1 citations · 1 across the 9 of their papers we have counts for
13 papers
ORIGAMISPACE: Benchmarking Multimodal LLMs in Multi-Step Spatial Reasoning with Mathematical Constraints
Rui Xu, Dakuan Lu, Zicheng Zhao +5
Spatial reasoning is a key capability in the field of artificial intelligence, especially crucial in areas such as robotics, computer vision, and natural language understanding. Ho…
Curse of Knowledge: When Complex Evaluation Context Benefits yet Biases LLM Judges
Weiyuan Li, Xintao Wang, Siyu Yuan +5
As large language models (LLMs) grow more capable, they face increasingly diverse and complex tasks, making reliable evaluation challenging. The paradigm of LLMs as judges has emer…
Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Jiangjie Chen, Qianyu He, Siyu Yuan +9
Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR…
ARIA: Training Language Agents with Intention-Driven Reward Aggregation
Ruihan Yang, Yikai Zhang, Aili Chen +5
Large language models (LLMs) have enabled agents to perform complex reasoning and decision-making through free-form language interactions. However, in open-ended language action en…
Can LLMs Learn to Map the World from Local Descriptions?
Sirui Xia, Aili Chen, Xintao Wang +4
Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in tasks such as code and mathematics. However, their potential to internalize structured spat…
PowerAttention: Exponentially Scaling of Receptive Fields for Effective Sparse Attention
Lida Chen, Dong Xu, Chenxin An +8
Large Language Models (LLMs) face efficiency bottlenecks due to the quadratic complexity of the attention mechanism when processing long contexts. Sparse attention methods offer a…
DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling
Aili Chen, Chengyu Du, Jiangjie Chen +6
To advance personalized applications such as recommendation systems and user behavior prediction, recent research increasingly adopts large language models (LLMs) for human -readab…
CoSER: A Comprehensive Literary Dataset and Framework for Training and Evaluating LLM Role-Playing and Persona Simulation
Xintao Wang, Heng Wang, Yifei Zhang +9
Role-playing language agents (RPLAs) have emerged as promising applications of large language models (LLMs). However, simulating established characters presents a challenging task…
Past Meets Present: Creating Historical Analogy with Large Language Models
Nianqi Li, Siyu Yuan, Jiangjie Chen +5
Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. How…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…
MINDECHO: Role-Playing Language Agents for Key Opinion Leaders
Rui Xu, Dakuan Lu, Xiaoyu Tan +5
Large language models~(LLMs) have demonstrated impressive performance in various applications, among which role-playing language agents (RPLAs) have engaged a broad user base. Now,…
EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms
Siyu Yuan, Kaitao Song, Jiangjie Chen +3
The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite…
Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?
Rui Xu, Xintao Wang, Jiangjie Chen +6
Can Large Language Models (LLMs) simulate humans in making important decisions? Recent research has unveiled the potential of using LLMs to develop role-playing language agents (RP…