most citedHide-and-Shill: A Reinforcement Learning Framework for Market Manipulation Detection in Symphony-a Decentralized Multi-Agent System

1 citations · 1 across the 2 of their papers we have counts for

collaborators

7 papers

cs.MA2026

CoWeaver: A Bi-directional, Learnable and Explainable Matching Engine for Mixed Human-Agent Science Collaboration

Jiayao Gu, Kexin Chu, Peidong Liu +5

LLM-based agents excel at writing articles, coding and information retrieval. However, they fail to form strong collaborations within the scientific community due to the bidirectio…

cs.AI20261 cited

Hide-and-Shill: A Reinforcement Learning Framework for Market Manipulation Detection in Symphony-a Decentralized Multi-Agent System

Ronghua Shi, Yiou Liu, Yuchun Feng +3

Decentralized finance (DeFi) has introduced a new era of permissionless financial innovation but also led to unprecedented market manipulation. Without centralized oversight, malic…

cs.AI2026

PeopleSearchBench: A Multi-Dimensional Benchmark for Evaluating AI-Powered People Search Platforms

Wei Wang, Tianyu Shi, Shuai Zhang +9

AI-powered people search platforms are increasingly used in recruiting, sales prospecting, and professional networking, yet no widely accepted benchmark exists for evaluating their…

cs.AI2026

Multi-Agent Collaborative Reward Design for Enhancing Reasoning in Reinforcement Learning

Pei Yang, Ke Zhang, Ji Wang +5

We present CRM (Multi-Agent Collaborative Reward Model), a framework that replaces a single black-box reward model with a coordinated team of specialist evaluators to improve robus…

cs.AI2025

SEDM: Scalable Self-Evolving Distributed Memory for Agents

Haoran Xu, Jiacong Hu, Ke Zhang +6

Long-term multi-agent systems inevitably generate vast amounts of trajectories and historical interactions, which makes efficient memory management essential for both performance a…

cs.LG2025

Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence

Ji Wang, Kashing Chen, Xinyuan Song +4

Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adap…