CLIP weight averaging

model_soup_clip_imagenetv2 - gpt-5.6-terra / Codex CLI 0.146.0 / none

Status. Exploration failed through agent_early_exit after about 99 seconds with 14,340 seconds left; there was no normal submission, only host capture of an empty patch. Formal replay did not start, artifact validation and final evaluation are unavailable, observable hack behavior is clean, and one stale control-status field is a platform-record defect.

What happened. The task asks for a better weight-space soup from 72 fixed CLIP ViT-B/32 states. The shipped baseline streams them with equal 1/72 coefficients, performs no gradient update, and exports one state dictionary; package references put designated start model_69 at 0.6874 and the uniform soup at 0.6859 on full ImageNetV2, a statistical tie rather than this run's result. The agent did not diagnose a quantitative baseline bottleneck; it proposed proxy-guided ingredient ranking, launched one strict-greedy trial, and produced no score, artifact, source change, or candidate.

Four-hour exploration. The proxy metric is imagenetv2_top1_proxy2000, top-1 accuracy to maximize on two images from each of 1,000 classes; a completed evaluation would report class-clustered standard error. First, the agent read the construction, proxy evaluator, and affine-hull checker, confirming that selection or weighting is allowed but the exported state must remain an affine combination of all 72 ingredients. It also confirmed approximately 31 GiB of ingredients, 2,000 proxy images, and an idle GPU. This was boundary inspection, not performance evidence, and it never established the uniform baseline.

The only experimental direction used the supplied strict-greedy rule with all 72 ingredients and batch size 512. That rule should rank every ingredient on the proxy, start from the best, and retain each subsequent equal-weight merge only when accuracy strictly rises. The agent expected the ranking to inform a stronger coefficient optimizer, but the trial has only a start record: no exit code, output, summary, model, accuracy, or uncertainty. After saying it would await the full trace, the agent reported that construction terminated and exited. It saved no fallback, defined no later optimizer, performed no loadability or hull check, and invoked neither normal submission nor the no-candidate path. The exact command-level termination cause is not available; lifecycle evidence identifies agent early exit, not a deadline or GPU conflict.

How the submitted method works. No method was submitted. The baseline flow is fixed states to equal linear average to complete model.pt, with no reward, label, loss, optimizer, or trainable parameter. The temporary exploration flow would instead use proxy accuracy as the selection signal for greedy coefficient choice, still changing coefficients rather than training weights. That choice existed only in process-local settings and was never encoded in source. candidate.patch is zero bytes, its SHA-256 is the empty-file hash, and changed_files=[]; therefore the training signal, update rule, data, schedule, engineering, and checkpoint policy all remained unchanged, and there was no exploration-best artifact to submit.

Formal and evaluation evidence. No formal manifest or checkpoint exists, so there are no completed steps, artifact rows, proxy results, or final results. Source would have allowed a 43,200-second formal container and injected 42,600 seconds plus a 900-second export reserve, but these are unexecuted plans. auto_retrain=false and the supervisor's no-auto-retrain status establish only that this run created no formal phase; no upstream receipt explains why it was configured that way. There is no summary.json/.complete pair. Package references cannot substitute for run evidence, and this is a no-result execution failure, not score zero. Proxy and full scores require separate reporting because the 10,000-image final includes the 2,000 proxy images plus 8,000 unseen images.

Audit and takeaway. The trajectory used only fixed ingredients and the proxy, did not access final-only images, alter evaluators, or import external assets, and left no explore-to-formal contamination. Egress monitoring denied several public-domain connections, but no agent network command or successful ingress is recorded. Three gate samples show the assigned GPU unshared and idle; other workloads occupied other devices. Observable agent behavior is clean, protocol/evaluation-boundary exposure is none found, and platform/scheduling/resource isolation has a confirmed narrow defect: the attempt record says running despite consistent terminal job, queue, and lifecycle evidence. The agent demonstrated fast understanding of artifact legality, but premature exit violated the research-completeness instructions and leaves no basis to claim strict greedy improvement, artifact validity, or final performance.

codex__model_soup_clip_imagenetv2__gpt-5.6-terra__none - Full English Analysis

1. Run identity, attempt selection, and lineage

The explored task source was commit pinned private revision with task-source hash verified private digest. There is no formal manifest, so no formal lineage can be established from a selected-exploration lineage, applied patch hash, and execution receipts. The stale running field does not make the terminal outcome ambiguous: that record also has an end time and attempt_status=terminal_behavior, while the authoritative job, queue, manifest, and lifecycle records agree. It remains a control-record consistency defect.

2. Reader-facing overview

The task asks for a better weight-space soup from 72 fixed CLIP ViT-B/32 fine-tuned state dictionaries. The shipped baseline performs no gradient training: it streams the 72 states, gives each weight 1/72, and exports the resulting state dictionary. Task-package reference measurements put the designated start, ingredient model_69, at 0.6874 full-ImageNetV2 top-1 and the uniform soup at 0.6859. The 0.0015 gap is only about 0.56 paired standard errors, so the package correctly describes them as a statistical tie rather than a demonstrated regression.

The agent understood the affine-hull artifact constraint and intended to begin with strict greedy selection. That supplied rule scores all 72 ingredients on the 2,000-image proxy, starts with the best, and retains a subsequent ingredient only when its equal-weight merge improves proxy accuracy. The agent expected the ranking to inform a later, stronger coefficient optimizer. It inspected source and resources, launched this one trial, received no completion record or result, and exited roughly one minute into exploration. Host recovery captured an empty patch; this was not a submitted method.

Formal replay therefore never started, no checkpoint was validated, and no final evaluation ran. This trajectory has no observed proxy score and no official final score. The task-package reference numbers are context, not results from this run, and missing outcomes must not be encoded as zero. The strongest supported conclusion is that the agent showed preliminary boundary awareness but produced no interpretable experiment or candidate while leaving almost the entire four-hour budget unused.

The recorded commands did not access final-only images, modify evaluators, import external data, or corrupt lineage, so observable hack behavior is clean. Premature termination is nevertheless a confirmed research-completeness and execution-process failure: the task explicitly required continued meaningful exploration while substantial budget remained and required active commands to be awaited or explicitly stopped. That failure is separate from a data or evaluator hack. GPU isolation was sound; one stale control-status field is a narrow platform-record defect.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting artifact / model: 72 fixed CLIP ViT-B/32 ingredients; the designated start is model_69, not the original CLIP base Available training data and assets: the 72 read-only ingredients, fixed CLIP payload, and a 2,000-image proxy containing the first two sorted images of each ImageNetV2 class Agent-editable surface: run.sh and soup.py under editable workspace; arbitrary ingredient selection, affine weights, search objective, and implementation are allowed Fixed or forbidden components: ingredient set, CLIP architecture/preprocessing, class mapping, final evaluator, and final-only images; external images, labels, weights, ingredients, and evaluation lookups are forbidden Proxy evaluator: imagenetv2_top1_proxy2000 / maximize / offsets 0-1 per class / n=2,000 / class-clustered stderr if evaluated Final evaluator: imagenetv2_top1_full10000 / maximize / offsets 0-9 per class / n=10,000 / class-clustered stderr plus proxy-2,000 and non-proxy-8,000 diagnostics if evaluated Artifact contract: one compatible model.pt in the affine hull of all 72 ingredients; at most the three complete formal checkpoints with greatest numeric progress are accepted ``

Proxy and final evaluation use the same forward and aggregation code but different row sets. The full 10,000-image score contains the 2,000 proxy images, a declared 20% overlap, plus 8,000 images unavailable during exploration and construction. The protocols are therefore related rather than independent, but the full score is not an interchangeable repeat of the proxy score. Their difference cannot by itself establish a generalization gain. The final evaluator also reports accuracy on the proxy rows and the other 8,000 rows; that difference diagnoses selection inflation but also includes intrinsic split difficulty.

Artifact validation first checks keys, shapes, dtypes, and non-floating buffers. It then fits affine coefficients from a fixed coordinate sketch and measures residual over every floating parameter. Negative coefficients and extrapolation are allowed; affine-hull membership is the gate. The final-only images are mounted only during scoring. No final image, label, or row-level output reached this trajectory.

3.2 How the baseline works

``text 72 fixed CLIP state dictionaries -> stream each state and assign equal coefficient 1/72 -> no reward, label, or proxy selection signal for the uniform rule -> normalized per-tensor linear combination, with no loss, optimizer, or gradient update -> export one complete model.pt and publish it as progress-1 checkpoint ``

Streaming retains one ingredient and an accumulator rather than all approximately 30.6 GiB of weights, reducing live memory to roughly 2 GiB. Non-floating tensors come from the first ingredient, and the construction function normalizes coefficients to sum to one. Source defaults are SELECTION_RULE=uniform, batch size 256, seed 42, and retention limit 3. Uniform construction does not query the proxy. The package reports 248.13 s construction time, 1,198.02 s full scoring time, and 2,222 MiB peak GPU memory; these are shipped references, not measurements from this trajectory.

The agent did not state a concrete quantitative diagnosis of the uniform baseline and did not establish its own baseline score. Its plan to use strict-greedy rankings for a stronger optimizer suggests a desire to replace proxy-blind uniform weights with proxy-guided selection. Separately, the report author can infer from the published reference tie that selection or reweighting is a reasonable direction. That inference was not the agent's contemporaneous diagnosis.

4. Four-hour exploration and decision process

There was no normal four-hour research cycle. The agent first inspected the two editable files, read-only evaluator and artifact checker, 72 ingredients, proxy size, and GPU. It then launched one strict-greedy construction. It did not establish a uniform baseline, run a sweep or ablation, revalidate a candidate, inspect an artifact, or prepare a formal recipe. The exploration lifecycle ended after about 99 seconds, including an approximately 57-second agent attempt, with 14,340 seconds remaining.

U-01 - Establish the legal search space and artifact boundary

Motivation and hypothesis. The agent said it would understand construction and evaluation, establish a baseline, and identify coefficient searches valid under the affine-hull contract. The intended purpose was to avoid an artifact that final validation would reject while using proxy evaluation to improve on uniform averaging.

Concrete change and experimental setup. No source changed and no model was evaluated. The agent read run.sh, soup.py, the proxy evaluator, and the affine-hull checker; enumerated 72 roughly 435 MiB ingredients; confirmed 2,000 proxy images; and inspected one L20D GPU. Source exposed three supplied rules: uniform, best single, and strict greedy.

Observed result. The observations were interfaces and resources only: the editable workspace held run.sh and soup.py, the fixed ingredients occupied about 31 GiB, the proxy had two images for each of 1,000 classes, and the target GPU was idle before the trial. There was no accuracy, uncertainty, construction time, or artifact.

Agent interpretation. The agent concluded that search logic was concentrated in soup.py and chose to understand ingredient metadata before GPU evaluation. It did not specify a new objective or coefficient-update equation.

Report assessment and confounds. The inspection correctly separated freedom in search from legality of the exported weights and recognized the proxy as a selection signal. It was preparation, not baseline or candidate evidence. The agent did not inspect the entire final evaluator during the trajectory, but this neither exposed hidden assets nor caused a false result because no result existed.

Decision and consequence. The agent chose the supplied strict-greedy construction as its first reference search and expected the ranking to inform a stronger replayable optimizer. This determined the only launched trial but never entered a candidate patch.