1 citations · 1 across the 9 of their papers we have counts for
9 papers
Cliff: Learning Process Rewards from the First Mistake
Peixuan Han, Runhui Wang, Ketan Ramaneti +3
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language model (LLM) post-training, but its reliance on coarse outcome rewards le…
-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues
Peixuan Han, Hongyi Du, Jiayu Liu +3
Personalization is a crucial capability of modern language agents. However, current research primarily positions personalized agents as passive responders to user preferences, limi…
GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs
Tao Feng, Haozhen Zhang, Zijie Lei +2
LLM routing has achieved promising results in integrating the strengths of diverse models while balancing efficiency and performance. However, to support more realistic and challen…
DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal
Peixuan Han, Yingjie Yu, Jingjun Xu +1
Despite the growing adoption of large language models (LLMs) in scientific research workflows, automated support for academic rebuttal, a crucial step in academic communication and…
ProtocolBench: Which LLM MultiAgent Protocol to Choose?
Hongyi Du, Jiaqi Su, Jisen Li +6
As large-scale multi-agent systems evolve, the communication protocol layer has become a critical yet under-evaluated factor shaping performance and reliability. Despite the existe…
Self-Aligned Reward: Towards Effective and Efficient Reasoners
Peixuan Han, Adit Krishnan, Gerald Friedland +2
Reinforcement learning with verifiable rewards has significantly advanced reasoning in large language models (LLMs), but such signals remain coarse, offering only binary correctnes…
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…
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…