5 papers
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…
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…
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…
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…
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…