activity
20232026
most citedTowards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters

2 citations · 5 across the 16 of their papers we have counts for

collaborators

16 papers

cs.CL2026

INSPIRE: An Internalize-Then-Improve Approach for Example-Driven Mathematical Reasoning

Shuai Wang, Jiayi Kuang, Yinghui Li +4

Mathematical reasoning has seen rapid progress in large language models (LLMs), yet existing methods optimize predominantly for final-answer correctness, raising the question wheth…

cs.AI2026

From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities

Jiayi Kuang, Yinghui Li, Yunze Song +11

Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing math benchmarks evaluate onl…

cs.AI2026

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

Yongheng Zhang, Ziang Liu, Jiaxuan Zhu +17

Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-im…

cs.RO2026

PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution

Yang Liu, Weixing Chen, Xinshuai Song +8

Vision-language-action models, world models, and agentic planners each advance physical intelligence, yet their composition lacks a common execution abstraction, shared state, sema…

cs.AI2026

TopoAgent: A Self-Evolving Topological Agent for Multimodal Scientific Reasoning

Mingze Xu, Yinghui Li, Jiayi Kuang +5

While Multimodal Large Language Models (MLLMs) excel in general tasks, rigorous scientific reasoning remains challenging due to the limitations of monolithic, linear planning. Such…

cs.AI2026

Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding

Yinghui Li, Jiayi Kuang, Peng Xing +11

Multimodal large language models (MLLMs) perform strongly on natural images, yet their ability to understand discrete visual symbols remains unclear. We present a multi-domain benc…

cs.SE2026

EvoConfig: Self-Evolving Multi-Agent Systems for Efficient Autonomous Environment Configuration

Xinshuai Guo, Jiayi Kuang, Linyue Pan +6

A reliable executable environment is the foundation for ensuring that large language models solve software engineering tasks. Due to the complex and tedious construction process, l…

cs.CV2026

TangramPuzzle: Evaluating Multimodal Large Language Models with Compositional Spatial Reasoning

Daixian Liu, Jiayi Kuang, Yinghui Li +8

Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual recognition and semantic understanding, yet precise compositional spatial reasoning under geome…

cs.SE2025

Process-Level Trajectory Evaluation for Environment Configuration in Software Engineering Agents

Jiayi Kuang, Yinghui Li, Xin Zhang +5

Large language model-based agents show promise for software engineering, but environment configuration remains a bottleneck due to heavy manual effort and scarce large-scale, high-…

cs.CL2025

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…

cs.CL2025

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…

cs.LG2025★ 1 cited

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…

cs.CL2024

CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction

Jingheng Ye, Zishan Xu, Yinghui Li +9

The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we intro…

cs.CL2024★ 1 cited

EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correction

Jingheng Ye, Shang Qin, Yinghui Li +8

Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited scenario, where they ignore the interaction between corrections and explanations and…

cs.CL2024★ 1 cited

Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework

Peng Xing, Yinghui Li, Shirong Ma +6

Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences. Recently, multi-domain CSC has gradually attracted the attention of researchers bec…

cs.CL2023★ 2 cited

Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters

Yinghui Li, Zishan Xu, Shaoshen Chen +7

Writing assistance is an application closely related to human life and is also a fundamental Natural Language Processing (NLP) research field. Its aim is to improve the correctness…