{
  "title": "Daily Research Brief 2026-04-13",
  "url": "/en/posts/research-brief-2026-04-13/",
  "permalink": "https://hackcv.com/en/posts/research-brief-2026-04-13/",
  "date": "2026-04-13",
  "lastmod": "2026-04-13",
  "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-2026-04-13/1200/675",
  "readingTime": 1,
  "wordCount": 107,
  "content": "\u003ch1 id=\"daily-research-brief-2026-04-13\"\u003eDaily Research Brief 2026-04-13\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\u003eThe paper thread today: Tango tames visual signals for efficient video understanding, LLMs generating harmful content, adaptive neural temporal compression, egocentric think-aloud chains, and VisionFoundry teaching VLMs visual perception.\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\u003eTango: Taming Visual Signals for Efficient Video Understanding\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2604.09547\"\u003ehttps://arxiv.org/abs/2604.09547\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eLarge Language Models Generate Harmful Content — Measurement and Mitigation\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2604.09544\"\u003ehttps://arxiv.org/abs/2604.09544\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eANTIC: Adaptive Neural Temporal In-Situ Compression\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2604.09543\"\u003ehttps://arxiv.org/abs/2604.09543\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEgoTL: Egocentric Think-Aloud Chains for Long-Horizon Understanding\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2604.09535\"\u003ehttps://arxiv.org/abs/2604.09535\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eVisionFoundry: Teaching VLMs Visual Perception Skills\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2604.09531\"\u003ehttps://arxiv.org/abs/2604.09531\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n",
  "summary": "Daily Research Brief 2026-04-13 📊 Token usage: estimated from retrieval and writing scale.\nCovers the latest AI research, open source and industry moves, updated daily.\nEditor\u0026rsquo;s Note The paper thread today: Tango tames visual signals for efficient video understanding, LLMs generating harmful content, adaptive neural temporal compression, egocentric think-aloud chains, and VisionFoundry teaching VLMs visual perception.\n1. Latest arXiv Papers Tango: Taming Visual Signals for Efficient Video Understanding — https://arxiv.org/abs/2604.09547\nLarge Language Models Generate Harmful Content — Measurement and Mitigation — https://arxiv.org/abs/2604.09544\n"
}