3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.AI2026
Current Agents Fail to Leverage World Model as Tool for Foresight
Cheng Qian, Emre Can Acikgoz, Bingxuan Li +8
Agents built on vision-language models increasingly face tasks that demand anticipating future states rather than relying on short-horizon reasoning. Generative world models offer…
cs.AI2025★ 3 cited
Where LLM Agents Fail and How They can Learn From Failures
Kunlun Zhu, Zijia Liu, Bingxuan Li +15
Large Language Model (LLM) agents, which integrate planning, memory, reflection, and tool-use modules, have shown promise in solving complex, multi-step tasks. Yet their sophistica…
cs.CV2025
MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning
Chengfei Wu, Ronald Seoh, Bingxuan Li +3
Recent advances in large vision-language models have led to impressive performance in visual question answering and multimodal reasoning. However, it remains unclear whether these…