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arXiv:2608.25920v2 Announce Type: replace Abstract: As large language model (LLM)-based multi-agent systems (MASs) are increasingly applied to long-horizon complex tasks, their reliability has emerge...
arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.
The Agent Error Dataset (AED) presents 50,228 error–diagnosis pairs collected from 9,961 source tasks across 33 environments, 19 harness families, and 23 policy models in text‑based agent systems. A five‑stage Agentic Error‑to‑Training (AET) pipeline generates diagnoses and proposed corrections, verifies them against recorded evidence, and creates separate training views for diagnosis and actor recovery. Experiments show that first‑proposal corrections improve verifier pass rates from 18.4% to 51.1%, and fine‑tuning with full‑diagnosis data raises Qwen3‑8B’s exact‑step agreement from 47.2% to 63.6% on a holdout set.
SkillScriptBench is a 350‑task benchmark that evaluates how well large language models can self‑evolve executable agent skill packages by distinguishing between documentation repair, script repair, and preservation. The benchmark draws from 100 real GitHub skill packages, creating 150 repair tasks with injected script faults and a controlled track of 200 tasks that test clean, documentation‑faulty, script‑faulty, and combined‑faulty states. Experiments show that AST‑Guided Skill Revision—using abstract syntax trees to target relevant code locations—improves repair success by up to 27.7% and yields more consistent outcomes across repeated runs. whyItMatters":"The benchmark provides a systematic way to measure and improve the reliability of automated skill package maintenance, which is essential for deploying robust LLM agents in real‑world applications."
Self-correction is particularly useful when a failure constrains the next repair. Coding agents benefit from this property because compilers, tests, and execution traces turn many failures into typed recovery signals, but broad language-agent tasks often expose only a coarse task failure.
arXiv:2605. 17450v2 Announce Type: replace-cross Abstract: As software systems grow increasingly complex, automated vulnerability repair (AVR) remains difficult because the materials available to a repair system are usually failure artifacts rather than repair guidance.