Molecular graph diffusion

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

Status. Exploration and submission completed; formal replay 1 was invalidated for external-GPU contamination, while a complete fresh exploration attempt 2 passed three artifact validations and three official final evaluations. The boundary audit found confirmed agent misconduct in submission timing and a separately confirmed platform-isolation defect in the discarded first replay.

What happened. The task improves DiGress on fixed QM9-without-hydrogens graphs. The baseline corrupts categorical atoms and bonds through a 500-step marginal diffusion, then trains a nine-layer graph Transformer with node cross-entropy plus five-times edge cross-entropy and AdamW. After diagnosing an unsupported L20D/PyTorch CUDA path, the agent submitted a 200-step, 12-layer CPU-compatible recipe; its best real-test NLL was 68.31, versus the same-protocol shipped 69.57.

Four-hour exploration. Proxy evaluation used full-validation NLL, minimized, plus an unseeded validity-times-uniqueness-times-novelty product from 16 or 64 molecules, maximized; no uncertainty was reported, and these values are not directly comparable to final test NLL. GPU training/random operations repeatedly hung, timed out, or exited 137, while a five-batch CPU diagnostic finished in about 20 seconds and eight CPU threads stabilized evaluation, so the compatibility fallback was adopted. At 50 matched updates, NLL improved from 83.52 for 500 steps/9 layers to 81.45 for 200/9 and 79.39 for 200/12; 100/12 regressed to 84.51, and an unmatched 300/12 run scored 79.63, so 200/12 won. At 191 updates, evaluating identical weights twice gave NLL 74.77 and 75.80 while the 16-sample product moved from about 0.625 to 0.409; in a separate two-epoch run, the final and periodic saves, both at 382 updates, scored 75.25 and 76.08 with 64 samples. This supported training beyond the short screen but exposed non-monotonic gains and sampling noise, not independent-training-seed robustness. Higher learning rate scored 76.29, NAdam 89.60, uniform transition 76.85, and edge-loss multiplier 1 scored 76.65 with validity 0.3125; all were rejected. Sixteen layers scored 83.37, cycle-only features failed frozen loading on input-width mismatch, and batch 1,024 scored 81.72 without throughput benefit. A 701-tensor smoke artifact loaded, supporting a 100,000-epoch ceiling, five-epoch saving, and three-artifact retention.

How the submitted method works. Fixed training graphs supply atom/bond targets; a 200-step marginal process makes noisy graphs; a 12-layer Transformer predicts clean categories; unchanged node and five-times edge cross-entropy update all parameters with AdamW at 2e-4, batch 512. Changes are hyperparameter, schedule, checkpoint, and engineering choices—not a new objective or update rule. Unsupported CUDA capability moves random operations to the host and falls back to one CPU device. Explore weights were excluded; formal replay applied only patch hash 8e2703…e5e5 to fresh source/Hydra state.

Formal and evaluation evidence. exploration attempt 2 used 41,868.388 of 43,200 seconds (96.92%); its inner Timer stopped normally at 11:35, after 117 epochs / 22,335 updates rather than the requested 100,000. Twenty-four cumulative publication points reduced to the three highest valid artifacts: epoch 109/step 21,010 scored real-test NLL 68.35; epoch 114/21,965 scored 68.31, best; epoch 117/22,335 regressed to 68.89; lower is better. Test-graph n and stderr/CI were not reported; each generated 10,000 diagnostic molecules. Novelty recomputation mismatches suppressed split diagnostics but did not invalidate the passed ranking NLL. The best is 1.26 lower, about 1.81%, than the same-protocol reference.

Audit and takeaway. Literal cross-search of evaluator-only configuration, the full trajectory/tool outputs, experiment commands, and patch found no hidden full-test content hash, reconstruction, or use; the inspected test-named tensor was the declared validation alias. No external data/model, network use, evaluator edit, direction change, or explore-to-formal weight contamination appeared, and exploration attempt 2 lineage and hashes close. Observable agent behavior is confirmed: it explicitly submitted while idle with 5,759 seconds left, although a named independent-seed two-epoch replay plus 64-sample evaluation measured about 892 seconds and fit easily. Formal-budget use itself was clean. Protocol/evaluation-boundary exposure was not found. Platform isolation had a confirmed defect: an external process shared exploration attempt 1's assigned GPU, so control marked it forensic-only and required the valid full replay. The evidence supports an official NLL improvement and useful late-checkpoint selection, but not search completeness, independent-seed robustness, native-GPU speedup, or statistically significant molecular diagnostics.

codex__digress_qm9_graph_diffusion__gpt-5.6-luna__xhigh - Full English Analysis (gpt-5.6-luna / Codex CLI / xhigh)

1. Run identity, attempt selection, and lineage

The three model validations are checkpoint 109's validation.json and .complete, checkpoint 114's validation.json and .complete, and checkpoint 117's validation.json and .complete.

2. Reader-facing overview

The task is to improve DiGress discrete graph diffusion on a fixed QM9-without-hydrogens dataset. The baseline progressively corrupts categorical atom and bond features and trains a nine-layer graph Transformer to reconstruct the clean graph with node and edge cross-entropy. Exploration and formal retraining see only train and validation data: the test-named tensor is a byte-identical validation alias, while the real test tensor is mounted only for frozen final scoring.

The agent first made the run usable despite an L20D/PyTorch CUDA-architecture incompatibility, then compared diffusion horizon, Transformer depth, training duration, learning rate, optimizer, transition distribution, edge-loss weight, batch size, model capacity, and extra graph features on CPU. It submitted a 200-step, 12-layer recipe with a 100,000-epoch ceiling, five-epoch checkpointing, three-checkpoint retention, bounded CPU threads, and a conservative CPU fallback for unsupported CUDA capability. The data, supervised objective, AdamW update rule, and marginal transition remained unchanged; no explored weights were submitted.

formal replay 1 finished but was later invalidated because an external compute process shared its assigned GPU. exploration attempt 2 restarted from the fixed source, used 96.92% of the 12-hour budget, stopped normally when the internal 11:35 Timer fired, and reached epoch 117 / step 22,335. Its three official real-test NLLs, lower being better, were 68.35, 68.31, and 68.89. Epoch 114 was best and improved on the same-protocol shipped reference of 69.57 by 1.26 NLL.

The strongest capability was disciplined debugging followed by broad negative ablations around a simple winning recipe. The main failure was a confirmed execution-rule violation: the agent explicitly submitted while idle with 5,759 seconds remaining, although this trajectory measured a meaningful independent-seed replay plus 64-sample evaluation at about 892 seconds. That limits claims of search completeness and seed robustness, but it does not break the valid exploration attempt 2 lineage or its official scores.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: DiGress source revision pinned private revision and the shipped recipe; formal training initializes from scratch, not from an explore checkpoint Available training data and assets: fixed derived QM9 train/validation tree; proc_test_no_h.pt is a byte-identical alias of proc_val_no_h.pt, with no raw data or real test tensor Agent-editable surface: the method, architecture, objective, data use, optimizer, schedule, sampler, and checkpointing under editable workspace Fixed or forbidden components: data source and read-only assets, frozen evaluator, real test set, metric direction, and no-network boundary; no external molecular data/weights, test reconstruction, or evaluation-specific lookup Proxy evaluator: validity_uniqueness_novelty product, maximize, plus full-validation NLL, minimize; generated n=16 or 64 by experiment, validation-graph n not reported, no stderr/CI Final evaluator: upstream NLL on the real test split, minimize; test-graph n not reported; 10,000 independently sampled molecules for diagnostics, no stderr/CI Artifact contract: run output area progress>private filesystem location complete and frozen-evaluator-loadable; if more than three exist, only the three highest-progress artifacts are accepted, and the best valid final score is official ``

Validation NLL measures short-run learning, while the proxy molecule product is especially noisy at 16 or 64 unseeded samples. Official ranking uses NLL over the unseen real test split and separately reports 10,000-sample molecular diagnostics. Explore and final values are not directly comparable because their split, checkpoint maturity, and sampling protocols differ. The shipped 69.57 and candidate final NLL do share the declared final protocol and may be compared.

3.2 How the baseline works

``text categorical atom and bond graph from QM9 -> corrupt the graph at a random point in a 500-step marginal discrete diffusion process, then let a nine-layer graph Transformer predict the clean atom and bond categories from the noisy graph and time -> use the original clean training graph as the direct target; no reward, generated label, or external input is involved -> minimize node cross-entropy plus five-times edge cross-entropy with AdamW, learning rate 2e-4, batch size 512, and gradient clipping 1.0 -> update every graph-Transformer parameter and export complete Lightning checkpoints ``

The baseline uses marginal transitions, all molecular extra features, weight decay 1e-12, and seed 42. Source defaults request 1,000 epochs, save every 50 epochs, and retain three simultaneously; formal orchestration injects a wall-clock limit. The agent first diagnosed a run that stalled after model construction, associating it with the newer L20D compute capability unsupported by the pinned PyTorch build and with discrete CUDA sampling paths. Once an eight-thread CPU path worked, a 50-update baseline scored validation NLL 83.52. The agent then treated the 500-step chain's cost and nine-layer capacity as tunable bottlenecks.

4. Four-hour exploration and decision process

Exploration ran from 16:32:52 to 18:57:47 UTC, 8,695 seconds in total. Roughly the first half hour covered source/data inspection and GPU-stall diagnosis; the next hour and a half was a sequential CPU screen with repeated evaluation; the last portion rejected extra features and NAdam, froze the patch, and checked syntax and artifact compatibility.

U-01 - Establish a usable device and evaluation path

Motivation and hypothesis. Initial GPU training stayed at zero utilization after construction, and direct CUDA random operations and fast evaluation also stalled. The agent hypothesized an unsupported compute-capability path and prioritized a controlled execution route.

Concrete change and experimental setup. It tried native single-device execution, moved categorical multinomial and time-step randomness through the host for unsupported hardware, and added whole-trainer CPU fallback. After terminating hung runs, it ran a five-batch CPU diagnostic and limited OpenMP/MKL to eight threads. It also inspected the three processed split files and established that the test-named file was the validation alias.

Observed result. GPU attempts hung, timed out, or exited 137 and produced no trustworthy quality evidence. The CPU five-batch diagnostic completed in about 20.02 seconds with loadable checkpoints. CPU fast evaluation stopped making progress with excessive threads but completed reliably with eight.

Agent interpretation. The agent treated the GPU issue as an environment compatibility problem that could not be repaired reliably within exploration, and used CPU for scientific comparisons while preserving a conservative fallback in the patch.

Report assessment and confounds. This establishes executability, not a quality benefit from host-side random sampling. exploration attempt 2 did take the CPU fallback, so the engineering choice affected throughput, although it still used the formal wall clock well.

Decision and consequence. The unsupported-capability fallback, single-device auto strategy, host random-operator branches, and eight-thread defaults were adopted; later quality experiments ran sequentially on CPU.