20 papers
MemoHarness: Agent Harnesses That Learn from Experience
Yue Huang, Wenjie Wang, Han Bao +7
MemoHarness is a framework that automatically adapts the control layer (harness) of large language model agents by learning from past executions, using a dual‑layer experience bank…
Fail-Aware and Explainable Test Oracle Prediction
Yue Zhao, Binish Tanveer, Jelena Zdravkovic
The paper introduces FOCAL, a code‑language‑model based system that predicts whether a test prefix will pass or fail and provides statement‑level explanations, improving fault dete…
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
Tianming Sha, Yue Zhao, Lichao Sun +1
Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but corre…
SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution
Aojie Yuan, Yi Nian, Haiyue Zhang +2
Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary label…
Agent Safety Is Action Alignment
Shawn Li, Yue Zhao
Large language models increasingly act as agents: they call tools, move money, delete records, and send messages on a user's behalf. To keep them safe, practitioners imported the c…
GRADE: Graph Representation of LLM Agent Dependency and Execution
Yue Zhao
Can one graph represent every kind of LLM agent's run? A trace records what each step did, never what it relied on, the state it read, and the results it reused. GRADE recovers tha…