works on

From the 1 of 12 linked papers with an AI index.

activity
20242026
collaborators

12 papers

cs.AI2026

MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning

Kawai Chung, Chunkit Chan, Yauwai Yim +12

The paper introduces MultivationBench, a benchmark that tests multimodal large language models on their ability to reason about evolving human motivations across sequential visual…

cs.CL2026

SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding

Yuqi Yang, Weiqi Wang, Baixuan Xu +13

Session history is a common way of recording user interacting behaviors throughout a browsing activity with multiple products. For example, if an user clicks a product webpage and…

cs.CL2025

CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?

Qing Zong, Jiayu Liu, Tianshi Zheng +7

Accurate confidence calibration in Large Language Models (LLMs) is critical for safe use in high-stakes domains, where clear verbalized confidence enhances user trust. Traditional…

cs.AI2025

DixitWorld: Evaluating Multimodal Abductive Reasoning in Vision-Language Models with Multi-Agent Dixit Gameplay

Yunxiang Mo, Tianshi Zheng, Qing Zong +6

Multimodal abductive reasoning--the generation and selection of explanatory hypotheses from partial observations--is a cornerstone of intelligence. Current evaluations of this abil…

cs.AI2025

LLM-Hanabi: Evaluating Multi-Agent Gameplays with Theory-of-Mind and Rationale Inference in Imperfect Information Collaboration Game

Fangzhou Liang, Tianshi Zheng, Chunkit Chan +2

Effective multi-agent collaboration requires agents to infer the rationale behind others' actions, a capability rooted in Theory-of-Mind (ToM). While recent Large Language Models (…

cs.CL2025

InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding

Cheng Jiayang, Qianqian Zhuang, Haoran Li +4

Grounding large language models (LLMs) in external knowledge sources is a promising method for faithful prediction. While existing grounding approaches work well for simple queries…