2 citations · 3 across the 9 of their papers we have counts for
9 papers
Beyond representational alignment with brain-guided language models for robust reasoning
Mingqing Xiao, Kai Du, Zhouchen Lin
The correspondence between large language models (LLMs) and the neural mechanisms underlying human higher-order cognition remains insufficiently characterized. Given that language…
ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics
Shunkai Zhang, Haoran Zhang, Yun Luo +15
Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight. Recent evidence…
Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
Yafu Li, Runzhe Zhan, Haoran Zhang +25
Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performa…
ProofVerifier: A Scalable, Diversity-Driven Framework for Natural-Language Proof Verification
Haotong Yang, Zitong Wang, Shijia Kang +7
While large language models (LLMs) have achieved strong performance on mathematical problems with verifiable answers, many advanced problems are proof-based and require evaluating…
LiteToken: Removing Intermediate Merge Residues From BPE Tokenizers
Yike Sun, Haotong Yang, Zhouchen Lin +1
Tokenization is fundamental to how language models represent and process text, yet the behavior of widely used BPE tokenizers has received far less study than model architectures a…
VACT: A Video Automatic Causal Testing System and a Benchmark
Haotong Yang, Qingyuan Zheng, Yunjian Gao +4
With the rapid advancement of text-conditioned Video Generation Models (VGMs), the quality of generated videos has significantly improved, bringing these models closer to functioni…
GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model
Haotong Yang, Xiyuan Wang, Qian Tao +3
Recent research on integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) typically follows two approaches: LLM-centered models, which convert graph data into t…
Number Cookbook: Number Understanding of Language Models and How to Improve It
Haotong Yang, Yi Hu, Shijia Kang +2
Large language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing (such as 9.1…
Parrot Mind: Towards Explaining the Complex Task Reasoning of Pretrained Large Language Models with Template-Content Structure
Haotong Yang, Fanxu Meng, Zhouchen Lin +1
The pre-trained large language models (LLMs) have shown their extraordinary capacity to solve reasoning tasks, even on tasks that require a complex process involving multiple sub-s…