AI Papers — Daily Top 5

2026-07-17 · FRIDAY

1. 1 VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding 111 UPVOTES · XINHAO LI ET AL. · MULTIMEDIA COMPUTING GROUP-NANJING UNIVERSITY · ARXIV 2607.14935 A fully open video multimodal LLM covering motion, long-video and streaming understanding, positioned as an open reference stack for video assistants.
2. 2 LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget 97 UPVOTES · CHANGHAI ZHOU ET AL. · MIND LAB · ARXIV 2607.14952 Reinforcement-learning post-training beyond 2 million tokens of context under a fixed GPU budget, attacking the widening gap between inference context lengths and what RL training can reach.
3. 3 SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning 73 UPVOTES · JINYANG WU ET AL. · ARXIV 2607.14777 Self-evolving on-policy distillation for agentic RL: models trained as interactive agents on long-horizon tasks learn from their own improving policy instead of a fixed teacher.
4. 4 SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration 52 UPVOTES · YUYAO ZHANG ET AL. · ANT GROUP · ARXIV 2607.15257 Works toward robust open-domain information seeking with tool-integrated LLMs, treating web search as a core model capability rather than a bolted-on tool.
5. 5 BadWAM: When World-Action Models Dream Right but Act Wrong 37 UPVOTES · QI LI ET AL. · ARXIV 2607.15207 Examines world-action models that predict the future correctly yet still act wrongly - an embodied-control failure mode where dreaming right does not mean acting right.