AIConfPaper
← Search

Yubin Kim

9 accepted papers

2026

Social Human Robot Embodied Conversation (SHREC) Dataset: Benchmarking Foundational Models’ Social Reasoning

Dong Won Lee, Yubin Kim, Sooyeon Jeong, Denison Guvenoz, Parker Malachowsky, Louis-Philippe Morency +2
RSS 2026poster

Our work focuses on the social reasoning capabilities of foundational models for real-world human–robot interactions. We introduce the Social Human Robot Embodied Conversation (SHREC) Dataset, a large-scale benchmark of 400 real-world human-robot interaction videos and over 10K annotations, capturin…

Cited by 0Source ↗Scholar ↗
2026

StretchTime: Adaptive Time Series Forecasting via Symplectic Attention

Yubin Kim, Viresh Pati, Jevon Twitty, Vinh Pham, Shihao Yang, Jiecheng Lu
ICML 2026poster

Transformer architectures have established strong baselines in time series forecasting, yet they typically rely on positional encodings that assume uniform, index-based temporal progression. However, real-world systems, from shifting financial cycles to elastic biological rhythms, frequently exhibit…

Cited by 0Source ↗Scholar ↗
2026

What Generative Search Engines Like and How to Optimize Web Content Cooperatively

Yujiang Wu, Shanshan Zhong, Yubin Kim, Chenyan Xiong
ICLR 2026poster

By employing large language models (LLMs) to retrieve documents and generate natural language responses, Generative Engines, such as Google AI overview and ChatGPT, provide significantly enhanced user experiences and have rapidly become the new form of search. Their rapid adoption also drives the ne…

Cited by 0Source ↗code ↗Scholar ↗
2025

BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum

Yubin Kim, Zhiyuan Hu, Hyewon Jeong, Eugene W Park, Shuyue Stella Li, Chanwoo Park +8
EMNLP 2025

Large Language Models (LLMs) as agents require careful behavioral adaptation. While adept at reactive tasks (e.g., medical reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce **BehaviorBench**, a comprehensi

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

ZeroS: Zero‑Sum Linear Attention for Efficient Transformers

Jiecheng Lu, Xu Han, Yan Sun, Viresh Pati, Yubin Kim, Siddhartha Somani +1
NeurIPS 2025spotlight

Linear attention methods offer Transformers $O(N)$ complexity but typically underperform standard softmax attention. We identify two fundamental limitations affecting these approaches: the restriction to convex combinations that only permits additive information blending, and uniform accumulated wei…

Cited by 0Source ↗Scholar ↗
2024

Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation

Hyunwoo Ryu, Jiwoo Kim, Hyunseok An, Junwoo Chang, Joohwan Seo, Taehan Kim +4
CVPR 2024highlight

Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper we present Diffusion-EDFs a novel SE(3)-equivariant diffusion-based approach for visual robotic manipulation tasks. We show that our proposed meth…

Cited by 40Source ↗PDF ↗code ↗Scholar ↗
2024

EmpathicStories++: A Multimodal Dataset for Empathy Towards Personal Experiences

Jocelyn Shen, Yubin Kim, Mohit Hulse, Wazeer Zulfikar, Sharifa Alghowinem, Cynthia Breazeal +1
ACL 2024findings

Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy datasets fall short in capturing the richness of empathy responses, often being confined to in-lab or acted scenarios, lack…

Cited by 9Source ↗PDF ↗Scholar ↗
2024

MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making

Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, Xuhai Xu, Daniel McDuff +4
NeurIPS 2024oral

Foundation models are becoming valuable tools in medicine. Yet despite their promise, the best way to leverage Large Language Models (LLMs) in complex medical tasks remains an open question. We introduce a novel multi-agent framework, named **M**edical **D**ecision-making **Agents** (**MDAgents**) t…

Cited by 35Source ↗PDF ↗code ↗Scholar ↗
2023

MultiPar-T: Multiparty-Transformer for Capturing Contingent Behaviors in Group Conversations

Dong Won Lee, Yubin Kim, Rosalind W. Picard, Cynthia Breazeal, Hae Won Park
IJCAI 2023poster

As we move closer to real-world social AI systems, AI agents must be able to deal with multiparty (group) conversations. Recognizing and interpreting multiparty behaviors is challenging, as the system must recognize individual behavioral cues, deal with the complexity of multiple streams of data fro…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗