SkillHone：一種透過持久決策歷史實現持續智能體技能演化的框架

摘要

Agent技能透過任務特定程序、腳本與參考資料擴展語言模型代理的功能，但這些技能所針對的任務與環境持續變動。現有方法僅在有限運行中改善技能，並僅保留最終產物，遺棄後續代理解讀先前修訂、評估與遭拒替代方案所需的決策歷程。我們提出SkillHone，一種基於持久化決策歷程的持續代理技能演化框架。SkillHone將技能修訂與提供實作回饋的評估端證據配對，記錄診斷、修訂、證據與結果的結構化歷程。角色分離的子代理在隱匿報告的測試探針上運行候選技能，並根據先前決策啟發提出修訂，實現跨階段的精煉，無需重新探索過往推理邏輯。在深度研究基準測試中，SkillHone無需預整合搜尋堆疊即可運行，且在GAIA與WebWalkerQA-EN上分別勝過商業支援的深度研究代理15.8個百分點與3.2個百分點，同時超越先前的技能演化方法。我們進一步將SkillHone部署於內部工具中介的分析場景，在七個設定中平均提升準確率18.8個百分點。

English

Agent skills extend language-model agents with task-specific procedures, scripts, and references, but the tasks and environments they target continually change. Existing methods improve skills in bounded runs and retain only the final artifact, discarding the decision history that later agents need to interpret prior revisions, evaluations, and rejected alternatives. We introduce SkillHone, a harness for continual agent skill evolution grounded in persistent decision history. SkillHone pairs skill revisions with evaluation-side evidence that supplies practice feedback, recording structured histories of diagnoses, revisions, evidence, and outcomes. Role-separated subagents run candidate skills on practice probes with redacted reporting and propose revisions informed by prior decisions, enabling cross-session refinement without rediscovering past rationale. On deep-research benchmarks, SkillHone runs without a pre-integrated search stack and outperforms the commercially backed deep-research agent by 15.8 points on GAIA and 3.2 points on WebWalkerQA-EN, while also exceeding prior skill-evolution methods. We further deploy SkillHone on internal tool-mediated analysis scenarios, where it improves accuracy by an average of 18.8 points across seven settings.