{"id":"8f793316-9af0-47a0-ae90-00d5e8e1aa53","arxiv_id":"2605.27760","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"SkillGrad applies a gradient-descent-inspired optimization to structured agent skill packages using trajectory-level losses, diagnostic text gradients, momentum accumulation, and LLM-based patching, yielding 6.7 percentage point average gains over training-based baselines on SpreadsheetBench Verifie","lead":"SkillGrad optimizes LLM agent skills by treating skill packages as parameters updated via text-based gradients from task trajectories, with momentum memory and LLM patching for stability. A smart generalist might read it to learn a systematic way to iteratively refine AI agent instructions for specialized tasks like data analysis without full model retraining.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Reliability of automatic diagnoses for producing effective text-based gradients is the central unverified assumption.","rationale":"The reader's weakest assumption correctly isolates the least-secured link in the central claim. No stronger internal inconsistency or missing control is evident from the provided abstract and description; the empirical result therefore remains conditional on that assumption holding.","tokens_in":1698,"tokens_out":322,"duration_ms":23978,"concrete_test":"Sample 50 trajectories from each benchmark; have two independent human raters score the automatic diagnoses for (a) factual accuracy of the identified failure mode and (b) actionability of the suggested edit on a 1-5 scale; then measure whether iterations whose diagnoses average >4.0 show statistically larger skill-quality gains than those with average <3.0.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The method frames skill optimization as gradient descent where task trajectories supply loss signals and automatic diagnoses supply the 'gradient' (correction direction). For the 6.7 pp average gain to be attributable to this formulation rather than the LLM patcher, momentum overlay, or prompt engineering, the diagnoses must reliably surface actionable, directionally correct edits. The abstract reports ablations only on momentum and contrastive diagnosis; it does not report any direct measurement (human or proxy) of diagnosis accuracy, precision of suggested fixes, or correlation between diagnosis quality and downstream skill improvement. If diagnoses frequently misidentify issues or propose ineffective edits, the observed gains could arise from other components or from the patcher simply rewriting skills more aggressively.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes SkillGrad, a gradient-descent-inspired framework for optimizing agent skills. It models skill packages as optimizable parameters, using task execution trajectories as loss signals and LLM-based automatic diagnoses as text-based gradients to guide corrections. A momentum agent accumulates diagnostic patterns, and an LLM patcher applies layer-aware edits. The method is evaluated on SpreadsheetBench Verified and WikiTableQuestions, claiming consistent outperformance over training-based baselines by an average of 6.7 percentage points across two backbone LLMs, with ablations indicating contributions from momentum and contrastive diagnosis.","tokens_in":1815,"tokens_out":575,"duration_ms":41432,"significance":"If the core assumption holds—that automatic diagnoses reliably produce effective text-based gradients—this work could provide a principled, training-free approach to skill optimization for LLM agents, addressing limitations of heuristic reflection methods. The reported performance gains and component ablations suggest potential practical utility in improving agent reliability on table-related tasks.","major_comments":[{"comment":"Experiments section: Ablations show that contrastive diagnosis contributes to final skill quality, but no direct measurement of automatic diagnosis accuracy, precision of suggested fixes, or correlation between diagnosis quality and downstream performance is reported. This is load-bearing for the central claim, as the 6.7 pp gain could arise from the LLM patcher or momentum rather than the text-gradient formulation.","section":"Experiments"},{"comment":"Method description: The framing relies on LLM calls for both diagnosis (gradient) and patching (update), yet no analysis or examples demonstrate that the process yields non-circular, directionally correct edits beyond what aggressive rewriting would achieve. Without this, attribution to the gradient-descent analogy remains unverified.","section":"§3 (Method)"},{"comment":"Results: The performance comparison lacks details on statistical tests, error bars, number of runs, or baseline descriptions (e.g., how training-based skill evolution baselines are implemented and trained), making it impossible to assess whether the average 6.7 pp improvement is robust.","section":"§4 (Experiments)"}],"minor_comments":[{"comment":"Abstract: Specify the two backbone LLMs used and the exact benchmarks to improve clarity and reproducibility.","section":"Abstract"},{"comment":"Ablations: Present quantitative results from momentum and contrastive diagnosis ablations in a dedicated table rather than summary statements.","section":"Ablations"}],"recommendation":"major_revision","confidential_remarks":"The manuscript's heavy dependence on proprietary or closed LLM calls for core components may limit reproducibility and raise questions about whether the contribution is primarily methodological or prompt-engineering based; this could affect fit for a methods-focused journal."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive and detailed feedback. The comments identify key areas where additional evidence and clarity would strengthen the manuscript. We respond to each major comment below and indicate the revisions we will make.","responses":[{"response":"We agree that direct quantification of diagnosis quality is important to support the central claim. In the revised manuscript we will add a new subsection (in §4) that evaluates automatic diagnosis accuracy and fix precision against human annotations on a sampled subset of trajectories, and reports the correlation between diagnosis quality scores and observed performance gains. This analysis will help isolate the contribution of the text-gradient component from the patcher and momentum.","revision_made":"yes","referee_comment":"[Experiments] Experiments section: Ablations show that contrastive diagnosis contributes to final skill quality, but no direct measurement of automatic diagnosis accuracy, precision of suggested fixes, or correlation between diagnosis quality and downstream performance is reported. This is load-bearing for the central claim, as the 6.7 pp gain could arise from the LLM patcher or momentum rather than the text-gradient formulation."},{"response":"We will strengthen the method section and add an appendix with concrete, side-by-side examples of diagnosis text and the resulting layer-aware patches. These examples will show that edits are localized and driven by specific diagnostic statements rather than wholesale rewriting. We will also add a short discussion in §3 clarifying the safeguards against circularity (trajectory-level loss evidence, contrastive diagnosis, and momentum memory) that differentiate the process from generic rewriting.","revision_made":"yes","referee_comment":"[§3 (Method)] Method description: The framing relies on LLM calls for both diagnosis (gradient) and patching (update), yet no analysis or examples demonstrate that the process yields non-circular, directionally correct edits beyond what aggressive rewriting would achieve. Without this, attribution to the gradient-descent analogy remains unverified."},{"response":"We will expand §4 to include: (i) results aggregated over three independent runs with different random seeds, reported with error bars; (ii) paired statistical significance tests; and (iii) detailed descriptions of the training-based baselines, including exact training procedures, data splits, and hyperparameters. These additions will allow readers to evaluate the robustness of the reported gains.","revision_made":"yes","referee_comment":"[§4 (Experiments)] Results: The performance comparison lacks details on statistical tests, error bars, number of runs, or baseline descriptions (e.g., how training-based skill evolution baselines are implemented and trained), making it impossible to assess whether the average 6.7 pp improvement is robust."}],"tokens_in":1424,"tokens_out":571,"duration_ms":20395,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"SkillGrad treats agent skills as a structured parameter set and runs something like gradient descent on them. Task trajectories supply the loss signal, automatic diagnoses produce text-based gradients that point to fixes, a momentum agent stores recurring patterns, and an LLM patcher applies layer-aware edits. The abstract positions this against heuristic reflection methods.\n\nThe explicit optimization framing and the momentum overlay are the clearest additions. The paper evaluates on SpreadsheetBench Verified and WikiTableQuestions, shows consistent gains across two backbone LLMs, and beats the strongest training-based baseline by 6.7 points on average. It also reports that removing momentum or contrastive diagnosis hurts results.\n\nThe main gap is the missing link between the diagnoses and actual improvement. The abstract gives no measurement of diagnosis accuracy, no human check on whether the suggested edits are on target, and no correlation between diagnosis quality and downstream gains. If the diagnoses are noisy or generic, the observed lift could come from the patcher simply rewriting skills more aggressively rather than from the gradient-descent structure.\n\nThis is for groups working on reusable skill libraries for domain-specific LLM agents. The experiments are concrete enough and the framing is distinct enough that it should go to peer review, though referees will likely press for tighter evidence on the diagnosis step.","headline":"SkillGrad frames skill updates as gradient descent with text gradients and momentum, reports 6.7pp gains on two benchmarks, but offers no direct check on whether the diagnoses actually supply useful correction directions.","tokens_in":2298,"tokens_out":341,"would_cite":false,"duration_ms":30015,"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":"SkillGrad optimizes unreliable agent skills by treating them as structured parameters updated through text-based gradients derived from task execution trajectories.","keywords":["agent skills","LLM agents","skill optimization","text-based gradients","momentum agent","skill evolution","procedural knowledge","gradient descent"],"falsifier":"Running the full SkillGrad pipeline on SpreadsheetBench Verified or WikiTableQuestions but observing no average gain over the strongest training baseline, or finding that random edits produce comparable results, would falsify the contribution of the diagnostic gradients.","tokens_in":2585,"feed_emoji":"📈","tokens_out":674,"duration_ms":33162,"temperature":0.7,"pith_summary":"The paper introduces SkillGrad to improve agent skills that are often unreliable or outdated by casting their refinement as a gradient-descent-style optimization process. Task executions generate loss evidence, automatic diagnoses produce text gradients that signal needed corrections, a momentum agent stabilizes updates by retaining recurring patterns, and an LLM patcher applies the changes through layer-aware edits to the skill package. This framework outperforms training-based evolution baselines by 6.7 percentage points on average on SpreadsheetBench Verified and WikiTableQuestions across two backbone LLMs. A reader would care because it replaces heuristic reflections with an explicit optimization loop that can iteratively refine procedural knowledge stored in files.","feed_headline":"SkillGrad updates agent skills via text gradients from task trajectories","feed_subtitle":"Framework outperforms training baselines by 6.7 points on spreadsheet and table benchmarks by treating skills as optimizable parameters.","key_machinery":"Gradient-descent loop that converts execution trajectories into diagnostic text gradients for iterative, momentum-stabilized updates to the skill package via an LLM patcher.","core_discovery":"SkillGrad treats the skill package as a structured parameter to optimize in a gradient descent fashion where task executions provide trajectory-level loss evidence, automatic diagnoses then provide text-based gradients that indicate the correction directions, a momentum agent accumulates recurring diagnostic patterns into a persistent memory overlay to stabilize optimization, and an LLM-based patcher executes the parameter update by applying layer-aware edits. On SpreadsheetBench Verified and WikiTableQuestions the method improves over the strongest training-based baseline by 6.7 percentage points on average across two backbone LLMs, with ablations confirming contributions from momentum and","pith_inferences":["The same trajectory-to-gradient mechanism could extend to optimizing other persistent agent components such as tool lists or memory stores.","If stronger LLMs improve diagnosis accuracy, the performance margin over baselines would likely increase on the same benchmarks.","Applying SkillGrad in domains with rapidly changing requirements, such as code repositories, could test whether the momentum overlay prevents overfitting to transient task patterns."],"forward_implications":["Text gradients from automatic diagnoses can drive measurable skill quality gains without model retraining.","Momentum accumulation of diagnostic patterns stabilizes iterative updates across multiple optimization steps.","Contrastive diagnosis improves the quality of generated gradients relative to non-contrastive versions.","Layer-aware patching allows precise, structured edits that preserve unrelated parts of the skill package."],"fun_headline_variants":["SkillGrad optimizes agent skills like gradient descent via text gradients","Text gradients from tasks refine skill packages with momentum stabilization","Layer-aware edits update skills using diagnostic gradients and trajectory loss","SkillGrad treats skills as parameters for gradient descent style optimization"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Automatic diagnoses can reliably generate text-based gradients that indicate effective correction directions for the skill package.","fun_headline_variants_meta":{"raw":{"variants":["SkillGrad optimizes agent skills like gradient descent via text gradients","Text gradients from tasks refine skill packages with momentum stabilization","Layer-aware edits update skills using diagnostic gradients and trajectory loss","SkillGrad treats skills as parameters for gradient descent style optimization"]},"model":"grok-4.3","cost_usd":0.003604,"raw_usage":{"total_tokens":1889,"prompt_tokens":680,"num_sources_used":0,"completion_tokens":64,"cost_in_usd_ticks":36037000,"prompt_tokens_details":{"text_tokens":680,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1145,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":680,"tokens_out":64,"duration_ms":10694,"temperature":1.0,"reasoning_tokens":1145,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T16:43:20.043590+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the full SkillGrad pipeline on SpreadsheetBench Verified or WikiTableQuestions but observing no average gain over the strongest training baseline, or finding that random edits produce comparable results, would falsify the contribution of the diagnostic gradients.","supporting_citations":[],"review_version":1}