MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems
MASCOT is a multi-agent socio-collaborative companion framework that coordinates specialized agents around social context and user goals to enable trustworthy, everyday companion experiences.
Jul 1, 2026
AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
AgentArk distills the collaborative behavior of multi-agent systems into a single LLM agent, decomposing trajectories into role-conditioned skills and recovering most of the ensemble's performance at a fraction of the cost.
Feb 4, 2026
AgentArk
Distilling multi-agent intelligence into a single LLM agent. Decomposes multi-agent trajectories into role-conditioned skills and trains a single agent to reproduce the collaborative behavior of the original ensemble. Under review at NeurIPS 2026.
Feb 4, 2026
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
We propose a framework for developing topology-aware Multi-Agent Systems (MAS), emphasizing agent selection, structure profiling, and topology synthesis, to enhance coordination and efficiency in complex task.
Jul 4, 2025
AgentReview
First LLM-based peer review simulation framework that disentangles latent factors driving reviewer decisions. Reveals a 37.1% variation in paper decisions due to reviewer biases. EMNLP 2024 Oral.
Nov 12, 2024
CompeteAI
Studies competition dynamics among LLM-based agents in a simulated virtual town with restaurant and customer agents. Reveals emergent behaviors and strategic patterns aligned with market and sociological theories. ICML 2024 Oral.
May 1, 2024
CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents
This work studies the competition dynamics among LLM-based agents, revealing emergent behaviors and strategic patterns in multi-agent systems....
Apr 30, 2024