4 citations · 14 across the 15 of their papers we have counts for
15 papers
ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
Tianyi Guan, Yiding Wang, Haotong Yang +5
Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve t…
Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs
Haotong Yang, Ting Long, Yi Chang
Tool learning enables LLMs to invoke external tools to accomplish tasks. Prior studies have demonstrated the effectiveness of a hierarchical structure: a high-level policy handles…
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…
GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization
Juntong Wang, Libin Chen, Xiyuan Wang +4
Repository-level bug localization-the task of identifying where code must be modified to fix a bug-is a critical software engineering challenge. Standard Large Language Modles (LLM…
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…
LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
Yansheng Mao, Yufei Xu, Jiaqi Li +5
Long-context understanding remains challenging for LLMs due to limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a framework that improves the long-cont…
Beyond Single-Task: Robust Multi-Task Length Generalization for LLMs
Yi Hu, Shijia Kang, Haotong Yang +2
Length generalization, the ability to solve problems longer than those seen during training, remains a critical challenge for large language models (LLMs). Previous work modifies p…
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…
Case-Based or Rule-Based: How Do Transformers Do the Math?
Yi Hu, Xiaojuan Tang, Haotong Yang +1
Despite the impressive performance in a variety of complex tasks, modern large language models (LLMs) still have trouble dealing with some math problems that are simple and intuiti…
Chain of Images for Intuitively Reasoning
Fanxu Meng, Haotong Yang, Yiding Wang +1
The human brain is naturally equipped to comprehend and interpret visual information rapidly. When confronted with complex problems or concepts, we use flowcharts, sketches, and di…
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…
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
Yi Hu, Haotong Yang, Zhouchen Lin +1
Large language models (LLMs) have scaled up to unlock a wide range of complex reasoning tasks with the aid of various prompting methods. However, current prompting methods generate…
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
Haotong Yang, Zhouchen Lin, Muhan Zhang
Most knowledge graphs (KGs) are incomplete, which motivates one important research topic on automatically complementing knowledge graphs. However, evaluation of knowledge graph com…