Newest papers

Adaptive Skill Granularity for Coding Agents: Runtime Routing and Macro-to-Micro Recovery

Dingxuan Zhang, Yanhui Jiang, Jun Wang, Hefeng Zhou · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31461 · cited by 0 works (OpenAlex) · open-access PDF

Arabic

وقد تم دراسة كيفية استرداد المهارات المستخدمة قبل الاستحواذ، أو كيفية تجميع المهارات المستخدمة قبل الاستحواذ، ومع ذلك، فإن الاستحواذ يخلق مشكلة إضافية في التحكم في الوقت المحدد: قد يتم حل هذه المهمة من خلال الدعوات الأساسية من الأدوات، والمهارات الكبيرة، أو الاستحواذ الإبداعي الذي يبدأ من مايكروسوفت ويدخل إلى الاستحواذ على المستوى المحدد. نحن نقدم نموذج Adaptive Skill Granularity، وهو نموذج يعمل على استرداد المهارات كخطوة عملية إضافية تتعلق بمستوى الاستحواذ، ومستوى الاستحواذ، والمهارات الكبيرة. يجمع النظام جهاز التحكم الجانبي المتكامل مع جهاز التحكم

Machine translation (Meta M2M100); the English original follows.

Abstract

Reusable skills allow large language model agents to package task-specific procedures, tool affordances, and execution guidance into modular units. Prior work has largely studied how agents retrieve, compose, or synthesize skills before execution. In coding agents, however, execution poses an additional runtime control problem: the same task may be solved through primitive tool calls, macro skills, or hybrid execution that starts with a macro and falls back to micro-level recovery. We introduce Adaptive Skill Granularity, a framework that treats skill granularity as an action-abstraction policy conditioned on task structure, execution state, and macro-skill failures. The system combines a feature-based granularity controller and a finite-state recovery executor. We evaluate the framework with a deterministic coding-agent protocol using verifier-defined success, total cost, and process metrics such as macro adoption, failure recovery, route match, and transition correctness. Results show that fixed macro execution is efficient when applicable but brittle under mismatch, while runtime routing and explicit macro-to-micro recovery improve robustness and recover controlled macro failures that fixed-granularity execution leaves unresolved. Our findings suggest that skill libraries for coding agents should be treated not only as retrievable resources, but also as controllable, verifiable, and recoverable execution abstractions.

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Topics: Computer science, Granularity, Modular design, Macro, Coding (social sciences), Distributed computing

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