{
  "title": "2026-09-15 AI Daily | When Language Models Meet the Real World: The Next Hurdle for AI Implementation is Verifiable Quantitative Capability",
  "url": "https://miaok.ong/en/ai-daily/ai-daily-2026-09-15/",
  "date": "2026-09-15T07:00:00+08:00",
  "lastmod": "2026-09-15T07:00:00+08:00",
  "type": "ai-daily",
  "kind": "page",
  "language": "en",
  "description": "Today\u0026rsquo;s focus shifts from model size to system trustworthiness: Fyxer and Perplexity demonstrate agents\u0026rsquo; ability for continuous execution in email, software, and production systems, while Occamy-1.0 explores more efficient collaborative open models. Meanwhile, several studies indicate that tool orchestration, data provenance, reproducibility, and quantitative reasoning are becoming key criteria for determining whether AI can be truly implemented in high-risk scenarios.",
  "keywords": null,
  "tags": [],
  "categories": [],
  "author": "Mark (Miao) Kong",
  "image": "https://miaok.ong/images/avatar.jpg",
  "content": "\u003ch1 id=\"2026-09-15-ai-daily--when-language-models-meet-the-real-world-the-next-challenge-for-ai-implementation-is-verifiable-quantification\"\u003e\n  2026-09-15 AI Daily | When Language Models Meet the Real World: The Next Challenge for AI Implementation is Verifiable Quantification\n  \u003ca class=\"heading-link\" href=\"#2026-09-15-ai-daily--when-language-models-meet-the-real-world-the-next-challenge-for-ai-implementation-is-verifiable-quantification\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h1\u003e\n\u003cblockquote\u003e\n\u003cp\u003eToday\u0026rsquo;s focus shifts from model scale to system trustworthiness: Fyxer and Perplexity demonstrate the continuous execution capabilities of agents in email, software, and production systems, while Occamy-1.0 explores more efficient collaborative open models. Meanwhile, several studies point out that tool orchestration, data traceability, reproducibility, and quantitative reasoning are becoming key factors in measuring whether AI can truly be implemented in high-risk scenarios.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2 id=\"-in-depth-guide-to-this-issues-watch-list\"\u003e\n  📖 In-depth Guide to This Issue\u0026rsquo;s Watch List\n  \u003ca class=\"heading-link\" href=\"#-in-depth-guide-to-this-issues-watch-list\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h2\u003e\n\u003cp\u003eThe first main thread most worthy of attention today is the agent\u0026rsquo;s evolution from \u0026ldquo;model invocation\u0026rdquo; to \u0026ldquo;trustworthy working system\u0026rdquo;: Fyxer\u0026rsquo;s AI administrative assistant focuses on long-term context across emails, meetings, and applications, while Occamy-1.0 showcases open models for complex collaborative processes. At the same time, research on model capabilities and Agent harness reminds engineering teams to re-evaluate the true contributions of tools, prompts, and control flow. The second main thread is reliability and verifiability: from R2VC\u0026rsquo;s Retrieve-Verify-Confidence Calibration, to draft-verify-revise for handling instruction ambiguity, and then to quantitative reasoning and event prediction, the core issue is how to make models \u0026ldquo;prove it\u0026rdquo; more and \u0026ldquo;sound like it\u0026rdquo; less in high-risk scenarios. In addition, discussions on frontier governance and infrastructure are also worth noting: from Dario Amodei\u0026rsquo;s debate on the pace of AI development, to the vertical integration of computing power, data centers, and software stacks, the capabilities race is simultaneously evolving into a competition of governance and industrial organizational capabilities.\u003c/p\u003e\n\u003ch2 id=\"-x-platform-ai-hot-news\"\u003e\n  🌐 X Platform AI Hot News\n  \u003ca class=\"heading-link\" href=\"#-x-platform-ai-hot-news\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h2\u003e\n\u003ch3 id=\"topic-1-anthropic-launches-claude-mods-for-custom-terminal-plugins\"\u003e\n  Topic 1: Anthropic Launches Claude Mods for Custom Terminal Plugins\n  \u003ca class=\"heading-link\" href=\"#topic-1-anthropic-launches-claude-mods-for-custom-terminal-plugins\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Hot for: 3 hours ago, Related Posts: 335\u003c/li\u003e\n\u003cli\u003eWhat happened: Anthropic launched a feature called Claude Mods for creating and customizing terminal plugins.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This indicates that AI assistants are expanding from standalone conversational tools to platforms embeddable in developer terminal workflows, which helps enhance automation capabilities and ecosystem expansion.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: Discussion on X is expected to focus on the scope of plugin customization, development experience, compatibility with existing terminal tools, as well as issues such as security, permissions, and maintenance costs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-2-yann-lecun-mocks-2019-gpt-2-release-fears\"\u003e\n  Topic 2: Yann LeCun Mocks 2019 GPT-2 Release Fears\n  \u003ca class=\"heading-link\" href=\"#topic-2-yann-lecun-mocks-2019-gpt-2-release-fears\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · Other\u003c/li\u003e\n\u003cli\u003eOverview: Hot for: 1 day ago, Related Posts: 5800\u003c/li\u003e\n\u003cli\u003eWhat happened: Yann LeCun looked back and mocked the concerns surrounding potential risks and impacts when GPT-2 was released in 2019.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This reflects the industry\u0026rsquo;s continuous introspection on generative AI risk assessment, the speed of technological progress, and open release strategies.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: Discussion on X mainly focuses on whether the judgment of GPT-2\u0026rsquo;s capabilities back then was overly cautious, whether subsequent model developments proved these concerns, and how AI companies should balance open sharing and security risks.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-3-metas-muse-ai-agent-wins-early-praise-for-everyday-help\"\u003e\n  Topic 3: Meta\u0026rsquo;s Muse AI Agent Wins Early Praise for Everyday Help\n  \u003ca class=\"heading-link\" href=\"#topic-3-metas-muse-ai-agent-wins-early-praise-for-everyday-help\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Hot for: 2 days ago, Related Posts: 1900\u003c/li\u003e\n\u003cli\u003eWhat happened: Meta\u0026rsquo;s Muse AI Agent received early praise for its practical performance in daily tasks.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This indicates that AI Agents are moving beyond mere Q\u0026amp;A to an application form capable of assisting with real-world daily affairs, which helps test their practical implementation value.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: Discussion on X mainly focuses on its convenience, task completion effectiveness, and user experience, while also raising questions about reliability, privacy, security, and whether early publicity was exaggerated.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-4-ai-speeds-product-building-but-demands-deeper-user-insight\"\u003e\n  Topic 4: AI Speeds Product Building but Demands Deeper User Insight\n  \u003ca class=\"heading-link\" href=\"#topic-4-ai-speeds-product-building-but-demands-deeper-user-insight\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Hot for: 7 hours ago, Related Posts: 804\u003c/li\u003e\n\u003cli\u003eWhat happened: Discussion points out that AI is accelerating product building, but product success still depends on a deeper understanding of user needs.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This indicates that AI can significantly improve development and iteration efficiency but cannot replace user research, demand judgment, and product decisions, reflecting that AI applications are shifting from simple efficiency improvement to human-computer collaboration.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: Discussion on X mainly revolves around the contradiction between AI-driven development acceleration and insufficient user insight: one side emphasizes that AI lowers the threshold for product building, while the other side worries that rapid output does not equate to solving real needs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-5-metas-muse-ai-agent-tops-download-charts-days-after-launch\"\u003e\n  Topic 5: Meta\u0026rsquo;s Muse AI Agent Tops Download Charts Days After Launch\n  \u003ca class=\"heading-link\" href=\"#topic-5-metas-muse-ai-agent-tops-download-charts-days-after-launch\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Hot for: 21 hours ago, Related Posts: 2100\u003c/li\u003e\n\u003cli\u003eWhat happened: Meta\u0026rsquo;s Muse AI Agent topped the download charts days after its launch, showing its rapid attention in the consumer market.\u003c/li\u003e\n\u003cli\u003eWhy it matters: This indicates that AI applications are shifting from one-off Q\u0026amp;A to intelligent agents capable of continuous task execution, which helps in observing real user demand for agent-based interactions and productized AI.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: Discussions on X are centered on the idea that \u0026ldquo;loops are the new prompts,\u0026rdquo; where continuous task iteration replaces single prompts. Meanwhile, some question whether download volume can represent actual usage and effectiveness, and are focusing on the accuracy, privacy, and risks of autonomous actions by agents.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-6-community-ranks-top-multi-agent-harnesses-for-ai-development\"\u003e\n  Topic 6: Community Ranks Top Multi-Agent Harnesses for AI Development\n  \u003ca class=\"heading-link\" href=\"#topic-6-community-ranks-top-multi-agent-harnesses-for-ai-development\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Trending 13 hours ago, 66 related posts\u003c/li\u003e\n\u003cli\u003eWhat it is: Users on the X platform are ranking and discussing the most suitable multi-agent runtime frameworks for AI development, linking them to issues like large-scale AI deployment, open weights, and governance.\u003c/li\u003e\n\u003cli\u003eWhy it matters: Multi-agent frameworks determine how multiple AI agents collaborate, invoke tools, and complete complex development tasks. They are the essential infrastructure for transitioning AI from single-model applications to engineered, large-scale deployment.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: Current discussions focus on the differences among various multi-agent frameworks regarding development efficiency, reliability, scalability, and security governance. Some perspectives are centered on their commercial and defense application prospects, while others are concerned about the risks posed by open weights, loss of agent control, and regulatory gaps.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4 id=\"summary-of-ai-public-opinion-on-x-today\"\u003e\n  Summary of AI Public Opinion on X Today\n  \u003ca class=\"heading-link\" href=\"#summary-of-ai-public-opinion-on-x-today\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h4\u003e\n\u003cp\u003eThe main theme of today\u0026rsquo;s public opinion is that AI is rapidly evolving from a conversational tool into an engineered platform embedded in terminals, capable of continuous task execution and supporting multi-agent collaboration. Discussions around Claude Mods, Muse Agent, and related frameworks collectively reflect this trend. The general consensus is that AI significantly lowers the barriers to product development and automation and is starting to demonstrate its value in handling real-world tasks. However, product success still hinges on genuine user needs, reliable user experience, and continuous iteration, rather than solely on model capabilities or download figures. Points of disagreement mainly center on whether early risk assessments (like those for GPT-2) were excessive, how far open-sourcing weights and open releases should go, and whether the hype surrounding agent-based products can translate into stable, real-world effectiveness. Potential risks include permission controls for plugins and agents, privacy leaks, task failures, loss of autonomous control, and governance gaps that could arise from the large-scale deployment of multi-agent systems in commercial and defense applications.\u003c/p\u003e\n\u003ch2 id=\"-influencer-insights\"\u003e\n  💡 Influencer Insights\n  \u003ca class=\"heading-link\" href=\"#-influencer-insights\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003eNo influencer insights for today. We recommend reading the in-depth content on the Watch List.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch2 id=\"-appendix-todays-watch-list-update-source-list\"\u003e\n  📚 Appendix: Today\u0026rsquo;s Watch List Update Source List\n  \u003ca class=\"heading-link\" href=\"#-appendix-todays-watch-list-update-source-list\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003eTime window: Last 3 days; 22 sources covered; 35 updates in total\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3 id=\"acquiredfm-a_full\"\u003e\n  Acquired.fm (A_full)\n  \u003ca class=\"heading-link\" href=\"#acquiredfm-a_full\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://www.acquired.fm/episodes/home-depot\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eThe Home Depot\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-09-14 11:35 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Awaiting Translation] - The Home Depot\u0026rsquo;s founding story is like an Avengers movie… if the Avengers got fired, went broke, and stacked empty paint cans ten feet high to look legitimate.\n\u003cul\u003e\n\u003cli\u003eAfter being unceremoniously fired from their previous hardware chain at ages 48 and 35, Bernie Marcus and Arthur Blank took the words of their New York banker Ken Langone (who had also just accidentally caused their firings) to heart: they\u0026rsquo;d just been \u0026ldquo;kicked in the ass with a golden horseshoe.” They proceeded to author the greatest compounding story in American retail history, helped by some legendary cameos along the way from Sol Price, Jamie Dimon, and Ross Perot (to name a few).\u003c/li\u003e\n\u003cli\u003eAnd the ending is as good as any superhero film: from its 1981 IPO to today, Home Depot has been the single highest-returning equity in the entire US stock market — higher than Apple, Microsoft, Berkshire Hathaway, and everything else\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMany thanks to our fantastic Fall \u0026lsquo;26 Season partners:.\n\u003cul\u003e\n\u003cli\u003e\n\u003cul\u003e\n\u003cli\u003eSign up for email updates , get our takeaways and research photos from each episode, and vote on future topics!\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eThe Home Depot\u0026rsquo;s founding story is like an Avengers movie… if the Avengers got fired, went broke, and stacked empty paint cans ten feet high to look legitimate\u003c/li\u003e\n\u003cli\u003eAfter being unceremoniously fired from their previous hardware chain at ages 48 and 35, Bernie Marcus and Arthur Blank took the words of their New York banker K…\u003c/li\u003e\n\u003cli\u003eAnd the ending is as good as any superhero film: from its 1981 IPO to today, Home Depot has been the single highest-returning equity in the entire US stock mark…\u003c/li\u003e\n\u003cli\u003eMany thanks to our fantastic Fall \u0026lsquo;26 Season partners:\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"all-in-podcast-a_full\"\u003e\n  All-In Podcast (A_full)\n  \u003ca class=\"heading-link\" href=\"#all-in-podcast-a_full\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://allinchamathjason.libsyn.com/jensen-huang-the-doomer-hoax-superintelligence-is-here-and-the-future-of-ai-ft-president-trump\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eJensen Huang: The Doomer Hoax, Superintelligence Is Here, and The Future of AI (ft. President Trump)\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-15 05:24 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [Pending Translation] - IREN is a vertically integrated AI Cloud platform, delivering data centers, compute and software for AI training and inference.\n\u003cul\u003e\n\u003cli\u003eOracle connects the data, applications, and infrastructure that turn AI into business outcomes—with the flexibility, choice, and control to optimize as AI evolves.\u003c/li\u003e\n\u003cli\u003eEY helps tech innovators scale from startup to exit to megacap.\u003c/li\u003e\n\u003cli\u003eWe\u0026rsquo;re focused on giving every person the tools to reach their full potential and making sure the benefits of technology are distributed to all.\u003c/li\u003e\n\u003cli\u003eKeel Infrastructure owns the power, land, and connectivity that HPC and AI run on - backed by secured energy assets and established grid interconnections across North America.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003e(0:00) Jensen Huang joins The Besties\u003c/li\u003e\n\u003cli\u003e(1:39) Thoughts on Dario\u0026rsquo;s blog, Frontier Labs calling to slow down AI, and Doomer psychology\u003c/li\u003e\n\u003cli\u003e(9:58) Sensible AI regulation and RSI\u003c/li\u003e\n\u003cli\u003e(16:05) Hugging Face acquisition, future of Open Source, and the race with China\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"stratechery-by-ben-thompson-a_full\"\u003e\n  Stratechery by Ben Thompson (A_full)\n  \u003ca class=\"heading-link\" href=\"#stratechery-by-ben-thompson-a_full\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://stratechery.com/2026/pacing-the-frontier-ais-digital-limits-ai-commissars/\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePacing the Frontier, AI’s Digital Limits, AI Commissars\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 18:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: Dario Amodei wants to pace the frontier; it’s an unrealistic proposal that seems mostly geared to political control of AI.\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e$15\u003c/strong\u003e / month \u003cem\u003eor\u003c/em\u003e \u003cstrong\u003e$150\u003c/strong\u003e / year.\u003c/li\u003e\n\u003cli\u003eSubstantial analysis of the news of the day delivered via three weekly emails or podcasts.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eStratechery Interviews\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eInterviews with leading public CEOs, private company founders, and discussions with fellow analysts.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eDario Amodei wants to pace the frontier; it\u0026rsquo;s an unrealistic proposal that seems mostly geared to political control of AI.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"openai-blog-a_full\"\u003e\n  OpenAI Blog (A_full)\n  \u003ca class=\"heading-link\" href=\"#openai-blog-a_full\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://openai.com/index/fyxer\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eHow Fyxer built an AI executive assistant people trust\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 20:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: For many professionals, work means keeping track of conversations and commitments across inboxes, meetings, messages, and apps.\n\u003cul\u003e\n\u003cli\u003eWithout rock-solid context, those commitments can fall through the cracks, damaging projects and relationships.\u003c/li\u003e\n\u003cli\u003eFyxer built an AI executive assistant that follows the thread as work moves across tools.\u003c/li\u003e\n\u003cli\u003eIt combines the latest OpenAI models with more than 500,000 hours of executive assistant workflows, dividing the work among dozens of specialized models that improve through real user feedback.\u003c/li\u003e\n\u003cli\u003eEmail is one of the clearest places to see it in action.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eFyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user’s voice.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://openai.com/index/perplexity-improving-accuracy-with-astra\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePerplexity trusts GPT-6 Astra with end-to-end systems\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 08:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: Perplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models.\n\u003cul\u003e\n\u003cli\u003eAs an AI-powered answer engine, Perplexity is deeply focused on search and accuracy.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIts ability to process large amounts of information is critically important.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJohnny Ho, Cofounder and Chief Strategy Officer, observes that every time the model gets better at writing code, Perplexity’s search engine improves too.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIt becomes able to write better programs that search the web and internal information and summarize it very concisely.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003ePerplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"arxiv-csai-b_introsearch\"\u003e\n  ArXiv cs.AI (B_intro+search)\n  \u003ca class=\"heading-link\" href=\"#arxiv-csai-b_introsearch\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11977\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eOccamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.11977v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Co-work agents execute complex workflows that combine information gathering, tool use, coding, and file manipulation across many model invocations.\u003c/li\u003e\n\u003cli\u003eBecause cost and latency accumulate over the full episode, their practical value depends not only on peak capability but also on how efficiently that capability is delivered.\u003c/li\u003e\n\u003cli\u003eYet many steps in everyday work emphasize state tracking, coordination, recovery, and follow-through rather than frontier-scale reasoning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11977v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Co-work agents execute complex workflows that combine information gathering, tool use, coding, and file manipulation across many model invocations\u003c/li\u003e\n\u003cli\u003eBecause cost and latency accumulate over the full episode, their practical value depends not only on peak capability but also on how efficiently that capability…\u003c/li\u003e\n\u003cli\u003eYet many steps in everyday work emphasize state tracking, coordination, recovery, and follow-through rather than frontier-scale reasoning\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11987\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eHarness or Model? Isolating the Harness Effect in Agentic Coding with a Contamination-Controlled Private Suite\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.11987v1 Announce Type: new.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11987\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eA Tale of Two Harnesses: Open-Source vs. Vendor-Native Agentic Coding Systems\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.11987v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: An agentic coding system couples a language model to a harness: the tools, prompts and control flow that turn a chat model into an autonomous software engineer.\u003c/li\u003e\n\u003cli\u003eVendors ship harnesses tuned to their own models, and practitioners assume the vendor-native pairing solves more tasks.\u003c/li\u003e\n\u003cli\u003eWe measure that assumption with paired same-model contrasts on a private, contamination-controlled suite of 256 repository and post-cutoff contest tasks.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11987v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: An agentic coding system couples a language model to a harness: the tools, prompts and control flow that turn a chat model into an autonomous software…\u003c/li\u003e\n\u003cli\u003eVendors ship harnesses tuned to their own models, and practitioners assume the vendor-native pairing solves more tasks\u003c/li\u003e\n\u003cli\u003eWe measure that assumption with paired same-model contrasts on a private, contamination-controlled suite of 256 repository and post-cutoff contest tasks\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12035\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eReading the Whole Heart: Latent-Attention Masked Autoencoders for Multimodal Cardiac Representation Learning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.12035v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Cardiovascular diagnosis rests on integrating complementary modalities, like ECG, echocardiography, chest radiographs, and clinical variables, each capturing distinct but correlated aspects of cardiac physiology.\u003c/li\u003e\n\u003cli\u003eYet most medical foundation models remain modality-specific, combining modalities only for finetuning or post-training.\u003c/li\u003e\n\u003cli\u003eThis discards the cross-modal evidence clinicians naturally integrate and ignores the structure within each modality.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12035v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Cardiovascular diagnosis rests on integrating complementary modalities, like ECG, echocardiography, chest radiographs, and clinical variables, each ca…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYet most medical foundation models remain modality-specific, combining modalities only for finetuning or post-training\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis discards the cross-modal evidence clinicians naturally integrate and ignores the structure within each modality\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12101\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCompetence-Gated Pooling of Language Models and Priors for Event Forecasting\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.12101v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: In hybrid forecasting, a language model is often one of several available signals.\u003c/li\u003e\n\u003cli\u003eA system may already have a market, crowd, or statistical forecast and must decide whether the model adds useful information or should be ignored.\u003c/li\u003e\n\u003cli\u003eThe relevant target is therefore not standalone model accuracy, but relative competence, defined as the model\u0026rsquo;s marginal value beyond the available external forecast.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12101v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: In hybrid forecasting, a language model is often one of several available signals\u003c/li\u003e\n\u003cli\u003eA system may already have a market, crowd, or statistical forecast and must decide whether the model adds useful information or should be ignored\u003c/li\u003e\n\u003cli\u003eThe relevant target is therefore not standalone model accuracy, but relative competence, defined as the model\u0026rsquo;s marginal value beyond the available external for…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12105\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLanguage Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.12105v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: The prevailing assumption in applied machine learning is that progress on consequential quantitative decisions such as pricing risk, allocating capital, triaging patients, or containing a network intrusion will follow from progress in large language models (LLMs).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA language model is trained on a representation of the world that was produced by human description; description is a lossy encoding of the quantitative record, and the loss is irreversible: no downstream model, at any scale, can recover from a description what the description did not encode.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe formalize this as a property of the representation on which a model is trained rather than of the model capacity, and we identify three further properties that consequential settings demand of a model and that a language substrate cannot supply by construction: reproducibility, lineage from every output back to the source records that produced.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12105v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: The prevailing assumption in applied machine learning is that progress on consequential quantitative decisions such as pricing risk, allocating capita…\u003c/li\u003e\n\u003cli\u003eA language model is trained on a representation of the world that was produced by human description; description is a lossy encoding of the quantitative record,…\u003c/li\u003e\n\u003cli\u003eWe formalize this as a property of the representation on which a model is trained rather than of the model capacity, and we identify three further properties th…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12115\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDU-NO: A Parameter-Efficient Double U-Shaped Neural Operator for Phase-Resolving Wave Modeling\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation pending] - arXiv:2609.12115v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Phase-resolving wave models such as FUNWAVE-TVD are the accuracy standard for nearshore dynamics, resolving the shoaling, refraction, and breaking of individual waves, but their cost rules them out for the ensembles, uncertainty quantification, and real-time warning that operational forecasting demands.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNeural operators promise solver-level accuracy at a fraction of that cost, yet on wave-dominated fields the accurate ones are large: hybrid spectral-convolutional operators such as U-FNO (the strongest baseline in our study after DU-NO) buy their fidelity with tens of millions of parameters.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe introduce DU-NO (Double U-shaped Neural Operator), a multiscale U-shaped spectral operator that attaches lightweight convolutional U-Net branches only at its two shallowest encoder and decoder levels.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Highlights:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12115v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Phase-resolving wave models such as FUNWAVE-TVD are the accuracy standard for nearshore dynamics, resolving the shoaling, refraction, and breaking of…\u003c/li\u003e\n\u003cli\u003eNeural operators promise solver-level accuracy at a fraction of that cost, yet on wave-dominated fields the accurate ones are large: hybrid spectral-convolution…\u003c/li\u003e\n\u003cli\u003eWe introduce DU-NO (Double U-shaped Neural Operator), a multiscale U-shaped spectral operator that attaches lightweight convolutional U-Net branches only at its…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12116\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eWhen Successful Knowledge Graph Edits Displace Correct Answers: Rank-Level Locality beyond Parameter Support\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:\n\u003cul\u003e\n\u003cli\u003eEditing a knowledge graph embedding (KGE) model to promote a desired answer can displace correct answers from the returned list.\u003c/li\u003e\n\u003cli\u003eLocality tests based only on facts that reuse the edited parameter can miss this ranking effect.\u003c/li\u003e\n\u003cli\u003eWe introduce a common rank-displacement audit at three scopes: facts supported by the edited parameter, other correct answers to the target query, and correct answers across queries with the same relation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12116v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Editing a knowledge graph embedding (KGE) model to promote a desired answer can displace correct answers from the returned list\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLocality tests based only on facts that reuse the edited parameter can miss this ranking effect\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe introduce a common rank-displacement audit at three scopes: facts supported by the edited parameter, other correct answers to the target query, and correct a…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12139\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eMined from Scientific Literature: Process Schemas for Atomic Layer Deposition and Etching in Materials Science\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: 【待翻译】- arXiv:2609.12139v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Atomic layer deposition (ALD) and atomic layer etching (ALE) are reported heterogeneously across experimental and simulation literature in materials science, hindering comparison and machine-actionable reuse.\u003c/li\u003e\n\u003cli\u003eWe present four domain-expert-reviewed JSON Schemas for ALD and ALE experimental and simulation processes.\u003c/li\u003e\n\u003cli\u003eCurated with schema-miner and grounded in QUDT using schema-miner pro, the schemas structure materials, process conditions, configurations , and measured or predicted results.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12139v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Atomic layer deposition (ALD) and atomic layer etching (ALE) are reported heterogeneously across experimental and simulation literature in materials s…\u003c/li\u003e\n\u003cli\u003eWe present four domain-expert-reviewed JSON Schemas for ALD and ALE experimental and simulation processes\u003c/li\u003e\n\u003cli\u003eCurated with schema-miner and grounded in QUDT using schema-miner pro, the schemas structure materials, process conditions, configurations , and measured or pre…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12162\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCan LLMs in Draft-Verify-Revise Pipelines Resolve Deictic Ambiguity?\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: 【待翻译】- arXiv:2609.12162v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Draft-verify-revise is a common LLM orchestration pattern for scaling inference-time compute.\u003c/li\u003e\n\u003cli\u003eOne LLM drafts, a second critiques the draft and provides feedback, and a third uses that feedback to revise the draft into the final output.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs context cascades between stages, LLMs at different stages can resolve a context-dependent expression such as \u0026ldquo;previous\u0026rdquo; differently.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12162v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Draft-verify-revise is a common LLM orchestration pattern for scaling inference-time compute\u003c/li\u003e\n\u003cli\u003eOne LLM drafts, a second critiques the draft and provides feedback, and a third uses that feedback to revise the draft into the final output\u003c/li\u003e\n\u003cli\u003eAs context cascades between stages, LLMs at different stages can resolve a context-dependent expression such as \u0026ldquo;previous\u0026rdquo; differently\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12165\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eGLARE: Generative Learning via Adversarial Reward Estimation For Social Dynamics Forecasting\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.12165v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Meeting continuation requires tracking the agenda, speaker roles, participant intentions, and disagreement across long multi-party discussions.\u003c/li\u003e\n\u003cli\u003eWe introduce the Meeting Dynamic Forecasting Benchmark (MDFB), constructed from 2,207 real-world meetings and 24,794 future-facing queries.\u003c/li\u003e\n\u003cli\u003eGiven a transcript prefix and an active question, a model generates a plausible multi-turn continuation in one call.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12165v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Meeting continuation requires tracking the agenda, speaker roles, participant intentions, and disagreement across long multi-party discussions\u003c/li\u003e\n\u003cli\u003eWe introduce the Meeting Dynamic Forecasting Benchmark (MDFB), constructed from 2,207 real-world meetings and 24,794 future-facing queries\u003c/li\u003e\n\u003cli\u003eGiven a transcript prefix and an active question, a model generates a plausible multi-turn continuation in one call\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"arxiv-cscl-b_introsearch\"\u003e\n  ArXiv cs.CL (B_intro+search)\n  \u003ca class=\"heading-link\" href=\"#arxiv-cscl-b_introsearch\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11955\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eR2VC: Modular Fact-Checking with Retrieval, Verification, and Confidence Calibration\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.11955v1 Announce Type: new.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: Large language models are increasingly used for automated fact checking, but end-to-end prompting often entangles evidence retrieval, reasoning, and uncertainty estimation, making failures difficult to diagnose and confidence difficult to trust.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe present R2VC, a modular retrieve, reason, verify, calibrate architecture for evidence-grounded fact checking with citations and abstention.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eR2VC combines hybrid sparse+dense retrieval over Wikipedia, a supervised fine-tuned and DPO-aligned generator that produces diverse structured verdict candidates, an external NLI cross-encoder for evidence-based candidate selection, and a lightweight sequence-level calibrator for confidence estimation and selective abstention.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11955v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large language models are increasingly used for automated fact checking, but end-to-end prompting often entangles evidence retrieval, reasoning, and u…\u003c/li\u003e\n\u003cli\u003eWe present R2VC, a modular retrieve, reason, verify, calibrate architecture for evidence-grounded fact checking with citations and abstention\u003c/li\u003e\n\u003cli\u003eR2VC combines hybrid sparse+dense retrieval over Wikipedia, a supervised fine-tuned and DPO-aligned generator that produces diverse structured verdict candidate…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12085\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eWhat Counts as a Mistake? Annotating Recitation Events in Quran Memorization Transcripts\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation Pending] - arXiv:2609.12085v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Checking Quran recitation from an ASR transcript requires distinguishing unresolved mistakes from repetitions, repairs, opening formulas and accepted spelling differences.\u003c/li\u003e\n\u003cli\u003eWe report a completed human annotation of 100 production recording cases: 348 scored units and 162 localized events across ten combined labels.\u003c/li\u003e\n\u003cli\u003eAn executable evaluator scores labels and word positions together.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12085v1 Announce Type: new\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: Checking Quran recitation from an ASR transcript requires distinguishing unresolved mistakes from repetitions, repairs, opening formulas and accepted…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe report a completed human annotation of 100 production recording cases: 348 scored units and 162 localized events across ten combined labels\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAn executable evaluator scores labels and word positions together\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12107\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eExtracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation Pending] - arXiv:2609.12107v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy, and operations, yet systematically identifying where these datasets are referenced remains difficult.\u003c/li\u003e\n\u003cli\u003eSuch references are dispersed across research papers, project documents, humanitarian reports, and other unstructured text, limiting both the ability to trace data use and to identify potential gaps in data availability or dissemination.\u003c/li\u003e\n\u003cli\u003eWe present a weakly supervised framework for adapting dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents without first constructing a large manually labeled training corpus.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12107v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy…\u003c/li\u003e\n\u003cli\u003eSuch references are dispersed across research papers, project documents, humanitarian reports, and other unstructured text, limiting both the ability to trace d…\u003c/li\u003e\n\u003cli\u003eWe present a weakly supervised framework for adapting dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents without firs…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12111\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eThe Cost of Compression: A Rate-Distortion Limit on Factual Hallucination\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublish Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.12111v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Factual hallucination in closed-book question answering is often treated as a coverage problem: a model fails because the relevant fact is absent from its internal memory.\u003c/li\u003e\n\u003cli\u003eThis view misses a second source of error.\u003c/li\u003e\n\u003cli\u003eEven when a fact has been observed, finite memory may force it to be stored only approximately.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eKey Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12111v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Factual hallucination in closed-book question answering is often treated as a coverage problem: a model fails because the relevant fact is absent from…\u003c/li\u003e\n\u003cli\u003eThis view misses a second source of error\u003c/li\u003e\n\u003cli\u003eEven when a fact has been observed, finite memory may force it to be stored only approximately\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12122\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eQuantifying Consonant Contributions to Word Intelligibility via Acoustic Masking\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublish Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.12122v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Consonants contribute unequally to whether a word is understood.\u003c/li\u003e\n\u003cli\u003eGiven the limited time available for therapy, ranking consonants by contribution to intelligibility helps prioritize intervention targets in motor speech disorders.\u003c/li\u003e\n\u003cli\u003eHowever, measuring this contribution relies on perceptual studies that are difficult to scale.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eKey Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12122v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Consonants contribute unequally to whether a word is understood\u003c/li\u003e\n\u003cli\u003eGiven the limited time available for therapy, ranking consonants by contribution to intelligibility helps prioritize intervention targets in motor speech disord…\u003c/li\u003e\n\u003cli\u003eHowever, measuring this contribution relies on perceptual studies that are difficult to scale\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12127\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLocal Edits, Global Ripples: Replay-Informed Policy Adaptation for Workflow Synthesis\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublish Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSummary: [To be translated] - arXiv:2609.12127v1 Announce Type: new.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: Prompt-policy editing offers a practical way to improve agents that synthesize executable workflows without updating the underlying model.\u003c/li\u003e\n\u003cli\u003eHowever, persistent prompt editing has two coupled properties.\u003c/li\u003e\n\u003cli\u003eFirst, edit locality does not imply effect locality: an edit confined to one policy segment can ripple through downstream execution, altering behavior beyond the edited segment.\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12127v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Prompt-policy editing offers a practical way to improve agents that synthesize executable workflows without updating the underlying model\u003c/li\u003e\n\u003cli\u003eHowever, persistent prompt editing has two coupled properties\u003c/li\u003e\n\u003cli\u003eFirst, edit locality does not imply effect locality: an edit confined to one policy segment can ripple through downstream execution, altering behavior beyond th…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12132\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePopulation-level measures of perceived food access reveal barriers beyond geographic proximity\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [To be translated] - arXiv:2609.12132v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Food access is multidimensional, but population-level measurement still relies heavily on geography because perceived dimensions of access are difficult to measure at scale.\u003c/li\u003e\n\u003cli\u003eHere, we use 25,125 Google Maps reviews from 49 grocery stores in Raleigh, North Carolina, to measure five dimensions of food access: availability, accessibility, affordability, accommodation, and acceptability.\u003c/li\u003e\n\u003cli\u003eWe identify review topics with unsupervised topic modeling and assign them to access dimensions using zero-shot classification, with 85.4% agreement against manual coding.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12132v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Food access is multidimensional, but population-level measurement still relies heavily on geography because perceived dimensions of access are difficu…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHere, we use 25,125 Google Maps reviews from 49 grocery stores in Raleigh, North Carolina, to measure five dimensions of food access: availability, accessibilit…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe identify review topics with unsupervised topic modeling and assign them to access dimensions using zero-shot classification, with 85.4% agreement against man…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12191\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eGAUGE: When Not to Trust LLM-as-a-Judge in User-Simulated Evaluation of Task-Oriented Agents\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePosted: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.12191v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Comparing and selecting task-oriented LLM agents increasingly relies on a low-cost offline evaluation gate: persona-driven LLM user-simulators converse with each candidate, an LLM-as-a-judge scores the transcripts, and the higher-scoring agent is promoted.\u003c/li\u003e\n\u003cli\u003eWe introduce GAUGE, a reusable offline protocol that measures whether this gate\u0026rsquo;s ranking matches a grounded verifiable reward across 25 agents from six providers on the $\\tau^2$-bench and SimulatorArena benchmarks, separating two kinds of evaluation validity that release practices conflate: ranking validity and construct validity.\u003c/li\u003e\n\u003cli\u003eFirst, a satisfaction-success gap: satisfaction carries essentially no information about task success, as conversations rated satisfied by our blind panel are decorrelated from actual success, with 57.5% of them failing the customer\u0026rsquo;s task, a pattern consistent across five rater populations, both benchmarks, and every subjective dimension we rated.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12191v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Comparing and selecting task-oriented LLM agents increasingly relies on a low-cost offline evaluation gate: persona-driven LLM user-simulators convers…\u003c/li\u003e\n\u003cli\u003eWe introduce GAUGE, a reusable offline protocol that measures whether this gate\u0026rsquo;s ranking matches a grounded verifiable reward across 25 agents from six provide…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFirst, a satisfaction-success gap: satisfaction carries essentially no information about task success, as conversations rated satisfied by our blind panel are d…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12230\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRepair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Date: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [[待翻译]]- arXiv:2609.12230v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Question-answering often requires reasoning across multiple connected facts rather than retrieving a single isolated relation.\u003c/li\u003e\n\u003cli\u003eKnowledge graphs (KGs) provide a structured way to represent such facts, but training large language models (LLMs) only on isolated KG head-relation-tail triples may limit their ability to learn the surrounding context needed for multi-hop reasoning.\u003c/li\u003e\n\u003cli\u003eIn this work, we propose a context-augmented training framework for multi-hop question-answering.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12230v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Question-answering often requires reasoning across multiple connected facts rather than retrieving a single isolated relation\u003c/li\u003e\n\u003cli\u003eKnowledge graphs (KGs) provide a structured way to represent such facts, but training large language models (LLMs) only on isolated KG head-relation-tail triple…\u003c/li\u003e\n\u003cli\u003eIn this work, we propose a context-augmented training framework for multi-hop question-answering\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.12243\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eChopthin-Consensus Power Sampling: A Diversity-Preserving Approach to LLM Decoding\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Date: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [[待翻译]]- arXiv:2609.12243v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-training.\u003c/li\u003e\n\u003cli\u003eHowever, many existing SMC approaches rely on equal-weight resampling, which can aggressively prune low-weight trajectories, discarding potentially correct reasoning paths and degrading the genealogical diversity of the search space.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo address this, we introduce Chopthin-Consensus Power Sampling (CCPS).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.12243v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-t…\u003c/li\u003e\n\u003cli\u003eHowever, many existing SMC approaches rely on equal-weight resampling, which can aggressively prune low-weight trajectories, discarding potentially correct reas…\u003c/li\u003e\n\u003cli\u003eTo address this, we introduce Chopthin-Consensus Power Sampling (CCPS)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"arxiv-cslg-b_introsearch\"\u003e\n  ArXiv cs.LG (B_intro+search)\n  \u003ca class=\"heading-link\" href=\"#arxiv-cslg-b_introsearch\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11934\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation Pending] - arXiv:2609.11934v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure.\u003c/li\u003e\n\u003cli\u003eRecovering this structure from perturbation time-series data is a fundamental identification problem, compounded by three coupled obstacles: the combinatorial complexity of interaction architectures, ambiguity of causal attribution under limited interventions, and state-dependent dynamics that confound structural inference.\u003c/li\u003e\n\u003cli\u003eEach obstacle is structural in origin and calls for a structural solution.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11934v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure\u003c/li\u003e\n\u003cli\u003eRecovering this structure from perturbation time-series data is a fundamental identification problem, compounded by three coupled obstacles: the combinatorial c…\u003c/li\u003e\n\u003cli\u003eEach obstacle is structural in origin and calls for a structural solution\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11935\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePhysics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSummary: [To be translated] - arXiv:2609.11935v1 Announce Type: new.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (PDEs).\u003c/li\u003e\n\u003cli\u003eHowever, providing rigorous uncertainty estimates remains an open challenge.\u003c/li\u003e\n\u003cli\u003eWe propose Physics-Informed Conformal Prediction (PI-CP), a framework that embeds PDE residuals into the nonconformity score of split conformal prediction, producing prediction intervals that are (i) distribution-free with provable coverage guarantees, and (ii) spatially adaptive when the PDE residual correlates with prediction error \u0026ndash; tighter where physics is well-satisfied, wider where it is violated.\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11935v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (P…\u003c/li\u003e\n\u003cli\u003eHowever, providing rigorous uncertainty estimates remains an open challenge\u003c/li\u003e\n\u003cli\u003eWe propose Physics-Informed Conformal Prediction (PI-CP), a framework that embeds PDE residuals into the nonconformity score of split conformal prediction, prod…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11937\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [To be translated] - arXiv:2609.11937v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: The deployment of Federated Learning (FL) in multi-center clinical networks faces the challenge of \u0026ldquo;knowledge dominance,\u0026rdquo; where high-volume hubs naturally overwhelm minority community nodes, implicitly treating the distinct clinical patterns of smaller cohorts as outliers.\u003c/li\u003e\n\u003cli\u003eExisting geometric defenses provide a security baseline but leave this efficiency-fairness dilemma unresolved.\u003c/li\u003e\n\u003cli\u003eTo bridge this gap, we propose Fed-Equilibrium, a framework that advances the paradigm from simple defense to topological equilibrium.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003earXiv:2609.11937v1 Announce Type: new\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: The deployment of Federated Learning (FL) in multi-center clinical networks faces the challenge of \u0026ldquo;knowledge dominance,\u0026rdquo; where high-volume hubs natur…\u003c/li\u003e\n\u003cli\u003eExisting geometric defenses provide a security baseline but leave this efficiency-fairness dilemma unresolved\u003c/li\u003e\n\u003cli\u003eTo bridge this gap, we propose Fed-Equilibrium, a framework that advances the paradigm from simple defense to topological equilibrium\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11954\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eEfficient AI Model Deployment Using Quantization Analysis Tool\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [Translation Pending] - arXiv:2609.11954v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: As deep learning models are increasingly deployed on resource constrained devices, the demand for efficient model optimization techniques continues to grow.\u003c/li\u003e\n\u003cli\u003eEffective deployment of AI models on edge and low power platforms requires optimization methods that reduce model size and computational cost while maintaining high accuracy.\u003c/li\u003e\n\u003cli\u003eThis paper presents Quantization Analysis Tool, a practical system designed to streamline quantization workflows and support performance efficient model deployment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11954v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: As deep learning models are increasingly deployed on resource constrained devices, the demand for efficient model optimization techniques continues to…\u003c/li\u003e\n\u003cli\u003eEffective deployment of AI models on edge and low power platforms requires optimization methods that reduce model size and computational cost while maintaining…\u003c/li\u003e\n\u003cli\u003eThis paper presents Quantization Analysis Tool, a practical system designed to streamline quantization workflows and support performance efficient model deploym…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11956\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePerformance, Efficiency and Collapse \u0026ndash; Advantages and Challenges in Offline Post-training of Code LLMs\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [Translation Pending] - arXiv:2609.11956v1 Announce Type: new.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: Post-training with reinforcement learning (RL) is a critical phase in the development of code-generating large language models (LLMs), as it ensures adherence to instructions and the production of functionally correct code.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis process typically requires computationally intensive code sample generation from Transformer-based LLMs and substantial GPU-CPU communication for sequence verification.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo address these computational challenges, this work examines whether RL-based post-training can be performed entirely offline by leveraging existing datasets rather than generating new samples.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11956v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Post-training with reinforcement learning (RL) is a critical phase in the development of code-generating large language models (LLMs), as it ensures a…\u003c/li\u003e\n\u003cli\u003eThis process typically requires computationally intensive code sample generation from Transformer-based LLMs and substantial GPU-CPU communication for sequence…\u003c/li\u003e\n\u003cli\u003eTo address these computational challenges, this work examines whether RL-based post-training can be performed entirely offline by leveraging existing datasets r…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11957\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLook Before You Leap: Pre-Action Verification for LLM Agents\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.11957v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: An LLM agent acts on the world by emitting actions: shell commands to run, edits to apply.\u003c/li\u003e\n\u003cli\u003eA wrong action does not always fail loudly; it can fail silently, producing a plausible but incorrect effect that raises no error.\u003c/li\u003e\n\u003cli\u003eWe argue that a cheap deterministic check, run before an action takes effect, is an effective and underused form of agent oversight, and we study it across two action modalities in one framework.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11957v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: An LLM agent acts on the world by emitting actions: shell commands to run, edits to apply\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA wrong action does not always fail loudly; it can fail silently, producing a plausible but incorrect effect that raises no error\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe argue that a cheap deterministic check, run before an action takes effect, is an effective and underused form of agent oversight, and we study it across two…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11958\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDecoding Mixture Perception through Computational Modeling of Component Interactions\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.11958v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Olfaction played an indispensable role throughout human evolution and civilization.\u003c/li\u003e\n\u003cli\u003eEven in the contemporary era of advanced technology, olfaction remains a critical channel for person to conduct danger discrimination, emotional experience, and memory formation.\u003c/li\u003e\n\u003cli\u003eHowever, most substances in nature exist as multi-molecule mixtures.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eKey English Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11958v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Olfaction played an indispensable role throughout human evolution and civilization\u003c/li\u003e\n\u003cli\u003eEven in the contemporary era of advanced technology, olfaction remains a critical channel for person to conduct danger discrimination, emotional experience, and…\u003c/li\u003e\n\u003cli\u003eHowever, most substances in nature exist as multi-molecule mixtures\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11959\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSpace as an Interventional Invariant: Cross-Modal Predictive Geometry for Stratified Cities and Em-Spaced Intelligence\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.11959v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Space is a foundational concept across mathematics, physics, spatial cognition, urban science, and embodied intelligence, yet these fields often treat spatial structure either as a shared geometric container or as a collection of disconnected representations.\u003c/li\u003e\n\u003cli\u003eSuch approaches struggle to explain how heterogeneous sensory and urban processes can jointly reveal a common spatial structure, particularly when different modalities do not share the same metric or representation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis paper addresses this gap by defining space as an interventional invariant: the minimal relational structure that preserves local compatibility and the conditional laws of future observations under admissible actions.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11959v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Space is a foundational concept across mathematics, physics, spatial cognition, urban science, and embodied intelligence, yet these fields often treat…\u003c/li\u003e\n\u003cli\u003eSuch approaches struggle to explain how heterogeneous sensory and urban processes can jointly reveal a common spatial structure, particularly when different mod…\u003c/li\u003e\n\u003cli\u003eThis paper addresses this gap by defining space as an interventional invariant: the minimal relational structure that preserves local compatibility and the cond…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11961\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eOn-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2609.11961v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Stress is a pervasive determinant of mental health and a key target for mobile health interventions.\u003c/li\u003e\n\u003cli\u003eOn-device language models (ODLMs) offer privacy-preserving inference without cloud dependency, yet their feasibility for health prediction under mobile resource constraints remains underexplored.\u003c/li\u003e\n\u003cli\u003eWe evaluate ODLMs for multi-modal stress prediction using zero-shot prompting, measuring predictive accuracy alongside latency and throughput.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11961v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Stress is a pervasive determinant of mental health and a key target for mobile health interventions\u003c/li\u003e\n\u003cli\u003eOn-device language models (ODLMs) offer privacy-preserving inference without cloud dependency, yet their feasibility for health prediction under mobile resource…\u003c/li\u003e\n\u003cli\u003eWe evaluate ODLMs for multi-modal stress prediction using zero-shot prompting, measuring predictive accuracy alongside latency and throughput\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2609.11993\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-09-14 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: arXiv:2609.11993v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Machine learning research in financial services is limited by the scarcity of representative open-source datasets.\u003c/li\u003e\n\u003cli\u003eExisting resources are often narrowly focused on a single modality or task and fail to reflect the structured, multimodal, and dynamic nature inherent to many problems in financial services.\u003c/li\u003e\n\u003cli\u003eIn this paper, we introduce FINESSE, a Financial Event Sequence Simulation Environment, an agent-based simulation framework for generating synthetic, structured datasets composed of multiple interdependent event streams.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2609.11993v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Machine learning research in financial services is limited by the scarcity of representative open-source datasets\u003c/li\u003e\n\u003cli\u003eExisting resources are often narrowly focused on a single modality or task and fail to reflect the structured, multimodal, and dynamic nature inherent to many p…\u003c/li\u003e\n\u003cli\u003eIn this paper, we introduce FINESSE, a Financial Event Sequence Simulation Environment, an agent-based simulation framework for generating synthetic, structured…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n",
  "wordCount": 6884,
  "readingTime": 33,
  "tableOfContents": "\u003cnav id=\"TableOfContents\"\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"#-in-depth-guide-to-this-issues-watch-list\"\u003e📖 In-depth Guide to This Issue\u0026rsquo;s Watch List\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#-x-platform-ai-hot-news\"\u003e🌐 X Platform AI Hot News\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#topic-1-anthropic-launches-claude-mods-for-custom-terminal-plugins\"\u003eTopic 1: Anthropic Launches Claude Mods for Custom Terminal Plugins\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-2-yann-lecun-mocks-2019-gpt-2-release-fears\"\u003eTopic 2: Yann LeCun Mocks 2019 GPT-2 Release Fears\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-3-metas-muse-ai-agent-wins-early-praise-for-everyday-help\"\u003eTopic 3: Meta\u0026rsquo;s Muse AI Agent Wins Early Praise for Everyday Help\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-4-ai-speeds-product-building-but-demands-deeper-user-insight\"\u003eTopic 4: AI Speeds Product Building but Demands Deeper User Insight\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-5-metas-muse-ai-agent-tops-download-charts-days-after-launch\"\u003eTopic 5: Meta\u0026rsquo;s Muse AI Agent Tops Download Charts Days After Launch\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-6-community-ranks-top-multi-agent-harnesses-for-ai-development\"\u003eTopic 6: Community Ranks Top Multi-Agent Harnesses for AI Development\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#-influencer-insights\"\u003e💡 Influencer Insights\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#-appendix-todays-watch-list-update-source-list\"\u003e📚 Appendix: Today\u0026rsquo;s Watch List Update Source List\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#acquiredfm-a_full\"\u003eAcquired.fm (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#all-in-podcast-a_full\"\u003eAll-In Podcast (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#stratechery-by-ben-thompson-a_full\"\u003eStratechery by Ben Thompson (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#openai-blog-a_full\"\u003eOpenAI Blog (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-csai-b_introsearch\"\u003eArXiv cs.AI (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-cscl-b_introsearch\"\u003eArXiv cs.CL (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-cslg-b_introsearch\"\u003eArXiv cs.LG (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n  \u003c/ul\u003e\n\u003c/nav\u003e",
  "isDraft": false
}