arxiv: 2605.10999 · v1 · submitted 2026-05-09 · 💻 cs.LG · cs.AI· cs.MA

Recognition: no theorem link

SkillGen: Verified Inference-Time Agent Skill Synthesis

Yuchen Ma , Yue Huang , Han Bao , Haomin Zhuang , Swadheen Shukla , Michel Galley , Xiangliang Zhang , Stefan Feuerriegel

Authors on Pith no claims yet

Pith reviewed 2026-05-13 06:05 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.MA

keywords LLM agentsskill synthesisinference-time improvementcontrastive inductiontrajectory analysisintervention verificationagent performance

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The pith

SkillGen generates human-readable, auditable skills from LLM agent trajectories by contrasting successes and failures, then verifies each skill's net effect through direct performance comparisons with and without it.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SkillGen as a way to improve LLM agents at inference time by creating reusable skills without retraining the model. It takes trajectories from a base agent, analyzes both successful and failed attempts using contrastive induction to spot what works and what doesn't, and turns that into a single skill description. The skill is then tested by applying it or not to the same tasks and measuring the overall change in outcomes, accounting for both fixes and new errors. This produces skills that are inspectable by humans and can be reused across different agents and tasks.

Core claim

SkillGen synthesizes a single auditable skill from trajectories by using contrastive induction over successful and failed trajectories to identify reusable success patterns and recurring failure modes. It models skills as interventions and verifies them by comparing outcomes on the same instances with and without the skill, measuring both repairs and regressions. This approach leads to consistent improvements in held-out performance across agents and datasets, outperforming baselines and enabling transfer across models.

What carries the argument

The SkillGen multi-agent framework that performs contrastive induction on trajectories and empirically verifies skills as interventions by with/without comparisons.

Load-bearing premise

That patterns identified by contrasting successful and failed trajectories represent reusable skills whose benefits can be reliably measured by simple with-and-without tests without being distorted by task-specific biases or hidden factors.

What would settle it

An experiment showing that skills generated by SkillGen do not improve or even decrease performance on held-out tasks when compared to the base agent without them, or that they fail to transfer to new models.

Figures

Figures reproduced from arXiv: 2605.10999 by Han Bao, Haomin Zhuang, Michel Galley, Stefan Feuerriegel, Swadheen Shukla, Xiangliang Zhang, Yuchen Ma, Yue Huang.

**Figure 1.** Figure 1: SKILLGEN overview. Our multi-agent framework synthesizes a single auditable skill from baseline trajectories. 1 It first elicits successful and failed rollouts as input. 2 It extracts reusable patterns of successful and failure modes. 3 It follows an iterative generation-verification-refinement loop to generate and refine new candidate skills. To formalize the outcome Y , we define a task-level evaluator E… view at source ↗

**Figure 2.** Figure 2: Comparison with skill-generation baselines. Accuracy improvement (∆) from adding a generated skill across representative benchmark–model entries. Mini, Grok, and Gemma denote GPT-5.4-Mini, Grok-4-Fast, and Gemma-4-26B, respectively. All methods use the same evaluation harness. models (+3.27 to +4.77 pp) and proprietary models (+4.79 to +10.08 pp); and (iii) out of 80 heldout benchmark–split–model entries… view at source ↗

**Figure 3.** Figure 3: SKILLGEN ablations. ∆ accuracy over a shared no-skill baseline on ALFWorld (OOD) and ChemLLMBench yield prediction. A1: ICL (k = 3) instead of the induced skill; A2: no refinement; A3: no verification gate; A4: no Failure Lessons; A5: plain-text skill (no script+reference bundle); Full: complete SKILLGEN. Full wins on every dataset–model pair, showing that each component contributes. gpt-5.4-mini gemma-4-2… view at source ↗

**Figure 4.** Figure 4: Cross-model skill transferability. Each heatmap reports ∆ accuracy when a skill generated by a source model (row) is executed by an evaluator model (column). Diagonal cells are self-transfer, while off-diagonal cells are cross-model transfer. Right and bottom margins show transfer-out and transfer-in means, respectively; color saturates at ±30 pp. The transfer matrix is evaluated on a shared pool of 100 he… view at source ↗

**Figure 5.** Figure 5: Insights for τ -Bench. Held-out accuracy on τ -Bench retail for the five models where the SKILLGEN verification gate activated. Gray bars are no-skill baselines and teal bars apply the induced skill; deltas are absolute percentage-point changes. RQ3 Which components are necessary for reliable skill construction? [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Insights for ChemLLMBench. Held-out accuracy on ChemLLMBench property prediction (left) and yield prediction (right). Gray bars are no-skill baselines and teal bars apply the SKILLGEN skill; bars labeled “±0.0” or “gate off” indicate no measurable change or rejection by the verification gate. 1 2 3 4 5 6 7 8 Refinement round r 20% 40% 60% 80% 100% Paired accuracy (with skill) = accuracy of the candidate pr… view at source ↗

**Figure 7.** Figure 7: Refinement rounds vs. skill accuracy. Each refinement round produces one candidate skill evaluated on the construction-time verification subset. (a) Per-round candidate accuracy for representative runs, with dashed no-skill baselines. (b) Best-so-far accuracy under a budget of K rounds. (c) Aggregate mean ∆ accuracy over all runs with 95% bootstrap confidence intervals. RQ5 In which additional task regimes… view at source ↗

**Figure 8.** Figure 8: t-SNE visualization of SkillGen’s induction on ALFWorld (gpt-5.4-nano). Red triangles [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗

read the original abstract

Skills are a promising way to improve LLM agent capabilities without retraining, while keeping the added procedure reusable and controllable. However, high-quality skills are still largely written by hand. We introduce SkillGen, a multi-agent framework that synthesizes a single auditable skill from trajectories generated by a base agent. The output is a human-readable artifact that can be inspected before use. Rather than merely summarizing trajectories, SkillGen leverages contrastive induction over both successful and failed trajectories to identify reusable success patterns, recurring failure modes, and behaviors that appear in nearby successes but are missing from failures. SkillGen then generates candidate skills and iteratively refines the skill. A key novelty in SkillGen is that we model agent skills as interventions to empirically verify the net effect of skills on the overall performance. Specifically, we compare outcomes on the same instances with and without the skill, so that we account for both repairs (cases where the skill fixes a baseline failure) and regressions (cases where the skill breaks a baseline success). Across a broad range of agents and datasets, SkillGen consistently improves held-out performance, outperforms existing skill-generation baselines, and produces skills that transfer across models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SkillGen's contrastive skill generation plus with/without intervention verification is a practical step for auditable LLM agent improvements, though the abstract supplies no numbers and the verification may not isolate effects cleanly.

read the letter

SkillGen generates human-readable skills for LLM agents by running contrastive induction over success and failure trajectories, then refines candidates and checks net impact by comparing the same task instances with and without the inserted skill. That verification step, which tallies both repairs and regressions, is the clearest addition relative to prior skill-generation work that mostly just summarizes trajectories or reports average gains.

Referee Report

2 major / 1 minor

Summary. The paper introduces SkillGen, a multi-agent framework that synthesizes a single human-readable, auditable skill from trajectories of a base LLM agent. It performs contrastive induction over success and failure trajectories to extract reusable success patterns and recurring failure modes, generates and iteratively refines candidate skills, and verifies net effect by comparing agent outcomes on identical task instances with versus without the skill (explicitly accounting for both repairs and regressions). The central claim is that this procedure yields consistent held-out performance gains across agents and datasets, outperforms existing skill-generation baselines, and produces skills that transfer across models.

Significance. If the verification methodology is shown to isolate skill effects without hidden confounders, the work would provide a practical, inference-time method for improving agent capabilities while producing inspectable artifacts. The explicit modeling of skills as interventions with repair/regression accounting is a methodological strength that could support more reliable skill reuse than purely summarization-based approaches.

major comments (2)

[Section 3] Verification procedure (Section 3): the with/without comparison on identical instances does not hold the underlying trajectory distribution fixed. Inserting the skill alters agent behavior from early steps onward, shifting the distribution of visited states, actions, and failure modes relative to the baseline trajectories used for contrastive induction. Consequently, measured net gains may partly reflect avoidance of certain paths rather than the reusable success patterns claimed, and this risk is highest for unconditional inference-time application.
[Section 4] Empirical evaluation (Section 4): the manuscript reports consistent outperformance and transfer but supplies no quantitative results, error bars, dataset sizes, number of runs, or explicit accounting of how regressions are balanced against repairs in the net-effect metric. Without these details the central claim that SkillGen 'consistently improves held-out performance' cannot be assessed for magnitude or reliability.

minor comments (1)

[Abstract] Abstract: the claim of broad applicability would be strengthened by at least naming the specific agents, datasets, and baseline methods evaluated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and indicate the corresponding revisions to the manuscript.

read point-by-point responses

Referee: [Section 3] Verification procedure (Section 3): the with/without comparison on identical instances does not hold the underlying trajectory distribution fixed. Inserting the skill alters agent behavior from early steps onward, shifting the distribution of visited states, actions, and failure modes relative to the baseline trajectories used for contrastive induction. Consequently, measured net gains may partly reflect avoidance of certain paths rather than the reusable success patterns claimed, and this risk is highest for unconditional inference-time application.

Authors: We appreciate the referee's careful analysis of the verification procedure. The design in Section 3 treats the skill explicitly as an intervention and measures its net impact on final task outcomes for identical instances. This captures both repairs and regressions in a single metric, which is the quantity most relevant for practical inference-time use. We agree that the trajectory distribution shifts because the skill influences early decisions and can steer the agent away from failure modes observed in the base trajectories. This shift is inherent to any behavioral intervention and is not a hidden confounder but the intended mechanism by which the contrastively induced patterns improve performance. The verification therefore reports the overall empirical effect rather than an isolated contribution of the patterns under a fixed distribution. We will add a clarifying paragraph in the revised Section 3 that explicitly discusses this distributional change and its implications for interpreting the results as net intervention effects. This is a textual clarification only. revision: partial
Referee: [Section 4] Empirical evaluation (Section 4): the manuscript reports consistent outperformance and transfer but supplies no quantitative results, error bars, dataset sizes, number of runs, or explicit accounting of how regressions are balanced against repairs in the net-effect metric. Without these details the central claim that SkillGen 'consistently improves held-out performance' cannot be assessed for magnitude or reliability.

Authors: We thank the referee for highlighting the need for more transparent reporting. While the manuscript describes the overall trends, we acknowledge that the specific quantitative values, error bars, dataset sizes, number of runs, and explicit repair/regression breakdown were not presented with sufficient prominence or in a dedicated table. We will revise Section 4 to include a clear summary table with mean performance gains and standard deviations (across 5 runs), dataset sizes, experimental run counts, and a breakdown of how repairs and regressions contribute to the net-effect metric. These details will be added in the next version of the manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical verification on held-out instances is independent of induction inputs

full rationale

The paper's core chain generates candidate skills via contrastive induction over success/failure trajectories, then applies them as interventions and measures net performance change by direct with/without comparison on the same task instances (accounting for repairs and regressions). This measurement is an external empirical outcome on held-out data, not a quantity defined in terms of the induction process or any fitted parameter. No equations reduce the reported improvement to the input trajectories by construction, no self-citation is invoked as a uniqueness theorem or load-bearing premise, and no ansatz is smuggled in. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are stated in the abstract; the approach rests on empirical paired comparisons rather than theoretical derivations.

pith-pipeline@v0.9.0 · 5525 in / 1112 out tokens · 102772 ms · 2026-05-13T06:05:20.364026+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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13 A Algorithm Algorithm 1 summarizes the full SKILLGENconstruction procedure. Algorithm 1SKILLGEN: contrastive induction with generation-verification-refinement loop Require: induction subset Dind, construction-time verification subset Dver, base agent A, evaluator E, round budgetK≥1, gate parametersg abs ∈Z ≥0 andg rel ∈[0,1] Ensure:skills ⋆ markedactiv...

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or tool creation (Qian et al., 2023), while interaction-based agents such as ReAct (Yao et al., 2023), Reflexion (Shinn et al., 2023), ExpeL (Zhao et al., 2024), and V oyager (Wang et al., 2023a) showed that trajectories can support reusable reasoning and action routines. Agent skills provide a first-class abstraction that often follows the Anthropic Agen...

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[21]

study web, dialogue, and RL deployment regimes. SKILLGENdiffers by making failure analysis central: it clusters error patterns, identifies capability boundaries, and verifies induced skills through multi-agent collaboration with a construction-time deployment rule over paired repairs and regressions. Synthetic data for LLM agents.Self-Instruct (Wang et al...

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For agent-specific capabilities, AgentTuning (Zeng et al., 2024), Agent-FLAN (Chen et al., 2024), FireAct (Chen et al., 2023), and Instruct-SkillMix (Kaur et al.,

and risk-injected safety trajectories (Huang et al., 2025a). For agent-specific capabilities, AgentTuning (Zeng et al., 2024), Agent-FLAN (Chen et al., 2024), FireAct (Chen et al., 2023), and Instruct-SkillMix (Kaur et al.,

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Success-branch and error-branch analysts process trajectories in parallel, and their proposed patches are consolidated through hierarchical LLM merging

17 C.6 Skill-Generation Baselines Trace2Skill.For Trace2Skill (Ni et al., 2026), we run one no-skill rollout over the training pool. Success-branch and error-branch analysts process trajectories in parallel, and their proposed patches are consolidated through hierarchical LLM merging. We preserve the original prompt structure and do not impose an addition...

work page 2026