{
  "title": "Daily Research Brief 2026-08-07",
  "url": "/en/posts/research-brief-2026-08-07/",
  "permalink": "https://hackcv.com/en/posts/research-brief-2026-08-07/",
  "date": "2026-08-07",
  "lastmod": "2026-08-07",
  "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-2026-08-07/1200/675",
  "readingTime": 1,
  "wordCount": 164,
  "content": "\u003ch1 id=\"daily-research-brief-2026-08-07\"\u003eDaily Research Brief 2026-08-07\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\u003ePapers and industry collide on the same point today: long-horizon reliability no longer comes from swapping models but from the \u0026lsquo;shell\u0026rsquo;. OneDayAgent hits 0.821 across five backend models with one harness, Mimir separates world memory from task memory for a 42.5% peak gain, and LeanMem sorts memory by compressibility.\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\u003eOneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2608.05013\"\u003ehttps://arxiv.org/abs/2608.05013\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMimir: A Neuro-Symbolic Memory System with Dynamic Grounding for Embodied Agents\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2608.04933\"\u003ehttps://arxiv.org/abs/2608.04933\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eLeanMem: Simple and Efficient Long-Term Memory for LLM Agents\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2608.03463\"\u003ehttps://arxiv.org/abs/2608.03463\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eToward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2608.05139\"\u003ehttps://arxiv.org/abs/2608.05139\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eUnified Agent: Managing Interactions across Devices\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2608.05729\"\u003ehttps://arxiv.org/abs/2608.05729\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAgentic Reinforcement Learning with Observation-Calibrated Self-Distillation (OCSD)\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2608.04788\"\u003ehttps://arxiv.org/abs/2608.04788\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTurnSight: Turn-Level Hindsight Self-Distillation for Tool-Integrated Reasoning\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2608.04007\"\u003ehttps://arxiv.org/abs/2608.04007\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStress-Testing AI Agents in a Real Machine-Catalysis Laboratory (USTC)\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2607.23045\"\u003ehttps://arxiv.org/abs/2607.23045\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n",
  "summary": "Daily Research Brief 2026-08-07 📊 Token usage: estimated from retrieval and writing scale.\nCovers the latest AI research, open source and industry moves, updated daily.\nEditor\u0026rsquo;s Note Papers and industry collide on the same point today: long-horizon reliability no longer comes from swapping models but from the \u0026lsquo;shell\u0026rsquo;. OneDayAgent hits 0.821 across five backend models with one harness, Mimir separates world memory from task memory for a 42.5% peak gain, and LeanMem sorts memory by compressibility.\n"
}