MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation Paper • 2605.27366 • Published May 26 • 30
When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs Paper • 2605.24202 • Published May 22 • 17
When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs Paper • 2605.24202 • Published May 22 • 17
Speculative Pipeline Decoding: Higher-Accruacy and Zero-Bubble Speculation via Pipeline Parallelism Paper • 2605.30852 • Published May 29 • 10
Foundation Protocol: A Coordination Layer for Agentic Society Paper • 2605.23218 • Published May 22 • 82
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning Paper • 2605.14212 • Published May 14 • 19
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning Paper • 2605.14212 • Published May 14 • 19
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning Paper • 2605.14212 • Published May 14 • 19
EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales Paper • 2605.11136 • Published May 11 • 11
Live-Evo: Online Evolution of Agentic Memory from Continuous Feedback Paper • 2602.02369 • Published Feb 2 • 1
EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales Paper • 2605.11136 • Published May 11 • 11