Large language model agents increasingly improve by distilling experience into reusable skills, but errors in that experience can persist in learned rules and affect later tasks. Existing evaluations largely examine clean learning or perturbations encountered after skills are frozen, leaving robustness during self-evol...
Tao Yu, Zhong-Tian Luo, Yu-Chen Long et al.· 0 citations
Lara, a machine-checkable language and protocol for checking and revising support for research claims, is introduced, and the metatheory of claim checking and cross-context argument transport is established, and semantic guarantees in Lean 4 are mechanized.
VulnGym is a real-world repository-level benchmark for evaluating vulnerability detection by coding agents that aligns reviewed GitHub advisories with their corresponding vulnerable version repositories and defines an end-to-end detection task and three oracle-based subtasks to jointly evaluate vulnerability detection...
Kexing Ji, Jiachen Liu, En-Ze Hu et al.· 2 citations
A Translation fRAmework Via rule-guided reasoning and rEinforcement Learning, namely TRAVEL, consisting of two modules that employs Monte Carlo Tree Search-based reasoning path construction guided by Rust-specific rules, and introduces reinforcement learning that couples execution feedback with reasoning-quality signal...