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20242026
most citedOne Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs

1 citations · 1 across the 15 of their papers we have counts for

collaborators

15 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.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.CL2026

Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models

Junru Lu, Jiarui Qin, Lingfeng Qiao +35

We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that r…

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

Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey

Jiayi Kuang, Jingyou Xie, Haohao Luo +6

Visual Question Answering (VQA) is a challenge task that combines natural language processing and computer vision techniques and gradually becomes a benchmark test task in multimod…

cs.CV2024

FLEX-CLIP: Feature-Level GEneration Network Enhanced CLIP for X-shot Cross-modal Retrieval

Jingyou Xie, Jiayi Kuang, Zhenzhou Lin +3

Given a query from one modality, few-shot cross-modal retrieval (CMR) retrieves semantically similar instances in another modality with the target domain including classes that are…

cs.CL2024

Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation

Zhe Xu, Daoyuan Chen, Jiayi Kuang +3

Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their…