collaborators

5 papers

cs.LG2026

Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization

Fei Bai, Zhipeng Chen, Chuan Hao +6

Recently, reinforcement learning~(RL) has become an important approach for improving the capabilities of large language models~(LLMs). In particular, reinforcement learning from ve…

cs.CL2026

Thinking with Tables: Enhancing Multi-Modal Tabular Understanding via Neuro-Symbolic Reasoning

Kun-Yang Yu, Zhi Zhou, Shi-Yu Tian +6

Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across modalities such as images and text. However, tabular data, despite being a criti…

cs.CL2026

SemEval-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models

Pengfei Cao, Mingxuan Yang, Yubo Chen +4

Understanding why real-world events occur is important for both natural language processing and practical decision-making, yet direct-cause inference remains underexplored in evide…

cs.AI2025

TabularMath: Understanding Math Reasoning over Tables with Large Language Models

Shi-Yu Tian, Zhi Zhou, Wei Dong +5

Mathematical reasoning has long been a key benchmark for evaluating large language models. Although substantial progress has been made on math word problems, the need for reasoning…

cs.LG2025

BMIP: Bi-directional Modality Interaction Prompt Learning for VLM

Song-Lin Lv, Yu-Yang Chen, Zhi Zhou +2

Vision-language models (VLMs) have exhibited remarkable generalization capabilities, and prompt learning for VLMs has attracted great attention for the ability to adapt pre-trained…