tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.List all Python files and submission directory
find /app -type f -name "*.py" | head -50 && ls /app/submission/
/app/run_checks.py /app/submission/nn.py /app/submission/autograd.py /app/submission/train.py /app/submission/optim.py /app/submission/data.py autograd.py data.py nn.py optim.py train.py
Check data files
ls /app/data/ 2>/dev/null && python3 -c "import numpy as np; d = np.load('/app/data/train.npz'); print({k: v.shape for k,v in d.items()})"train.npz
{'X': (2500, 3, 16, 16), 'y': (2500,)}/app/submission/autograd.py
1 """Reverse-mode automatic differentiation over numpy arrays. 2 3 YOU MUST IMPLEMENT EVERYTHING MARKED ``# TODO`` / ``raise NotImplementedError`` using numpy 4 ONLY. No torch/jax/tensorflow/autograd/etc. The grader's conformance suite (a) differentiates the 5 differentiable ops below with central finite differences and compares against YOUR analytic 6 backward, and (b) checks the LSQ ``fake_quant`` STE + scale gradient against the reference 7 definition, so every backward must be correct (a single wrong reduction / STE / Jacobian fails 8 the gradient checks and zeroes the reward). 9 10 A ``Tensor`` wraps one ``numpy.ndarray`` (stored as ``float64`` in ``self.data``) and records the 11 op that produced it so ``.backward()`` can accumulate ``.grad`` into every leaf. 12 13 CONTRACT (the grader builds its own reference against this exact API): 14 * ``Tensor(data, requires_grad=False)`` -- ``self.data`` is ``float64``; ``self.grad`` is None 15 until backward, then a same-shape ``float64`` array. requires_grad leaves accumulate grad. 16 * Every op returns a NEW Tensor whose ``requires_grad`` is the OR of its differentiable inputs, 17 and whose ``_backward`` closure does ``input._accum(local_grad)`` (so multiple paths add up). 18 * ``t.backward(grad=None)`` -- seeds ``grad`` (all-ones for a scalar) and walks the graph in 19 reverse topological order, calling each node's ``_backward``. Repeated backward accumulates. 20 * BROADCASTING: binary ops broadcast like numpy; the ba …[truncated 54775 chars]
/app/submission/nn.py
1 """QAT layers + a small INT2 CNN, built on YOUR autograd engine.
2
3 Implement every ``# TODO`` forward (the parameters + ``named_params``/``buffers`` naming are
4 already wired for you; you compose the autograd ops). The grader checks each layer's forward
5 against its OWN reference AND finite-difference-checks the gradients that flow through your
6 autograd, so the composition must be exactly right.
7
8 CHECKPOINT NAMING CONTRACT (the grader loads YOUR ``qat_model.npz`` into its own reference
9 QuantCNN by these exact keys/shapes -- do not rename):
10 stem.conv.weight (W0,3,3,3) | stem.conv.wq.scale (1,) | stem.bn.gamma/.beta (W0,) | stem.aq.scale (1,)
11 blocks.{i}.conv.weight (Cout,Cin,3,3) | blocks.{i}.conv.wq.scale (1,)
12 blocks.{i}.bn.gamma/.beta (Cout,) | blocks.{i}.aq.scale (1,)
13 head.weight (Cfeat,n_classes) | head.wq.scale (1,) | head.bias (n_classes,)
14 BN running stats are BUFFERS (saved alongside params, NOT trained by the optimizer, NOT audited):
15 stem.bn.running_mean/.running_var (W0,) | blocks.{i}.bn.running_mean/.running_var (Cout,)
16
17 Quantization: every conv/head WEIGHT is LSQ-quantized to a signed 2-bit grid {-2,-1,0,1} via a
18 per-tensor learnable step before use; every block activation (post-relu) is LSQ-quantized to an
19 unsigned 2-bit grid {0,1,2,3}. Conv = 3x3 same-padding cross-correlation. Linear head = x@weight+bias.
20 """
21 from __future__ import annotations
22
23 from typing import Dict, List
24
25 import numpy as n
…[truncated 35167 chars]/app/submission/optim.py
1 """Optimizers over autograd ``Tensor`` parameters.
2
3 Implement the ``# TODO`` update math. Both optimizers update ``param.data`` IN PLACE using
4 ``param.grad``. The grader runs one (and several) steps and compares your updated params against
5 its reference within a tight tolerance, so the math must be exact.
6
7 SGD (with momentum ``mu`` and coupled weight decay ``wd``):
8 g = grad + wd * param; v = mu * v + g; param -= lr * v
9
10 AdamW (DECOUPLED weight decay -- the decay is applied to the PARAM, not folded into the moments):
11 m = b1*m + (1-b1)*g; v = b2*v + (1-b2)*g^2
12 mhat = m / (1 - b1^t); vhat = v / (1 - b2^t) # t = 1-based step count
13 param -= lr * ( mhat / (sqrt(vhat) + eps) + wd * param )
14
15 PARAM GROUPS: both optimizers must accept EITHER a flat param list OR a list of
16 ``{"params": [...], "weight_decay": wd}`` dicts, so weight decay can be applied to SOME params
17 and not others. This matters for low-bit LSQ QAT: the per-tensor quantizer ``scale`` params,
18 BatchNorm gamma/beta, and biases must be EXCLUDED from weight decay. ``_normalize_groups`` and
19 ``split_decay_params`` are provided; honour the per-group weight decay in ``step``.
20 """
21 from __future__ import annotations
22
23 import math
24
25 from typing import Dict, List
26
27 import numpy as np
28
29 from autograd import Tensor
30
31
32 def split_decay_params(model) -> Dict[str, List[Tensor]]:
33 """Partition a model's named params into weight
…[truncated 11129 chars]/app/submission/data.py
1 """Image data loading + normalization + batching.
2
3 The dataset is a procedurally-generated CIFAR-style set: ``X`` is ``uint8`` (N,3,S,S) in [0,255],
4 ``y`` is the integer class label in [0, n_classes). The shipped training split is at
5 ``/app/data/train.npz``; the SEALED held-out split is regenerated by the verifier (never shipped).
6
7 Implement the ``# TODO`` bodies (numpy only). The verifier normalizes the held-out split with the
8 SAME transform, so match it exactly.
9
10 Contract:
11 load_npz(path) -> (X uint8 (N,3,S,S), y int64 (N,)) [provided]
12 normalize(X) -> float64: x = (X/255 - 0.5) / 0.25 [per-channel, the grader's transform]
13 iter_minibatches(Xn, y, batch_size, rng, shuffle=True) -> yields (xb, yb)
14 """
15 from __future__ import annotations
16
17 from typing import Tuple
18
19 import numpy as np
20
21 NORM_MEAN = 0.5
22 NORM_STD = 0.25
23
24
25 def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
26 d = np.load(path)
27 X = np.asarray(d["X"]).astype(np.uint8)
28 y = np.asarray(d["y"]).astype(np.int64).reshape(-1)
29 return X, y
30
31
32 def normalize(X: np.ndarray) -> np.ndarray:
33 """uint8 (N,3,S,S) -> float64, x = (X/255 - NORM_MEAN) / NORM_STD."""
34 # TODO: cast to float, scale to [0,1], subtract NORM_MEAN, divide by NORM_STD.
35 raise NotImplementedError("normalize")
36
37
38 def iter_minibatches(Xn: np.ndarray, y: np.ndarray, batch_size: int,
39 rng: np.rando
…[truncated 281 chars]/app/submission/train.py
1 """Training entry point. Implement the QAT loop, train your INT2 CNN on /app/data/train.npz, and 2 SAVE the checkpoint to /app/submission/qat_model.npz (numpy .npz of the model state_dict: every 3 param + every BN running buffer, by the nn.QuantCNN naming contract). 4 5 The grader (verify.py) will: 6 (1) run the conformance suite over your autograd/nn/optim, 7 (2) BIT-AUDIT every conv/head weight tensor in your checkpoint to <= 4 distinct float values, 8 (3) load your weights into its OWN QuantCNN and score held-out top-1 on a SEALED split. 9 10 So at SAVE time you must BAKE the quantized weights back into each ``weight`` tensor (replace it 11 with round(clip(w/s, -W_NEG, W_POS)) * s using the learned wq.scale) so the saved weights already 12 lie on the 2-bit grid and pass the audit. Reaching the top-1 floor at INT-2 needs a real recipe: 13 sensible LSQ STEP-SIZE INITIALIZATION (a default scale of 1.0 quantizes the small init weights to 14 all-zeros -> a dead network at chance), per-tensor learnable scales, weight decay EXCLUDED from the 15 scale/BN/bias params, gradient clipping, a decaying LR, and enough epochs. Naive post-training 16 rounding or under-training lands at chance. 17 18 This file is a skeleton; the loop body is yours to write. 19 """ 20 from __future__ import annotations 21 22 import argparse 23 import os 24 import sys 25 26 import numpy as np 27 28 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) 29 sys.path.insert(0, "/app") 30 3 …[truncated 1513 chars]
/app/run_checks.py
1 #!/usr/bin/env python3 2 """PUBLIC component check runner for the from-scratch numpy QAT framework. 3 4 Run this AS OFTEN AS YOU LIKE while you implement, to get a per-check progress signal: 5 6 python3 /app/run_checks.py # check the framework in /app/submission 7 python3 /app/run_checks.py --submission /some/dir 8 python3 /app/run_checks.py --only autograd_grads # run one milestone 9 python3 /app/run_checks.py --quiet # summary line only 10 11 It imports YOUR modules from /app/submission (autograd.py / nn.py / optim.py / data.py), exactly as 12 the grader does, and runs a PUBLIC portion of the component conformance suite, printing a clear 13 per-check report so you can implement -> run -> read failures -> fix -> repeat. 14 15 HOW THIS RELATES TO YOUR GRADE 16 ------------------------------ 17 This runner is a DEBUGGING AID, not the grader. It checks YOUR framework against references it can 18 compute on its own: 19 * autograd_grads : central FINITE-DIFFERENCE of YOUR forward vs YOUR analytic backward, for every 20 differentiable op + the domain ops (conv2d/avgpool2d/maxpool2d/batchnorm2d/ 21 batchnorm1d/layernorm) + end-to-end through the full QuantCNN / QuantMLP. 22 * quant_grads : the LSQ fake_quant STE indicator + scale-gradient CLIP RULES (the exact 23 structural identities the straight-through estimator must satisfy). 24 * nn_forward : …[truncated 3362 chars]
Check file sizes
wc -c /app/submission/autograd.py /app/submission/nn.py /app/submission/optim.py /app/submission/data.py /app/submission/train.py
50472 /app/submission/autograd.py 33295 /app/submission/nn.py 11509 /app/submission/optim.py 1661 /app/submission/data.py 2827 /app/submission/train.py 99764 total
"""Reverse-mode automatic differentiation over numpy arrays."""
from __future__ import annotations
from typing import Callable, Iterable, List, Optional
import numpy as np
_bi_max = max
_bi_min = min
def _as_f64(x) -> np.ndarray:
return np.asarray(x, dtype=np.float64)
def _unbroadcast(grad: np.ndarray, shape: tuple) -> np.ndarray:
while grad.ndim > len(shape):
grad = grad.sum(axis=0)
for ax, sz in enumerate(shape):
if sz == 1 and grad.shape[ax] != 1:
grad = grad.sum(axis=ax, keepdims=True)
return grad.reshape(shape)
def _expand_to(g, axis, ndim, keepdims):
"""Re-insert reduced axes so g can be broadcast back to input shape."""
if keepdims or axis is None:
return g
axes = (axis,) if isinstance(axis, int) else tuple(axis)
for ax in sorted(a % ndim for a in axes):
g = np.expand_dims(g, axis=ax)
return g
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")
def __init__(self, data, requires_grad: bool = False, _parents: Iterable["Tensor"] = ()):
self.data: np.ndarray = _as_f64(data)
self.grad: Optional[np.ndarray] = None
self.requires_grad: bool = bool(requires_grad)
self._backward: Callable[[], None] = lambda: None
self._parents: tuple = tuple(_parents)
@property
def shape(self):
return self.data.shape
@property
def ndim(self):
return self.data.ndim
def zero_grad(self):
…[truncated 48517 chars]The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
"""QAT layers + INT2 CNN/MLP/SepCNN, built on the autograd engine."""
from __future__ import annotations
from typing import Dict, List
import numpy as np
import autograd as ag
from autograd import Tensor
W_NEG, W_POS = 2, 1 # signed weights: {-2,-1,0,1}
A_NEG, A_POS = 0, 3 # unsigned activations (post-relu): {0,1,2,3}
class Module:
def params(self) -> List[Tensor]:
return list(self.named_params().values())
def named_params(self) -> Dict[str, Tensor]:
raise NotImplementedError
def buffers(self) -> Dict[str, np.ndarray]:
return {}
def __call__(self, *a, **k):
return self.forward(*a, **k)
def _kaiming(rng, fan_in, shape, gain=1.0):
return rng.standard_normal(shape) * (gain / np.sqrt(fan_in))
# ===================================================================== generic (full-precision) layers
class Linear(Module):
def __init__(self, n_in, n_out, rng, bias=True, gain=1.0):
self.weight = Tensor(_kaiming(rng, n_in, (n_in, n_out), gain), requires_grad=True)
self.bias = Tensor(np.zeros(n_out), requires_grad=True) if bias else None
def forward(self, x: Tensor) -> Tensor:
out = ag.matmul(x, self.weight)
if self.bias is not None:
out = ag.add(out, self.bias)
return out
def named_params(self):
d = {"weight": self.weight}
if self.bias is not None:
d["bias"] = self.bias
return d
class ReLU(Module):
def forwar
…[truncated 25732 chars]The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
"""Optimizers over autograd Tensor parameters."""
from __future__ import annotations
import math
from typing import Dict, List
import numpy as np
from autograd import Tensor
def split_decay_params(model) -> Dict[str, List[Tensor]]:
decay, no_decay = [], []
for name, p in model.named_params().items():
lname = name.lower()
if lname.endswith("scale") or "bn." in lname or lname.endswith("gamma") \
or lname.endswith("beta") or lname.endswith("bias"):
no_decay.append(p)
else:
decay.append(p)
return {"decay": decay, "no_decay": no_decay}
def _normalize_groups(params):
plist = list(params)
if plist and isinstance(plist[0], dict):
return [(list(g["params"]), g.get("weight_decay", None)) for g in plist]
return [(plist, None)]
class Optimizer:
def __init__(self, params):
self._groups = _normalize_groups(params)
self.params: List[Tensor] = [p for g, _ in self._groups for p in g]
def zero_grad(self):
for p in self.params:
p.grad = None
class SGD(Optimizer):
def __init__(self, params, lr=0.1, momentum=0.0, weight_decay=0.0):
super().__init__(params)
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
self._v = [np.zeros_like(p.data) for p in self.params]
def step(self):
idx = 0
for group_params, wd_override in self._groups:
wd = self.weight_decay
…[truncated 11214 chars]The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
"""Image data loading + normalization + batching."""
from __future__ import annotations
from typing import Tuple
import numpy as np
NORM_MEAN = 0.5
NORM_STD = 0.25
def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
d = np.load(path)
X = np.asarray(d["X"]).astype(np.uint8)
y = np.asarray(d["y"]).astype(np.int64).reshape(-1)
return X, y
def normalize(X: np.ndarray) -> np.ndarray:
return (X.astype(np.float64) / 255.0 - NORM_MEAN) / NORM_STD
def iter_minibatches(Xn: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator, shuffle: bool = True):
n = len(y)
idx = np.arange(n)
if shuffle:
rng.shuffle(idx)
for start in range(0, n, batch_size):
batch_idx = idx[start:start + batch_size]
yield Xn[batch_idx], y[batch_idx]
The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run public checks
python3 run_checks.py 2>&1 | head -100
[autograd_grads] 115/115 grad/add_bcast OK (max_abs=3.17e-10) grad/sub_bcast OK (max_abs=3.17e-10) grad/mul_bcast OK (max_abs=1.02e-10) grad/div OK (max_abs=1.89e-11) grad/div_denom OK (max_abs=6.86e-11) grad/power2 OK (max_abs=2.34e-10) grad/power3 OK (max_abs=2.06e-10) grad/maximum OK (max_abs=6.55e-12) grad/minimum OK (max_abs=6.55e-12) grad/where OK (max_abs=7.48e-11) grad/exp OK (max_abs=1.72e-10) grad/log OK (max_abs=2.05e-10) grad/sqrt OK (max_abs=3.39e-10) grad/abs OK (max_abs=5.10e-11) grad/clip OK (max_abs=6.55e-12) grad/matmul OK (max_abs=1.14e-10) grad/matmul_batched OK (max_abs=2.42e-10) grad/transpose OK (max_abs=5.10e-11) grad/transpose_axes OK (max_abs=5.10e-11) grad/reshape OK (max_abs=3.79e-11) grad/getitem_index OK (max_abs=3.79e-11) grad/getitem_slice OK …[truncated 4613 chars]
Run public checks - tail
python3 run_checks.py 2>&1 | tail -100
quant/lsqplus_reduces_to_fake_quant OK [nn_forward] 53/53 fwd/softmax_sums_to_1 OK (row_sum_err=1.1e-16) fwd/log_softmax_eq_log_softmax OK fwd/relu_def OK fwd/leaky_relu_def OK fwd/sigmoid_def OK fwd/tanh_def OK fwd/gelu_erf_def OK fwd/exp_log_inverse OK fwd/cross_entropy_value OK (got=3.2296 ref=3.2296) fwd/mse_value OK fwd/layernorm_zero_mean_unit_var OK fwd/batchnorm2d_zero_mean_unit_var OK fwd/batchnorm1d_zero_mean_unit_var OK fwd/conv2d_shape OK (shape=(2, 5, 8, 8)) fwd/conv2d_stride_shape OK (shape=(2, 4, 4, 4)) fwd/maxpool2d_def OK fwd/avgpool2d_def OK fwd/fake_quant_grid_w OK (unique=4) fwd/fake_quant_grid_a OK (unique=4) fwd/Linear_def OK fwd/MLP_shape OK (shape=(2, 3, 8)) fwd/QuantCNN_shape OK fwd/QuantMLP_shape OK fwd/QuantCNN_state_dict_roundtrip OK (keys=24) fwd/QuantMLP_state_dict_roundtrip OK (keys=19) fwd/square_def OK fwd/rsqrt_def …[truncated 3952 chars]
Benchmark training speed
python3 -c "
import sys; sys.path.insert(0, 'submission')
import numpy as np
import autograd as ag
from nn import QuantCNN
import time
model = QuantCNN(10, (16,32), seed=0)
model.set_training(True)
X = np.random.randn(32, 3, 16, 16)
y = np.random.randint(0, 10, 32)
t0 = time.time()
for _ in range(3):
for p in model.params(): p.zero_grad()
logits = model.forward(X)
loss = ag.cross_entropy(logits, y)
loss.backward()
t1 = time.time()
print(f'3 batches (N=32) in {t1-t0:.2f}s -> {(t1-t0)/3:.2f}s/batch')
"
3 batches (N=32) in 0.43s -> 0.14s/batch
Benchmark with batch size 64
python3 -c "
import sys; sys.path.insert(0, 'submission')
import numpy as np
import autograd as ag
from nn import QuantCNN
import time
model = QuantCNN(10, (16,32), seed=0)
model.set_training(True)
X = np.random.randn(64, 3, 16, 16)
y = np.random.randint(0, 10, 64)
t0 = time.time()
for _ in range(3):
for p in model.params(): p.zero_grad()
logits = model.forward(X)
loss = ag.cross_entropy(logits, y)
loss.backward()
t1 = time.time()
print(f'3 batches (N=64) in {t1-t0:.2f}s -> {(t1-t0)/3:.2f}s/batch')
n_batches = 2500 // 64 + 1
print(f'Estimated: {n_batches * (t1-t0)/3:.1f}s/epoch -> {100 * n_batches * (t1-t0)/3/60:.1f}m for 100 epochs')
"
3 batches (N=64) in 0.86s -> 0.29s/batch Estimated: 11.4s/epoch -> 19.0m for 100 epochs
Benchmark with batch size 128
python3 -c "
import sys; sys.path.insert(0, 'submission')
import numpy as np
import autograd as ag
from nn import QuantCNN
import time
model = QuantCNN(10, (16,32), seed=0)
model.set_training(True)
X = np.random.randn(128, 3, 16, 16)
y = np.random.randint(0, 10, 128)
t0 = time.time()
for _ in range(3):
for p in model.params(): p.zero_grad()
logits = model.forward(X)
loss = ag.cross_entropy(logits, y)
loss.backward()
t1 = time.time()
print(f'N=128: {(t1-t0)/3:.2f}s/batch')
n_batches = 2500 // 128 + 1
print(f'Estimated: {n_batches * (t1-t0)/3:.1f}s/epoch -> {100 * n_batches * (t1-t0)/3/60:.1f}m for 100 epochs')
"
Exit code 137
/bin/bash: line 46: 1573 Killed python3 -c "
import sys; sys.path.insert(0, 'submission')
import numpy as np
import autograd as ag
from nn import QuantCNN
import time
model = QuantCNN(10, (16,32), seed=0)
model.set_training(True)
X = np.random.randn(128, 3, 16, 16)
y = np.random.randint(0, 10, 128)
t0 = time.time()
for _ in range(3):
for p in model.params(): p.zero_grad()
logits = model.forward(X)
loss = ag.cross_entropy(logits, y)
loss.backward()
t1 = time.time()
print(f'N=128: {(t1-t0)/3:.2f}s/batch')
n_batches = 2500 // 128 + 1
print(f'Estimated: {n_batches * (t1-t0)/3:.1f}s/epoch -> {100 * n_batches * (t1-t0)/3/60:.1f}m for 100 epochs')
""""QAT training for INT2 QuantCNN."""
from __future__ import annotations
import argparse
import os
import sys
import numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, "/app")
import autograd as ag
import data as D
import optim as O
from nn import QuantCNN, W_NEG, W_POS, A_NEG, A_POS
WIDTHS = (16, 32)
def init_lsq_scales(model):
"""Initialize LSQ step sizes:
- Weight scales: mean(abs(w)) to avoid dead network
- Activation scales: 1.0 (reasonable for BN+relu outputs)
"""
named = model.named_params()
# Weight quantizer scales
for name, p in named.items():
if 'wq.scale' in name:
# Find the corresponding weight tensor
weight_name = name.replace('.wq.scale', '.weight')
if weight_name not in named:
# head weight uses head.wq.scale -> head.weight
weight_name = name.replace('wq.scale', 'weight').replace('head.weight', 'head.weight')
# Try to find weight via naming convention
prefix = name[:-len('wq.scale')] # e.g., "stem.conv."
w_name = prefix + 'weight'
if w_name not in named:
# For "head.wq.scale", look for "head.weight"
parts = name.split('.')
if 'wq' in parts:
wq_idx = parts.index('wq')
w_parts = parts[:wq_idx] + ['weight']
w_name = '.'.join(w_parts)
if w_name in
…[truncated 5804 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Test training for 5 epochs
python3 submission/train.py --epochs 5 --data /app/data/train.npz --out /tmp/test_checkpoint.npz 2>&1 | head -20
No file changes, the agent left the workspace untouched.
=== SyncVals verifier (local re-grade of the agent's own checkpoint) === COMPONENT CONFORMANCE SUITE: 276/278 cases passed (all milestones pass: False) failing milestones: integration (21/23) FINAL METRIC: held-out INT2 top-1 = 0.8973 >= threshold 0.7 REWARD = 0 (1 iff every component milestone passes AND the sealed held-out metric clears its threshold) NOTE: the in-sandbox training in the 481MB e2b box was OOM-killed (0-byte checkpoint), so the raw in-sandbox reward was an infrastructure artifact. This reward is from a faithful LOCAL re-train of the agent's OWN train.py (24GB, no OOM) with its own default hyperparameters, then re-graded by the real verifier.
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_213032864c3b4f07. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_213032864c3b4f07 · verifier authoritative; classifier explanatory.