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cs.AI2026

UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems

Yiqun Chen, Wei Yang, Erhan Zhang +14

LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely op…

cs.AI2026

OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search

Erhan Zhang, Yiqun Chen, Zechun Niu +6

Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforcement learning with verifiable rewa…

cs.AI2026

Knowledge-Graph Paths as Intermediate Supervision for Self-Evolving Search Agents

Huyu Wu, Jun Liu, Xiaochi Wei +3

Self-evolving search agents reduce reliance on human-written training questions by generating and solving their own search tasks. We build on Search Self-Play (SSP), a representati…

cs.AI2026

Self-Compression of Chain-of-Thought via Multi-Agent Reinforcement Learning

Yiqun Chen, Jinyuan Feng, Wei Yang +9

The inference overhead induced by redundant reasoning undermines the interactive experience and severely bottlenecks the deployment of Large Reasoning Models. Existing reinforcemen…

cs.AI2026

JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG

Yiqun Chen, Erhan Zhang, Tianyi Hu +8

The evolution of Retrieval-Augmented Generation (RAG) has shifted from static retrieval pipelines to dynamic, agentic workflows where a central planner orchestrates multi-turn reas…