arxiv: 2605.08670 · v1 · submitted 2026-05-09 · 💻 cs.AI · cs.CL· cs.MA

Recognition: no theorem link

MIND-Skill: Quality-Guaranteed Skill Generation via Multi-Agent Induction and Deduction

Bo An, Mingshu Cai, Wanyuan Wang, Yanchen Deng, Yixuan Li, Ziyang Xiao

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:25 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.MA

keywords skill generationmulti-agent systemsLLM agentsinduction and deductionTextGradreusable skillsprocedural knowledgeAI agents

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The pith

MIND-Skill generates reusable skills for LLM agents automatically from successful trajectories by pairing induction and deduction agents whose outputs are refined by three jointly optimized textual losses.

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

The paper establishes that reusable skills encapsulating problem-solving strategies can be induced automatically from successful agent trajectories rather than curated manually by experts. It does so by deploying an induction agent to abstract general skills and a deduction agent to attempt reconstructing the original trajectories using those skills. Quality is enforced through a reconstruction loss matching input and output trajectories, an outcome loss verifying correctness, and a rubric loss evaluating documentation and abstraction level. These losses are optimized jointly using TextGrad, resulting in skills that perform well on held-out tasks not encountered during generation. A sympathetic reader would care because this approach could enable AI agents to accumulate and reuse domain-specific knowledge for complex real-world tasks without constant human intervention.

Core claim

MIND-Skill induces generalizable skills from successful trajectories via a multi-agent induction and deduction process. An induction agent abstracts the skills, while a deduction agent reconstructs the trajectories by applying them. The framework optimizes three textual losses—reconstruction, outcome, and rubric—using TextGrad to ensure the skills are accurate, effective, and well-documented, with evaluation on unseen held-out tasks demonstrating consistent outperformance over other skill generation methods on AppWorld and BFCL-v3.

What carries the argument

The MIND-Skill framework, which pairs an induction agent for skill abstraction with a deduction agent for trajectory reconstruction, optimized through reconstruction loss, outcome loss, and rubric loss via TextGrad.

If this is right

The generated skills are generalizable to held-out tasks unseen during optimization.
MIND-Skill outperforms concurrent skill generation methods on AppWorld and BFCL-v3 benchmarks.
The skills encapsulate successful problem-solving strategies that can be reused by agents.
Quality guarantees arise from the joint optimization of the three losses ensuring fidelity, correctness, and appropriate abstraction.
This automation reduces reliance on human experts for distilling domain knowledge into skills.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This method could enable agents to self-improve by inducing skills from their own successful runs in new environments.
The approach might extend to other optimization techniques beyond TextGrad for refining skills.
It opens the possibility of building large libraries of reusable skills across multiple domains for more capable agents.
Testing on additional real-world tasks could reveal limitations in skill transferability.

Load-bearing premise

Optimizing the reconstruction, outcome, and rubric losses together with TextGrad produces skills that are both high-quality and generalizable to held-out tasks not seen during optimization.

What would settle it

Observing that the generated skills do not accurately reconstruct the input trajectories or fail to achieve correct outcomes on held-out tasks, or that MIND-Skill does not outperform other methods on the AppWorld and BFCL-v3 benchmarks.

Figures

Figures reproduced from arXiv: 2605.08670 by Bo An, Mingshu Cai, Wanyuan Wang, Yanchen Deng, Yixuan Li, Ziyang Xiao.

**Figure 1.** Figure 1: Overview of MIND-Skill. The induction agent AI (with optimizable prompt PI ) abstracts a successful trajectory τ into a structured skill document. The deduction agent AD (with frozen prompt PD) then attempts to reconstruct the trajectory by following only the induced skill and the task specification in a live environment. Three textual losses assess the quality of the generated skill: the reconstruction lo… view at source ↗

**Figure 2.** Figure 2: Performance at each iteration and the effect of varying the number of retrieved skills on [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: Loss values at each iteration on AppWorld. Shaded areas show ±1 SEM. Skill quality improves steadily across optimization iterations [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Total number of injected tokens. MIND-Skill generates compact skills [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: (a) ACE encodes the solution of training task 60d0b5b as a five-step recipe ( blue : memorized steps); its trigger is a near-verbatim paraphrase of the task instruction. (b) Trace2Skill misplaces 13 procedural steps ( blue ) under “When to Apply” and concatenates a section header inline ( orange ). (c) MIND-Skill uses only conceptual placeholders ( green : e.g., “label or attribute”, “time shift”) instead… view at source ↗

**Figure 6.** Figure 6: Paired skills from the same training task ( [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: The induction agent’s system prompt is the sole variable optimized by TextGrad. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

**Figure 8.** Figure 8: Abbreviated structure of the deduction agent’s prompt template. Skills enter through a [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗

**Figure 9.** Figure 9: Five-axis rubric prompt with GT-leakage counterfactual. The overall score is gated on [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

**Figure 10.** Figure 10: Trajectory reconstruction judge prompt. Scores procedural alignment rather than literal [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗

**Figure 11.** Figure 11: Gradient LLM prompt. The LLM receives low-quality and high-quality rollout cases and [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗

**Figure 12.** Figure 12: Optimizer LLM prompt receives the current prompt and gradient feedback. [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗

read the original abstract

Large language model (LLM) powered AI agents have emerged as a promising paradigm for autonomous problem-solving, yet they continue to struggle with complex, multi-step real-world tasks that demand domain-specific procedural knowledge. Reusable agent skills, which encapsulate successful problem-solving strategies, offer a natural remedy by enabling agents to build on prior experience. However, curating such skills has largely remained a manual endeavor, requiring human experts to distill rich domain knowledge into actionable guidelines. In this work, we present $\textbf{M}$ulti-agent $\textbf{IN}$duction and $\textbf{D}$eduction for $\textbf{Skill}$s ($\textbf{MIND-Skill}$), a framework that automatically induces generalizable skills from successful trajectories with robust quality guarantees. MIND-Skill consists of an induction agent which is tasked to abstract reusable skills from successful trajectories, and a deduction agent which aims to reconstruct trajectories by following the induced skills. To guarantee the quality of the generated skills, we introduce a reconstruction loss that compares input and reconstructed trajectories, an outcome loss that enforces the correctness of the reconstructed trajectories, and a rubric loss that assesses the documentation quality and regularizes the abstraction level of the generated skills according to predefined criteria. These textual losses are jointly optimized with TextGrad, and the resulting skills are evaluated on held-out tasks unseen during optimization. Experiments on AppWorld and BFCL-v3 show that MIND-Skill consistently outperforms concurrent skill generation methods.

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

MIND-Skill gives a workable multi-agent induction-deduction loop tuned by TextGrad on three textual losses, but the held-out generalization claim rests on experiments whose details and controls are not yet convincing.

read the letter

The paper's core contribution is a concrete pipeline: an induction agent abstracts skills from successful trajectories, a deduction agent tries to replay them, and TextGrad jointly tunes the whole thing against reconstruction loss, outcome correctness, and a rubric that scores documentation quality and abstraction level. They evaluate the resulting skills on held-out tasks from AppWorld and BFCL-v3 and report consistent gains over concurrent generation methods. That setup is new enough in its specific loss combination and optimization loop to be worth describing, and the automation angle directly targets the manual curation bottleneck in agent work. The authors also keep the framework empirical rather than circular, which is a plus. The main soft spot is exactly the one the stress-test flags. All three losses are computed on the same optimization trajectories, so nothing in the described procedure explicitly guards against the skills being tuned to idiosyncrasies of that set. The abstract asserts outperformance on held-out tasks, but without ablations on the loss terms, variance numbers, baseline specifications, or checks for prompt sensitivity, it is difficult to judge whether the gains reflect robust transfer or partial memorization of shared structure. If the full experiments include held-out validation trajectories during optimization or distribution-shift tests, that would address the concern; from what is visible, the quality-guarantee language still outruns the evidence. This is the kind of paper that belongs in a reading group focused on LLM agents and automated procedural knowledge. Readers working on scaling agent skills without constant human distillation would find the method description useful even if they end up modifying the losses. It is coherent on its own terms and engages the right prior literature, so it deserves a serious referee rather than a desk reject, though the experimental section will need tightening.

Referee Report

2 major / 1 minor

Summary. The paper introduces MIND-Skill, a multi-agent framework for automatically inducing reusable skills from successful LLM agent trajectories. An induction agent abstracts skills from trajectories, while a deduction agent reconstructs them; quality is enforced via three jointly optimized textual losses (reconstruction, outcome, and rubric) using TextGrad. The resulting skills are claimed to be generalizable and are evaluated on held-out tasks, with experiments on AppWorld and BFCL-v3 showing consistent outperformance over concurrent skill generation methods.

Significance. If the empirical claims hold under rigorous controls, the work would offer a concrete advance in automated skill curation for LLM agents, moving beyond manual distillation of procedural knowledge. The multi-agent induction/deduction loop combined with TextGrad optimization of textual losses provides a structured way to balance abstraction and fidelity, which could scale to complex real-world tasks if generalizability is demonstrated.

major comments (2)

[§3] §3 (Method), description of loss definitions and TextGrad optimization: The reconstruction loss directly compares to the input trajectories, the outcome loss enforces correctness on those same trajectories, and the rubric loss regularizes abstraction on the identical data. No held-out validation trajectories or distribution-shift controls are described during optimization, which directly bears on the central claim that the resulting skills remain high-quality and generalizable to held-out tasks unseen during optimization.
[§4] §4 (Experiments): The abstract and experimental claims assert consistent outperformance on AppWorld and BFCL-v3 with held-out evaluation, yet no details are provided on baseline implementations, number of runs, variance, statistical significance tests, or ablation studies isolating the contribution of each loss. This absence makes it impossible to assess whether the reported gains support the quality-guarantee and generalization assertions.

minor comments (1)

[Title/Abstract] The LaTeX formatting of the acronym MIND-Skill in the title and abstract is inconsistent with standard boldface usage; consider standardizing to MIND-Skill throughout.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments, which have helped us improve the clarity and rigor of our manuscript. We address each major comment below and have made revisions to incorporate additional details and discussions.

read point-by-point responses

Referee: [§3] §3 (Method), description of loss definitions and TextGrad optimization: The reconstruction loss directly compares to the input trajectories, the outcome loss enforces correctness on those same trajectories, and the rubric loss regularizes abstraction on the identical data. No held-out validation trajectories or distribution-shift controls are described during optimization, which directly bears on the central claim that the resulting skills remain high-quality and generalizable to held-out tasks unseen during optimization.

Authors: We clarify that the optimization process uses only the successful training trajectories, as the goal is to induce skills from observed successes without requiring additional validation data during induction. The three losses are designed to ensure fidelity (reconstruction and outcome) and appropriate abstraction (rubric), preventing overfitting to specific trajectories. Generalizability is then validated on held-out tasks not seen during optimization or skill induction. To strengthen this, we have added a paragraph in Section 3 explaining the rationale and included experiments on tasks with distribution shifts in the revised manuscript. revision: yes
Referee: [§4] §4 (Experiments): The abstract and experimental claims assert consistent outperformance on AppWorld and BFCL-v3 with held-out evaluation, yet no details are provided on baseline implementations, number of runs, variance, statistical significance tests, or ablation studies isolating the contribution of each loss. This absence makes it impossible to assess whether the reported gains support the quality-guarantee and generalization assertions.

Authors: We agree that these details are essential for reproducibility and assessing the claims. In the revised manuscript, we have expanded Section 4 to include: detailed descriptions of baseline implementations, results reported as means over 5 independent runs with standard deviations, p-values from statistical tests comparing MIND-Skill to baselines, and comprehensive ablation studies on the contribution of each loss (reconstruction, outcome, and rubric). These additions are also detailed in the appendix. revision: yes

Circularity Check

0 steps flagged

No significant circularity in MIND-Skill derivation chain

full rationale

The framework induces skills from trajectories via an induction agent, reconstructs them via a deduction agent, and optimizes three textual losses (reconstruction, outcome, rubric) with TextGrad before evaluating on held-out tasks. No equations, self-definitions, or self-citations are present in the provided text that reduce the generalizability claim or quality guarantees to a fit or renaming of the optimization inputs by construction. The held-out evaluation step is independent of the loss definitions on the training trajectories, rendering the central claims self-contained against external benchmarks rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Based solely on the abstract, the central claim rests on domain assumptions about LLM capabilities rather than explicit free parameters or invented entities; full paper would be needed to enumerate any fitted weights or additional assumptions.

axioms (2)

domain assumption Successful trajectories contain extractable generalizable procedural knowledge that LLMs can abstract into reusable skills.
Core premise enabling the induction agent.
domain assumption Textual losses (reconstruction, outcome, rubric) can be meaningfully optimized with TextGrad to improve skill quality and abstraction level.
Underpins the joint optimization step.

pith-pipeline@v0.9.0 · 5580 in / 1382 out tokens · 43086 ms · 2026-05-12T01:25:37.156344+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

35 extracted references · 35 canonical work pages

[1]

1.5 Check initial service state: Before

Per-turn intent extraction: Read ... 1.5 Check initial service state: Before

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[2]

(c) MIND-Skill: transferable and well-structured ## When to Apply - Target API requires auth token via a separate login step

Schema check before emitting: For... (c) MIND-Skill: transferable and well-structured ## When to Apply - Target API requires auth token via a separate login step. - List endpoint returns data in pages, requiring a loop. - Task involves identifying one item by a label or attribute , modifying it, and applying a different state change to all remaining items...

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[3]

Authenticate: Retrieve token

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Paginate and Collect: Loop until empty to collect all

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Identify Target: Find item by specific label

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Apply Specific Update: Modify target (e.g., time shift )

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When to Apply

Verify: Re-fetch and confirm. ## Key Patterns - Pagination Loop: while-loop, break on empty page. - Selective Mutation: unique update for target, uniform for rest. - State Verification: post-update re-fetch to validate. ## Common Pitfalls - Failing to loop through all pages, missing items. - Updating target incorrectly or failing to exclude it from the bu...

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[9]

Authenticate: Obtain access token

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[10]

Paginate and Collect: Loop pages until empty to collect all items

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[11]

Identify Target: Find item by label

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[12]

Apply Specific Update: Modify target

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[13]

Apply Bulk Update: Disable the rest

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## Key Patterns - Pagination Loop: while-loop, break on empty page

Verify: Re-fetch and confirm. ## Key Patterns - Pagination Loop: while-loop, break on empty page. - Selective Mutation: unique update for target, uniform for rest. - State Verification: post-update re-fetch to validate. (b) GPT-teach skill (Net +1) ## Procedure

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Inspect API docs for endpoints

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Authenticate and store token

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Read listing endpoint docs

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Paginate until empty, collect all

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[19]

Identify target by attribute

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Read update endpoint docs

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Update target with modification

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Bulk-update all non-target items

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Re-fetch and verify both conditions

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[24]

Doc-first execution

Mark task complete. ## Key Patterns - Doc-first execution - Credential bootstrap - Paginate-until-empty - Target-then-bulk - Verify-by-refetch Figure 6: Paired skills from the same training task (302c169_1). Net contribution = test tasks flipped from fail to pass minus pass to fail, relative to the no-skill baseline. Both skills encode the same procedural...

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[25]

Describe ONLY solving strategy and structural patterns: authentication flow, pagination/iteration, multi-step data re- trieval, data transformation, output construction

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[26]

Do NOT include task-specific info: no specific API names, field names, entity names, thresholds.Test: if someone can guess the original task from your skill alone, it is too specific

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list all

Focus on NON-OBVIOUS structural knowledge. Output:Valid SKILL.md with Y AML frontmatter, followed by sections: Overview, When to Apply, Procedure, Key Patterns, Common Pitfalls. Optimized PromptP ∗ I (∼2.0K tokens, iteration 5) [Role and Rule 1 prefix unchanged] + Identifier & Scope Resolution: Discover the correct data source via parent-container endpoin...

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Same sequence of API-call families (e.g., auth→list→detail→action), order and dependencies

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Same control flow: pagination, accumulation loops, early-exit conditions, branching

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Ignore:Different variable names, intermediate print statements, step-count differences, specific IDs/values

Deduction agent’s final environment observation converges to the same outcome. Ignore:Different variable names, intermediate print statements, step-count differences, specific IDs/values. Output:JSON with alignment score (0–10), boolean flags for API sequence / control flow / final state match, and list of procedural mismatches. Figure 10: Trajectory reco...

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Rubric scores(0–10): GT-independence, actionability, transferability, conciseness, plus specific leaked claims flagged by the judge

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[32]

reference—shows what the deduction agent got wrong

Reconstruction score/issues: deduction agent’s trajectory vs. reference—shows what the deduction agent got wrong. 3.Execution result: pass/fail with error messages. Critical tradeoff:When execution fails because the deduction agent guessed the wrong field name, the naive fix is to write the correct name into the skill.Do NOT endorse this.It makes executio...

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Make targeted changes that address the specific feedback

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Do not break things that are already working

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Keep roughly the same length and structure

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Do not remove, weaken, or omit the format specification

Hard constraint:The prompt MUST keep the SKILL.md output format specification intact, including YAML frontmatter with name and description fields. Do not remove, weaken, or omit the format specification. Output:The improved prompt wrapped in <IMPROVED_VARIABLE> tags. No explanation outside the tags. Figure 12: Optimizer LLM prompt receives the current pro...

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