{
  "title": "Daily Research Brief 2026-05-20",
  "url": "/en/posts/research-brief-2026-05-20/",
  "permalink": "https://hackcv.com/en/posts/research-brief-2026-05-20/",
  "date": "2026-05-20",
  "lastmod": "2026-05-20",
  "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-2026-05-20/1200/675",
  "readingTime": 1,
  "wordCount": 151,
  "content": "\u003ch1 id=\"daily-research-brief-2026-05-20\"\u003eDaily Research Brief 2026-05-20\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: agentic discovery for test-time compute (LLMs Improving LLMs), normalizing trajectory models, conformal path reasoning for trustworthy KGQA, Mixture-of-Experts pretraining (UniPool/EMO), and why global LLM leaderboards mislead.\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\u003eLLMs Improving LLMs: Agentic Discovery for Test-Time Compute\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.08083\"\u003ehttps://arxiv.org/abs/2605.08083\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eNormalizing Trajectory Models\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.08078\"\u003ehttps://arxiv.org/abs/2605.08078\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eConformal Path Reasoning: Trustworthy KGQA via Path-Based Conformal Prediction\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.08077\"\u003ehttps://arxiv.org/abs/2605.08077\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGRAPHLCP: Structure-Aware Localized Conformal Prediction\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.08074\"\u003ehttps://arxiv.org/abs/2605.08074\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSTARFlow2: Bridging Language Models and Normalizing Flows\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.08021\"\u003ehttps://arxiv.org/abs/2605.08021\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eUniPool: A Globally Shared Expert Pool for Mixture-of-Experts\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.06665\"\u003ehttps://arxiv.org/abs/2605.06665\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEMO: Pretraining Mixture of Experts for Emergent Modularity\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.06663\"\u003ehttps://arxiv.org/abs/2605.06663\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCrafting Reversible SFT Behaviors in Large Language Models\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.06632\"\u003ehttps://arxiv.org/abs/2605.06632\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWhy Global LLM Leaderboards Are Misleading\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.06656\"\u003ehttps://arxiv.org/abs/2605.06656\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMASPO: Joint Prompt Optimization for LLM-Based Multi-Agent Systems\u003c/strong\u003e — \u003ca href=\"https://arxiv.org/abs/2605.06641\"\u003ehttps://arxiv.org/abs/2605.06641\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n",
  "summary": "Daily Research Brief 2026-05-20 📊 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: agentic discovery for test-time compute (LLMs Improving LLMs), normalizing trajectory models, conformal path reasoning for trustworthy KGQA, Mixture-of-Experts pretraining (UniPool/EMO), and why global LLM leaderboards mislead.\n1. Latest arXiv Papers LLMs Improving LLMs: Agentic Discovery for Test-Time Compute — https://arxiv.org/abs/2605.08083\n"
}