arXiv:2606. 26669v1 Announce Type: new Abstract: Agents often repeatedly solve similar task instances from scratch, leading to unnecessary reasoning cost and long execution traces.
By Zhongxin Guo, Danrui Qi, Hanwen Gu, Peng Cheng, Yongqiang Xiong
arXiv:2607. 25853v1 Announce Type: new Abstract: Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks.
By Yu Hao, Jinxuan Cai, Qi Zhang, Yawen Li, Zhiqiang Zhang, Chuan Shi, Cheng Yang
arXiv:2606. 06893v1 Announce Type: new Abstract: Large language model agents increasingly rely on Skills to encode procedural knowledge, yet high-quality Skills remain costly to hand-write.
By Yuyang Zhang, Xinyuan Han, Xudong Jiang, Run Wang
arXiv:2609.09233v1 Announce Type: cross
Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused...
By Wasu Top Piriyakulkij, Rachel Lawrence, Alicia Curth, Sushrut Karmalkar, Niranjani Prasad
HEXIS is a system that compiles agent skills into extended finite state machines, separating skill knowledge from control flow. It uses local instructions within states to guide reasoning and generation, while explicit transition conditions manage execution progress. The incremental compiler maps skill clauses and tool interfaces to state operations, aligns development traces to identify missing operations, and updates are validated through static checks and replay of traces, resulting in improved success rates and reduced execution tokens across benchmarks.
By Minghao LI
SkillLens introduces a hierarchical skill-evolution framework that organizes skills into a four-layer graph of policies, strategies, procedures, and primitives, allowing retrieval at mixed granularity. The system first retrieves semantically relevant skill seeds, expands them via a degree‑corrected random walk, and uses a verifier to decide whether to accept, decompose, rewrite, or skip each visited unit. This approach enables agents to reuse compatible subskills while locally adapting mismatched components, and theoretical analysis shows sublinear cost under sparse mismatch assumptions, with empirical results on MuLocbench and ALFWorld demonstrating consistent improvements over strong baselines.
By Ziyang Yu, Yongliang Miao, Liang Zhao, Bowen Zhu, Hasibul Haque