arXiv 2610.05030 · cs.AI · 2026-10-04
EVISKILL: Grounding Skill Evolution in Replayable Evidence
Yan Zhou, Yili Wang, Yiwei Dai, Qinggang Zhang, Xin Wang
DEEP Group at Jilin University
In latest · #245 Added on 2026-10-21
One-line takeaway
EVISKILL grounds agent skill evolution in replayable evidence to judge which edits should persist.
EVISKILL 把智能体技能演化建立在可回放的行为证据上,判断每次修改是否该保留。
Read on arXiv PDF Hugging Face discussion Code Project page
Topics & disciplines
More on Agents
- Raven: The Harness of Harnesses for Composable Agentic Intelligence ▲748
- False Frontiers: Diagnosing and Mitigating Co-Cheating in Self-Evolving Search Agents ▲642
- BizAgentBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business ▲490
- Omni-IO Skills: Harnessing Your Agent Omni-Native ▲485
- Player-1: Learning to Play from Synthetic Experience ▲348
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-22.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.