{"id":"a79e7598-1384-4a25-bfce-267e6274bb7f","arxiv_id":"2606.22330","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"HDSO validates candidate skills for frozen LLM agents through curator-proposed hypotheses tested in paired executions, delivering +6.9 and +4.0 Avg. SR gains on ALFWorld for two Qwen3 models even under noisy feedback.","lead":"The paper introduces Hypothesis-Driven Skill Optimization (HDSO), a train-free framework where a frozen curator proposes falsifiable hypotheses about skills, validates them via paired control/treatment executions on an LLM executor, and only consolidates supported skills. This approach aims to enable safer, auditable skill updates for action-oriented agents without model retraining or unconstrained memory growth.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Paired control/treatment validation does not isolate skill effect from executor stochasticity","rationale":"The reader's weakest_assumption exactly matches the load-bearing risk in the validation procedure. Because the supplied review was abstract-only and the full text is not reproduced here, no additional internal inconsistency or mitigating statistical detail can be checked; the concern therefore remains unchanged.","tokens_in":1819,"tokens_out":339,"duration_ms":21430,"concrete_test":"Re-execute the ALFWorld protocol for both Qwen3-8B and Qwen3.6-27B using at least 10 independent random seeds per condition (baseline vs. HDSO); report mean SR ± std and a paired statistical test on the per-seed deltas. If the mean gain is not significant at p < 0.05 or if std > reported gain, the attribution to skills fails.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline gains (+6.9 / +4.0 Avg. SR) rest on the curator correctly attributing behavior differences in paired executions to the candidate skill. The executor is a frozen stochastic LLM; even identical prompts and skills can produce different trajectories across runs. The abstract describes no repeated trials per pair, no variance or confidence intervals on the SR deltas, and no statistical test (e.g., paired t-test or bootstrap) on whether the observed difference exceeds sampling noise. The 20 % flip experiment tests feedback noise but leaves execution variance untouched. If the reported gains lie within the natural run-to-run fluctuation of the executor, the causal claim that the skill repository drives the improvement does not hold.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes Hypothesis-Driven Skill Optimization (HDSO), a train-free framework in which a frozen curator LLM observes executor traces, generates falsifiable hypotheses with validation plans, instantiates candidate skills, validates them via paired control/treatment executions on the frozen stochastic executor, and consolidates only supported skills into an approved repository. The executor then uses progressive disclosure of approved skills. On ALFWorld, the method reports +6.9 and +4.0 average success-rate gains over executor-only baselines for Qwen3-8B and Qwen3.6-27B respectively, with the gains preserved (+7.1 for the 8B model) under 20% random feedback flips; transfer and cross-model diagnostics are also presented.","tokens_in":1924,"tokens_out":629,"duration_ms":15746,"significance":"If the reported gains survive rigorous isolation from executor stochasticity, HDSO would supply an auditable, hypothesis-driven alternative to unconstrained skill accumulation for frozen action agents, with demonstrated robustness to feedback noise and some evidence of transferability.","major_comments":[{"comment":"The paired control/treatment validation procedure (described in the abstract and the ALFWorld experimental section) does not report repeated trials per pair, variance estimates, confidence intervals, or any statistical test (paired t-test, bootstrap, etc.) on the observed success-rate deltas. Because the executor is a frozen stochastic LLM, run-to-run trajectory variation can produce behavior differences unrelated to the candidate skill; without these controls the attribution of the headline +6.9 / +4.0 Avg. SR gains to the skill repository is not yet established.","section":"ALFWorld experiments / abstract"},{"comment":"The 20% feedback-flip experiment tests curator robustness to noisy success/failure labels but leaves execution stochasticity untouched; the same paired-validation design is used, so the preserved +7.1-point gain remains subject to the same attribution concern.","section":"noise-robustness experiment"},{"comment":"No quantitative details are supplied on the exact validation metrics, the decision rule for “supported” vs. “unsupported” hypotheses, or the hypothesis-generation prompt template; these omissions make the central claim that only causally effective skills enter the repository difficult to evaluate or reproduce.","section":"method description / curator pipeline"}],"minor_comments":[{"comment":"The abstract states that “transfer and heterogeneous-pair diagnostics further show that validated repositories can be useful beyond the run that produced them,” yet the manuscript would benefit from a dedicated table or figure summarizing the transfer success rates and the conditions under which cross-model curation succeeds or fails.","section":"transfer diagnostics"},{"comment":"Notation for success rate (SR) and average SR is used without an explicit definition or reference to the ALFWorld evaluation protocol in the main text.","section":"evaluation metrics"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback emphasizing statistical rigor and reproducibility. We address each major comment below and commit to revisions that strengthen the attribution of gains and clarify the method.","responses":[{"response":"We agree that the current presentation lacks repeated trials, variance estimates, and formal statistical tests, leaving attribution vulnerable to executor stochasticity. Although the paired design compares control and treatment on the same task instances, this does not fully address run-to-run variation. In revision we will perform multiple independent paired executions per candidate skill, report standard deviations and confidence intervals, and apply appropriate paired tests (e.g., McNemar or bootstrap) to the success-rate deltas.","revision_made":"yes","referee_comment":"[ALFWorld experiments / abstract] The paired control/treatment validation procedure (described in the abstract and the ALFWorld experimental section) does not report repeated trials per pair, variance estimates, confidence intervals, or any statistical test (paired t-test, bootstrap, etc.) on the observed success-rate deltas. Because the executor is a frozen stochastic LLM, run-to-run trajectory variation can produce behavior differences unrelated to the candidate skill; without these controls the attribution of the headline +6.9 / +4.0 Avg. SR gains to the skill repository is not yet established."},{"response":"This observation is correct; the noise-robustness results inherit the same limitation. We will apply the same expanded statistical protocol (repeated trials, variance, and paired tests) to the 20% flip condition in the revised manuscript so that the preserved gain is evaluated under equivalent controls.","revision_made":"yes","referee_comment":"[noise-robustness experiment] The 20% feedback-flip experiment tests curator robustness to noisy success/failure labels but leaves execution stochasticity untouched; the same paired-validation design is used, so the preserved +7.1-point gain remains subject to the same attribution concern."},{"response":"We will add the requested details: the validation metric (success-rate delta between paired executions), the precise decision rule (minimum delta threshold and number of supporting validations required), and the full curator prompt templates (hypothesis generation and validation review) in a new appendix. These additions will make the supported-skill consolidation process fully reproducible.","revision_made":"yes","referee_comment":"[method description / curator pipeline] No quantitative details are supplied on the exact validation metrics, the decision rule for “supported” vs. “unsupported” hypotheses, or the hypothesis-generation prompt template; these omissions make the central claim that only causally effective skills enter the repository difficult to evaluate or reproduce."}],"tokens_in":1546,"tokens_out":571,"duration_ms":16039,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that HDSO makes skill addition to frozen LLM agents more deliberate: a curator model watches traces, writes a falsifiable hypothesis with a validation plan, turns it into a skill package, and only keeps the skill if paired control and treatment runs show a difference. The abstract reports +6.9 average success rate points on ALFWorld for Qwen3-8B and +4.0 for the 27B version, with the gains holding under 20% flipped feedback.\n\nThe structured loop is the clearest advance. Requiring an explicit validation plan and then checking behavior differences before consolidation is more disciplined than the memory-accumulation methods it cites. Progressive disclosure of skills also keeps the executor from drowning in an ever-growing list. The noise-robustness check is a practical addition that addresses one real failure mode in these systems.\n\nThe soft spot is the validation itself. The abstract gives no sign of repeated trials per pair, variance estimates, or any statistical test on the success-rate deltas. Because the executor is a stochastic LLM, identical prompts can produce different trajectories across runs. A single control-treatment pair can easily produce a difference that has nothing to do with the candidate skill. The 20% flip test only perturbs feedback; it does not touch execution variance. Without those controls the causal claim that the skill repository drives the improvement rests on thin evidence.\n\nThe cross-model transfer results are mentioned but not quantified enough to judge their strength. This paper is aimed at researchers who maintain skill libraries for action agents and want an auditable process rather than unchecked accumulation. It is worth sending to peer review so the authors can supply the missing statistical checks and protocol details; the core mechanism is reasonable even if the current numbers need tighter grounding.","headline":"HDSO adds a hypothesis-plus-paired-validation step for skill addition, but single-run pairs leave the gains vulnerable to executor noise.","tokens_in":2392,"tokens_out":428,"would_cite":false,"duration_ms":21826,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"HDSO improves frozen LLM agents on ALFWorld by validating curator-proposed skills through paired control and treatment executions.","keywords":["LLM agents","skill optimization","hypothesis validation","ALFWorld","frozen models","paired execution","train-free","auditable skills"],"falsifier":"Re-running the same validated skill on identical task instances with the executor forced to temperature zero and deterministic sampling; if the success-rate gain disappears, the attribution to the skill itself does not hold.","tokens_in":2694,"feed_emoji":"🤖","tokens_out":776,"duration_ms":18441,"temperature":0.7,"pith_summary":"The paper presents Hypothesis-Driven Skill Optimization as a way to add external skills to action-oriented LLM agents without updating model weights or accumulating unverified memory. A frozen curator watches the executor, generates a falsifiable hypothesis together with a validation plan, packages it as a candidate skill, and tests it by running the executor with and without the skill on matched tasks. Only hypotheses that produce clear behavior differences are kept in an approved repository; the executor then applies skills selectively through progressive disclosure. On ALFWorld this yields average success-rate gains of 6.9 points for Qwen3-8B and 4.0 points for Qwen3.6-27B, and the gains hold at +7.1 points even when 20 percent of the success or failure signals are randomly flipped during discovery and validation. The framework keeps every addition auditable and stops short of unconstrained skill accumulation.","feed_headline":"Paired tests let frozen agents adopt only validated skills","feed_subtitle":"A curator proposes falsifiable hypotheses, confirms them via control and treatment runs, and adds only those that improve performance, prese","key_machinery":"The hypothesis curator that proposes falsifiable hypotheses with validation plans and confirms them via paired control/treatment executions on the frozen executor.","core_discovery":"HDSO is a train-free framework in which both the skill curator and the agent executor remain frozen inference endpoints; the curator observes executor traces, proposes a falsifiable hypothesis with an explicit validation plan, instantiates the hypothesis as a candidate skill package, validates the package through paired control and treatment executions, reviews observed behavior differences, and consolidates only supported candidates into an approved repository, while the executor consumes approved skills through progressive disclosure and falls back to the executor-only path when no skill is selected.","pith_inferences":["The paired-validation step could be reused as a template for auditing other LLM-generated procedures such as plans or code snippets.","If validation plans are made more quantitative, the method might reduce the need for fine-tuning in environments where retraining is expensive or restricted.","Heterogeneous curator-executor pairs suggest that skill quality is highest when the curator's diagnostic strength matches the executor's capability profile.","The auditable lifecycle could be extended to track skill provenance across multiple agents sharing the same repository."],"forward_implications":["Executor success rates rise on ALFWorld without any weight updates or retraining.","Validated skill repositories transfer to new runs produced by the same executor.","The system maintains most of its gain even when 20 percent of feedback signals are flipped during discovery.","Cross-model curation works only when curator diagnosis, executor capability, and validation evidence align.","Skills enter the repository only after explicit paired validation, limiting addition of spurious rules."],"fun_headline_variants":["Paired tests validate skills for frozen LLM agents","Hypothesis-driven validation for agent skill packages","Frozen endpoints test skills before repository approval","HDSO curates skills via falsifiable hypothesis checks"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Observed differences between paired control and treatment executions can be attributed to the candidate skill rather than stochastic variation in the executor or the curator's phrasing of the hypothesis.","fun_headline_variants_meta":{"raw":{"variants":["Paired tests validate skills for frozen LLM agents","Hypothesis-driven validation for agent skill packages","Frozen endpoints test skills before repository approval","HDSO curates skills via falsifiable hypothesis checks"]},"model":"grok-4.3","cost_usd":0.005081,"raw_usage":{"total_tokens":2508,"prompt_tokens":736,"num_sources_used":0,"completion_tokens":55,"cost_in_usd_ticks":50812000,"prompt_tokens_details":{"text_tokens":736,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1717,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":736,"tokens_out":55,"duration_ms":11649,"temperature":1.0,"reasoning_tokens":1717,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T11:12:57.942879+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Re-running the same validated skill on identical task instances with the executor forced to temperature zero and deterministic sampling; if the success-rate gain disappears, the attribution to the skill itself does not hold.","supporting_citations":[],"review_version":1}