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Jiachen Liu

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#machine learning Preprint Oct 2026

When Non-Adversarial Noise Hurts Learning: Benchmarking Skill Self-Evolution in Agents

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
Review Aug 2026

VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection

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
Jul 2026

Towards Reliable C-to-Rust Translation with Rule-Guided Reasoning and Reinforcement Learning

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...

Feng Luo, Jiachen Liu, Cuiyun Gao et al. · 0 citations

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