MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation Paper • 2605.27366 • Published May 26 • 29
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 29 days ago • 10
Foundation Protocol: A Coordination Layer for Agentic Society Paper • 2605.23218 • Published May 22 • 81
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning Paper • 2605.14212 • Published May 14 • 18
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning Paper • 2605.14212 • Published May 14 • 18
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning Paper • 2605.14212 • Published May 14 • 18
SenseNova-U1 Collection SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-Unify Architecture • 10 items • Updated 14 days ago • 74
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
EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales Paper • 2605.11136 • Published May 11 • 11
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture Paper • 2605.12500 • Published May 12 • 194