3 citations · 4 across the 5 of their papers we have counts for
6 papers
AgentWorld: Benchmarking Long-Horizon Collaboration of Multi-agent LLMs
Raphael Shu, Yusen Zhang, Young Min Cho +7
Existing multi-agent benchmarks primarily test in competitive settings, short-horizon interactions under 20 steps, or simply aggregate individual performance, failing to isolate an…
CONFETTI: Conversational Function-Calling Evaluation Through Turn-Level Interactions
Tamer Alkhouli, Katerina Margatina, James Gung +4
We introduce Conversational Function-Calling Evaluation Through Turn-Level Interactions (CONFETTI), a conversational benchmark1 designed to evaluate the function-calling capabiliti…
Optimizing LLM-Based Multi-Agent System with Textual Feedback: A Case Study on Software Development
Ming Shen, Raphael Shu, Anurag Pratik +4
We have seen remarkable progress in large language models (LLMs) empowered multi-agent systems solving complex tasks necessitating cooperation among experts with diverse skills. Ho…
TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues
Yubin Ge, Salvatore Romeo, Jason Cai +4
Temporal reasoning in multi-session dialogues presents a significant challenge which has been under-studied in previous temporal reasoning benchmarks. To bridge this gap, we propos…
Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications
Raphael Shu, Nilaksh Das, Michelle Yuan +2
AI agents powered by large language models (LLMs) have shown strong capabilities in problem solving. Through combining many intelligent agents, multi-agent collaboration has emerge…
Structured List-Grounded Question Answering
Mujeen Sung, Song Feng, James Gung +3
Document-grounded dialogue systems aim to answer user queries by leveraging external information. Previous studies have mainly focused on handling free-form documents, often overlo…