4 papers
How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning
Zeyu He, Hannah Kim, Dan Zhang +1
In orchestrated multi-agent systems, humans often struggle to manage plans due to their complexity and limited transparency. Existing approaches rely on outcome-level supervision,…
Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
Naoki Otani, Nikita Bhutani, Hannah Kim +2
Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external data sources. Existing strategi…
Snippet-based Conversational Recommender System
Haibo Sun, Naoki Otani, Hannah Kim +2
Conversational Recommender Systems (CRS) engage users in interactive dialogues to gather preferences and provide personalized recommendations. While existing studies have advanced…
VeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failures
Yoo Yeon Sung, Hannah Kim, Dan Zhang
AI practitioners increasingly use large language model (LLM) agents in compound AI systems to solve complex reasoning tasks, these agent executions often fail to meet human standar…