SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning Paper • 2607.14777 • Published 11 days ago • 100
Cosmos3 Collection Omnimodal World Models for Physical AI • 11 items • Updated about 22 hours ago • 169
Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation Paper • 2607.11886 • Published 14 days ago • 84
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning Paper • 2606.26790 • Published Jun 25 • 56
UnityShots: Memory-Driven Multi-Shot Audio-Video Generation with Boundary-Aware Gating Paper • 2606.21661 • Published Jun 19 • 28
DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation Paper • 2606.26058 • Published Jun 24 • 67
Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models Paper • 2606.25041 • Published Jun 23 • 120
EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions Paper • 2606.23654 • Published Jun 22 • 79
NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers? Paper • 2606.24530 • Published Jun 23 • 64
PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models Paper • 2606.19534 • Published Jun 17 • 65
Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models Paper • 2606.11324 • Published Jun 9 • 172
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Paper • 2606.19980 • Published Jun 18 • 15