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

0 comments

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 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: 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 casebased learning not only enhances general analytical ability,… view at source ↗

**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.

Desk Editor's Note private letter to a colleague

The paper applies case-based reasoning to LLM agents by turning past task experience into reusable assets and reports gains over baselines that grow with complexity, but the methods and metrics stay too thin to verify the claims.

read the letter

The main point for you is that this work takes established case-based reasoning and turns it into a framework for LLM agents, extracting knowledge, prompts, and skills from real past tasks so they transfer to new ones. The abstract shows results on a benchmark of six task categories where the approach matches or beats zero-shot, few-shot, checklist, and rule-memory baselines, with bigger edges on harder tasks and some cross-agent reuse.

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)

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.
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)

Abstract: consider briefly naming the six task categories to give readers immediate context for the benchmark scope.
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

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
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

discussion (0)

Forward citations

Cited by 1 Pith paper

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

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

Works this paper leans on

50 extracted references · 9 canonical work pages · cited by 1 Pith paper · 8 internal anchors

[1]

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...
[2]

Related Work 2.1 LLM-based Autonomous Agents With recent advances in large language models (LLMs) for language understanding and generation, researchers have increasingly explored their potential as agents for solving complex tasks3,7. Unlike traditional dialogue systems, autonomous agents must not only generate text, but also plan tasks, use tools, inter...
[3]

field formats should be rechecked

Case-Based Learning (CBL) Framework 3.1 Design Rationale and Overall Workflow The core goal of the Case-Based Learning (CBL) framework proposed in this paper is not simply to provide LLM-based agents with more contextual information, but to build a learning mechanism that more closely resembles the way human experts develop. In real scientific research, e...
[4]

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 ...
[5]

case -based learning works

Results 5.1 Overall Results Across the Six Tasks Figure 1 illustrates the proposed Case -Based Learning (CBL) framework and its core operating mechanisms. Unlike approaches that enhance LLM agents merely by increasing context length or injecting static knowledge, the central idea of CBL is to treat each r eal task execution as a learnable case. In this wa...
[6]

knows a rule,

Discussion The experimental results show that the core value of case -based learning is not simply to provide LLMs with more background information, but to equip agents with a learning mechanism that more closely resembles the growth process of real experts. Unlike approaches that rely on pretrained knowledge, prompt engineering, or few -shot examples 4,5...
[7]

First, the organization of case assets remains rela tively static

Limitations and Future Work Although our results show that case -driven experience transfer has clear potential for improving both performance on complex tasks and reasoning efficiency, the current work still has several limitations. First, the organization of case assets remains rela tively static. In this study, experience is represented as structured a...
[8]

knowing the rules,

Conclusion The results of this study show that the value of case -based learning lies not merely in providing LLMs with more prompts or background knowledge, but in giving agents a learning mechanism that more closely resembles the growth process of real experts. Unli ke methods that rely on pretrained knowledge, prompt engineering, or few -shot examples,...
[9]

A Survey on Large Language Model Based Autonomous Agents

Wang, L., Ma, C., and Feng, X. A Survey on Large Language Model Based Autonomous Agents. Frontiers of Computer Science, 2024

2024
[10]

A., MacKnight, R., and Kline, B

Boiko, D. A., MacKnight, R., and Kline, B. Autonomous Chemical Research with Large Language Models. Nature, 2023

2023
[11]

A Survey on the Memory Mechanism of Large Language Model based Agents

Zhang, Z., Bo, X., and Ma, C. A Survey on the Memory Mechanism of Large Language Model Based Agents. arXiv preprint arXiv:2404.13501, 2024

work page internal anchor Pith review arXiv 2024
[12]

B., Mann, B., Ryder, N., et al

Brown, T. B., Mann, B., Ryder, N., et al. Language Models are Few -Shot Learners. In Advances in Neural Information Processing Systems (NeurIPS), 2020

2020
[13]

Retrieval -Augmented Generation for Knowledge- Intensive NLP Tasks

Lewis, P., Perez, E., Piktus, A., et al. Retrieval -Augmented Generation for Knowledge- Intensive NLP Tasks. In Advances in Neural Information Processing Systems (NeurIPS), 2020

2020
[14]

Reflexion: Language Agents with Verbal Reinforcement Learning

Shinn, N., Cassano, F., Gopinath, A., et al. Reflexion: Language Agents with Verbal Reinforcement Learning. In Advances in Neural Information Processing Systems (NeurIPS), 2023

2023
[15]

Emergent Abilities of Large Language Models

Wei, J., Tay, Y., Bommasani, R., et al. Emergent Abilities of Large Language Models. Transactions on Machine Learning Research (TMLR), 2022

2022
[16]

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

Karpas, E., Scharfe, C., and others. MRKL Systems: A Modular, Neuro -Symbolic Architecture that Combines Large Language Models, External Knowledge Sources and Discrete Reasoning. arXiv preprint arXiv:2205.00445, 2022

work page internal anchor Pith review arXiv 2022
[17]

ReAct: Synergizing Reasoning and Acting in Language Models

Yao, S., Zhao, J., Yu, D., et al. ReAct: Synergizing Reasoning and Acting in Language Models. In International Conference on Learning Representations (ICLR), 2023

2023
[18]

Toolformer: Language Models Can Teach Themselves to Use Tools

Schick, T., Dwivedi-Yu, J., Dessi, R., et al. Toolformer: Language Models Can Teach Themselves to Use Tools. In Advances in Neural Information Processing Systems (NeurIPS), 2023

2023
[19]

HuggingGPT: Solving AI Tasks with ChatGPT and Its Friends in Hugging Face

Shen, Y., Song, K., Tan, X., et al. HuggingGPT: Solving AI Tasks with ChatGPT and Its Friends in Hugging Face. In Advances in Neural Information Processing Systems (NeurIPS), 2023

2023
[20]

G., Zhang, T., Wang, X., and Gonzalez, J

Patil, S. G., Zhang, T., Wang, X., and Gonzalez, J. E. Gorilla: Large Language Model Connected with Massive APIs. In Advances in Neural Information Processing Systems (NeurIPS), 2024

2024
[21]

OpenAGI: When LLM Meets Domain Experts

Ge, Y., Hua, W., Mei, K., et al. OpenAGI: When LLM Meets Domain Experts. In Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2023

2023
[22]

AutoGPT: An Autonomous GPT-4 Experiment

Significant Gravitas. AutoGPT: An Autonomous GPT-4 Experiment. GitHub repository, 2023

2023
[23]

Voyager: An Open-Ended Embodied Agent with Large Language Models

Wang, G., Xie, Y., Jiang, Y., et al. Voyager: An Open -Ended Embodied Agent with Large Language Models. arXiv preprint arXiv:2305.16291, 2023

work page internal anchor Pith review Pith/arXiv arXiv 2023
[24]

AgentBench : Evaluating LLMs as Agents

Liu, X., Yu, H., Zhang, H., et al. AgentBench : Evaluating LLMs as Agents. In International Conference on Learning Representations (ICLR), 2024

2024
[25]

GAIA: A Benchmark for General AI Assistants

Mialon, G., Fourrier, C., Swift, C., et al. GAIA: A Benchmark for General AI Assistants. In International Conference on Learning Representations (ICLR), 2024

2024
[26]

Reasoning with language model is planning with world model.arXiv preprint arXiv:2305.14992,

Hao, S., Gu, Y., Ma, H., et al. Reasoning with Language Model Is Planning with World Model. arXiv preprint arXiv:2305.14992, 2023

work page arXiv 2023
[27]

S., O’Brien, J

Park, J. S., O’Brien, J. C., Cai, C. J., et al. Generative Agents: Interactive Simulacra of Human Behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST), 2023

2023
[28]

MemoryBank: Enhancing Large Language Models with Long-Term Memory

Zhong, W., Guo, L., Gao, Q., et al. MemoryBank: Enhancing Large Language Models with Long-Term Memory. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024

2024
[29]

MemGPT: Towards LLMs as Operating Systems

Packer, C., Fang, V., Patil, S. G., et al. MemGPT: Towards LLMs as Operating Systems. arXiv preprint arXiv:2310.08560, 2023

work page internal anchor Pith review Pith/arXiv arXiv 2023
[30]

ExpeL: LLM Agents Are Experiential Learners

Zhao, A., Huang, D., Xu, Q., et al. ExpeL: LLM Agents Are Experiential Learners. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024

2024
[31]

Augmenting Language Models with Long -Term Memory

Wang, W., Dong, L., Cheng, H., et al. Augmenting Language Models with Long -Term Memory. In Advances in Neural Information Processing Systems (NeurIPS), 2023

2023
[32]

Kolodner, J. L. Case-Based Reasoning. Morgan Kaufmann, 1993

1993
[33]

Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches

Aamodt, A., and Plaza, E. Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 1994

1994
[34]

Retrieval-Augmented Generation for Large Language Models: A Survey

Gao, Y., Xiong, Y., Gao, X., et al. Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv preprint arXiv:2312.10997, 2023

work page internal anchor Pith review Pith/arXiv arXiv 2023
[35]

Chain -of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., et al. Chain -of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems (NeurIPS), 2022

2022
[36]

F., Lin, K., Hewitt, J., et al

Liu, N. F., Lin, K., Hewitt, J., et al. Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics (TACL), 2024

2024
[37]

WebGPT: Browser-assisted question-answering with human feedback

Nakano, R., Hilton, J., Balaji, S., et al. Browser -Assisted Question -Answering with Human Feedback. arXiv preprint arXiv:2112.09332, 2021

work page internal anchor Pith review arXiv 2021
[38]

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Sarthi, P., Abdullah, S., Tuli, A., et al. RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval. In International Conference on Learning Representations (ICLR), 2024

2024
[39]

Self-Consistency Improves Chain of Thought Reasoning in Language Models

Wang, X., Wei, J., Schuurmans, D., et al. Self-Consistency Improves Chain of Thought Reasoning in Language Models. In International Conference on Learning Representations (ICLR), 2023

2023
[40]

PAL: Program -Aided Language Models

Gao, L., Madaan, A., Zhou, S., et al. PAL: Program -Aided Language Models. In International Conference on Machine Learning (ICML), 2023

2023
[41]

Plan -and-Solve Prompting: Improving Zero -Shot Chain-of-Thought Reasoning by Large Language Models

Wang, L., Xu, W., Lan, Y., et al. Plan -and-Solve Prompting: Improving Zero -Shot Chain-of-Thought Reasoning by Large Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 2023

2023
[42]

Chen, W., Ma, X., Wang, X., and Cohen, W. W. Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks. Transactions on Machine Learning Research (TMLR), 2023

2023
[43]

Curriculum Learning

Bengio, Y., Louradour, J., Collobert, R., and Weston, J. Curriculum Learning. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML), 2009

2009
[44]

Vygotsky, L. S. Mind in Society: The Development of Higher Psychological Processes. Harvard University Press, 1978

1978
[45]

Training Language Models to Follow Instructions with Human Feedback

Ouyang, L., Wu, J., Jiang, X., et al. Training Language Models to Follow Instructions with Human Feedback. In Advances in Neural Information Processing Systems (NeurIPS), 2022

2022
[46]

F., Leike, J., Brown, T., et al

Christiano, P. F., Leike, J., Brown, T., et al. Deep Reinforcement Learning from Human Preferences. In Advances in Neural Information Processing Systems (NeurIPS), 2017

2017
[47]

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Bai, Y., Jones, A., Ndousse, K., et al. Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. arXiv preprint arXiv:2204.05862, 2022

work page internal anchor Pith review Pith/arXiv arXiv 2022
[48]

MetaGPT: Meta -Programming for A Multi-Agent Collaborative Framework

Hong, S., Zhuge, M., Chen, J., et al. MetaGPT: Meta -Programming for A Multi-Agent Collaborative Framework. In International Conference on Learning Representations (ICLR), 2024

2024
[49]

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Wu, Q., Bansal, G., Zhang, J., et al. AutoGen: Enabling Next -Gen LLM Applications via Multi-Agent Conversation. arXiv preprint arXiv:2308.08155, 2023

work page internal anchor Pith review Pith/arXiv arXiv 2023
[50]

S., Reid, M., et al

Kojima, T., Gu, S. S., Reid, M., et al. Large Language Models are Zero-Shot Reasoners. In Advances in Neural Information Processing Systems (NeurIPS), 2022. Appendix Appendix A. Additional Details of the Eight Benchmark Tasks The six task categories in this study are not ordinary question -answering samples, but a collection of cases built around high -co...

2022