1 citations · 2 across the 19 of their papers we have counts for
25 papers
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Xiujin Liu +77
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…
CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds
Ang Li, Haofei Yu, Zhengwei Tao +7
The growing use of language models for forecasting motivates a broader evaluation of their world-model capabilities. Such evaluation must go beyond predictive accuracy to assess hi…
Learning to Simulate Individuals from Macro Social Signals
Yining Zhao, Bushi Liu, Haofei Yu +5
Large language models are increasingly used to simulate how individuals respond to new situations, yet the behavioral reasoning behind these responses is either inherited from pret…
ReSolve: Reusing Candidate Reasoning through Selective Generative Moderation
Bangji Yang, Jiajun Fan, Hongba Ma +7
Sampling multiple solutions spends computation on intermediate deductions and unfinished arguments as well as final answers. We introduce ReSolve, a training-free inference procedu…
RINI: Seeing the Prior Is Not Enough
Hongyi Du, Tianyi Zhang, Heng Wang +7
A research proposal can describe an established mechanism correctly while claiming to introduce it. We study whether providing the earlier paper corrects such contribution claims.…
Relic: From Multi-Agent Collaboration to Persistent Organizational Capability
Hongyi Du, Tianyi Zhang, Weijia Zhang +9
Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to develop on the old version where…
Can Large Language Models Forecast What Researchers Study Next?
Fenghai Li, Zihan Tang, Haofei Yu +2
Large language models increasingly generate research ideas, yet judging their novelty or feasibility at generation time does not establish whether they anticipate subsequent work.…
Multi-Turn Agentic Scientific Literature Search via Workflow Induction
Jisen Li, Bingxuan Li, Nanyi Jiang +10
Scientific literature search often requires more than retrieving papers from a single query: users' intents are underspecified, preference-dependent, and evolve through interaction…
Building Social World Models with Large Language Models
Haofei Yu, Yining Zhao, Guanyu Lin +1
Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social science…
Auto-Dreamer: Learning Offline Memory Consolidation for Language Agents
Chongrui Ye, Yuxiang Liu, Yu Wang +5
Language agents increasingly operate over streams of related tasks, yet existing memory systems struggle to convert accumulated experience into reusable knowledge. Retrieval-augmen…
Interactive Evaluation Requires a Design Science
Keyang Xuan, Peiyang Song, Pan Lu +10
AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other…
ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery
Haofei Yu, Jiaxuan You, Peter Clark +2
Scientific artifacts such as models and datasets are foundations for research. With the rapid growth of platforms like HuggingFace, researchers now have access to a large number of…
SocialVeil: Probing Social Intelligence of Language Agents under Communication Barriers
Keyang Xuan, Pengda Wang, Chongrui Ye +3
Large language models (LLMs) are increasingly evaluated in interactive environments to test their social intelligence. However, existing benchmarks often assume idealized communica…
LiveTradeBench: Seeking Real-World Alpha with Large Language Models
Haofei Yu, Fenghai Li, Jiaxuan You
Large language models (LLMs) achieve strong performance across benchmarks--from knowledge quizzes and math reasoning to web-agent tasks--but these tests occur in static settings, l…
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…
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…
Sotopia-RL: Reward Design for Social Intelligence
Haofei Yu, Zhengyang Qi, Yining Zhao +6
Social intelligence has become a critical capability for large language models (LLMs), enabling them to engage effectively in real-world social tasks such as collaboration and nego…
ConsistencyChecker: Tree-based Evaluation of LLM Generalization Capabilities
Zhaochen Hong, Haofei Yu, Jiaxuan You
Evaluating consistency in large language models (LLMs) is crucial for ensuring reliability, particularly in complex, multi-step interactions between humans and LLMs. Traditional se…
Beyond Facts: Evaluating Intent Hallucination in Large Language Models
Yijie Hao, Haofei Yu, Jiaxuan You
When exposed to complex queries containing multiple conditions, today's large language models (LLMs) tend to produce responses that only partially satisfy the query while neglectin…
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…
Table as Thought: Exploring Structured Thoughts in LLM Reasoning
Zhenjie Sun, Naihao Deng, Haofei Yu +1
Large language models' reasoning abilities benefit from methods that organize their thought processes, such as chain-of-thought prompting, which employs a sequential structure to g…
ResearchTown: Simulator of Human Research Community
Haofei Yu, Zhaochen Hong, Zirui Cheng +5
Large Language Models (LLMs) have demonstrated remarkable potential in scientific domains, yet a fundamental question remains unanswered: Can we simulate human research communities…