Author

Bing Li

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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

Preprint Sep 2026

Frame Differential On-Policy Self-Distillation for Video Reasoning

Reinforcement learning (RL) has substantially improved the reasoning ability of multimodal language models through verifiable rewards and increasingly fine-grainedvisual or temporal credit assignment. In video reasoning, however, current RL methods typically train with a fixed sparse frame budget: increasing the number...

Haiying He, Xin Zheng, Shao-Li Hu et al. · 0 citations