11 citations · 21 across the 15 of their papers we have counts for
17 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…
Atomic Thinking of LLMs: Decoupling and Exploring Mathematical Reasoning Abilities
Jiayi Kuang, Haojing Huang, Yinghui Li +8
Large Language Models (LLMs) have demonstrated outstanding performance in mathematical reasoning capabilities. However, we argue that current large-scale reasoning models primarily…
The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
Long Li, Zhijian Zhou, Jiaran Hao +9
A central paradox in fine-tuning Large Language Models (LLMs) with Reinforcement Learning with Verifiable Reward (RLVR) is the frequent degradation of multi-attempt performance (Pa…
OR-VSKC: Resolving Visual-Semantic Knowledge Conflicts in Operating Rooms with Synthetic Data-Guided Alignment
Weiyi Zhao, Xiaoyu Tan, Liang Liu +3
Automated identification of surgical safety risks is critical for improving patient outcomes; however, Multimodal Large Language Models (MLLMs) frequently suffer from Visual-Semant…
Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents
Rui Xu, MingYu Wang, XinTao Wang +4
Recent advances in LLM-based role-playing language agents (RPLAs) have attracted broad attention in various applications. While chain-of-thought reasoning has shown importance in m…
AURORA:Automated Training Framework of Universal Process Reward Models via Ensemble Prompting and Reverse Verification
Xiaoyu Tan, Tianchu Yao, Chao Qu +8
The reasoning capabilities of advanced large language models (LLMs) like o1 have revolutionized artificial intelligence applications. Nevertheless, evaluating and optimizing comple…
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning
Yinghui Li, Haojing Huang, Jiayi Kuang +7
How to alleviate the hallucinations of Large Language Models (LLMs) has always been the fundamental goal pursued by the LLMs research community. Looking through numerous hallucinat…
One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs
Yinghui Li, Jiayi Kuang, Haojing Huang +10
Leveraging mathematical Large Language Models (LLMs) for proof generation is a fundamental topic in LLMs research. We argue that the ability of current LLMs to prove statements lar…
SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain
Dakuan Lu, Xiaoyu Tan, Rui Xu +5
Recent breakthroughs in large language models (LLMs) exemplified by the impressive mathematical and scientific reasoning capabilities of the o1 model have spotlighted the critical…
An Attentive Dual-Encoder Framework Leveraging Multimodal Visual and Semantic Information for Automatic OSAHS Diagnosis
Yingchen Wei, Xihe Qiu, Xiaoyu Tan +4
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder caused by upper airway blockage, leading to oxygen deprivation and disrupted sleep. Traditional diagnos…
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework
Yongxin Deng, Xihe Qiu, Jue Chen +1
The inherent uncertainty in the environmental transition model of Reinforcement Learning (RL) necessitates a delicate balance between exploration and exploitation. This balance is…
BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +8
Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary prompting, but can surface when the…
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought
Xiaoyu Tan, Yongxin Deng, Xihe Qiu +7
Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supporting various aspects of daily…
Robust Deep Hawkes Process under Label Noise of Both Event and Occurrence
Xiaoyu Tan, Bin Li, Xihe Qiu +3
Integrating deep neural networks with the Hawkes process has significantly improved predictive capabilities in finance, health informatics, and information technology. Nevertheless…
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models
Xihe Qiu, Haoyu Wang, Xiaoyu Tan +6
Effective collaboration in multi-agent systems requires communicating goals and intentions between agents. Current agent frameworks often suffer from dependencies on single-agent e…
Struct-X: Enhancing Large Language Models Reasoning with Structured Data
Xiaoyu Tan, Haoyu Wang, Xihe Qiu +4
Structured data, rich in logical and relational information, has the potential to enhance the reasoning abilities of large language models (LLMs). Still, its integration poses a ch…
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,…