{"ID":5937009,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T15:05:50.046563074Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05202","arxiv_id":"2607.05202","title":"EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer","abstract":"Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test single-episode task solving; memory benchmarks target information retention rather than procedural reuse. We introduce EvoAgentBench, a benchmark for agent self-evolution via Ability-guided transfer across four agentic domains: web research, algorithmic reasoning, software engineering, and knowledge work. EvoAgentBench extracts trace-grounded Abilities from agent executions, canonicalizes them into operational units, and builds domain-specific Ability Graphs linking tasks that share procedural overlap. By design, every test task is backed by verified training-side Ability support. Across a 528/267 train/test split, two scaffolds, and three backbones, curated Ability content transfers reliably across model families, but no current automatic method sustains positive gain in all settings. EvoAgentBench shifts self-evolution evaluation from aggregate accuracy comparison to fine-grained diagnosis of experience encoding, routing, and uptake. The benchmark is publicly available at https://huggingface.co/datasets/EverMind-AI/EvoAgentBench.","short_abstract":"Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test single-episode task solving; memory benchmar...","url_abs":"https://arxiv.org/abs/2607.05202","url_pdf":"https://arxiv.org/pdf/2607.05202v1","authors":"[\"Xingze Gao\",\"Chuanrui Hu\",\"Hongda Chen\",\"Pengfei Yao\",\"Zhao Wang\",\"Yi Bai\",\"Zhengwei Wu\",\"Yunyun Han\",\"Xiaofeng Cong\",\"Jie Gui\",\"Yafeng Deng\",\"Teng Li\"]","published":"2026-07-06T15:17:09Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}