3 papers
cs.LG2026
Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels
Do Nguyen-Thanh, Junlin Fang, Bing Li +2
Failure attribution for LLM agents aims to identify decisive steps responsible for task failure, which is critical for debugging and improving agentic systems. Existing methods rel…
cs.AI2026
Toolcompass: Guiding Tool Trialing, Not Suppressing It
Junlin Fang, Chong Zhang, Do Nguyen-Thanh +3
Large language model (LLM) agents must generalize from tools seen during training to unseen tools at deployment. A key challenge is tool trialing, i.e., excessive trials waste the…
cs.AI2026
Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models
Junlin Fang, Do Nguyen-Thanh, Xiaogang Xu +2
Large reasoning models (LRMs) generate long reasoning traces before producing final answers. While these traces may contain useful signals for hallucination detection, harnessing t…