Molecular graph diffusion

digress_qm9_graph_diffusion - gpt-5.6-luna / Codex CLI 0.146.0 / low

Status. Explore closed with a receipt, although the agent process exited 137 during submission; candidate.patch was empty. Formal retraining, artifact validation, and final evaluation were not created. The audit confirms early-submission/background-work noncompliance, but no data or evaluator cheating.

What happened. The task is to improve DiGress discrete graph diffusion on fixed hydrogen-removed QM9, ranked by lower real-test negative log-likelihood (NLL). The nine-layer graph Transformer learns to recover clean atom/bond categories from 500-step discrete corruption, updating approximately 8.2 million parameters with node cross-entropy plus five times edge cross-entropy. The supplied 69.57 is a shipped-reference test NLL; this trajectory produced no score. The agent claimed the trainable recipe was the opportunity but measured no bottleneck and submitted no change.

Four-hour exploration. The proxy maximizes validity_uniqueness_novelty on 2,000 independently drawn molecules and reports lower-is-better validation NLL; it emits no uncertainty and its declared sampling seed is ineffective. No proxy evaluation ran.

The agent named model capacity, diffusion horizon, and optimizer/regularization, but only inspected the shipped settings: nine layers, 500 steps, learning rate 2e-4, AdamW/AMSGrad, tiny weight decay, and disabled EMA. None was tested. A three-epoch GPU baseline loaded data, built the model, and initialized Gloo. PyTorch warned that the L20D capability was absent from its compiled architectures, but no CUDA exception appeared. No first batch, checkpoint, or metric completed. The agent blamed CUDA initialization and claimed it stopped the run; receipts show the GPU child still using 810 MiB at submission, leaving cause and cleanup inconclusive.

A nominal one-batch CPU diagnostic first hit the phase lock; its retry built the same model with batch size 512, then was manually interrupted after roughly a minute. Export failed because no checkpoint existed. This shows only that construction did not crash, not that infrastructure or the GPU caused the delay. Source inspection also found an EMA reference without an implementation, while a lightweight wrapper self-check passed; neither is performance or artifact evidence.

How the submitted method works. There is no candidate method. A zero-byte patch implies the unchanged baseline flow: fixed graph → marginal discrete corruption → graph-Transformer clean-category prediction → weighted node/edge cross-entropy → AdamW update → 500 reverse-diffusion sampling steps. Training signal, data, algorithm, hyperparameters, schedule, engineering, and checkpoint policy all remained unchanged; no generated labels, external data, extra model, or pre-existing rationale entered training, and there was no “best” explore weight to submit.

Source alone suggests a hypothetical fresh formal run with seed 42, batch 512, up to 1,000 epochs, wall-clock guards, and three retained checkpoints. These are planned defaults, not executed settings. The shipped reference trained for only 3,305.81 seconds, so the empty patch did not actively satisfy the long-recipe requirement.

Formal and evaluation evidence. Formal replay never started. The supervisor records --no-auto-retrain, and the analysis manifest has no linked or unlinked formal directory; this does not establish broader scheduler intent. Checkpoint publications and simultaneous retention were both zero. No artifact, validation receipt, final summary.json, or matching .complete exists. Best checkpoint, trend, and final NLL are not available, never zero. The historical 69.57 and the final protocol's 10,000-molecule diagnostics are not this run's results.

Audit and takeaway. Both probes used fixed read-only train/validation data and random initialization. No command accessed the real test tensor, altered the evaluator/direction, fetched external inputs, or contaminated formal replay. Observable behavior is confirmed noncompliance solely because meaningful exploration and stopped background work were explicit submission conditions: 14,170 of 14,400 seconds remained and the GPU child was live. Protocol exposure was not found. Launch isolation looked normal, but the warning and early interruption leave platform/scheduling/resource isolation inconclusive.

The agent showed quick comprehension of diffusion training and correctly refused to call startup logs performance evidence. Its decisive limitation was ending after about 1.6% of the timer with every scientific direction untested. The evidence supports only “empty patch, no candidate, no official result, and confirmed execution-protocol breach”; it cannot rank a method against the shipped baseline or prove a GPU-stack defect.

codex__digress_qm9_graph_diffusion__gpt-5.6-luna__low - Full English Analysis

1. Run identity, attempt selection, and lineage

The primary run lasted from 2026-08-10 13:43:03 to 13:47:51 UTC, with 288 seconds recorded for the explore phase. At submission, 14,170 seconds of the four-hour timer remained. The supervisor directly records that this batch used --no-auto-retrain and therefore did not start formal replay after explore termination. The analysis manifest likewise contains no linked or unlinked formal run, expected final root, or later attempt. The evidence cannot establish whether a scheduler outside this batch ever contemplated a separate run, but there is no exploration attempt 2, correction, or additional formal status in this batch's control tree.

2. Reader-facing overview

The task asks the agent to improve DiGress discrete graph diffusion on fixed hydrogen-removed QM9, with lower real-test negative log-likelihood (NLL) as the official ranking metric. The shipped recipe randomly initializes a nine-layer graph Transformer, corrupts atom and bond categories over a 500-step cosine schedule toward their dataset marginals, and trains all approximately 8.2 million parameters with node cross-entropy plus five times edge cross-entropy. The task declaration supplies a shipped-reference test NLL of 69.57, but that is reference evidence, not a result produced by this trajectory.

The agent named model capacity, diffusion horizon, and optimizer/regularization as potentially high-leverage directions, but changed and compared none of them. It launched a three-epoch baseline probe and then a nominal one-batch CPU diagnostic. Both reached model construction but not a first completed training batch, and neither produced a checkpoint, proxy score, or performance measurement. The agent attributed the problem mainly to CUDA initialization in the environment; the record establishes an architecture-compatibility warning and two manually interrupted runs, but not the cause of the delay.

The final submission action produced a zero-byte patch and no candidate method. Formal replay, artifact validation, and final evaluation never started, so there is no official result to encode as zero or compare with 69.57. The principal failure is execution completeness: the agent submitted with 14,170 seconds remaining, an agent-owned GPU child still active, and all three proposed directions untested. That violates the task's explicit requirements to continue while a meaningful experiment can finish and to wait for or stop all background work before submission. No hidden-test access, frozen-evaluator modification, or external-data use was found.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: fixed DiGress revision pinned private revision; randomly initialized nine-layer graph Transformer, with no pretrained weights Available training data and assets: fixed hydrogen-removed QM9 train/validation derivative; a byte-identical validation tensor occupies the test-named compatibility path required by the upstream datamodule Agent-editable surface: training source, architecture, transition/noise model, objective, data selection/resampling, optimizer, schedule, sampling, and checkpointing under editable workspace Fixed or forbidden components: the data universe, real test tensor, frozen evaluator, and metric direction; external molecule data/weights, test reconstruction, evaluator-only training access, and evaluation lookup behavior are forbidden Proxy evaluator: validity_uniqueness_novelty, maximize, 2,000 generated molecules from an independent draw with an ineffective declared sampling seed; also validation NLL, minimize, validation-graph n not available; no standard error or confidence interval Final evaluator: upstream test NLL, minimize, test-graph n not available; ancillary validity/uniqueness/novelty on 10,000 generated molecules; no standard error or confidence interval Artifact contract: run output area>private filesystem location; accept at most the three greatest valid progress values, score each, and select the lowest NLL ``

The proxy headline is the fraction of generated samples that are simultaneously valid, distinct, and absent from the training set: validity, uniqueness, and novelty are multiplied. Its 2,000 molecules come from an independent draw whose declared seed is not consumed by the pinned sampler. Proxy evaluation also computes NLL on the training-visible validation tensor. Final ranking instead computes NLL on the real test tensor mounted only for scoring, with a separate 10,000-molecule diagnostic draw. The proxy headline and final NLL differ in quantity and direction, while validation and test NLL differ in split; neither pair supports direct subtraction. The first 2,000 molecules of a final run are an internal prefix diagnostic, but a separate fast-evaluation call is not that prefix.

3.2 How the baseline works

``text Fixed QM9 molecular graph (C/N/O/F node classes and no/single/double/triple/aromatic bond classes) -> sample a diffusion time and corrupt nodes/edges with a cosine schedule and dataset-marginal transition matrices -> a nine-layer graph Transformer predicts clean node and edge classes from the noisy graph, time, and structural/molecular features -> use the clean graph as the target for node cross-entropy + 5 × edge cross-entropy; this task has no weighted global-y target -> AdamW with AMSGrad updates all approximately 8.2 million parameters; generation reverses 500 diffusion steps from marginal noise ``

After wrapper overrides, the shipped recipe uses training seed 42, batch size 512, learning rate 2e-4, nine layers, 500 diffusion steps, weight decay 1e-12, gradient clipping at 1.0, no exponential moving average, and no learning-rate scheduler. Source requests up to 1,000 epochs. If a formal run existed, orchestration source would force an outer 42,600-second training wall clock plus a 600-second reserve, and the trainer's internal timer would be planned for approximately 41,700 seconds. Source plans an export every 50 epochs, keeps three simultaneously, and always saves a final checkpoint. Because no formal run exists, these are source-derived plans, not executed settings.

The task instruction reports the shipped B300 reference as test NLL 78.05 after a one-epoch training start and 69.57 for the current shipped solution, a reduction of 8.48 (10.86%). Training took 3,305.81 seconds and final scoring 2,108.34 seconds. On 10,000 diagnostic molecules, validity, uniqueness, and upstream novelty were 0.9805, 0.9769, and 0.5190; no uncertainty was supplied. These are fixed historical references, not trajectory results.

The agent asserted only that the “main opportunity” was in the trainable recipe rather than the evaluator contract, then named capacity, diffusion horizon, and optimization/regularization. It did not provide a measured bottleneck. Fixed capacity, the absence of a scheduler, disabled EMA, and tiny weight decay are source facts, but treating any of them as the performance cause would go beyond the evidence.

4. Four-hour exploration and decision process

The agent spent roughly its first minute reading the launcher, wrapper, and upstream configuration; about two minutes launching and polling the GPU baseline, attempting a CPU diagnostic, and killing processes; and less than another minute inspecting checkpoint source and running a lightweight self-check before submission. The harness recorded only 288 seconds of explore time. There was no interpretable performance experiment, stochastic revalidation, or formal recipe embodying the proposed research directions.

U-01 - Could capacity, diffusion horizon, or optimization/regularization improve the baseline?

Motivation and hypothesis. After seeing a nine-layer graph Transformer with node width 256, edge width 64, 500 diffusion steps, and AdamW, the agent proposed comparing model capacity, diffusion horizon, and optimizer/regularization settings. It hoped to improve both validation behavior and final test NLL, but did not associate those directions with a measured failure mode.

Concrete change and experimental setup. Nothing changed. The agent inspected configuration and training source but did not alter layer count, width, diffusion steps, learning rate, weight decay, EMA, or optimizer, and did not launch a sweep.

Observed result. Source and resolved configuration establish approximately 8.2 million trainable parameters, nine layers, 500 steps, learning rate 2e-4, AdamW/AMSGrad, weight decay 1e-12, and disabled EMA. None of the three proposed directions has a performance result, sample count, or uncertainty estimate.

Agent interpretation. It called these settings “high-leverage” candidates and judged recipe work more promising than changing the evaluator contract.

Report assessment and confounds. This was an untested research plan, not a bottleneck diagnosis. With no baseline checkpoint or proxy result, the sign and magnitude of any capacity, horizon, or regularization effect are unknown.

Decision and consequence. All three directions remained inconclusive rather than adopted or rejected. The empty patch means none affected submission.