Author

Jun-Lin Fang

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#machine learning Preprint Oct 2026

Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels

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 rely on costly LLM prompting, step-level error annotations, or verified successful trajectories that lack failure signals for assistance. In th...

Do Nguyen-Thanh, Jun-Lin Fang, Bing Li et al. · 1 citation

Sep 2026

Beyond Known Objects: Prompting 6D Pose Estimator to Learn Novel Objects Continually

Robust 6D object pose estimation is essential for enabling intelligent systems to interact with the physical world. While existing methods have achieved notable progress, they remain constrained by two major limitations: poor scalability when new objects are introduced and severe performance degradation caused by catas...

Long Tian, Yang Liu, Jun-Lin Fang et al. · IEEE Transactions on Image P... · 0 citations

#artificial intelligence Preprint Sep 2026

Toolcompass: Guiding Tool Trialing, Not Suppressing It

This work introduces ToolCompass, a post-training framework that guides tool trialing by organizing tool-call representations according to shared functions and jointly reduces intra-function variation across domains and increases inter-function separation.

Jun-Lin Fang, Chong-Chong Zhang, Do Nguyen-Thanh et al. · 0 citations