activity
20232026
collaborators

7 papers

cs.AI2026

SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models

Josue Torres-Fonseca, Naihao Deng, Yinpei Dai +5

Multimodal Large Language Models are increasingly adopted as autonomous agents in interactive environments, yet their ability to proactively address safety hazards remains insuffic…

cs.IR2026

LUCid: Redefining Relevance For Lifelong Personalization

Chimaobi Okite, Anika Misra, Joyce Chai +1

Work to date has mainly relied on semantic proximity to identify relevant content for lifelong personalization. However, situational relevance is often more important for determini…

cs.LG2025

Evaluating Long-Context Reasoning in LLM-Based WebAgents

Andy Chung, Yichi Zhang, Kaixiang Lin +3

As large language model (LLM)-based agents become increasingly integrated into daily digital interactions, their ability to reason across long interaction histories becomes crucial…

cs.AI2025

Proactive Assistant Dialogue Generation from Streaming Egocentric Videos

Yichi Zhang, Xin Luna Dong, Zhaojiang Lin +5

Recent advances in conversational AI have been substantial, but developing real-time systems for perceptual task guidance remains challenging. These systems must provide interactiv…

cs.CV2025

Temporally-Grounded Language Generation: Towards Real-Time Vision-Language Models

Keunwoo Peter Yu, Joyce Chai

Vision-language models (VLMs) have shown remarkable progress in offline tasks such as image captioning and video question answering. However, real-time interactive environments imp…

cs.AI2023

Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake?

Yuwei Bao, Keunwoo Peter Yu, Yichi Zhang +6

Despite tremendous advances in AI, it remains a significant challenge to develop interactive task guidance systems that can offer situated, personalized guidance and assist humans…

cs.AI2023

Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?

Yichi Zhang, Jiayi Pan, Yuchen Zhou +2

Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. However, known as visual illusions, human's perceptio…