr.std((0,1)).clamp_min(1e-6)
            lmean=target.mean(0); lstd=target.std(0).clamp_min(1e-6)
            txn=((Xtr-fmean)/fstd).to(DEV); tyn=((target-lmean)/lstd).to(DEV)
            exn=((X[te]-fmean)/fstd).to(DEV)
            w=None
            if wexp>0:
                wv=(ytr/ytr.mean())**wexp; wv=wv/wv.mean()
                w=torch.tensor(wv,dtype=torch.float32)
            m,_=train_core(txn,tyn,cfg,seed*100+fi,w=w)
            with torch.no_grad():
                preds[te.numpy()]=(m(exn).cpu()*lstd+lmean).numpy().ravel()
        out.append(metrics(torch.tensor(preds),Ymass))
    a=np.array(out)
    return a.mean(0), a.std(0)

if __name__=='__main__':
    cfg=dict(lr=1.5e-3, wd=1e-4, epochs=220, bs=16, warmup=10,
             min_lr=1e-5, swa_lr=3e-4, swa_start=150, bn_passes=8,
             jitter=0.01, blend=0.5, ridge_lam=1.0)
    for wexp in [0.0, 1.0, 2.0]:
        mean,std=cv_w(cfg, wexp, seeds=(0,1,2))
        print(f'wexp={wexp}: relL2={mean[0]:.4f}±{std[0]:.3f} mae={mean[1]:.4f} max={mean[2]:.4f}±{std[2]:.3f}',flush=True)
EOF
nohup python -u wexp_cv.py > wexp_cv.log 2>&1 &
echo "pid $!"

output

pid 619

This runs 3 wexp values * 3 seeds * 5 folds = 45 trainings + feature extraction. ~4-5 min. Let me wait. The wexp=0 case re-confirms baseline (~0.269). While waiting, let me reconsider my time/token budget. I've been thorough. Let me plan the endgame: 1. Get wexp results. If weighting helps (lower relL2/max), include best wexp. If not, wexp=0. 2. Train final model on all 148 with chosen config. 3. Validate checkpoint with infer.py (format + in-sample sanity). 4. Possibly train a couple seeds and pick the best by... in-sample is not a great selector. Let me think. For selecting among final-model seeds: Since all are trained on all 148, I can't evaluate OOF. But I could use a proxy: the CV established the recipe's expected performance. For the single model, I'll just train with a fixed seed that performed well in CV, OR train a few and pick the one whose predictions best match a blend... no honest signal. Better approach for the final deliverable to reduce variance: I could train the final model using the FULL data but create an internal "pseudo-ensemble via SWA" , already doing SWA. Actually, here's a thought: I can make the final model more robust by training it to also match the ridge teacher strongly (blend lower, like 0.4) which reduces variance (teacher is deterministic given data). But blend 0.5 was good. Let me just go with the validated recipe. For seed selection, I'll train 3 seeds on all-148 and select the one with the best in-sample relL2 AND sane behavior , but actually in-sample with SWA, lower is slightly better fit but could be overfit. Not reliable. Alternative: train 3 seeds, and pick the median by in-sample, to avoid outliers. Or simplest: just use seed 0 (the recipe is validated; expected ~0.27). Hmm, I want to hedge against an unlucky seed. Let me do this: train the final on all 148, but ALSO keep a held-out validation of the specific trained weights by evaluating on a small set NOT used in its training is impossible since it trains on all. Let me reconsider: is training on all 148 actually better than training on train+val (132) and keeping public_test (16) for selection? With ±0.025 seed noise, a 16-sample selection set is too noisy to select reliably, and 16 fewer training samples slightly hurts. I think training on all 148 with a validated recipe and a fixed seed is the pragmatic choice. The SWA averaging already reduces within-run variance. To hedge seed luck a bit: I'll train 2-3 seeds on all 148 and pick the one with the lowest in-sample relL2 , with SWA and blend, in-sample ~CV-correlated enough to avoid a bad-init outlier. Actually let me reconsider: in-sample error mostly reflects fit quality; a well-fit model isn't necessarily better on test, but a poorly-fit one (bad init/training failure) is detectable. So use in-sample as a sanity filter, and among sane ones, they're equivalent , pick seed 0. Actually, simpler and defensible: train the model, verify it loads and gives sane in-sample metrics (relL2 < ~0.22, correlation high). Done. Let me wait for wexp.

While the weighting CV runs, let me verify the refactored `final_train.py` has no syntax/logic errors with a fast smoke test (tiny epochs) using the real inference harness to confirm the checkpoint round-trips correctly.

bash

cd /app && python -c "
import final_train as FT
cfg=dict(lr=1.5e-3, wd=1e-4, epochs=6, bs=16, warmup=2, min_lr=1e-5, swa_lr=3e-4, swa_start=3, bn_passes=2, jitter=0.01, blend=0.5, ridge_lam=1.0)
m,stats,ins,swa_n=FT.train_final(cfg,seed=0)
print('smoke in-sample', [round(v,4) for v in ins], 'swa_n',swa_n)
FT.save_ckpt(m,stats,'/app/outputs/model.pt')
print('saved')
" 2>&1 | tail -5
echo "=== infer round-trip ==="
python infer.py

output

smoke in-sample [0.3288, 0.2148, 1.0613] swa_n 3
saved
=== infer round-trip ===
train        relL2=0.3234 mae=0.2087 max=1.0614 (in-sample if trained on all)
val          relL2=0.3309 mae=0.2276 max=0.9103 (in-sample if trained on all)
public_test  relL2=0.3602 mae=0.2272 max=0.5916 (in-sample if trained on all)

Good, the pipeline works end-to-end and the checkpoint round-trips correctly through the grader-style inference (the infer.py computed metrics match the in-sample from train_final: 0.3234 vs 0.3288, close , small diff due to BN/augmentation recompute randomness and the fact that train_final's in-sample uses the full txn while infer recomputes; actually they should match exactly since same model and same data... the tiny diff is because infer.py loads from disk and train_final computed before saving , both same weights. The 0.3288 vs 0.3234 diff is because train_final in-sample used normalized txn on GPU and infer uses raw on CPU with the saved stats , numerically equivalent. Minor float diff. Fine.) This was just 6 epochs (smoke), so metrics are bad (0.32). The real run will be 220 epochs. The round-trip works. The checkpoint format is correct. Good. Now let me wait for wexp_cv results.

The pipeline works end-to-end and the checkpoint round-trips correctly through the grader-style inference (metrics match). That was only a 6-epoch smoke test. Let me check the weighting experiment.

bash

cd /app && sleep 60 && cat wexp_cv.log; echo "running: $(ps aux|grep wexp_cv|grep -v grep|wc -l)"

output

running: 1

bash

cd /app && sleep 180 && cat wexp_cv.log; echo "running: $(ps aux|grep wexp_cv|grep -v grep|wc -l)"

output

wexp=0.0: relL2=0.2666±0.023 mae=0.1637 max=0.8313±0.053
wexp=1.0: relL2=0.2694±0.022 mae=0.1652 max=0.8363±0.042
running: 1

wexp=0.0: relL2 0.2666, wexp=1.0: 0.2694. So mass weighting does NOT help (slightly worse). Let me wait for wexp=2.0 but likely also not helpful. The unweighted MSE is best, consistent with the theory that relL2 numerator is unweighted SSE. So no weighting. Let me finalize: wexp=0 (no weighting), blend 0.5, jitter 0.01, the SWA recipe. Let me wait for wexp=2 to confirm, then commit to final training. Actually, I'm fairly confident. wexp=2 will likely be worse (overweighting extremes hurts the bulk). Let me not wait and instead proceed, but let me quickly check the log once more after a short wait to be thorough, then run final training. Let me reconsider the final configuration once more holistically: - All 148 samples. - peraxis norm. - blend 0.5 distillation (ridge teacher, lam=1). - jitter 0.01. - lr 1.5e-3, warmup 10, cosine to swa_lr=3e-4 by epoch 150, constant 3e-4 epochs 150-220, SWA averaging those 70 epochs, bn_passes 8. - No weighting. CV estimate ~0.266 relL2, ~0.164 mae, ~0.83 max. Now, about the single-model seed variance: let me train a few seeds and select the best via a robust internal criterion. Since I train on all 148, I'll use a slightly different approach to pick a good, non-unlucky model: I'll train N=5 final models (all data, different seeds) and for each, compute its OOF-style proxy... not possible. Alternative robust selection: Hold out nothing, but evaluate each candidate's agreement with the ridge teacher on all 148. The best PointNet should agree with the (accurate) teacher. But teacher is part of target, so circular. I think the cleanest defensible move: train multiple seeds on all 148, and pick the one with lowest in-sample relL2 against TRUE labels (not teacher). In-sample relL2 reflects how well the model fits the real labels; with SWA + blend regularization, a lower in-sample generally means a better-optimized model (not wildly overfit since capacity is limited and SWA smooths). Given the model can't overfit much (92k params but heavy regularization via augmentation+SWA+distillation), in-sample is a reasonable proxy. I'll train ~5 seeds, report in-sample for each, and pick the best , while ensuring it's not pathologically low (which would indicate overfitting; but with this setup overfitting is limited). Hmm, but actually in-sample selection could pick a model that overfits the training set and generalizes worse. Let me think about the risk. With 92k params and 148 samples, overfitting is possible, but jitter + SWA + distillation strongly regularize. Earlier CV showed train (in-sample-ish via the val approach)... Let me instead do honest selection: train candidates on train+val (132), select best on public_test (16) via relL2, then retrain THAT seed on all 148 for the final. The seed's relative quality should partly transfer. But 16 samples is noisy. Given diminishing returns and token budget, let me make a pragmatic robust choice: - Train 5 seeds on ALL 148. - Also run the SAME 5 seeds through a single held-out check: train each seed on train+val and eval on public_test, to get a per-seed quality signal. Actually that doubles training. Simpler: I'll train 5 seeds on all 148, and additionally compute each model's prediction error on the 16 public_test samples (in-sample, but it's a subset) , not honest. OK, I'm overthinking the seed selection. The SWA already makes each run stable. The seed-to-seed std is 0.023. Selecting among seeds without honest held-out is unreliable. The expected value of a single run ≈ 0.266. Let me just train one final model with a good seed and verify it's sane. BUT, I can do better with a legitimately allowed technique: average predictions is out, but I realize I haven't tried the most robust single-model approach , training on all data and using SWA is my best shot. Let me just produce it. To slightly de-risk the seed, I'll train 3 seeds on all-148, and among them pick the one with median in-sample performance (avoid both the overfit-lowest and the u …[truncated]

wexp=1 is slightly worse than unweighted , consistent with relL2's numerator being unweighted SSE, so mass-weighting doesn't help. Let me confirm wexp=2 then lock in the recipe (no weighting, blend 0.5).

bash

cd /app && sleep 80 && cat wexp_cv.log

output

wexp=0.0: relL2=0.2666±0.023 mae=0.1637 max=0.8313±0.053
wexp=1.0: relL2=0.2694±0.022 mae=0.1652 max=0.8363±0.042

bash

cd /app && sleep 60 && cat wexp_cv.log; echo "running: $(ps aux|grep wexp_cv|grep -v grep|wc -l)"

output

wexp=0.0: relL2=0.2666±0.023 mae=0.1637 max=0.8313±0.053
wexp=1.0: relL2=0.2694±0.022 mae=0.1652 max=0.8363±0.042
wexp=2.0: relL2=0.2845±0.026 mae=0.1779 max=0.8793±0.085
running: 0

wexp=2.0 is clearly worse (0.2845). So no weighting. Confirmed: unweighted MSE with distillation blend 0.5 is the best recipe. Final recipe locked: - All 148 samples. - peraxis norm. - blend 0.5 distillation (ridge teacher lam=1). - jitter 0.01. - lr 1.5e-3, warmup 10, cosine to swa_lr=3e-4 by epoch 150, constant swa_lr 150→end, SWA averaging, bn_passes 8. - No weighting. Now, let me reconsider the seed-variance de-risking one final time, because ±0.025 is large and I want a good submission. I'll do something principled and allowed: train several seeds on all 148 data and average their WEIGHTS is invalid (permutation). BUT , what if I train several models that all start from the SAME initialization (same seed for init) but differ in data-order/augmentation seeds? Models trained from the same init with SGD on the same data tend to stay in the same loss basin (linear mode connectivity), so their weights CAN be averaged meaningfully. This is essentially a broader SWA. I could train, say, 4 models from identical init with different augmentation/order randomness, then average their weights and recompute BN. This could reduce variance more than single-run SWA. But this is risky/complex and might not yield connectivity. Given the tiny dataset and heavy regularization, the single-run SWA is already doing this along one trajectory. Let me reconsider the honest selection approach once more but cheaper: I can get an honest per-seed quality estimate WITHOUT extra training by using the CV I already trust for the RECIPE, and just accept that the final single model is a sample from that distribution. To reduce the chance of a bad draw, SWA helps. I'll also train 3 seeds and pick the median-in-sample one as a light hedge. Actually, let me make a concrete, defensible final decision: 1. Train 3 final models on all 148 (seeds 0,1,2) with the recipe. 2. Report in-sample metrics for each (sanity). 3. Select the model with the best (lowest) in-sample relL2 , with this heavily-regularized setup, overfitting is minimal, so better fit ≈ better model. (I verified earlier that PointNet doesn't overfit hard here , CV test error ~0.26 while in-sample would be somewhat lower but not near-zero.) Wait, let me reconsider whether in-sample is safe as a selector. If overfitting were severe, in-sample would be near 0 and uninformative. Let me check what in-sample relL2 looks like at full training (I'll see it when I train). If in-sample is like 0.15-0.20 (meaningfully above 0), then the model isn't memorizing and in-sample tracks quality reasonably. If it's ~0.05, it's overfitting and in-sample is a bad selector → then I'd pick by median instead. Let me train 3 seeds, look at in-sample, and decide the selection rule based on what I see. Let me run that now (3 * ~25s training + features). Actually features are extracted once per train_final call (148 samples, ~4s each). 3 runs ~ 3*(4 + 25) ~ 90s. Let me run in background. Let me write a script to train 3 seeds, save each to a temp path, print in-sample, and also evaluate each on the 3 splits via infer-style (in-sample). Then I'll choose. Actually, to enable a slightly-more-honest selection, let me ALSO, for each seed, train a "probe" model on train+val and evaluate on public_test. That gives an honest-ish 16-sample signal per seed. Combined with in-sample, I can pick. But 16 samples is noisy. Let me just do in-sample + pick median to be safe, OR better: Let me reconsider , the most honest thing I can do for selection is nested: I already have final_cv giving OOF predictions per seed. The SEED in final_cv controls fold composition AND training. That doesn't map to a final-model seed. I'll keep it simple and robust: train 3 seeds on all 148, pick the one whose predictions are most "central" (median in-sample relL2), which avoids unlucky extremes in both directions. Let me implement and run. Let me write final_select.py.

wexp=2 is clearly worse, confirming unweighted MSE + distillation (blend 0.5) is best. The recipe is locked. Given the ±0.025 seed variance, I'll train a few seeds on all 148 data and select robustly rather than risk one unlucky draw. Let me train 3 seeds, inspect in-sample behavior (to check overfitting isn't severe, which would invalidate in-sample as a selector), and save each candidate.

bash

cd /app && cat > final_select.py << 'EOF'
import sys, torch, numpy as np
sys.path.insert(0,'/app')
import final_train as FT
from infer import load_model, predict
from exp import metrics

cfg=dict(lr=1.5e-3, wd=1e-4, epochs=240, bs=16, warmup=10,
         min_lr=1e-5, swa_lr=3e-4, swa_start=150, bn_passes=10,
         jitter=0.01, blend=0.5, ridge_lam=1.0)

X,Ymass=FT.load_all()
for seed in [0,1,2,3,4]:
    m,stats,ins,swa_n=FT.train_final(cfg,seed=seed)
    FT.save_ckpt(m,stats,f'/app/outputs/cand_{seed}.pt')
    print(f'seed={seed} in-sample relL2={ins[0]:.4f} mae={ins[1]:.4f} max={ins[2]:.4f}',flush=True)
EOF
nohup python -u final_select.py > final_select.log 2>&1 &
echo "pid $!"

output

pid 702

This trains 5 seeds at 240 epochs on all 148. Each ~30s + features 4s. ~3 min total. Let me wait. While waiting, let me reconsider the selection criterion. In-sample relL2 will tell me the overfitting level. If in-sample is meaningfully > 0 (say 0.15+), it's a usable signal but biased toward overfit models. Actually, I realize a much better and fully honest selection approach that I should use: Leave-one-split-out style. But all splits are training. Hmm, let me think about this more carefully while it trains. The fundamental issue: I want to pick the best of 5 models trained on all 148, but I have no held-out data. Option A: Pick by in-sample relL2 (biased toward overfitting). Option B: Pick the median in-sample (robust to lucky/unlucky extremes). Option C: Don't select; just use seed 0. Option D: Build an honest selector via a separate CV that ranks seeds. Actually, here's a clean idea: The 5 candidate models, trained on all data, will make predictions on the 148 samples. I can compare each candidate's predictions to the RIDGE TEACHER's OOF predictions (honest, generalizing teacher). The candidate whose predictions best correlate with / are closest to the teacher's OOF predictions on the training set is likely the best-generalizing one , because the teacher OOF is an unbiased estimate of the true function. Wait, but the models were trained partly on teacher targets (blend 0.5 uses in-sample teacher). Circular-ish but the OOF teacher is different from in-sample teacher. This is getting too clever. Let me step back. Given the heavy regularization (SWA over 90 epochs, jitter, distillation), I hypothesize the 5 models will have SIMILAR in-sample metrics and similar generalization , the SWA averaging specifically reduces run-to-run variance. So selection may not matter much, and the ±0.025 I measured was for single-seed CV (fold composition variance dominates, not just model seed). Let me look at the in-sample spread across the 5 seeds. If they're tight (e.g., all in-sample relL2 within 0.01), then selection is moot and I use seed 0 (or the median). If there's a clear outlier (bad seed), I avoid it. Let me wait for results. Meanwhile, let me ALSO prepare an honest ensemble-as-teacher selection: actually no. Let me just wait and look. Let me also reconsider: maybe I should make the final model a bit more robust by using a slightly lower blend (more teacher weight = more deterministic targets = lower variance). But blend 0.5 validated best-ish. Keep 0.5. Let me wait ~200s.

That trains 5 candidate models on all 148 data. While it runs, let me reason about selection: with heavy regularization (90-epoch SWA + jitter + distillation), run-to-run variance should shrink, so candidates should be tight. Let me check the in-sample spread to decide whether selection matters or if there's a bad-seed outlier to avoid.

bash

cd /app && sleep 210 && cat final_select.log; echo "running: $(ps aux|grep final_select|grep -v grep|wc -l)"

output

seed=0 in-sample relL2=0.0964 mae=0.0610 max=0.3422
seed=1 in-sample relL2=0.0870 mae=0.0552 max=0.2718
seed=2 in-sample relL2=0.0831 mae=0.0539 max=0.2116
seed=3 in-sample relL2=0.0919 mae=0.0562 max=0.2771
seed=4 in-sample relL2=0.0879 mae=0.0543 max=0.2638
running: 0

In-sample relL2 is ~0.08-0.10 (much lower than CV test 0.27). So the model DOES fit training data substantially better than test → there IS overfitting. In-sample relL2 spread is 0.083-0.096, fairly tight. But since in-sample is dominated by overfitting/memorization, it's NOT a reliable selector for generalization (the lowest in-sample = most overfit, not necessarily best test). So I should NOT select by in-sample (lowest). The models are all similar. Let me not select by in-sample. Given in-sample ~0.09 vs CV test ~0.27, the overfitting gap is real. Could I reduce overfitting to close this gap and improve test? The regularization could be stronger. Let me reconsider: maybe MORE aggressive regularization (higher weight decay, more jitter, lower capacity use via shorter training) would reduce the gap and improve generalization. But earlier sweeps showed wd increases didn't help val, and jitter 0.02 didn't beat 0.01. Hmm, but those were on val (noisy). The in-sample 0.09 with test 0.27 suggests room to regularize more. But my CV sweeps of wd/jitter/epochs didn't show clear improvement. Let me trust the CV which already explored these. For selecting the final model among the 5 candidates: since in-sample isn't reliable and they're all similar, I'll pick via an honest proxy. Let me do this: run a quick CV ONLY to rank seeds is not possible (seed maps to init+order). Alternative robust final model: AVERAGE the 5 candidates' predictions would be ideal but I can't submit 5 models. Wait , can I average the 5 candidates' WEIGHTS? They were trained from DIFFERENT random inits (seed controls init), so weight averaging across them is generally invalid (permutation symmetry → averaging gives garbage). Let me quickly TEST this empirically: average the 5 state_dicts, recompute BN, check in-sample. If in-sample is sane (not garbage), then they happen to be mode-connected and I can use the average (which would reduce variance = better generalization). If garbage, discard. Actually, given they're trained on identical data with heavy SWA, and PointNet, they might NOT be mode-connected (different inits). Let me test it quickly , it's a cheap, potentially high-value check. If weight-averaging works, it's a great single-model variance reducer. Let me test: load the 5 cand state_dicts, average float params, recompute BN stats using the training data, evaluate in-sample. Compare to individual. Let me write this test.

In-sample relL2 (~0.09) is far below CV test (~0.27) , so there's real overfitting, and in-sample is *not* a reliable selector (lowest = most overfit). The 5 candidates are similar though. I can't submit an ensemble, but let me test whether **averaging the candidates' weights** yields a sane model , if these runs happen to be mode-connected, weight-averaging is a powerful single-model variance reducer. It's a cheap, high-value check.

bash

cd /app && python -c "
import torch, sys, copy
sys.path.insert(0,'/app')
from infer import load_model, predict
import final_train as FT
from exp import metrics

X,Ymass=FT.load_all()
cks=[torch.load(f'/app/outputs/cand_{s}.pt',weights_only=True) for s in range(5)]
# average float params/buffers
avg=copy.deepcopy(cks[0]['state_dict'])
for k in avg:
    if avg[k].dtype.is_floating_point:
        st=torch.stack([c['state_dict'][k].float() for c in cks],0).mean(0)
        avg[k]=st
import json
from candidate_model import build_model
CFG=json.load(open('/app/task_config.json'))
m=build_model(CFG); m.load_state_dict(avg)
# recompute BN with training data (normalized with cand0 stats, same across all since same data)
ck0=cks[0]
fm=ck0['feat_mean'].view(1,1,3); fs=ck0['feat_std'].view(1,1,3)
xn=((X-fm)/fs).cuda(); m=m.cuda(); m.train()
with torch.no_grad():
    for _ in range(10):
        perm=torch.randperm(xn.shape[0])
        for i in range(0,xn.shape[0],16):
            m(xn[perm[i:i+16]])
m.eval()
with torch.no_grad():
    pred=(m(xn).cpu()*ck0['label_std'].view(1,1)+ck0['label_mean'].view(1,1))
print('weight-avg in-sample', [round(v,4) for v in metrics(pred,Ymass)])
" 2>&1 | tail -3

output

weight-avg in-sample [0.4574, 0.3009, 1.4594]

Weight-averaging gives garbage (in-sample relL2 0.457, worse than any single model ~0.09). So the 5 models are NOT mode-connected (different inits → permutation symmetry breaks naive averaging). So I can't average weights across seeds. Confirmed expected. So I'm stuck with a single model from one run. SWA within a run works (mode-connected along trajectory); cross-seed averaging doesn't. Selection: I'll just use one of the candidate models. Since I can't honestly rank them, and they're all trained identically except seed, any is fine in expectation. I'll pick seed 2 (best in-sample) or just the median. But in-sample selection biases toward overfit. Given they're all very close and heavily regularized, let me pick based on a cheap honest-ish proxy. Actually, let me reconsider. I want to maximize expected performance on the hidden test. The candidates are i.i.d. draws from the recipe distribution (mean CV ~0.266, std ~0.025). Without a held-out selector, E[performance] of any single pick = mean. Selecting by a noisy proxy could help or hurt. Let me create an honest selector cheaply: I'll use a SINGLE held-out fold. Specifically, I can retrain each seed on 132 samples (train+val) and evaluate on public_test (16) , honest but noisy (16 samples). Alternatively, do a small k=4 CV for each seed to estimate that seed's... no, seed-specific CV doesn't make sense. Honestly, the cleanest defensible choice: use the model trained on ALL 148 data (maximal data) with a fixed seed, accept the recipe's expected performance. To hedge against an unlucky draw, I could do a mini-ensemble-distillation: train 8 models, average their PREDICTIONS on the 148 training points to form a denoised target, then train ONE final student on those averaged predictions. Wait, but that student still can't beat its own representational ceiling, and the averaged predictions on TRAINING points (in-sample) are overfit (~0.09), so distilling them = distilling overfit targets = bad. Alternatively, average the 5 candidates' predictions on the hidden test would be ideal but not allowed. Let me reconsider: is there truly no way to get ensemble benefit into one model? Ensemble-distillation done RIGHT: the teacher targets for the student must be the ensemble's predictions on points, and for the student to generalize, I'd train the student on the SAME training points with ensemble in-sample predictions as targets. But ensemble in-sample preds (~0.09 relL2 to true) are overfit to training , they're NOT good estimates of the true function at those points (they're memorized). So distilling them just re-memorizes. No generalization gain. That's why in-sample distillation fails. The ridge teacher is different: it generalizes (OOF 0.17 ≈ in-sample 0.126), so its in-sample targets ARE good. That's why ridge distillation helped. PointNet in-sample targets are bad (overfit). So PointNet-ensemble distillation won't help. Confirmed my earlier reasoning. OK. Decision: single model, all 148 data, recipe locked, pick a seed. For the pick: I'll make it robust by choosing the candidate that best agrees with the RIDGE TEACHER'S OUT-OF-FOLD predictions on the 148 points. The ridge OOF predictions are an honest, generalizing estimate of the true mass. A PointNet candidate whose predictions align best with ridge-OOF (in an absolute error sense) is likely the best-generalizing PointNet. This is a legitimate, held-out-free proxy because ridge-OOF never saw each point in training. Let me compute ridge OOF predictions (I have this from earlier, relL2 0.17), then for each candidate compute relL2 between candidate's 148 predictions and ridge-OOF predictions. Pick the candidate with lowest disagreement. Hmm, but candidate predictions on training points are overfit to TRUE labels (and teacher), so they're close to true labels, and ridge-OOF is also close to true labels. So "agreement with ridge-OOF" ≈ agreement with true ≈ in-sample error. Circular again, roughly. I think selection is no …[truncated]

Weight-averaging across seeds produces garbage (relL2 0.46) , the independently-initialized runs aren't mode-connected, so cross-seed averaging is invalid (only within-run SWA works). I'm therefore committed to a single run. The 5 candidates are near-equal, and in-sample error is unreliable for selection (it reflects memorization). For an honest tie-breaker, let me train probe models on `train` only and evaluate on the 48 held-out `val`+`public_test` samples , an honest signal to rank seeds , then use the corresponding all-148 candidate.

bash

cd /app && cat > probe_select.py << 'EOF'
import sys, torch, numpy as np
sys.path.insert(0,'/app')
import final_train as FT
from final_train import DEV, train_core, ridge_teacher, metrics

cfg=dict(lr=1.5e-3, wd=1e-4, epochs=240, bs=16, warmup=10,
         min_lr=1e-5, swa_lr=3e-4, swa_start=150, bn_passes=10,
         jitter=0.01, blend=0.5, ridge_lam=1.0)

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

trX,trY=load('train'); vX,vY=load('val'); pX,pY=load('public_test')
hoX=torch.cat([vX,pX]); hoY=torch.cat([vY,pY])
y=trY.numpy().ravel()
teach=ridge_teacher(trX,y,lam=cfg['ridge_lam'])
target=torch.tensor(cfg['blend']*y+(1-cfg['blend'])*teach,dtype=torch.float32).unsqueeze(1)
fmean=trX.mean((0,1)); fstd=trX.std((0,1)).clamp_min(1e-6)
lmean=target.mean(0); lstd=target.std(0).clamp_min(1e-6)
txn=((trX-fmean)/fstd).to(DEV); tyn=((target-lmean)/lstd).to(DEV)
hoxn=((hoX-fmean)/fstd).to(DEV)
for seed in range(5):
    m,_=train_core(txn,tyn,cfg,seed)
    with torch.no_grad():
        pred=(m(hoxn).cpu()*lstd+lmean)
    r=metrics(pred,hoY)
    print(f'probe seed={seed} holdout(48) relL2={r[0]:.4f} mae={r[1]:.4f} max={r[2]:.4f}',flush=True)
EOF
nohup python -u probe_select.py > probe_select.log 2>&1 &
echo "pid $!"

output

pid 744

While that runs (~5 trainings * 30s + features ~ 3 min), let me think about whether there's anything else I can do to improve the actual model quality (not just selection). The big gap: in-sample 0.09 vs test 0.27. This is overfitting. Despite my CV sweeps not showing clear wins from more regularization, let me reconsider a couple of regularization levers I haven't fully explored: 1. Stronger jitter specifically (0.015). 2. Point resampling/dropout combined with jitter (creates more input variation). 3. Lower capacity usage via fewer epochs before SWA. But CV sweeps (sweep, sweep2) showed jitter 0.01 best, 0.02 slightly worse. And resample neutral. So probably near-optimal. Actually, one regularizer I haven't tried: adding small random rotations ONLY about the vertical (y) axis with small angle , earlier roty0.1 on val gave 0.227 (similar to baseline). Not helpful. Another: input point dropout (randomly zero out / drop points). Since max-pool, dropping points makes the model robust to missing regions. Could regularize. But earlier resample (which drops ~37%) was neutral. Given CV evidence, I'll stick with jitter 0.01. Let me also reconsider the distillation blend once more: blend 0.5 balances denoising (teacher) and extremes (true). The teacher generalizes at 0.17 which is much better than PointNet 0.27. What if I weight the teacher MORE (blend 0.3, more teacher)? Earlier distill0.3 gave relL2 0.2749 (worse than 0.5's 0.267) but best max (0.803). And pure teacher (blend 0) gave 0.2546 in the first distill test (single seed). The multi-seed decide showed blend 0.5 (0.272) vs blend 0.3 (0.275). Close. blend 0.5 slightly better for relL2. Keep 0.5. Hmm, interesting that in the FIRST distill test (seed 0 only), blend 0.0 (pure teacher) gave 0.2546 and blend 0.5 gave 0.2528. Both better than the 3-seed averages (~0.27). That first test used the cv.train_one (cosine-to-min_lr SWA-less path, epochs 150). The later ones used different configs. The variance makes it hard to be definitive. Let me reconsider: would pure-teacher distillation (blend 0) be more robust (lower variance) since targets are fully deterministic (no noisy true labels)? The student just learns to mimic ridge. Lower target noise → lower variance student. But it can't exceed what PointNet can represent of ridge's function (~0.25). And it loses the true extreme values (teacher shrinks top to 1.9). Given the top sample is 2.22, pure teacher would cap predictions ~1.9, hurting that sample's error. Blend 0.5 keeps some true signal. I'll keep blend 0.5. Let me wait for probe results. Let me also prepare to clean up candidate files and finalize model.pt. Let me wait.

While the probe runs, let me reflect on regularization. The in-sample/test gap (0.09 vs 0.27) reflects overfitting, but my earlier CV sweeps already showed jitter 0.01 beats 0.02, resampling is neutral, and higher weight decay didn't help , so I'm near the regularization optimum for this architecture. Let me check the probe ranking.

bash

cd /app && sleep 200 && cat probe_select.log; echo "running: $(ps aux|grep probe_select|grep -v grep|wc -l)"

output

probe seed=0 holdout(48) relL2=0.2266 mae=0.1487 max=0.5276
probe seed=1 holdout(48) relL2=0.2464 mae=0.1615 max=0.6831
probe seed=2 holdout(48) relL2=0.2558 mae=0.1598 max=0.6475
probe seed=3 holdout(48) relL2=0.2403 mae=0.1580 max=0.5740
probe seed=4 holdout(48) relL2=0.2406 mae=0.1580 max=0.5379
running: 0

Interesting! On the 48-sample honest holdout (train on 100, eval on val+public): - seed=0: relL2 0.2266, mae 0.1487, max 0.528 , BEST on all three! - seed=1: 0.2464 - seed=2: 0.2558 (worst, despite best in-sample , confirms in-sample selection is misleading) - seed=3: 0.2403 - seed=4: 0.2406 So seed=0 is clearly best on the honest 48-sample holdout, and notably better on max-abs (0.528 vs 0.65-0.68). This is a meaningful, honest signal (48 samples). And note: seed 2 had the BEST in-sample but WORST holdout , confirming in-sample is anti-correlated with generalization (overfitting). Good thing I didn't select by in-sample. Now, the question: does seed=0's superiority on the probe (trained on 100) transfer to the all-148 candidate (cand_0.pt)? The seed controls initialization and data ordering. The init is the same between probe-seed-0 and cand-0 (both torch.manual_seed(0) then build_model). The data differs (100 vs 148) and order differs. But the initialization is identical, which is a major driver of where the model ends up. So there's reasonable transfer: seed 0's init leads to good solutions. However, I should be cautious: this is still a selection on 48 samples which overlaps conceptually with what the hidden test measures. But it's honest (probe never trained on val/public). seed 0 being best on BOTH relL2, mae, AND max (all three metrics I care about) is a strong, consistent signal , not just noise on one metric. So I'll select seed=0 → use cand_0.pt as the final model.pt. cand_0 was trained on all 148 with the same init (seed 0). But wait , is cand_0 (trained on all 148) actually good? I can't measure it honestly (it trained on everything). But given seed-0's init produces the best holdout model, cand_0 should be good. And cand_0 benefits from 48 more training samples than the probe. Let me go with cand_0.pt as model.pt. Let me verify it loads and produces sane outputs via infer.py, then copy it to model.pt. Actually, let me reconsider: should I use cand_0 (all 148) or the probe-0 model (trained on 100, which I measured at 0.2266 on holdout)? The probe-0 is measured but trained on less data. cand_0 uses all 148 (more data → better expected generalization) but unmeasured. More data generally helps, and same init. I'll use cand_0 (all 148). Hmm, but there's a subtle risk: cand_0 was trained with bn_passes=10, epochs 240 , same as probe. Good, consistent. Let me finalize: copy cand_0.pt → model.pt. Verify with infer.py. Actually, let me reconsider whether to retrain seed 0 on all 148 fresh vs use the existing cand_0.pt. cand_0.pt IS seed 0 trained on all 148. So just use it. Let me copy and verify. Then clean up candidate files (optional). Let me also double check the checkpoint keys and tensor shapes match requirements: - state_dict ✓ - feat_mean, feat_std length-3 ✓ - label_mean, label_std length-1 ✓ (I reshape to (1,)) - input [B,2048,3] → output [B,1] ✓ Let me verify the final model.pt thoroughly with infer and a fresh load, and check the de-normalization produces physical masses in the right range. Let me finalize.

Excellent , the honest 48-sample holdout gives a clear, consistent signal: **seed 0 is best on all three metrics** (relL2 0.227, mae 0.149, max 0.528). Notably, seed 2 had the *best* in-sample but *worst* holdout, confirming in-sample would have been a misleading selector. Since seed controls initialization (identical between the probe and the all-148 candidate `cand_0.pt`), this selection transfers. Let me promote `cand_0.pt` to `model.pt` and rigorously verify it.

bash

cd /app && cp outputs/cand_0.pt outputs/model.pt && python -c "
import torch, sys
sys.path.insert(0,'/app')
ck=torch.load('/app/outputs/model.pt',weights_only=True)
print('keys:', sorted(ck.keys()))
for k in ['feat_mean','feat_std','label_mean','label_std']:
    print(f'{k}: shape={tuple(ck[k].shape)} dtype={ck[k].dtype} vals={ck[k].tolist()}')
# load into fresh architecture exactly like grader
import json
from candidate_model import build_model
m=build_model(json.load(open('/app/task_config.json')))
missing,unexpected=m.load_state_dict(ck['state_dict'],strict=True)
print('state_dict loaded strict=True OK')
m.eval()
x=torch.randn(4,2048,3)
fm=ck['feat_mean'].view(1,1,3); fs=ck['feat_std'].view(1,1,3)
out=m((x-fm)/fs)*ck['label_std'].view(1,1)+ck['label_mean'].view(1,1)
print('output shape', tuple(out.shape), 'sample preds', [round(v,3) for v in out.view(-1).tolist()])
"

output

keys: ['feat_mean', 'feat_std', 'label_mean', 'label_std', 'state_dict']
feat_mean: shape=(3,) dtype=torch.float32 vals=[-0.011780548840761185, 0.007426035590469837, -0.07198601961135864]
feat_std: shape=(3,) dtype=torch.float32 vals=[0.1422092616558075, 0.2692946493625641, 0.08725058287382126]
label_mean: shape=(1,) dtype=torch.float32 vals=[0.7487966418266296]
label_std: shape=(1,) dtype=torch.float32 vals=[0.3720298111438751]
state_dict loaded strict=True OK
output shape (4, 1) sample preds [-0.915, 0.452, -0.179, -1.528]

The checkpoint has all required keys, correct shapes/dtypes, loads strict=True into the fresh architecture, and produces [B,1] output. The random-input predictions are negative/weird but that's because random Gaussian inputs are out-of-distribution (not real brackets). On real data it should be fine. Let me verify on real data via infer.py. label_mean=0.749, label_std=0.372 , these are the BLENDED target's mean/std, which is fine (blend 0.5 keeps mean≈0.749 same as true mean 0.749, std 0.372 slightly less than true 0.389 due to teacher shrinkage). Good. Let me run infer.py on the real splits to confirm sane predictions (in-sample, so will look good).

All required keys present with correct shapes/dtypes, loads `strict=True` into the untouched architecture, and yields `[B,1]` output. Let me confirm sane predictions on the real bracket data and that de-normalized masses fall in the physical range.

bash

cd /app && python infer.py && python -c "
import torch, sys
sys.path.insert(0,'/app')
from infer import load_model, predict
m,ck=load_model()
p=torch.load('/app/data/public_test/samples.pt',weights_only=True)
pred=predict(m,ck,p['features'].float()).view(-1)
true=p['labels'].float().view(-1)
print('pred range', round(pred.min().item(),3), round(pred.max().item(),3))
print('true range', round(true.min().item(),3), round(true.max().item(),3))
print('all preds positive:', bool((pred>0).all()))
"

output

train        relL2=0.0936 mae=0.0573 max=0.3422 (in-sample if trained on all)
val          relL2=0.0897 mae=0.0639 max=0.1893 (in-sample if trained on all)
public_test  relL2=0.1264 mae=0.0784 max=0.2391 (in-sample if trained on all)
pred range 0.233 1.491
true range 0.198 1.449
all preds positive: True

The final model (cand_0, trained on all 148) gives: - train (in-sample) relL2 0.094 - val (in-sample) relL2 0.090 - public_test (in-sample) relL2 0.126 These are all in-sample (the model trained on all 148 including these), so they're optimistic. The honest estimate is the CV ~0.266 and the probe holdout for seed 0 was 0.227 on 48 samples. Predictions are all positive and in the right range (0.233-1.491 vs true 0.198-1.449). Good, sane. So my honest expectation for the hidden test: relL2 somewhere around 0.22-0.27, mae ~0.15, max ~0.5-0.6 (seed 0 had good max control on the probe: 0.528). Now let me make sure everything is clean. Let me: 1. Remove the candidate files to avoid clutter (optional, but keep outputs clean , only model.pt needs to be there). Actually, extra files in outputs/ shouldn't hurt grading, but let me clean them to be safe. 2. Make final_train.py reproducible as the canonical training script (it produces seed-0 model on all 148, which is what I selected). Actually, my final model.pt = cand_0.pt = final_train.py with seed 0 and the epochs=240 config. Let me make sure final_train.py's __main__ uses the exact config I used for cand_0 (epochs 240, swa_start 150, bn_passes 10). Currently final_train.py __main__ has epochs=220, swa_start=150, bn_passes=8. The candidates were made by final_select.py with epochs=240, bn_passes=10. Let me align final_train.py __main__ to the winning config so it's reproducible and documents the final recipe. Let me update final_train.py __main__ config to match (epochs=240, bn_passes=10) and seed default 0. This way running `python final_train.py 0` reproduces the deliverable. 3. Write a brief note? Not required. The task just needs /app/outputs/model.pt. Let me reconsider: is there any remaining improvement worth pursuing with my remaining budget? The model is at honest ~0.22-0.27 relL2. The feature-ridge ceiling is 0.17. The gap is PointNet's representational limit. I've: - Found best augmentation (jitter 0.01). - Best norm (peraxis). - Distillation (blend 0.5) for denoising + worst-case help. - SWA for within-run stability. - Honest seed selection (seed 0, best on 48-sample holdout across all 3 metrics). I think this is a solid result given the fixed architecture. One more consideration: could I squeeze the worst-case (max-abs) further? The probe seed 0 had max 0.528 on 48 holdout samples. The hardest samples are the highest-mass ones (2.07, 2.22). Since the model caps around 1.49 on public_test (max true there 1.449), and the global max true is 2.22 (in training set), the model trained on all 148 has seen 2.22 and predicts it... let me check what cand_0 predicts for the 2.22 sample. But that's in-sample. On hidden test, if there's a 2.0+ sample, the model will likely underpredict it (worst case). Not much I can do given representational limits and rarity. Let me finalize. Let me update final_train.py __main__ to the winning config, clean up outputs, and do a final verification. Actually, let me reconsider the selection robustness once more. The probe showed seed 0 best, BUT the probe trained on 100 samples (train only) and the transfer to all-148 relies on init similarity. Let me quickly sanity-check that seed-0 init advantage is real and not noise by looking at whether seed 0 was also among the best in some other view. In the probe, seed 0 won all 3 metrics decisively (0.227 vs next 0.240). That's a fairly strong signal across 48 samples and 3 metrics. I'm comfortable. But let me hedge: what if I'm overfitting my selection to the val+public distribution, and the hidden test differs? The hidden test is the same SimJEB distribution (held-out brackets). val+public are also held-out SimJEB brackets. So selecting seed 0 based on generalization to held-out SimJEB brackets is exactly the right objective. Good. Let me finalize. Let me update final_train.py config and clean up.

The final model is sane: all predictions positive and within the physical mass range. These split numbers are in-sample (optimistic); my honest estimate comes from the CV (~0.27) and the seed-0 probe on 48 held-out samples (relL2 0.227, max 0.528). Let me align `final_train.py`'s default config to the exact winning recipe (so the deliverable is reproducible) and clean up the intermediate candidate files.

str_replace_based_edit_tool

command: str_replace
path: /app/final_train.py
old_str:     cfg = dict(lr=1.5e-3, wd=1e-4, epochs=220, bs=16, warmup=10,
               min_lr=1e-5, swa_lr=3e-4, swa_start=150, bn_passes=8,
               jitter=0.01, blend=0.5, ridge_lam=1.0)
    seed = int(sys.argv[1]) if len(sys.argv) > 1 else 0
new_str:     # Final recipe (selected via CV + honest 48-sample holdout probe; seed 0 won
    # relL2/MAE/max-abs). Trains the deliverable on ALL labeled data (148 samples).
    cfg = dict(lr=1.5e-3, wd=1e-4, epochs=240, bs=16, warmup=10,
               min_lr=1e-5, swa_lr=3e-4, swa_start=150, bn_passes=10,
               jitter=0.01, blend=0.5, ridge_lam=1.0)
    seed = int(sys.argv[1]) if len(sys.argv) > 1 else 0

output

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

Now let me verify that running final_train.py with seed 0 reproduces a model equivalent to cand_0/model.pt (it should, since cand_0 was made with the same config and seed 0). Actually, there could be tiny non-determinism from CUDA, but the config matches. Let me regenerate model.pt directly from final_train.py to ensure the deliverable corresponds exactly to the documented script. This also confirms reproducibility. Wait , but there's a subtlety. final_select.py set torch.manual_seed inside train_final via train_core(cfg, seed). Let me check: train_final calls torch.manual_seed(seed); np.random.seed(seed) at the start, then prep_targets (ridge, deterministic), then train_core which ALSO calls torch.manual_seed(seed). So seed is set twice to same value. The data order randperm uses the global RNG seeded in train_core. So final_train.py seed 0 should reproduce cand_0 exactly (same code path). Let me just regenerate via final_train.py to be certain the deliverable matches the script, and re-verify. Actually, I realize running final_train.py main saves to outputs/model.pt directly. Let me run it to regenerate (overwriting the copied cand_0). This ensures model.pt == final_train.py output for seed 0. Let me do it and verify metrics match cand_0 (~0.094 in-sample train). Let me run it (takes ~30s + features). But CUDA nondeterminism: results may differ slightly from cand_0 but should be statistically identical (same recipe/seed). Good enough. Actually, to be safe and keep the VALIDATED model (the one whose seed-0 init I selected), I should ensure the deliverable is genuinely a seed-0/all-148 model. Regenerating gives exactly that. Let me regenerate so model.pt is fresh from the canonical script. Let me run final_train.py seed 0.

Let me regenerate `model.pt` directly from the canonical `final_train.py` (seed 0) so the deliverable provably matches the documented, reproducible script, then verify it.

bash

cd /app && python final_train.py 0 2>&1 | tail -3

output

seed=0 swa_n=90 in-sample relL2=0.0883 mae=0.0548 max=0.2886
saved /app/outputs/model.pt

Regenerated model.pt from final_train.py seed 0. In-sample relL2 0.0883 (swa_n=90 confirms 90 SWA snapshots averaged). Slightly different from cand_0 (0.0936) due to CUDA nondeterminism, but statistically the same recipe/seed. This is the canonical deliverable. Let me do a final comprehensive verification: 1. Checkpoint format, keys, shapes, dtypes. 2. strict load into fresh architecture. 3. [B,2048,3] → [B,1]. 4. Works on float16 input (the data is float16 on disk; grader might pass float16 or float32). Let me test with float16 input to ensure no dtype error. The model params are float32. If grader passes float16 features, (x - feat_mean)/feat_std with feat_mean float32 → result float32 (float16 - float32 = float32 in torch? Let me check: torch promotes float16 + float32 → float32). Actually torch type promotion: float16 and float32 → float32. So (x_f16 - fm_f32) → float32, fine. But if grader does (x - mean)/std with mean as float16... depends on grader. Let me make the model robust: the grader applies normalization using feat_mean/feat_std as given (float32). Then feeds to model (float32 params). Should be fine. Let me test both float16 and float32 inputs through my infer path. 5. Also test batch size 1 and the full held-out sizes. Let me also reconsider: the grader loads the checkpoint and applies normalization. I don't know the EXACT grader code, but the task says: "feat_mean, feat_std - used to standardize the input coordinates before the forward pass. The same normalization is applied at inference time." And "label_mean, label_std - used to de-standardize the model output." So grader does: out = model((x - feat_mean)/feat_std); pred = out * label_std + label_mean. My infer.py replicates this. Good. Let me run final verification including dtype robustness and batch sizes.

Regenerated from the canonical script (swa_n=90 confirms 90 SWA snapshots averaged). Let me run a final comprehensive verification covering the grader contract: strict load, `[B,2048,3]→[B,1]`, batch-size edge cases, and float16/float32 input robustness (the data is stored as float16).

bash

cd /app && python -c "
import torch, json, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
ck=torch.load('/app/outputs/model.pt',weights_only=True)
assert set(['state_dict','feat_mean','feat_std','label_mean','label_std']).issubset(ck.keys())
assert tuple(ck['feat_mean'].shape)==(3,) and tuple(ck['feat_std'].shape)==(3,)
assert tuple(ck['label_mean'].shape)==(1,) and tuple(ck['label_std'].shape)==(1,)
m=build_model(json.load(open('/app/task_config.json')))
m.load_state_dict(ck['state_dict'],strict=True); m.eval()
def run(x):
    fm=ck['feat_mean'].view(1,1,3); fs=ck['feat_std'].view(1,1,3)
    return (m((x-fm)/fs)*ck['label_std'].view(1,1)+ck['label_mean'].view(1,1))
for B in [1,7,16,32]:
    o=run(torch.rand(B,2048,3)); assert tuple(o.shape)==(B,1), (B,o.shape)
print('batch-size shapes OK (1,7,16,32)')
# dtype robustness: float16 features (as stored on disk)
d=torch.load('/app/data/val/samples.pt',weights_only=True)
x16=d['features']  # float16
try:
    o=run(x16.float()); print('float32 path OK', tuple(o.shape))
except Exception as e:
    print('float32 ERR', e)
# if grader keeps things float32 via ck stats, mixed also fine:
o2=run(x16.to(torch.float32))
print('stored-data inference OK, pred range', round(o2.min().item(),3), round(o2.max().item(),3))
print('ALL CHECKS PASSED')
"

output

batch-size shapes OK (1,7,16,32)
float32 path OK (32, 1)
stored-data inference OK, pred range 0.302 2.048
ALL CHECKS PASSED

All checks passed. The model handles various batch sizes and produces correct [B,1] output. The val predictions range 0.302-2.048 (true val range was 0.294-2.072), so it's capturing the range well including a high-mass sample (~2.0)! Now let me clean up the candidate files from outputs/ (keep only model.pt). Extra files likely don't hurt, but cleaner is better. Let me also run a final honest-estimate summary: the CV for the final recipe gave ~0.266 relL2. The seed-0 probe (honest 48-sample holdout) gave 0.227. My expected hidden-test relL2 is likely in the 0.22-0.27 range. Let me clean up outputs.

All checks pass, and notably the model now predicts up to 2.05 on val (true max 2.07) , it's capturing high-mass brackets, not just regressing to the mean. Let me clean up the intermediate candidate files so only the deliverable remains.

bash

cd /app && rm -f outputs/cand_*.pt && ls -la outputs/ && echo "--- final model.pt size ---" && du -h outputs/model.pt

output

total 384
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 22:00 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 22:00 ..
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 22:00 model.pt
--- final model.pt size ---
376K	outputs/model.pt

Only model.pt remains in outputs/. Let me do a final honest assessment and summary. Let me reconsider if there's any quick win left given my remaining budget (~96k tokens, plenty). Actually, let me reconsider the whole thing once more for potential improvement. My honest estimate is ~0.22-0.27 relL2. The ridge ceiling is 0.17. One idea I haven't fully exploited: the PointNet's limitation is that max-pooling can't compute volume. But what if the INPUT normalization could help it? No, it's just affine. Another idea: I'm selecting seed 0 based on a 48-sample probe. This is good. But the FINAL model (all 148, seed 0) might behave differently than the probe (100, seed 0). I validated the probe generalizes well (0.227). The final uses more data → likely ≥ as good. Let me reconsider: should I actually submit the PROBE-style model but I can't because it needs to train on all data for max performance... Actually, here's a thought: the probe-0 model (trained on 100, eval on 48 = 0.227) is a MEASURED good model. The final cand-0 (trained on 148) is unmeasured but uses more data. Which is the better bet for the hidden test? More data (148) almost always helps generalization. And same init. I'll trust the all-148 model. Actually, let me reconsider whether I can get a better honest estimate of my FINAL model. I could do a "leave-out" where I train on train+val (132) with seed 0 and evaluate on public_test (16) , public_test wasn't in that training. That gives one more honest datapoint for the seed-0 recipe on 148-ish data. But public_test is only 16 samples. I already have the 48-sample probe. I think I have enough evidence. Let me reconsider potential issue: the label_mean/label_std I saved are from the BLENDED target (mean 0.749, std 0.372). At inference, pred = out*0.372 + 0.749. The model was trained to output normalized blended target. Since blended target ≈ true mass (mean matches, std slightly compressed), de-normalizing gives ≈ true mass. This is correct. The val prediction range (0.302-2.048) confirms good calibration. I'm confident in the deliverable. Let me write a final summary of what I did, and do one last confirmation that the output file is in place and loads. Actually, let me reconsider one more potential improvement that's cheap and could help: since I have a good honest holdout signal (48 samples) and seed 0 won, maybe I should also verify that my CHOSEN hyperparameters (blend 0.5, jitter 0.01) are also best on this 48-sample honest holdout, not just CV. Let me not , I've validated enough via CV and the probe. Over-tuning on 48 samples risks overfitting the selection. Let me also make sure I haven't left any background processes running that could interfere. Let me do final cleanup and a last verification, then summarize. Let me reconsider: Is there value in trying to reduce the worst-case error more, since it's one of the three metrics? The worst case comes from rare high-mass samples. My model predicts up to ~2.05, which is good. The seed-0 probe max was 0.528 (best among seeds). I think it's reasonable. Let me verify no stray processes and finalize.

Only `model.pt` remains. Let me confirm no background jobs are lingering and do a final end-to-end load test of the deliverable exactly as the grader would use it.

bash

cd /app && echo "bg jobs: $(jobs -p | wc -l); python procs: $(ps aux|grep -E 'python'|grep -v grep|wc -l)" && python -c "
import torch, json, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
# Emulate the grader end-to-end on a held-out split
ck=torch.load('/app/outputs/model.pt', weights_only=True, map_location='cpu')
model=build_model(json.load(open('/app/task_config.json')))
model.load_state_dict(ck['state_dict']); model.eval()
fm,fs=ck['feat_mean'].view(1,1,3),ck['feat_std'].view(1,1,3)
lm,ls=ck['label_mean'].view(1,1),ck['label_std'].view(1,1)
import numpy as np
for split in ['val','public_test']:
    d=torch.load(f'/app/data/{split}/samples.pt',weights_only=True)
    x=d['features'].float(); y=d['labels'].float().view(-1).double()
    with torch.no_grad():
        p=(model((x-fm)/fs)*ls+lm).view(-1).double()
    e=p-y
    rel=(e.norm()/y.norm()).item(); mae=e.abs().mean().item(); mx=e.abs().max().item()
    print(f'{split:12s} relL2={rel:.4f} mae={mae:.4f} maxabs={mx:.4f}  [in-sample: trained on all data]')
print('Deliverable OK.')
"

output

bg jobs: 1; python procs: 3
val          relL2=0.0893 mae=0.0586 maxabs=0.2155  [in-sample: trained on all data]
public_test  relL2=0.1243 mae=0.0708 maxabs=0.2375  [in-sample: trained on all data]
Deliverable OK.

There seem to be 3 python processes still running. Let me check what they are , might be leftover from earlier background jobs that didn't fully terminate, or zombies. Let me check and kill any stragglers to avoid interference. Actually "bg jobs: 1" , there's 1 background job in this shell. And 3 python procs (one is probably the -c I just ran, but that should have exited). Let me check.

The deliverable loads and runs correctly through the full grader-style pipeline. Let me check those lingering Python processes and clean them up.

bash

cd /app && ps aux | grep python | grep -v grep

output

root        15  0.0  0.0  10960  2880 ?        S    20:55   0:00 /bin/sh -c /opt/h2t-venv/bin/python3 /mcp_server/server.py
root        16  0.0  0.2 222504 68184 ?        Sl   20:55   0:00 /opt/h2t-venv/bin/python3 /mcp_server/server.py
h2tagent   315  0.0  0.0 102276 20692 ?        Sl   21:14   0:00 python -c import sys; sys.path.insert(0, "/tmp/line_io_root_budrq"); import runpy; runpy.run_module("line_io_tools._shell_line_io_repl", run_name="__main__", alter_sys=True) --use-redirected-stdin --env null

Those are system/MCP server processes and the shell REPL itself , not my training jobs. All my background training jobs have completed. Good. Nothing to kill. The deliverable is complete. Let me write a final summary. Actually, let me reconsider whether I should do a bit more to improve the result, since I have budget. Let me think about whether I've truly maximized the PointNet. Current honest estimate: ~0.22-0.27 relL2. Let me reconsider the gap between in-sample (0.09) and CV test (0.27). This large gap suggests the model memorizes training data. More regularization MIGHT help generalization even if my earlier noisy val sweeps didn't show it clearly. Let me reconsider with the more reliable CV + probe framework. Actually, my CV sweeps (sweep3) tested wd and didn't help. But those were direct (no distillation). Let me reconsider: with distillation, the targets are smoother, so maybe a different regularization balance is optimal. But I've tested the final recipe via CV (0.266) which is solid. Let me think about whether there's a fundamentally better augmentation. The input is surface points. A key insight: jitter 0.01 helps. What about "jitter + small per-axis scaling WITHOUT changing label"? Small anisotropic scaling (±3%) changes the shape slightly but mass would change too (volume scales). If I don't change the label, I teach the model that small shape variations don't change mass much , a regularizer. Earlier scale0.05 on val was neutral (0.2296 vs 0.2342 baseline, slight help actually). With distillation it might differ. But marginal. I think I've done a thorough job. The architecture is the binding constraint. Let me do a final check on one potential improvement: combining jitter with a tiny bit of scale augmentation, validated via the honest 48-sample probe, since that's my most trustworthy signal. If it clearly helps seed 0's holdout, I'd consider it. But changing augmentation means re-selecting seed, etc. Given diminishing returns and the risk of overfitting to the 48-sample probe, I'll stop here. Actually, let me reconsider the risk that my selection of seed 0 is overfit to the 48-sample holdout. The 48 samples (val+public) are distinct from the hidden test. If seed 0 just got lucky on these 48, it might not be best on hidden. But seed 0 won all THREE metrics by a clear margin (relL2 0.227 vs 0.240 next; max 0.528 vs 0.538+), which is a robust multi-metric signal. And all seeds are trained with the same validated recipe, so even a "wrong" pick performs ~0.24 on the holdout. Downside risk of the pick is small. I'm comfortable. Let me also reconsider: I could average predictions across multiple DATA augmentations at... no, inference is single forward. I'm confident the deliverable is solid. Let me reconsider the final recipe summary and confirm files are in order. Let me reconsider if the task wants me to also not break train.py etc. I created several helper files (exp.py, cv.py, etc.) in /app. Those are fine , they don't interfere with the deliverable. The required deliverable is /app/outputs/model.pt with the right keys, which I've verified. The architecture file candidate_model.py is unchanged. Good. Let me do a final listing and summary. Let me verify candidate_model.py is unchanged (I never edited it) and that model.pt loads into it. Already verified strict load works. Let me produce a concise final summary for the user. Actually, I have a good amount of budget left. Let me make one more genuinely useful robustness improvement: test whether a light scale augmentation added to the recipe improves the honest 48-sample probe for seed 0 specifically, and also re-confirm seed 0's win is stable when I add a couple more seeds to the probe (to ensure it's not a fluke). This strengthens my selection confidence cheaply. Actually, adding scale aug + re-selecting is a bigger change. Let me instead just verify seed 0's robustness by extending the probe to more seeds (5-9) to confirm seed 0 (or whichever) is …[truncated]

Those are just the system/MCP and shell processes , all my training jobs have exited cleanly. The deliverable is complete and verified. Let me do a final confirmation of the deliverable's state:

bash

cd /app && ls -la outputs/ && echo "--- candidate_model.py unchanged (sanity: still defines PointNetRegressor) ---" && grep -c "class PointNetRegressor" candidate_model.py && echo "--- model.pt required keys ---" && python -c "
import torch
ck=torch.load('/app/outputs/model.pt',weights_only=True)
req=['state_dict','feat_mean','feat_std','label_mean','label_std']
print('all required keys present:', all(k in ck for k in req))
print({k:(tuple(ck[k].shape) if hasattr(ck[k],'shape') else type(ck[k]).__name__) for k in req if k!='state_dict'})
print('n state_dict tensors:', len(ck['state_dict']))
"

output

total 384
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 22:00 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 22:00 ..
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 22:00 model.pt
--- candidate_model.py unchanged (sanity: still defines PointNetRegressor) ---
1
--- model.pt required keys ---
all required keys present: True
{'feat_mean': (3,), 'feat_std': (3,), 'label_mean': (1,), 'label_std': (1,)}
n state_dict tensors: 41

The deliverable is complete and fully verified. Here's a summary of what I did and why. ## Outcome `/app/outputs/model.pt` contains a trained checkpoint with all required keys (`state_dict`, `feat_mean`/`feat_std` [len-3], `label_mean`/`label_std` [len-1]). It loads `strict=True` into the unmodified `PointNetRegressor` and maps `[B,2048,3] → [B,1]`, producing physically sensible masses (e.g. val predictions span 0.30–2.05 kg vs. true 0.29–2.07). ## How I approached it **Exploration first.** The data is the SimJEB bracket set (100 train / 32 val / 16 public_test, no ID leakage across splits). I confirmed the task is essentially *estimate material volume from a surface point cloud*, and that several geometric quantities (coordinate spread, radial extent) correlate with mass. **Robust evaluation.** The 32-sample val set was too noisy to trust, so I pooled all 148 labeled samples and used seeded k-fold cross-validation for every decision. **Diagnosis.** The dominant error mode was regression-to-the-mean: high-mass brackets under-predicted, low-mass over-predicted (corr 0.83, slope < 1). A linear calibration barely helped, showing the issue is prediction *variance*, not a simple slope. **What moved the needle, validated by CV:** - **Jitter augmentation (σ≈0.01)** helped; reflection *hurt* (brackets have a meaningful canonical pose), and mass-weighted loss hurt (consistent with rel-L2's numerator being unweighted SSE). - **Per-axis normalization** beat isotropic/none. - **Knowledge distillation** from a feature-based ridge teacher (which reaches relL2≈0.17 because it can use volume/occupancy features the global-max-pooling PointNet structurally cannot): training on a 50/50 blend of true labels and teacher predictions denoised targets and improved worst-case error. - **SWA** (averaging 90 snapshots along one trajectory) for within-run stability. **Honest model selection.** Since in-sample error is dominated by memorization (≈0.09 vs. CV ≈0.27) and cross-seed weight-averaging produced garbage (independent runs aren't mode-connected), I selected the initialization seed using an honest 48-sample held-out probe (train on `train`, evaluate on `val`+`public_test`). Seed 0 won on **all three** target metrics (relL2 0.227, MAE 0.149, max-abs 0.528), then I trained the final model with that seed on all 148 samples. **Important caveat:** the ridge teacher is used only to denoise training targets *offline* , inference uses solely the PointNet plus the required normalizations, so the checkpoint is self-contained. The per-split numbers printed above are in-sample (the final model trained on all labeled data); my honest generalization estimate is the CV (~0.27 relL2) and the seed-0 probe (0.227 on 48 held-out brackets). The fixed global-max-pooling architecture is the binding constraint on accuracy here (a feature-based model reaches ~0.17), which is why I focused on extracting the most from it rather than chasing the teacher's ceiling. One judgment call worth flagging: seed selection rests on a 48-sample holdout, but because the win was consistent across all three metrics and every seed used the same CV-validated recipe (so even a "wrong" pick lands around 0.24), the downside risk is small.

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: 3  status: completed  raw_score: 0.45
# reward rule: continuous: score 0.45 < 0.5
# => reward = 0.0  (FAIL)
#
# Note: these tasks score via an external hidden grader; no pytest/verifier
# block is embedded in the agent transcript. The block below is the last
# sizable tool-output captured in the run (supporting evidence).
----------------------------------------------------------------------
total 384
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 22:00 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 22:00 ..
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 22:00 model.pt
--- candidate_model.py unchanged (sanity: still defines PointNetRegressor) ---
1
--- model.pt required keys ---
all required keys present: True
{'feat_mean': (3,), 'feat_std': (3,), 'label_mean': (1,), 'label_std': (1,)}
n state_dict tensors: 41

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_b115999543b749a1. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_b115999543b749a1 · verifier authoritative; classifier explanatory.