Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents Paper • 2606.26080 • Published Jun 24 • 12
DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models Paper • 2606.05758 • Published Jun 4 • 5
When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs Paper • 2605.24202 • Published May 22 • 18
Capturing LLM Capabilities via Evidence-Calibrated Query Clustering Paper • 2605.17110 • Published May 16 • 2
Capturing LLM Capabilities via Evidence-Calibrated Query Clustering Paper • 2605.17110 • Published May 16 • 2
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
From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing Paper • 2605.15181 • Published May 14 • 12
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
Exploration and Exploitation Errors Are Measurable for Language Model Agents Paper • 2604.13151 • Published Apr 14 • 25
SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks Paper • 2603.24755 • Published Mar 25 • 30
Live-Evo: Online Evolution of Agentic Memory from Continuous Feedback Paper • 2602.02369 • Published Feb 2 • 1
EVA: Efficient Reinforcement Learning for End-to-End Video Agent Paper • 2603.22918 • Published Mar 24 • 44
EVA: Efficient Reinforcement Learning for End-to-End Video Agent Paper • 2603.22918 • Published Mar 24 • 44