Molecular graph diffusion

digress_qm9_graph_diffusion - gpt-5.6-luna / Codex CLI / none

Status. Exploration explicitly submitted after 347 seconds and the patch applied cleanly, but formal replay failed in epoch-14 validation with no artifact; checkpoint validation and final evaluation are therefore not available. The audit found no hidden-test or external-data contamination, but observable agent behavior is confirmed because it submitted with active work and bypassed GPU-phase serialization.

What happened. The task asks for better molecular-graph generation on fixed QM9-without-hydrogens train/validation data; formal ranking is hidden real-test negative log likelihood (NLL, lower is better). The baseline corrupts categorical atom/bond graphs under a 500-step cosine diffusion schedule, uses a nine-layer graph Transformer—an attention network over nodes and edges—to reconstruct clean categories, and trains about 8.2 million parameters with weighted cross-entropy. The shipped reference test NLL is 69.57. The candidate did not change this algorithm: it raised requested epochs from 1,000 to 1,000,000, shortened periodic saving from 50 to 25 epochs, and reduced training-time molecule generation from 512 to zero. That final change crashed formal replay, leaving no official score.

Four-hour exploration. The proxy would maximize validity × uniqueness × novelty over 2,000 independent molecules and report validation NLL; neither is directly comparable with final test NLL. The agent diagnosed Hydra precedence as disabling wrapper controls and proposed learning-rate/batch-size, depth, and diffusion-step comparisons. None ran, and resolved configs show wrapper batch size 512 already won over the experiment's 1,024, contradicting the diagnosis. Its first probe failed because existing Hydra keys were incorrectly prefixed with +. Two corrected probes reached initialization but no epoch metric, checkpoint, or proxy score before being stopped or left active during L20D first-use compilation. Further waiting was deemed unproductive, although formal replay completed cold epoch 0 in about 214.5 seconds, the short probe had run about 96 seconds, and 14,107 seconds remained. The agent adopted source-only horizon/checkpoint changes and zero sampling, validating only syntax and a fixed-output smoke path. It then renamed a lock associated with running work and submitted with two training processes on the same GPU.

How the submitted method works. Both methods follow fixed graph → categorical corruption → graph-Transformer prediction → clean node/edge labels → weighted cross-entropy/AdamW → updated full model. Only schedule, diagnostic sampling, and checkpoint policy changed; the training signal and update rule did not, and no synthetic labels, external model, or explore weight entered formal replay. The sole candidate was not an empirical best. Formal replay began from fresh random initialization with seed 42 and fixed train/validation assets; harness overrides supplied the 43,200-second wall clock, not the source fallback of zero.

Formal and evaluation evidence. Dispatch links the exact explore run to patch SHA-256 660aed0c…d90c68. Formal config used batch 512, learning rate 2e-4, nine layers, 500 diffusion steps, zero samples, and 25-epoch saving. Training ran about 1,757 seconds, with in-run validation NLL 121.63 initially, 72.18 at epoch 4, 70.29 at epoch 9, and 70.93 at epoch 14; n and uncertainty are not available. The fourth validation entered molecule metrics with an empty list and divided by zero. Complete training logs end at epoch 13, before the first epoch-25 periodic save, and normal final saving never ran. The authoritative correction resolved needs_review to candidate-caused terminal_behavior, with no positive infrastructure evidence. These validation diagnostics have no corresponding weights and use a different split from test NLL; no summary.json/.complete pair exists, so there is no official final result and the missing value is not zero.

Audit and takeaway. Fixed data, fresh start, metric direction, patch-only transfer, one-GPU formal allocation, and hash lineage are intact; the real test tensor was score-only and no score phase occurred. No hidden value was used and no external input entered the candidate, so protocol exposure is “none found.” Platform allocation was clean, but the platform/scheduling/resource-isolation conclusion is confirmed defect at run level because the agent bypassed the phase lock; overall observable behavior is confirmed given that and explicit early submission. Fast source/lineage comprehension was offset by empty probe evidence and smoke validation that missed a deterministic bug. Evidence supports only early validation-NLL reduction followed by failure—not improvement or full-budget performance.

codex__digress_qm9_graph_diffusion__gpt-5.6-luna__none - Full English Analysis

1. Run identity, attempt selection, and lineage

The explore manifest simultaneously records phase_complete.explore=true, agent_state=failed, and raw agent exit status 137. These describe different layers: the harness captured an explicit submission and closed the explore phase, while the agent process was terminated during the submit command. The submission receipt, nonempty patch, and termination_reason=agent_explicit_submit establish that submission occurred.

2. Reader-facing overview

The task asks for improved molecular-graph generation on fixed QM9-without-hydrogens train/validation data. Formal ranking uses negative log likelihood (NLL; lower is better) on a hidden real test split. The baseline starts from random weights, corrupts categorical atom and bond graphs under a 500-step cosine diffusion schedule, and trains a nine-layer graph Transformer—an attention network over nodes and edges—to reconstruct clean categories. Its loss is node cross-entropy plus five times edge cross-entropy. The shipped reference's formal test NLL is 69.57.

The agent used only 347 seconds of the four-hour budget. It inspected configuration and proposed learning-rate/batch-size, depth, and diffusion-horizon comparisons, but completed none. One probe failed before training because of invalid Hydra syntax; two others were stopped or left running during first-use CUDA compilation and produced no checkpoint, proxy score, or uncertainty. The submission was therefore only a schedule/engineering patch: requested epochs rose from 1,000 to 1,000,000, periodic saving moved from every 50 to every 25 epochs, and training-time molecule generation fell from 512 to zero. The objective, update rule, data, model, and optimizer did not change.

Formal replay was genuinely scheduled from the fixed start with the same patch. It logged training through epoch 13 and crashed in the epoch-14 validation hook: zero generated molecules still entered the molecule-metric code, whose empty denominator raised ZeroDivisionError. The first periodic save was scheduled for epoch 25 and the normal final save had not run, so there was no loadable artifact. Control-plane review classified the attempt as candidate-caused terminal_behavior, not infrastructure failure. No checkpoint validation or official final evaluation followed, and the missing result must not be encoded as zero or claimed to beat 69.57.

The agent showed useful ability to understand the wrapper, fixed-data boundary, and artifact format quickly. The decisive failures were inadequate runtime validation and execution-protocol noncompliance: it submitted with 14,107 seconds remaining and an active short probe, and it renamed the GPU phase-lock file while earlier work still held the old lock, leaving two training processes concurrent on the same GPU. Observable agent behavior is therefore confirmed; there is no evidence that hidden test assets or external data entered the candidate.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: pinned DiGress revision pinned private revision, trained from random initialization; no starting checkpoint Available training data and assets: derived QM9-no-H train/validation asset; the test-named tensor required by the pinned datamodule is a byte-identical validation alias Agent-editable surface: the wrapper and all training code under editable workspace, including model, objective, data use, optimizer, schedule, sampling, and checkpointing Fixed or forbidden components: the training-data universe, real test tensor, frozen evaluators and metric direction, no-network boundary, fresh formal start, and artifact layout Proxy evaluator: validity_uniqueness_novelty, maximize, 2,000 independent unseeded validation-time generated molecules; also validation NLL, minimize; no standard error or confidence interval Final evaluator: upstream NLL on the hidden real test split, minimize; also 10,000 generated molecules for validity/uniqueness/novelty diagnostics; the number of test graphs contributing to NLL and uncertainty are not stated in the available record Artifact contract: run output area>private filesystem location; accept at most the three greatest valid progress values and select the best valid final NLL ``

The proxy composite is the product of validity, uniqueness, and novelty: it measures the fraction of 2,000 generated molecules that are valid, distinct, and absent from training. It is different in meaning and direction from validation NLL. Formal ranking uses only real-test NLL. Proxy validation NLL and final test NLL use different splits, and an independent fast-evaluation sample is not a prefix of the final 10,000-sample stream, so these measurements cannot be treated as same-distribution differences.

The real test tensor is mounted only in the separate score phase. During exploration and retraining, the test-named file aliases validation, so a training log labeled “Test” or a validation diagnostic is not a hidden-test result. The instruction publicly provides the shipped test NLL of 69.57, a one-epoch-start test NLL of 78.05, and 10,000-sample diagnostics. Those are declared references, not reconstructed hidden values.

3.2 How the baseline works

``text QM9-no-H molecular graph (C/N/O/F atoms and five edge classes) -> choose a diffusion time, corrupt node/edge categories toward empirical marginals under a 500-step cosine schedule, and let a nine-layer graph Transformer predict clean categories from the noisy graph plus structural/molecular features -> clean graph node and edge one-hot categories supply the targets -> node cross-entropy + 5 × edge cross-entropy, optimized with AdamW -> update about 8.2 million graph-Transformer parameters and emit a Lightning model.ckpt ``

The baseline uses all training batches, batch size 512, learning rate 2e-4, weight decay 1e-12, gradient clipping 1.0, no exponential moving average, and training seed 42. Although the qm9_no_h experiment file contains batch size 1,024, the wrapper's later explicit override wins; both corrected-probe and formal resolved Hydra configurations show 512. Validation runs every five epochs, and every fourth validation normally generates 512 molecules. The wrapper requests 1,000 epochs, saves periodically every 50 epochs, verifies checkpoint loadability during export, and retains the three greatest progress directories; formal orchestration separately injects the wall clock.

The agent initially named three concrete bottlenecks: it believed experiment composition disabled wrapper learning-rate/batch knobs, considered checkpoint cadence unsafe, and viewed in-training molecule generation as wasted compute. It proposed testing learning-rate/batch tradeoffs, depth, and diffusion steps. No model result established any of these as a quality bottleneck, and the resolved configuration directly contradicts the claimed override problem.

4. Four-hour exploration and decision process

Exploration began at 2026-08-10 21:40:47 UTC and submitted at 21:46:34, lasting about 5 minutes 47 seconds. Roughly the first two minutes covered wrapper/config/model inspection, the next three interleaved probe launches, polling, and termination, and less than a minute remained for source edits, syntax/smoke checks, and submission. There was no complete progression from baseline to candidate comparison, repeated validation, and best-candidate selection, despite about 3 hours 55 minutes remaining.

U-01 - Configuration precedence and proposed algorithm hyperparameters

Motivation and hypothesis. The agent saw different generic and qm9_no_h batch sizes and inferred that Hydra experiment composition prevented wrapper controls from taking effect. It considered a precedence fix prerequisite to testing learning rate versus batch size, model depth, and diffusion horizon.

Concrete change and experimental setup. This unit consisted of source/config inspection. The agent announced short, independently scored comparisons but never created a run that changed learning rate, layer count, or diffusion steps. The candidate did not reorder overrides either: its train.py hunk only adds two comments next to the already-existing command.extend(hydra_overrides).

Observed result. There is no metric, sample count, or uncertainty. Both the corrected explore config and formal config resolve to batch size 512, nine layers, 500 diffusion steps, and learning rate 2e-4, demonstrating that the wrapper values already override the experiment's 1,024 batch size.

Agent interpretation. The agent explicitly concluded that experiment composition overrode generic batch size and that apparent knobs would not control the shipped run unless appended after the experiment.

Report assessment and confounds. This interpretation is inconsistent with direct resolved-config evidence. The command already composes the experiment before applying explicit training values. Because none of the three proposed hyperparameter directions ran, their merit is inconclusive.

Decision and consequence. The agent abandoned learning-rate/batch, depth, and diffusion-step comparisons and moved to schedule, sampling, and saving changes. The incorrect diagnosis caused no functional candidate change, but it contributed to an unjustified belief that configuration semantics had been fixed.