most citedWhere LLM Agents Fail and How They can Learn From Failures

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MA2025

SeeingEye: Agentic Information Flow Unlocks Multimodal Reasoning In Text-only LLMs

Weijia Zhang, Zijia Liu, Haoru Li +2

Recent advances in text-only large language models (LLMs), such as DeepSeek-R1, demonstrate remarkable reasoning ability. However, these models remain fragile or entirely incapable…

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.CL2025

Time-R1: Towards Comprehensive Temporal Reasoning in LLMs

Zijia Liu, Peixuan Han, Haofei Yu +2

Large Language Models (LLMs) demonstrate impressive capabilities but lack robust temporal intelligence, struggling to integrate reasoning about the past with predictions and plausi…

cs.AI2025★ 1 cited

SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents

Kunlun Zhu, Jiaxun Zhang, Ziheng Qi +6

Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concern…

cs.CL2025

ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind

Peixuan Han, Zijia Liu, Jiaxuan You

Large language models (LLMs) have shown promising potential in persuasion, but existing works on training LLM persuaders are still preliminary. Notably, while humans are skilled in…