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arxiv: 2604.12717 · v1 · submitted 2026-04-14 · 💻 cs.AI

Recognition: unknown

Transferable Expertise for Autonomous Agents via Real-World Case-Based Learning

Zhenyu Ma , Yuyang Song , Chunyi Yang , Jingyi Zhu , Letian Yang , Xukai Jiang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:40 UTC · model grok-4.3

classification 💻 cs.AI
keywords case-based learningLLM agentsautonomous agentstransferable knowledgereal-world tasksprompting baselinestask complexity
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The pith

A case-based learning framework lets LLM agents extract and reuse knowledge from past tasks to improve structured performance on new complex real-world work.

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

The paper presents a framework that turns completed task experiences into reusable knowledge assets, analytical prompts, and operational skills so agents can transfer prior expertise instead of relying only on general pretraining or fixed prompts. It tests the approach on a benchmark covering six categories of complex tasks and compares it against zero-shot, few-shot, checklist prompt, and rule memory baselines. Results indicate consistent strong performance that matches or exceeds the best baseline in every case, with larger gains on harder tasks. Additional checks show that the benefit grows with task complexity and that practical knowledge learned by one agent can be applied by others. The authors conclude that this case-based method offers a route to building more reliable agents for professional real-world use.

Core claim

Converting experience from past tasks into reusable knowledge assets, analytical prompts, and operational skills allows agents to transfer task-relevant expertise and perform more structured analysis on new tasks, producing performance that matches or exceeds standard prompting baselines across six complex task categories with the clearest advantages on harder problems.

What carries the argument

The case-based learning framework, which extracts task-relevant knowledge, analytical prompts, and operational skills from real past cases and stores them as transferable assets.

If this is right

  • Agents achieve stronger or equal performance on every tested task category compared with zero-shot, few-shot, checklist, and rule-memory prompting.
  • The performance advantage of case-based learning widens as task complexity increases.
  • Knowledge assets acquired by one agent transfer directly to other agents without additional training.
  • The method supports construction of agents that can handle professional real-world work more reliably than prompt-only approaches.

Where Pith is reading between the lines

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

  • A library of stored cases could let agents accumulate expertise incrementally across many interactions rather than resetting with each new prompt.
  • Shared case assets might enable networks of agents to pool experience, reducing duplication of effort on similar problems.
  • Automatic extraction of assets will require mechanisms to detect and drop case-specific noise that could mislead on dissimilar future tasks.

Load-bearing premise

Experience from past tasks can be converted into reusable knowledge assets, prompts, and skills that apply to new tasks without introducing irrelevant details or errors.

What would settle it

A new set of complex tasks where agents using the extracted case assets perform below the strongest baseline or where transferred knowledge produces repeated errors traceable to mismatched prior cases.

Figures

Figures reproduced from arXiv: 2604.12717 by Chunyi Yang, Jingyi Zhu, Letian Yang, Xukai Jiang, Yuyang Song, Zhenyu Ma.

Figure 3
Figure 3. Figure 3: Performance gains on structured and semi [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Stronger gains on complex workflow tasks. [PITH_FULL_IMAGE:figures/full_fig_p022_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Largest advantage on the open-ended scientific discovery task. This figure focuses on the multi-agent scientific discovery platform task and compares the proposed method with the best baseline in terms of score and success rate. The results show that our method achieves the most substantial margin of improvement on this task, indicating that case￾based learning not only enhances general analytical ability,… view at source ↗
Figure 6
Figure 6. Figure 6: Analysis of the inheritability of practical knowledge. [PITH_FULL_IMAGE:figures/full_fig_p027_6.png] view at source ↗
read the original abstract

LLM-based autonomous agents perform well on general reasoning tasks but still struggle to reliably use task structure, key constraints, and prior experience in complex real-world settings. We propose a case-based learning framework that converts experience from past tasks into reusable knowledge assets, allowing agents to transfer prior case experience to new tasks and perform more structured analysis. Unlike methods based mainly on pretrained knowledge or static prompts, our framework emphasizes extracting and reusing task-relevant knowledge, analytical prompts, and operational skills from real cases. We evaluate the method on a unified benchmark of six complex task categories and compare it with Zero-Shot, Few-Shot, Checklist Prompt, and Rule Memory baselines. Results show that our method achieves consistently strong performance across all tasks and matches or outperforms the best baseline in every case, with especially clear gains on more complex tasks. Further analysis shows that the advantage of case-based learning increases with task complexity, and that practical knowledge acquired by one agent can be reused by others. These findings suggest that case-based learning offers a promising path for building professional agents for real-world work.

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.

Referee Report

2 major / 2 minor

Summary. The paper proposes a case-based learning framework for LLM-based autonomous agents that converts experience from past tasks into reusable knowledge assets, analytical prompts, and operational skills. Unlike approaches relying primarily on pretrained knowledge or static prompts, the framework emphasizes extracting task-relevant knowledge for structured analysis on new tasks. It is evaluated on a unified benchmark of six complex task categories against explicit baselines (Zero-Shot, Few-Shot, Checklist Prompt, Rule Memory), with results claiming consistent strong performance that matches or exceeds the best baseline in every case, larger gains on complex tasks, increasing advantage with task complexity, and successful reuse of knowledge across agents.

Significance. If the empirical results hold, this work is significant for advancing reliable autonomous agents in real-world settings. It provides a concrete alternative to static prompting by demonstrating transferable expertise via case-based learning, supported by comparisons to multiple baselines on a unified benchmark and evidence of cross-agent reuse. The observation that benefits scale with task complexity is a notable strength that could inform practical agent design.

major comments (2)
  1. Evaluation section: the central claim of consistent outperformance and complexity-dependent gains rests on the benchmark results, but the manuscript must explicitly report the evaluation metrics, statistical significance tests, task definitions, and controls for prompt engineering quality. Without these, the comparisons to baselines cannot be fully verified as load-bearing evidence.
  2. Framework section: the assumption that past experience converts reliably into reusable assets without introducing irrelevant details or errors is load-bearing for the transferability claim. The paper should include concrete examples, ablation studies, or validation steps showing error-free extraction to support the reported cross-agent reuse.
minor comments (2)
  1. Abstract: consider briefly naming the six task categories to give readers immediate context for the benchmark scope.
  2. Notation and figures: ensure all figures comparing performance across baselines include error bars or confidence intervals for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. We address each major comment point by point below, incorporating revisions to improve the verifiability and rigor of the manuscript.

read point-by-point responses

  1. Referee: Evaluation section: the central claim of consistent outperformance and complexity-dependent gains rests on the benchmark results, but the manuscript must explicitly report the evaluation metrics, statistical significance tests, task definitions, and controls for prompt engineering quality. Without these, the comparisons to baselines cannot be fully verified as load-bearing evidence.

    Authors: We agree that explicit details on metrics, statistical tests, task definitions, and prompt controls are required to make the benchmark comparisons fully verifiable. In the revised manuscript, we have expanded the Evaluation section with a new subsection that reports: the primary metrics (task success rate and structured analysis quality score), results of statistical significance tests (paired t-tests with p-values against each baseline), precise definitions and examples for all six task categories, and controls for prompt engineering (including fixed prompt templates, length standardization, and independent validation of baseline prompts by multiple annotators). These additions directly substantiate the claims of consistent outperformance and increasing gains with task complexity. revision: yes

  2. Referee: Framework section: the assumption that past experience converts reliably into reusable assets without introducing irrelevant details or errors is load-bearing for the transferability claim. The paper should include concrete examples, ablation studies, or validation steps showing error-free extraction to support the reported cross-agent reuse.

    Authors: The reliability of the extraction process is indeed central to the transferability results. We have revised the Framework section to include: (1) concrete examples of extracted knowledge assets, analytical prompts, and operational skills from sample past tasks, showing the conversion steps; (2) an ablation study comparing full framework performance to a variant without the structured extraction module; and (3) validation results from manual review of 50 randomly sampled extractions, reporting low rates of irrelevant details or errors (under 5%). These additions provide direct support for the cross-agent reuse findings without altering the original experimental outcomes. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper proposes an empirical case-based learning framework for LLM agents, converting past task experience into reusable knowledge assets, and validates it via direct performance comparisons on a unified benchmark of six task categories against explicit external baselines (Zero-Shot, Few-Shot, Checklist Prompt, Rule Memory). No mathematical derivations, equations, fitted parameters presented as predictions, or self-citation chains appear in the abstract or described structure. The central claims rest on observable outperformance and cross-agent reuse measured against independent baselines, making the argument self-contained without reduction to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that real task experiences contain extractable, transferable knowledge that LLMs can reliably process into reusable assets. No free parameters or invented entities are mentioned in the abstract.

axioms (1)
  • domain assumption Past task experiences contain structured, reusable knowledge that can be extracted and applied to new tasks without significant loss or distortion.
    Invoked in the description of converting experience into knowledge assets and the claim of transferability.

pith-pipeline@v0.9.0 · 5495 in / 1362 out tokens · 28018 ms · 2026-05-10T14:40:31.427138+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MDAgent: A Multi-Agent Framework for End-to-End Molecular Dynamics Research

    q-bio.QM 2026-04 unverdicted novelty 5.0

    MDAgent combines multiple AI agents with case-based learning to handle end-to-end molecular dynamics workflows including strategy design, simulation, analysis, and interpretation.

Reference graph

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    Introduction In recent years, LLM-based autonomous agents have shown strong capabilities in open - ended tasks such as planning, reasoning, and tool use, raising expectations that they may eventually support complex professional work in scientific research, enterprise pl atform management, biomedical analysis, and software engineering1,2. However, despite...

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    Experimental Setup 4.1 Task Design and Case Construction To systematically evaluate the analytical ability and transferability of agents in complex real-world tasks, we construct a case set consisting of six representative task categories. These tasks are drawn from high-complexity scenarios in real system development and deployment, requiring agents not ...

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