{
  "title": "Daily Research Brief 2026-04-16",
  "url": "/en/posts/research-brief-2026-04-16/",
  "permalink": "https://hackcv.com/en/posts/research-brief-2026-04-16/",
  "date": "2026-04-16",
  "lastmod": "2026-04-16",
  "author": "",
  "description": "Daily research brief — AI / LLM / Agent / Computer Vision / Audio-Video / Engineering",
  "categories": ["Research Brief"],
  "tags": ["AI","LLM","Agent","Computer Vision","Audio-Video","Engineering","Daily Brief"],
  "cover": "https://picsum.photos/seed/daily-research-brief-2026-04-16/1200/675",
  "readingTime": 1,
  "wordCount": 125,
  "content": "\u003ch1 id=\"daily-research-brief-2026-04-16\"\u003eDaily Research Brief 2026-04-16\u003c/h1\u003e\n\u003cp\u003e📊 Token usage: estimated from retrieval and writing scale.\u003c/p\u003e\n\u003cp\u003eCovers the latest AI research, open source and industry moves, updated daily.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2 id=\"editors-note\"\u003eEditor\u0026rsquo;s Note\u003c/h2\u003e\n\u003cp\u003eAsk HN: why are so many rolling out their own AI/LLM agent sandboxes? Show HN: Mirror AI — an LLM agent that takes actions, not just chats. Practical tips for optimizing documentation for LLMs, agents and chatbots.\u003c/p\u003e\n\u003ch2 id=\"1-latest-arxiv-papers\"\u003e1. Latest arXiv Papers\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eOne Token per Highly Selective Frame: Towards Efficient Video Understanding\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSeedance 2.0: Advancing Video Generation for the World\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eROSE: Retrieval-Oriented Segmentation Enhancement\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSpatialEvo: Self-Evolving Spatial Intelligence\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFrom P(y|x) to P(y): Investigating Reinforcement Learning Reward Shifts\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGeometric Context Transformer for Streaming 3D Understanding\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eLongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDon\u0026rsquo;t Let the Video Speak: Audio-Contrastive Prompting for Video QA\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n",
  "summary": "Daily Research Brief 2026-04-16 📊 Token usage: estimated from retrieval and writing scale.\nCovers the latest AI research, open source and industry moves, updated daily.\nEditor\u0026rsquo;s Note Ask HN: why are so many rolling out their own AI/LLM agent sandboxes? Show HN: Mirror AI — an LLM agent that takes actions, not just chats. Practical tips for optimizing documentation for LLMs, agents and chatbots.\n1. Latest arXiv Papers One Token per Highly Selective Frame: Towards Efficient Video Understanding\n"
}