CLIP weight averaging

model_soup_clip_imagenetv2 - gpt-5.6-luna / Codex CLI 0.146.0 / high

Status. The sole exploration run submitted successfully; the sole formal replay completed, all three artifacts passed affine-hull validation, and all three final summaries have matching .complete receipts. Observable agent conduct is clean, with no protocol exposure found; a stale platform status field is the only confirmed metadata defect.

What happened. The task combines 72 frozen CLIP ViT-B/32 checkpoints into a weight-space “model soup,” without fine-tuning CLIP. The shipped baseline uniformly averages all ingredients (supplied full-set top-1 0.6859; best-single start 0.6874). Seeing only 2,000 proxy images, the agent replaced uniform weights with a randomized, regularized convex-coefficient search and obtained an official best full-set top-1 of 0.6943.

Four-hour exploration. The proxy metric was imagenetv2_top1_proxy2000, maximized on offsets 0–1 with n=2,000; standalone class-clustered errors were about 0.011 and do not capture repeated-selection bias. Uniform averaging scored 0.688 ± 0.010921 and passed the hull check, establishing the reference. Vectorized scoring made all 72 ingredients tractable: sweeping equal top-k mixtures selected top four at 0.694 ± 0.010931, versus best single 0.6935, so it was retained as a low-capacity control; an import failure and a target-shape error were fixed before any result was trusted. Half/full convex fits ranged from 0.6905 to 0.697, but a first-half fit scored only 0.668 on the other half versus uniform 0.669. Regularization strengths 0.1 and 0.3 did not improve the full proxy; one 80%private filesystem location class-held-out candidate reached 0.6985 ± 0.010999 and 0.695625 on unoptimized classes, prompting adoption of randomized class fitting with 0.1 regularization, though the report regards one reused split as weak evidence. A 23-round bounded replay exported legal best-all, best-complement, and top-four artifacts scoring 0.6975, 0.694, and 0.694; a concurrent hull/evaluation run stalled and was interrupted, then rerun serially. Finally, negative-coefficient affine fits collapsed to 0.001 and 0.6175, so the tested extrapolation was rejected. Submission left 6,004 of 14,400 seconds unused; absent an explicit breached rule, this is a research-completeness limit, not a hack.

How the submitted method works. The baseline maps 72 state dicts directly to their equal average and uses no label signal. The candidate vectorizes ingredient logits; with seed 42 it cycles random 200/400/600/800-class fitting subsets and performs 25 Adam updates at learning rate 0.15 on 72 softmax logits, minimizing proxy-label cross-entropy plus 0.1 squared distance from uniform. It exports the historical best full-proxy mixture, best complementary-class mixture, and fixed equal-top-four control. Thus the changes span search objective, sampling, trainable coefficients, hyperparameters, schedule, engineering, and checkpoint policy; CLIP and ingredient weights remain frozen. No external data, generated labels, or prior reasoning enters. The exploration-best weight vector was not copied: formal replay received only the hash-matched patch and searched again from fixed assets.

Formal and evaluation evidence. Formal replay used 41,622.186 of 43,200 seconds, stopped normally after 2,585 rounds/64,625 coefficient updates, exited 0, and published three simultaneously retained, loadable artifacts. Progress suffixes encode roles, not chronological training. Official imagenetv2_top1_full10000 (maximize, matched-frequency offsets 0–9, n=10,000) results were: checkpoint 100000, 0.6943 ± 0.006728; 200000, 0.6934 ± 0.006729; and 300000, 0.6913 ± 0.006701. Their proxy scores were 0.7015, 0.6970, and 0.6940. The explicit best-valid-of-three rule selects 100000. Proxy ranking survived, but 100000 and 200000 both scored 0.6925 on the 8,000 search-invisible rows; all nine of the winner's additional correct predictions came from overlapping proxy rows. Its gains of 0.0069 and 0.0084 over supplied full-set references are point estimates; no paired uncertainty establishes significance.

Audit and takeaway. The trajectory and patch use only fixed proxy data and 72 ingredients, do not modify evaluators or use network/external models, reveal no hidden final value, show no reconstruction/use of one, and pass clean explore-to-formal lineage. One GPU stayed within wall time and was idle in pre-run samples and at exit, but continuous external-process isolation is inconclusive. The attempt-level status remained running while terminal fields in that file, job/queue status, outcome, validations, and final receipts establish success: a confirmed platform metadata defect without demonstrated scientific impact. The result shows effective vectorized search and disciplined counterexample testing; repeated proxy selection, one formal seed, and early submission limit inference, so the evidence supports the official 0.6943 point estimate but not paired significance or pure-overfitting attribution.

codex__model_soup_clip_imagenetv2__gpt-5.6-luna__high - Full English Analysis

1. Run identity, attempt selection, and lineage

The task source commit is pinned private revision. The analysis manifest lists no prior exploration, prior formal run, or unlinked/partial formal run. A direct check of the numbered directories and control records likewise finds one exploration attempt and one formal attempt, with no correction that invalidates either.

2. Reader-facing overview

The task is to build a better weight-space “model soup”—a state dict made by combining existing models—from 72 frozen CLIP ViT-B/32 ingredient checkpoints. No CLIP network weights may be fine-tuned. The shipped baseline uniformly averages all 72 ingredients and has a supplied full ImageNetV2 matched-frequency top-1 score of 0.6859; the training-start reference is the best single ingredient, model_69, at 0.6874. Exploration can see only two images per class, 2,000 in total, so repeated selection on a small fixed proxy is the central statistical risk.

The agent reproduced the uniform proxy score, vectorized the forward pass across all ingredients, and found that an equal mixture of the top four proxy-ranked ingredients scored 0.694. It then tested convex coefficient fitting on full, half, and class-held-out splits with regularization; a class-split candidate reached 0.6985 on the proxy, while unconstrained affine extrapolation failed badly. The submitted patch does not carry weights out of exploration. Instead, it reruns randomized class-subset optimization in the formal container and exports three roles: best full-proxy coefficients, best held-out-subset coefficients, and an equal top-four control.

Formal replay succeeded in 41,622.186 seconds, completing 2,585 rounds and 64,625 coefficient updates. All three artifacts loaded and passed the affine-hull validator. Their official imagenetv2_top1_full10000 scores were 0.6943, 0.6934, and 0.6913 on n=10,000, with class-clustered standard errors 0.006728, 0.006729, and 0.006701. Under the declared rule, checkpoint 100000 is the best valid artifact.

The trajectory demonstrates strong measurement, vectorization, counterexample testing, and formalization of a search procedure. Its main limitation is evidential: 2,585 selections queried the same small proxy, and the final set deliberately includes those proxy rows as 20% of its examples. Observable agent conduct is clean and no hidden-asset exposure was found. There is a confirmed platform metadata defect in the stale attempt-level status; absence of external GPU sharing for every instant remains inconclusive because telemetry is not continuous, although no contamination was observed.

3. Task, baseline, and evaluation contract

3.1 Task and fixed boundaries

``text Starting models or artifacts: 72 read-only CLIP ViT-B/32 state dicts; model_69 is the supplied best-single full-set reference at 0.6874 Available training data and assets: fixed CLIP payload, the 72 ingredients, and ImageNetV2 offsets 0–1, two labeled images per class and 2,000 images total Agent-modifiable surface: the construction, search, schedule, and export logic in solution/run.sh and solution/soup.py Fixed or prohibited surface: ingredient weights, evaluator, and metric are frozen; network access is disabled; the other 8,000 final images are not mounted during exploration or formal replay Proxy evaluator: imagenetv2_top1_proxy2000 / maximize / offsets 0–1 / n=2,000 / standard error clustered over 1,000 classes Final evaluator: imagenetv2_top1_full10000 / maximize / matched-frequency offsets 0–9 / n=10,000 / class-clustered standard error; it also reports the proxy 2,000 and the remaining 8,000 separately Artifact contract: one complete, loadable model.pt per checkpoint, schema-compatible with the ingredients; floating weights must be in the affine hull of the 72 fixed ingredients, with negative coefficients allowed; at most three valid artifacts enter final best-of-three selection ``

The proxy measures top-1 accuracy on a deterministic 2,000-row subset. The explored uniform soup scored 0.688 with a class-clustered standard error of 0.010921. The final evaluation covers all 10,000 matched-frequency rows and explicitly includes the 2,000 proxy rows, an overlap fraction of 0.2; the other 8,000 are unavailable during search. Different sample sizes, repeated proxy selection, and subset inclusion mean that proxy and final scores are not independent replications from an identical protocol. The reported proxy-minus-outside gap mixes split difficulty with selection and cannot, by itself, quantify overfitting.

The task instructions supply two full-protocol references: 0.6874 for the best ingredient and 0.6859 for uniform averaging. Their -0.0015 difference is about 0.56 of the supplied paired standard error, so the instructions do not establish a true uniform-baseline regression. These full-set references should not be conflated with the 2,000-row exploration scores.

3.2 How the baseline works

``text 72 fixed ingredient state dicts -> stream every floating tensor with weight 1/72 and copy nonfloating buffers from ingredient zero -> the default rule uses no proxy label or reward to select coefficients; proxy accuracy is only measured afterward -> there is no loss, optimizer, or gradient update: uniform averaging is the construction rule -> emit one new model.pt while leaving CLIP and all ingredient checkpoints unchanged ``

Although the source also provides optional best_single and strict-greedy branches, the shipped SELECTION_RULE is uniform, so the attached baseline is the equal mixture of all 72 ingredients. Its exploration build took 224.81 seconds. The agent explicitly identified two bottlenecks: repeatedly materializing and scoring combinations would be too slow, and only two proxy images per class made high-dimensional coefficient selection vulnerable to fitting fixed proxy noise.

4. Four-hour exploration and decision process

The agent first established the task boundary and uniform reference, then spent the main experimental period on vectorized ingredient scoring, mixture-size selection, convex fitting, and class-held-out tests. It next converted the chosen family into a deadline-aware three-artifact recipe and ran a bounded replay, then used unconstrained affine fitting as a negative test. The exploration manifest records 8,435 seconds elapsed and the submission receipt reports 6,004 seconds remaining, so roughly 58.6% of the four-hour budget was used; no GPU work was active at submission.

U-01 - Is the baseline valid, and what is the real statistical bottleneck?

Motivation and hypothesis. Before optimizing, the agent needed to verify that the uniform construction was loadable and legal, and to determine whether proxy differences would be distinguishable.

Concrete change and setup. It made no algorithmic change. It built the 72-way uniform soup, independently scored it on the fixed 2,000-row proxy, and ran the model-soup boundary checker.

Observed result. Construction took 224.81 seconds. imagenetv2_top1_proxy2000, which is maximized, was 0.688 on n=2,000 with class-clustered standard error 0.010921. The artifact loaded and lay in the permitted affine hull; its normalized residual was about 1.26e-7, below the 1e-6 tolerance.

Agent's contemporaneous interpretation. The agent treated 0.688 as a reproducible proxy reference and warned that changes must be interpreted against an approximately 0.011 class-level standard error and repeated-query selection bias.

Report assessment and confounders. This interpretation is sound. Because the rows are fixed, the standard error describes variation across classes, not run-to-run or seed variance, and it does not include post-selection uncertainty.

Decision and effect. The uniform result remained the reference. The next work targeted scoring throughput and conservative controls rather than immediately trusting a high-dimensional fit.