30 citations · 34 across the 16 of their papers we have counts for
16 papers
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
Jundong Xu, Qingchuan Li, Jiaying Wu +11
Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deploymen…
TeamBench: Evaluating Agent Coordination under Enforced Role Separation
Yubin Kim, Chanwoo Park, Taehan Kim +9
Agent systems often decompose a task across multiple roles, but these roles are typically specified by prompts rather than enforced by access controls. Without enforcement, a team…
An AI Co-Data-Scientist for Prioritizing Candidate Biomarkers from Wearable Sensor Data
Yubin Kim, Salman Rahman, Samuel Schmidgall +33
Wearable devices generate continuous physiological and behavioral data, but converting these signals into clinically reviewable biomarker hypotheses remains labor-intensive. We int…
A Modern System Recipe for Situated Embodied Human-Robot Conversation with Real-Time Multimodal LLMs and Tool-Calling
Dong Won Lee, Sarah Gillet, Louis-Philippe Morency +2
Situated embodied conversation requires robots to interleave real-time dialogue with active perception: deciding what to look at, when to look, and what to say under tight latency…
Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning
Zhiyuan Hu, Yunhai Hu, Juncheng Liu +9
Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, multi-agent RL (MARL)…
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs
Zhiyuan Hu, Yucheng Wang, Yufei He +7
Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from explor…
Towards a Science of Scaling Agent Systems
Yubin Kim, Ken Gu, Chanwoo Park +17
Agents, language model-based systems capable of reasoning, planning, and acting are widely adopted in real-world tasks, yet how their performance changes as these systems scale acr…
InvThink: Premortem Reasoning for Safer Language Models
Yubin Kim, Taehan Kim, Eugene Park +4
We present InvThink, a training and prompting framework that requires the model to enumerate, analyze, and constrain potential failures before generating its final response. Unlike…
Tiered Agentic Oversight: A Hierarchical Multi-Agent System for Healthcare Safety
Yubin Kim, Hyewon Jeong, Chanwoo Park +9
Large language models (LLMs) deployed as agents introduce significant safety risks in clinical settings due to their potential for error and single points of failure. We introduce…
BehaviorSFT: Behavioral Token Conditioning for Clinical Agents Across the Proactivity Spectrum
Yubin Kim, Zhiyuan Hu, Hyewon Jeong +11
Large Language Models (LLMs) as clinical agents require careful behavioral adaptation. While adept at reactive tasks (e.g., diagnosis reasoning), LLMs often struggle with proactive…
Aligning Dialogue Agents with Global Feedback via Large Language Model Multimodal Reward Decomposition
Dong Won Lee, Hae Won Park, Cynthia Breazeal +1
We propose a large language model based reward decomposition framework for aligning dialogue agents using only a single session-level feedback signal. We leverage the reasoning cap…
VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation
Yubin Kim, Taehan Kim, Wonjune Kang +8
Vocal health plays a crucial role in peoples' lives, significantly impacting their communicative abilities and interactions. However, despite the global prevalence of voice disorde…
Social Human Robot Embodied Conversation (SHREC) Dataset: Benchmarking Foundational Models' Social Reasoning
Dong Won Lee, Yubin Kim, Denison Guvenoz +5
Our work focuses on the social reasoning capabilities of foundation models for real-world human-robot interactions. We introduce the Social Human Robot Embodied Conversation (SHREC…
Does "Reasoning" with Large Language Models Improve Recognizing, Generating, and Reframing Unhelpful Thoughts?
Yilin Qi, Dong Won Lee, Cynthia Breazeal +1
Cognitive Reframing, a core element of Cognitive Behavioral Therapy (CBT), helps individuals reinterpret negative experiences by finding positive meaning. Recent advances in Large…
Medical Hallucinations in Foundation Models and Their Impact on Healthcare
Yubin Kim, Hyewon Jeong, Shan Chen +24
Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accuracy, fostering overconfidence an…
A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
Yubin Kim, Chanwoo Park, Hyewon Jeong +7
Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (…