Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
Summary
This paper introduces SkeMex, a self-evolving framework that enhances medical agents by distilling interaction trajectories into structured skill memory, enabling better long-term clinical reasoning through context-dependent utility estimation and governance.
View Cached Full Text
Cached at: 06/09/26, 08:41 AM
Paper page - Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
Source: https://huggingface.co/papers/2606.09365 Published on Jun 8
·
Submitted byhttps://huggingface.co/manglu3935
Mangluon Jun 9
Authors:
,
,
,
,
,
,
,
,
,
Abstract
SkeMex is a self-evolving framework that enhances medical agents through structured skill memory, improving long-term clinical reasoning by distinguishing useful experiences and governing memory retention based on contextual utility.
Medical agent systemsare increasingly expected to supportinteractive clinical decision makingrather than only static question answering. In such settings, effective agents must reuse prior experience across evolving cases, yet existingmemory mechanismsoften retain raw historical traces that are redundant, noisy, and difficult to govern. More importantly, they rarely distinguish which memories are truly useful for future reasoning. This limits their ability to accumulate compact and reliable experience for long-horizon clinical reasoning. To close this gap, we propose SkeMex, apost-deployment self-evolutionframework that improves medical agents through askill-based memorywithout updating model weights. SkeMex distills informativeinteraction trajectoriesinto structured skills that encode reusableprocedural knowledge, and organizes them into amulti-branch repositoryspanning general, task-specific, and action-level experience. To determine which memories should be reused and retained, SkeMex estimatescontext-dependent utilityfrom environment feedback and uses it to guidevalue-aware retrievalandrepository governance. A closed-loop ``Read--Write--Assess--Govern“ lifecycle further supportscontinual evolutionby writing new skills, updating utilities, promoting useful memories, and removing harmful entries. Experiments across diverse clinical tasks show that SkeMex consistently outperforms representative memory-based agents in bothoffline and online settings. It also generalizes across model backbones and supportstransferable skill memory. All data and code will be released publicly.
View arXiv pageView PDFAdd to collection
Get this paper in your agent:
hf papers read 2606\.09365
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2606.09365 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2606.09365 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2606.09365 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents
MSCE is a training-free framework that organizes LLM agent experience into three memory levels and converts them into reusable skills with evidence links, outperforming existing memory and skill-augmented baselines.
Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember
This paper introduces SESA, a self-evolving skill-augmented search agent that co-evolves task generation and skill memory via tool-augmented search self-play. It improves accuracy across seven QA benchmarks over baselines while supporting memory-free deployment.
@dair_ai: // MetaSkill-Evolve // Great paper on self-improving agents. Most self-improving agents rewrite what the agent does and…
MetaSkill-Evolve introduces a recursive two-timescale framework for LLM agents to evolve both task skills and the improvement procedure itself, achieving notable accuracy gains on OfficeQA, SealQA, and ALFWorld benchmarks.
MedSkillAudit: A Domain-Specific Audit Framework for Medical Research Agent Skills
This paper introduces MedSkillAudit, a domain-specific framework for auditing the safety and quality of medical research AI agent skills before deployment. The study demonstrates that the system achieves reliable assessment consistency comparable to or better than human expert review.
GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning
Introduces GuideSkill, an external reasoning layer that compiles clinical practice guidelines into executable diagnostic skills, improving LLM accuracy on clinical reasoning benchmarks without backbone updates.