{
  "title": "2026-08-26 AI Daily | OpenAI Pushes AI Competition Towards Full-Stack: Self-Developed Inference Chips, Computing Power Systems, and Trusted Intelligent Agents Simultaneously Accelerate",
  "url": "https://miaok.ong/en/ai-daily/ai-daily-2026-08-26/",
  "date": "2026-08-26T07:00:00+08:00",
  "lastmod": "2026-08-26T07:00:00+08:00",
  "type": "ai-daily",
  "kind": "page",
  "language": "en",
  "description": "Today\u0026rsquo;s main theme shifts from model capabilities to system capabilities. OpenAI continues to strengthen its full-stack strategy, from data centers and chips to products, with Jalapeño aiming for lower-cost inference. Concurrently, agent governance, citation attribution, and runtime evidence protocols are gaining traction, and reliability evaluations are beginning to delve into long-tail language and cultural scenarios.",
  "keywords": null,
  "tags": [],
  "categories": [],
  "author": "Mark (Miao) Kong",
  "image": "https://miaok.ong/images/avatar.jpg",
  "content": "\u003ch1 id=\"2026-08-26-ai-daily--openai-pushes-ai-competition-to-full-stack-accelerating-in-house-inference-chips-compute-systems-and-trustworthy-agents-in-parallel\"\u003e\n  2026-08-26 AI Daily | OpenAI Pushes AI Competition to Full-Stack: Accelerating In-House Inference Chips, Compute Systems, and Trustworthy Agents in Parallel\n  \u003ca class=\"heading-link\" href=\"#2026-08-26-ai-daily--openai-pushes-ai-competition-to-full-stack-accelerating-in-house-inference-chips-compute-systems-and-trustworthy-agents-in-parallel\"\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 main theme shifts from model capabilities to system capabilities. OpenAI continues to strengthen its full-stack strategy, from data centers and chips to products, with Jalapeño pointing toward lower-cost inference. Meanwhile, agent governance, citation attribution, and runtime evidence protocols are gaining traction, as reliability evaluation begins to delve into long-tail languages and cultural contexts.\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\u003eThere are three main themes worth following today. The first is the comprehensive \u0026ldquo;full-stack\u0026rdquo; trend in AI infrastructure: OpenAI continues to integrate its compute, chips, and inference systems, and early results from Jalapeño also point to a more efficient inference architecture, a key area for engineering teams to watch. The second is that agents are entering the hard-problem phase of governance and security: from sycophancy and citation attribution to agentic security and runtime evidence protocols, it\u0026rsquo;s clear that after achieving the ability to \u0026ldquo;get things done,\u0026rdquo; trustworthiness and auditability are becoming core requirements. The third is the push for model reliability in long-tail languages and cultural contexts. Several studies today on Khmer, Nigerian Pidgin, Cyrillic, and visual-language biases remind us that evaluation systems are still far from mature. On a side note, Netflix\u0026rsquo;s exploration of becoming a streaming aggregation portal is also worth watching, as it reflects a reorganization of platform-based distribution logic.\u003c/p\u003e\n\u003ch2 id=\"-ai-hot-topics-on-x\"\u003e\n  🌐 AI Hot Topics on X\n  \u003ca class=\"heading-link\" href=\"#-ai-hot-topics-on-x\"\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-xai-and-cursor-boost-grok-model-usage-limits-again\"\u003e\n  Topic 1: xAI and Cursor Boost Grok Model Usage Limits Again\n  \u003ca class=\"heading-link\" href=\"#topic-1-xai-and-cursor-boost-grok-model-usage-limits-again\"\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 for: 7 hours ago, Related posts: 5,400\u003c/li\u003e\n\u003cli\u003eWhat happened: xAI and Cursor have once again increased the usage limits for the Grok model, drawing attention from developers and AI users to its availability and cost.\u003c/li\u003e\n\u003cli\u003eWhy it matters: This reflects a trend of AI products competing for developer adoption and workflow integration by offering higher call limits. It also indicates intensifying competition in compute supply, cost control, and commercialization for inference models.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: Discussions on X center on whether the increased limits signify a genuine enhancement in Grok\u0026rsquo;s infrastructure and model serving capabilities, whether Cursor users will experience more stable coding, and if this will squeeze the market share of models like Claude and GPT in developer tools. Some also question the cost sustainability and the actual extent of quality improvement.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-2-shopify-ceo-pushes-for-claude-code-to-adopt-agentsmd-standard\"\u003e\n  Topic 2: Shopify CEO Pushes for Claude Code to Adopt AGENTS.md Standard\n  \u003ca class=\"heading-link\" href=\"#topic-2-shopify-ceo-pushes-for-claude-code-to-adopt-agentsmd-standard\"\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 for: 9 hours ago, Related posts: 2,900\u003c/li\u003e\n\u003cli\u003eWhat happened: The CEO of Shopify has publicly called for Claude Code to adopt the AGENTS.md standard, which uses a unified file to describe the rules, context, and collaboration conventions for AI programming agents within a codebase.\u003c/li\u003e\n\u003cli\u003eWhy it matters: This is about the standardization and interoperability of AI programming tools. If different agents can read the same set of project conventions, it could reduce configuration costs, minimize errors, and improve the stability of multi-tool and multi-agent collaboration in real-world codebases.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: The debate on X focuses on whether Claude Code\u0026rsquo;s adoption could help AGENTS.md become a de facto standard and whether a unified specification can improve an agent\u0026rsquo;s understanding of project context. The point of contention is whether it\u0026rsquo;s essential industry infrastructure or just another project file that adds to the maintenance burden and risks configuration fragmentation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-3-openai-engineer-surprises-with-changed-appearance-in-interview\"\u003e\n  Topic 3: OpenAI Engineer Surprises with Changed Appearance in Interview\n  \u003ca class=\"heading-link\" href=\"#topic-3-openai-engineer-surprises-with-changed-appearance-in-interview\"\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 for: 2 days ago, Related posts: 4,600\u003c/li\u003e\n\u003cli\u003eWhat happened: An X user\u0026rsquo;s post discussing the changed appearance of an OpenAI engineer in an interview has gone viral, sparking conversations about their identity, well-being, and background.\u003c/li\u003e\n\u003cli\u003eWhy it matters: This kind of topic is significant because it reflects the high level of public attention on OpenAI and its key employees. It can easily impact the company\u0026rsquo;s image, talent narrative, and public perception of the AI industry\u0026rsquo;s culture and work pressure.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: The discussion mainly revolves around whether the change in appearance is real, its potential causes, and whether this level of attention is normal observation or an invasion of privacy. Some have also used it as a jumping-off point to discuss the high-intensity work environment at AI companies and the magnifying effect of public opinion.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-4-openai-unveils-jalapeño-chip-outpacing-nvidia-in-speed-and-efficiency\"\u003e\n  Topic 4: OpenAI Unveils Jalapeño Chip Outpacing Nvidia in Speed and Efficiency\n  \u003ca class=\"heading-link\" href=\"#topic-4-openai-unveils-jalape%c3%b1o-chip-outpacing-nvidia-in-speed-and-efficiency\"\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 for: 9 hours ago, Related posts: 13,000\u003c/li\u003e\n\u003cli\u003eWhat happened: OpenAI has reportedly announced a chip named Jalapeño, claiming it surpasses Nvidia\u0026rsquo;s comparable products in speed and energy efficiency.\u003c/li\u003e\n\u003cli\u003eWhy it matters: If this claim holds true, it signifies that leading AI companies are reducing their reliance on general-purpose GPUs through in-house chip development. This would impact compute costs, the supply chain, and future model deployment strategies.\u003c/li\u003e\n\u003cli\u003eDiscussion Overview: Discussions on X are mainly focused on three points: whether performance comparisons are backed by public benchmarks, whether this implies OpenAI is pursuing deeper in-house hardware development, and the potential impact on Nvidia\u0026rsquo;s position in the AI compute market.\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 on X today is that the AI competition is shifting from \u0026ldquo;who has the stronger model\u0026rdquo; to \u0026ldquo;who can offer cheaper, more stable, and more deeply integrated solutions into developer workflows.\u0026rdquo; Whether it\u0026rsquo;s Grok increasing its limits, the calls to standardize Claude Code \u003ccode\u003eAGENTS.md\u003c/code\u003e, or the rumors of OpenAI developing its own chips, everything points to three key areas: compute power, interface standards, and entry-point control. The general consensus is that the core of developer tools is not just the model\u0026rsquo;s capability itself, but also its usability, cost, and collaborative efficiency within the project context. The disagreements center on whether these moves represent genuine capability improvements, marketing-driven volume releases, a burden of standardization, or unverified performance narratives. Opinions on \u003ccode\u003eAGENTS.md\u003c/code\u003e are also sharply divided: supporters see it as infrastructure for reducing operational errors and enhancing multi-agent collaboration, while opponents worry it will become an additional maintenance cost and create new fragmentation. There are three main potential risks: first, whether the compute costs behind high limits and in-house chips are sustainable; second, that performance claims without sufficient benchmark support could mislead market judgment; and third, the magnified discussions around individual engineers\u0026rsquo; appearances and well-being, which tend to conflate industry pressure, privacy boundaries, and public curiosity.\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\u003eInfluencer insights are unavailable today. We recommend reading the in-depth content from 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; Covering 22 sources; 35 updates in total\u003c/p\u003e\n\u003c/blockquote\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/netflix-to-sell-streaming-services-streamers-as-aggregators-revisiting-roku/\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eNetflix to Sell Streaming Services?, Streamers as Aggregators, Revisiting Roku\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-25 18:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: Netflix is considering selling other streaming services, and I think it’s a good idea; it’s also a let-down for Netflix’s original goals and potential pivots.\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\u003eNetflix is considering selling other streaming services, and I think it\u0026rsquo;s a good idea; it\u0026rsquo;s also a let-down for Netflix\u0026rsquo;s original goals and potential pivots.\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/the-full-stack-behind-abundant-intelligence\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eThe full stack behind abundant intelligence\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-25 15:05 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: Progress in AI compounds fastest when the entire system improves together.\n\u003cul\u003e\n\u003cli\u003eThat is how I think about OpenAI’s compute strategy: one integrated system spanning data centers and chips, frontier models, our developer platform, consumer and enterprise products, and AI-native devices, with each layer strengthening the next.\u003c/li\u003e\n\u003cli\u003eBetter software makes hardware more productive.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHardware designed for our workloads improves speed and efficiency.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMore capable models unlock better products, which generate more demand, usage, and learning.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Key points:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOpenAI CFO Sarah Friar explains how advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower…\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/jalapeno-first-results\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eJalapeño’s first results show industry-leading speed and efficiency in AI inference\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-25 15:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [TO BE TRANSLATED] - Since announcing Jalapeño, OpenAI’s first custom inference chip, we have been testing the chip and the system built around it.\n\u003cul\u003e\n\u003cli\u003eThe results show a significant performance advance: Jalapeño can serve more AI work per unit of power while also returning responses more quickly.\u003c/li\u003e\n\u003cli\u003eJalapeño delivers both higher throughput and lower latency with one architecture, where existing hardware systems often have to make a tradeoff between the two.\u003c/li\u003e\n\u003cli\u003eFor customers, that can mean faster responses, more responsive agents, and more reliable access as demand grows.\u003c/li\u003e\n\u003cli\u003eOur mission is to ensure that artificial general intelligence benefits all of humanity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key points:\n\u003cul\u003e\n\u003cli\u003eJalapeño is a custom inference chip from OpenAI that delivers faster, more power-efficient AI inference, with higher throughput and lower latency for modern mod…\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/disrupting-malicious-uses-of-ai-influence-campaign-russia\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDisrupting a new covert influence campaign from Russia\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-25 08:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [TO BE TRANSLATED] - OpenAI banned Russia-origin accounts using AI to promote a fake Israel-based think tank and a “sovereignty” index praising Russia and criticizing the West.\n\u003cul\u003e\n\u003cli\u003eThis piece from OpenAI Blog explains how Disrupting a new covert influence campaign from Russia shapes the broader AI and infrastructure landscape.\u003c/li\u003e\n\u003cli\u003eIt also surfaces practical implications for founders, operators, and investors following Disrupting a new covert influence campaign from Russia.\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\u003eOpenAI banned Russia-origin accounts using AI to promote a fake Israel-based think tank and a “sovereignty” index praising Russia and criticizing the West.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://openai.com/index/introducing-admin-plugin\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eIntroducing the Admin plugin for ChatGPT Work and Codex\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 08:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [TO BE TRANSLATED] - Use the Admin plugin for ChatGPT Work and Codex to analyze workspace usage, manage members and permissions, adjust limits, and act on admin requests.\n\u003cul\u003e\n\u003cli\u003eThis piece from OpenAI Blog explains how Introducing the Admin plugin for ChatGPT Work and Codex shapes the broader AI and infrastructure landscape.\u003c/li\u003e\n\u003cli\u003eIt also surfaces practical implications for founders, operators, and investors following Introducing the Admin plugin for ChatGPT Work and Codex.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eKey Takeaways (EN):\n\u003cul\u003e\n\u003cli\u003eUse the Admin plugin for ChatGPT Work and Codex to analyze workspace usage, manage members and permissions, adjust limits, and act on admin requests.\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/2608.21362\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eKVBoost: Chunk-Level Key-Value Cache Reuse with Deviation-Guided Recomputation for Efficient Large Language Model Inference\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [TO BE TRANSLATED] - arXiv:2608.21362v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Transformer-based large language models (LLMs) incur high prefill latency because key-value (KV) tensors must be recomputed for each request.\u003c/li\u003e\n\u003cli\u003eExisting prefix-caching systems reduce this cost but require prompts to share a leading contiguous prefix, limiting effectiveness when shared content appears at arbitrary positions.\u003c/li\u003e\n\u003cli\u003eWe present KVBoost, a chunk-level KV cache reuse system for HuggingFace-compatible decoder models that enables reuse regardless of content position.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eKey Takeaways (EN):\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21362v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Transformer-based large language models (LLMs) incur high prefill latency because key-value (KV) tensors must be recomputed for each request\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExisting prefix-caching systems reduce this cost but require prompts to share a leading contiguous prefix, limiting effectiveness when shared content appears at…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe present KVBoost, a chunk-level KV cache reuse system for HuggingFace-compatible decoder models that enables reuse regardless of content position\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2608.21363\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAIREP: A Protocol for Per-Decision Evidence in AI Runtime Governance\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [To be translated]- arXiv:2608.21363v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: A protocol is presented for recording the governance decisions of automated AI runtimes.\u003c/li\u003e\n\u003cli\u003eWhen a runtime releases, blocks, defers, redacts, or escalates an individual output, AIREP records that decision as a single signed object that any party can check offline, independent of the runtime that produced it.\u003c/li\u003e\n\u003cli\u003eA record carries the decision as one of a closed set of verbs under a stated policy basis, references its input, output, and evidence by hash rather than by value, and declares both what its evidence covers and what it does not.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21363v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: A protocol is presented for recording the governance decisions of automated AI runtimes\u003c/li\u003e\n\u003cli\u003eWhen a runtime releases, blocks, defers, redacts, or escalates an individual output, AIREP records that decision as a single signed object that any party can ch…\u003c/li\u003e\n\u003cli\u003eA record carries the decision as one of a closed set of verbs under a stated policy basis, references its input, output, and evidence by hash rather than by val…\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/2608.21366\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eReviewing Model Collapse and Countermeasures\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [To be translated]- arXiv:2608.21366v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Driven by massive amounts of web-scale data, generative AI (GenAI) has achieved remarkable progress, enabling various applications in diverse sectors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe advances of GenAI have actuated practitioners to use AI-synthesized data for training next-generation AI models.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUndeniably, using synthetic data has alleviated the increasing stringent demand for data supply.\u003c/li\u003e\n\u003cli\u003eEN Key points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21366v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Driven by massive amounts of web-scale data, generative AI (GenAI) has achieved remarkable progress, enabling various applications in diverse sectors\u003c/li\u003e\n\u003cli\u003eThe advances of GenAI have actuated practitioners to use AI-synthesized data for training next-generation AI models\u003c/li\u003e\n\u003cli\u003eUndeniably, using synthetic data has alleviated the increasing stringent demand for data supply\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/2608.21372\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAI Learning and Conceptual Transfer in the Game of Hidden Rules\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2608.21372v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: This report summarizes the work conducted on the Game of Hidden Rules (GOHR), focusing on reinforcement learning agents trained to infer hidden rules from trial-and-error feedback, representation design, rule difficulty analysis, transfer learning, generalization, and pseudo-bot-assisted human learning analysis.\u003c/li\u003e\n\u003cli\u003eThe report focuses on the Transformer-based A2C framework, Feature-Centric and Object-Centric representations, experimental findings, and classification of human learning data.\u003c/li\u003e\n\u003cli\u003earXiv:2608.21372v1 Announce Type: new Abstract: This report summarizes the work conducted on the Game of Hidden Rules (GOHR), focusing on reinforcement learning agents trained to infer hidden rules… The report focuses on the Transformer-based A2C framework, Feature-Centric and Object-Centric representations, experimental findings, and classification of huma….\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21372v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: This report summarizes the work conducted on the Game of Hidden Rules (GOHR), focusing on reinforcement learning agents trained to infer hidden rules…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe report focuses on the Transformer-based A2C framework, Feature-Centric and Object-Centric representations, experimental findings, and classification of huma…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2608.21374\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLitReview Arena: Evaluating Literature Review Agents with Battle-Style Peer Review Platform\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: 【To be translated】- arXiv:2608.21374v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspects of research utility depend on expert judgment rather than reference-overlap metrics.\u003c/li\u003e\n\u003cli\u003eWe introduce LitReview Arena, a battle-style evaluation platform with a structured protocol tailored to literature review quality: domain experts with AI paper-writing experience compare anonymized drafts, are matched to topics within their expertise, and provide dimension-wise outcomes over five literature-review-specific criteria.\u003c/li\u003e\n\u003cli\u003eFrom this protocol, we collect approximately 3k expert judgments, each containing five dimension-wise outcomes, and show that even the strongest current systems win only 23.0% of decisive matches against human drafts on overall utility, while agentic LLMs such as Sonar Deep Research substantially outperform base language models by over 60%.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21374v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspe…\u003c/li\u003e\n\u003cli\u003eWe introduce LitReview Arena, a battle-style evaluation platform with a structured protocol tailored to literature review quality: domain experts with AI paper-…\u003c/li\u003e\n\u003cli\u003eFrom this protocol, we collect approximately 3k expert judgments, each containing five dimension-wise outcomes, and show that even the strongest current systems…\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/2608.21375\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSchemaRouter: Field-Aware Tool Routing for Efficient Heterogeneous Agentic RAG\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: [Translation pending] - arXiv:2608.21375v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph stores.\u003c/li\u003e\n\u003cli\u003eExposing all tool descriptions to an LLM agent, or selecting tools only by vector similarity, causes two costly failures: over-fetching, which increases payload size, token use, and latency, and under-fetching, which omits fields needed to answer the query.\u003c/li\u003e\n\u003cli\u003eWe present SchemaRouter, a lightweight routing layer that represents tools, endpoints, parameters, response fields, domain concepts, units, provenance, and license policies as a schema graph.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21375v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and grap…\u003c/li\u003e\n\u003cli\u003eExposing all tool descriptions to an LLM agent, or selecting tools only by vector similarity, causes two costly failures: over-fetching, which increases payload…\u003c/li\u003e\n\u003cli\u003eWe present SchemaRouter, a lightweight routing layer that represents tools, endpoints, parameters, response fields, domain concepts, units, provenance, and lice…\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/2608.21379\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRIACT: A Responsible AI System for Personalized Study Habit Tracking and Early Burnout Signal Detection in University Students\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation pending] - arXiv:2608.21379v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Student burnout is highly prevalent in higher education, with reported rates ranging from 12% to over 70% and consistently exceeding those of the working population - yet it is typically identified only retrospectively, after academic decline has already occurred.\u003c/li\u003e\n\u003cli\u003eA contributing factor is that students have little structured visibility into their own study behaviour, and existing productivity tools record activity without interpreting it.\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 presents RIACT (Record, Insight, Analyze, Coach, Track), a web-based application that combines structured study session logging with a hybrid AI architecture to surface personalized insights and early burnout signals.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21379v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Student burnout is highly prevalent in higher education, with reported rates ranging from 12% to over 70% and consistently exceeding those of the work…\u003c/li\u003e\n\u003cli\u003eA contributing factor is that students have little structured visibility into their own study behaviour, and existing productivity tools record activity without…\u003c/li\u003e\n\u003cli\u003eThis paper presents RIACT (Record, Insight, Analyze, Coach, Track), a web-based application that combines structured study session logging with a hybrid AI arch…\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/2608.21382\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eThere Is No Neutral Harness: Modern LLM Leaderboards Are Manufactured by Config-Fragile Items\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2608.21382v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Multiple-choice benchmarks fix the questions and the correct answers, but not the harness: the order of the options, the wording of the prompt, and whether a language model\u0026rsquo;s answer is read from generated text or from per-option likelihoods.\u003c/li\u003e\n\u003cli\u003eWork on this harness sensitivity reports it as aggregate score variance, leaving unexamined which items the variance falls on and whether they are the items that separate one model from the next.\u003c/li\u003e\n\u003cli\u003eWe treat the evaluation harness of large language models (LLMs) as an independent variable and resolve its effect to single items.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21382v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Multiple-choice benchmarks fix the questions and the correct answers, but not the harness: the order of the options, the wording of the prompt, and wh…\u003c/li\u003e\n\u003cli\u003eWork on this harness sensitivity reports it as aggregate score variance, leaving unexamined which items the variance falls on and whether they are the items tha…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe treat the evaluation harness of large language models (LLMs) as an independent variable and resolve its effect to single items\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2608.21393\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSpyre-Accelerated Retrieval-Augmented Generation on IBM LinuxONE: A Cloud-Native Architecture for Secure, High-Throughput Enterprise AI Inference\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [TO BE TRANSLATED] - arXiv:2608.21393v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Running large language models inside enterprise environments has always bumped up against a practical wall: the data lives in one place, the AI horsepower sits somewhere else, and moving sensitive records between the two creates real headaches around latency, security, and regulatory exposure.\u003c/li\u003e\n\u003cli\u003eIBM\u0026rsquo;s Spyre accelerator PCIe inference card built for LinuxONE and the broader IBM Z family changes that equation.\u003c/li\u003e\n\u003cli\u003eIn this paper we lay out a six-subsystem RAG architecture that runs entirely on IBM LinuxONE, using Spyre for generative inference, the Telum II on-chip accelerator for lightweight classification tasks, and Red Hat OpenShift for container orchestration.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21393v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Running large language models inside enterprise environments has always bumped up against a practical wall: the data lives in one place, the AI horsep…\u003c/li\u003e\n\u003cli\u003eIBM\u0026rsquo;s Spyre accelerator PCIe inference card built for LinuxONE and the broader IBM Z family changes that equation\u003c/li\u003e\n\u003cli\u003eIn this paper we lay out a six-subsystem RAG architecture that runs entirely on IBM LinuxONE, using Spyre for generative inference, the Telum II on-chip acceler…\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/2608.21408\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eHate Speech Classification In Roman Urdu: A Comparative Study On Parameter Efficient Fine-Tuning And Prompt Engineering\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [TO BE TRANSLATED] - arXiv:2608.21408v1 Announce Type: new.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: Due to the widespread accessibility of the internet and social media, toxic and hateful con-tent has grown exponentially, causing significant distress and negative societal impacts.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRo-man Urdu, a low-resource language used in Pakistan and among Urdu-speaking communities worldwide, presents additional challenges because of its informal grammar, inconsistent sen-tence structures, and multiple variations in word spellings.\u003c/li\u003e\n\u003cli\u003eThis research aims to identify the most effective techniques for hate speech classification in such low-resource settings with limited data.\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21408v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Due to the widespread accessibility of the internet and social media, toxic and hateful con-tent has grown exponentially, causing significant distress…\u003c/li\u003e\n\u003cli\u003eRo-man Urdu, a low-resource language used in Pakistan and among Urdu-speaking communities worldwide, presents additional challenges because of its informal gram…\u003c/li\u003e\n\u003cli\u003eThis research aims to identify the most effective techniques for hate speech classification in such low-resource settings with limited data\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/2608.21364\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDistinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation pending] - arXiv:2608.21364v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Both human and AI systems that process narrative or long-form content operate incrementally: input is received over time, and internal representations must be updated accordingly.\u003c/li\u003e\n\u003cli\u003eIncremental interpretation, therefore, depends not only on what is represented but also on how the representational state evolves under new evidence.\u003c/li\u003e\n\u003cli\u003eWe distinguish two structurally different update operators that arise in narrative interpretation: revision-driven update and delayed elaboration.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21364v1 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: Both human and AI systems that process narrative or long-form content operate incrementally: input is received over time, and internal representations…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIncremental interpretation, therefore, depends not only on what is represented but also on how the representational state evolves under new evidence\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe distinguish two structurally different update operators that arise in narrative interpretation: revision-driven update and delayed elaboration\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2608.21365\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eKSE-Web: An Analysis of Hybrid Retrieval and LLM-Assisted Query Expansion for Low-Resource Khmer Semantic Search\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: arXiv:2608.21365v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: As a low-resource language, Khmer presents several retrieval challenges, including limited annotated data, ambiguous word boundaries, weak support in multilingual embedding models, and frequent mixed Khmer-English usage.\u003c/li\u003e\n\u003cli\u003eThis paper presents KSE-Web, an analysis of hybrid retrieval and LLM-assisted query expansion for Khmer semantic search.\u003c/li\u003e\n\u003cli\u003eWe construct the dataset from approximately 17K candidate Khmer titles and retain 3K cleaned full-text Khmer documents after filtering, normalization, deduplication, and document-length control.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21365v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: As a low-resource language, Khmer presents several retrieval challenges, including limited annotated data, ambiguous word boundaries, weak support in…\u003c/li\u003e\n\u003cli\u003eThis paper presents KSE-Web, an analysis of hybrid retrieval and LLM-assisted query expansion for Khmer semantic search\u003c/li\u003e\n\u003cli\u003eWe construct the dataset from approximately 17K candidate Khmer titles and retain 3K cleaned full-text Khmer documents after filtering, normalization, deduplica…\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/2608.21369\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eWazobia Eval: A Benchmark for Nigerian Pidgin Emotion Understanding, Sarcasm Detection, and Cultural Reasoning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-25 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:2608.21369v1 Announce Type: new.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: Nigerian Pidgin is one of Africa\u0026rsquo;s most widely spoken languages, yet remains severely underrepresented in language model evaluation.\u003c/li\u003e\n\u003cli\u003eExisting benchmarks primarily focus on translation, transcription, or generic sentiment analysis, leaving critical aspects of culturally grounded language understanding unmeasured.\u003c/li\u003e\n\u003cli\u003eWe introduce Wazobia Eval, a benchmark for evaluating Nigerian Pidgin emotion understanding, sarcasm detection, and cultural reasoning.\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21369v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Nigerian Pidgin is one of Africa\u0026rsquo;s most widely spoken languages, yet remains severely underrepresented in language model evaluation\u003c/li\u003e\n\u003cli\u003eExisting benchmarks primarily focus on translation, transcription, or generic sentiment analysis, leaving critical aspects of culturally grounded language under…\u003c/li\u003e\n\u003cli\u003eWe introduce Wazobia Eval, a benchmark for evaluating Nigerian Pidgin emotion understanding, sarcasm detection, and cultural 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/2608.21376\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eOn the Role of Citations in Preference Data\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [To be translated] - arXiv:2608.21376v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Many NLP tasks require systems to provide attribution in their outputs\u0026ndash;i.e.\u003c/li\u003e\n\u003cli\u003ecitations to grounding sources.\u003c/li\u003e\n\u003cli\u003eAttribution serves as a bulwark against model hallucination and as a means for users to verify the credibility of model outputs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21376v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Many NLP tasks require systems to provide attribution in their outputs\u0026ndash;i.e\u003c/li\u003e\n\u003cli\u003ecitations to grounding sources\u003c/li\u003e\n\u003cli\u003eAttribution serves as a bulwark against model hallucination and as a means for users to verify the credibility of model outputs\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/2608.21377\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAgentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [To be translated] - arXiv:2608.21377v1 Announce Type: new.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied primarily in single-turn settings.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThis paper investigates a critical question: does subjecting LLMs to greater interaction scaffolding make sycophancy better or worse?\u003c/li\u003e\n\u003cli\u003eAcross 4,800 veracity judgments (200 statements $\\times$ 6 models $\\times$ 4 conditions), we find that the interaction scaffolding characteristic of agentic systems (feedback loops, reconsideration checkpoints, and iterative refinement) systematically amplifies sycophantic behavior.\u003c/li\u003e\n\u003cli\u003eKey Points (EN):\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21377v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied pr…\u003c/li\u003e\n\u003cli\u003eThis paper investigates a critical question: does subjecting LLMs to greater interaction scaffolding make sycophancy better or worse\u003c/li\u003e\n\u003cli\u003eAcross 4,800 veracity judgments (200 statements $\\times$ 6 models $\\times$ 4 conditions), we find that the interaction scaffolding characteristic of agentic sys…\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/2608.21384\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBeyond Two Bytes per Letter: Tokenization Overhead in Cyrillic AI Systems\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublish Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2608.21384v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Modern multilingual tokenizers often fragment Ukrainian and other underrepresented Cyrillic-script languages more heavily than English, creating disparities in cost and context capacity.\u003c/li\u003e\n\u003cli\u003eWe quantify this overhead across nine production tokenizers and five languages with standardized Cyrillic and Latin representations, covering 8.37 million word forms.\u003c/li\u003e\n\u003cli\u003eOn a corpus benchmark, Ukrainian shows 68-121% token overhead on modern tokenizers and 220% on the older cl100k, measured through full-text fertility on the BrUK and Brown corpora.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eKey Points (EN):\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21384v1 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\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2608.21385\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eA Social Media Analysis of Discourse on the Israel\u0026ndash;Palestine Conflict on Telegram\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePosted on: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation Pending] - arXiv:2608.21385v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose broadcast architecture yields an unusually direct record of deliberate political communication, have not been systematically compared at scale.\u003c/li\u003e\n\u003cli\u003eThis study presents a multi-method computational analysis of 87,617 messages from sixteen Telegram channels, eight pro-Israel and eight pro-Palestine, spanning May 2021 to June 2026 and covering multiple conflict escalations.\u003c/li\u003e\n\u003cli\u003eIt combines sentiment analysis, three stance detection methods drawn from distinct paradigms (keyword matching, zero-shot DeBERTa via natural language inference, and a fine-tuned BERTweet model), and a framing analysis, all evaluated against 736 manually annotated messages.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21385v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose br…\u003c/li\u003e\n\u003cli\u003eThis study presents a multi-method computational analysis of 87,617 messages from sixteen Telegram channels, eight pro-Israel and eight pro-Palestine, spanning…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIt combines sentiment analysis, three stance detection methods drawn from distinct paradigms (keyword matching, zero-shot DeBERTa via natural language inference…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2608.21415\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eMitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2608.21415v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from their training data, resulting in biased behavior when processing portraits from different social groups.\u003c/li\u003e\n\u003cli\u003eExisting debiasing approaches typically compare token probabilities between the original and biased generations during decoding, but they are fundamentally limited by their reliance on a single, stereotyped viewpoint and fail to account for the diversity of social perspectives.\u003c/li\u003e\n\u003cli\u003eInspired by the social science principle that diversity fosters fairness, we propose Counterfactual Ensemble Decoding (CED), a novel framework that constructs multi-group counterfactual perspectives within the visual representation space and integrates them during decoding to promote equitable model behavior.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21415v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from…\u003c/li\u003e\n\u003cli\u003eExisting debiasing approaches typically compare token probabilities between the original and biased generations during decoding, but they are fundamentally limi…\u003c/li\u003e\n\u003cli\u003eInspired by the social science principle that diversity fosters fairness, we propose Counterfactual Ensemble Decoding (CED), a novel framework that constructs m…\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/2608.21423\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAgentic Security: A Systematization of Tools, Failure Modes, and Design Laws for LLM-Driven Penetration Testing\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-08-25 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:2608.21423v1 Announce Type: new.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: Agentic security uses large-language-model (LLM) agents to plan, dispatch, and interpret security tools.\u003c/li\u003e\n\u003cli\u003eAs these systems move from demonstrations to deployed products, practitioners repeatedly encounter the same operational failures.\u003c/li\u003e\n\u003cli\u003eWe systematize these failures through a hands-on evaluation of ten widely used static, dynamic, cloud, orchestration, and AI red-teaming tools for unattended pipelines.\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21423v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Agentic security uses large-language-model (LLM) agents to plan, dispatch, and interpret security tools\u003c/li\u003e\n\u003cli\u003eAs these systems move from demonstrations to deployed products, practitioners repeatedly encounter the same operational failures\u003c/li\u003e\n\u003cli\u003eWe systematize these failures through a hands-on evaluation of ten widely used static, dynamic, cloud, orchestration, and AI red-teaming tools for unattended pi…\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/2608.21462\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: [TO BE TRANSLATED] - arXiv:2608.21462v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Due to the selection of their training data, large language models (LLMs) perform best on standard-language inputs from languages using the Latin alphabet with large speaker populations, while disadvantaging other language varieties.\u003c/li\u003e\n\u003cli\u003eNevertheless, they can also be a versatile tool for preserving precisely such endangered languages.\u003c/li\u003e\n\u003cli\u003eBut do they also possess the necessary creativity and capacity for abstraction to decode phonetically encoded language the same way humans do?\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21462v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Due to the selection of their training data, large language models (LLMs) perform best on standard-language inputs from languages using the Latin alph…\u003c/li\u003e\n\u003cli\u003eNevertheless, they can also be a versatile tool for preserving precisely such endangered languages\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBut do they also possess the necessary creativity and capacity for abstraction to decode phonetically encoded language the same way humans do\u003c/p\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/2608.21386\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eModel of Models: When Does Emitting a Specialist Beat Attending, Adapting, or Tuning?\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To Be Translated] - arXiv:2608.21386v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Given a task described by a few examples, how should a model be specialized to it?\u003c/li\u003e\n\u003cli\u003eFour mechanisms are available \u0026ndash; zero-shot, in-context attention, test-time gradient adaptation, and emitting specialist weights from a hypernetwork \u0026ndash; yet the operating regime of the last is rarely mapped.\u003c/li\u003e\n\u003cli\u003eWe run the identical four-way comparison across six tasks spanning regression, generation, language modeling, reinforcement learning, and clinical and genomic classification, holding the specialist, the context, and (where we can) the training budget fixed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21386v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Given a task described by a few examples, how should a model be specialized to it\u003c/li\u003e\n\u003cli\u003eFour mechanisms are available \u0026ndash; zero-shot, in-context attention, test-time gradient adaptation, and emitting specialist weights from a hypernetwork \u0026ndash; yet the…\u003c/li\u003e\n\u003cli\u003eWe run the identical four-way comparison across six tasks spanning regression, generation, language modeling, reinforcement learning, and clinical and genomic c…\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/2608.21398\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRuntime Action Interference for AI Control of AlphaStar in StarCraft II\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To Be Translated] - arXiv:2608.21398v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: A trained reinforcement learning policy does not determine the complete behavior that users encounter: deployment code still schedules, admits, suppresses, or replaces its proposed actions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe contribute \\emph{runtime action interference} (RAI), an AI control mechanism that preserves policy parameters while regulating action pacing and filtering configured action patterns after inference.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRAI releases a proposed action only when its cooldown condition is satisfied and its content detector does not flag the action; otherwise, it dispatches a no-op.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Key Points:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21398v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: A trained reinforcement learning policy does not determine the complete behavior that users encounter: deployment code still schedules, admits, suppre…\u003c/li\u003e\n\u003cli\u003eWe contribute \\emph{runtime action interference} (RAI), an AI control mechanism that preserves policy parameters while regulating action pacing and filtering co…\u003c/li\u003e\n\u003cli\u003eRAI releases a proposed action only when its cooldown condition is satisfied and its content detector does not flag the action; otherwise, it dispatches a no-op\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/2608.21399\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFederated Ensemble Forecasting Under Supply-Chain Market Volatility\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Pending Translation] - arXiv:2608.21399v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Supply chain forecasting systems increasingly operate under market shocks, non-identically distributed regional demand, and limited willingness to centralize commercial data.\u003c/li\u003e\n\u003cli\u003eThis work proposes Federated Ensemble Forecasting with Negative-Correlation Learning (FEF NCL), a distributed method that trains specialized forecasting experts across client nodes while discouraging redundant model errors.\u003c/li\u003e\n\u003cli\u003eThe framework combines temporal feature encoders, client level drift scoring, reliability-weighted aggregation, and an explain ability layer that exposes the market and supplier variables most responsible for each forecast.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21399v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Supply chain forecasting systems increasingly operate under market shocks, non-identically distributed regional demand, and limited willingness to cen…\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 work proposes Federated Ensemble Forecasting with Negative-Correlation Learning (FEF NCL), a distributed method that trains specialized forecasting experts…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe framework combines temporal feature encoders, client level drift scoring, reliability-weighted aggregation, and an explain ability layer that exposes the ma…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2608.21473\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eClass-Conditioned Gaussian Mixture Modeling for Imbalanced Time Series Quantification\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2608.21473v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Quantification, estimating class prevalences in bags of unlabeled instances is vital in domains where aggregate statistics are more important than individual instance labels, such as biosignal monitoring, fall detection, and activity recognition.\u003c/li\u003e\n\u003cli\u003eWe investigate this issue in the challenging setting of imbalanced time series data and develop CC-GMNet-TS, a class-conditioned Gaussian mixture quantifier that combines a Transformer-based feature extractor with per-class latent mixtures.\u003c/li\u003e\n\u003cli\u003eUnlike previous mixture-based quantifiers, which use a single Gaussian mixture shared by all classes, CC-GMNet-TS assigns each class its own compact mixture in a bounded latent space and scores segment embeddings against these class-specific components to create bag-level representations that emphasize rare but informative patterns.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21473v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Quantification, estimating class prevalences in bags of unlabeled instances is vital in domains where aggregate statistics are more important than ind…\u003c/li\u003e\n\u003cli\u003eWe investigate this issue in the challenging setting of imbalanced time series data and develop CC-GMNet-TS, a class-conditioned Gaussian mixture quantifier tha…\u003c/li\u003e\n\u003cli\u003eUnlike previous mixture-based quantifiers, which use a single Gaussian mixture shared by all classes, CC-GMNet-TS assigns each class its own compact mixture in…\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/2608.21485\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCongruence Decomposition with Neural Block Solvers for Large-Scale PCI Assignment\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation Pending] - arXiv:2608.21485v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Physical Cell Identity (PCI) assignment is essential for interference management in dense 5G networks.\u003c/li\u003e\n\u003cli\u003eAs cellular networks scale, PCI reuse becomes unavoidable, which may cause collisions, confusions, and multiple forms of modular interference.\u003c/li\u003e\n\u003cli\u003eJointly mitigating these effects gives rise to a large-scale, multi-objective combinatorial optimization problem that is difficult to solve efficiently at practical network scales.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21485v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Physical Cell Identity (PCI) assignment is essential for interference management in dense 5G networks\u003c/li\u003e\n\u003cli\u003eAs cellular networks scale, PCI reuse becomes unavoidable, which may cause collisions, confusions, and multiple forms of modular interference\u003c/li\u003e\n\u003cli\u003eJointly mitigating these effects gives rise to a large-scale, multi-objective combinatorial optimization problem that is difficult to solve efficiently at pract…\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/2608.21488\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eKAN-Robust-Bench: A Benchmark for Evaluating the Robustness of Kolmogorov-Arnold Networks\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [Translation Pending] - arXiv:2608.21488v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exposed to adversarial threats.\u003c/li\u003e\n\u003cli\u003eWhile adversarial attacks fall into various categories, the most prominent category in research studies is evasion.\u003c/li\u003e\n\u003cli\u003eIn evasion attacks, the adversary generates perturbed versions of samples, which might not be observable by human eyes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21488v1 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\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2608.21496\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eThe geometry of AI validation: Exact certification limits for iid best-of-N search\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2608.21496v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: AI systems increasingly generate alternatives, inspect evidence, and deploy a selected output.\u003c/li\u003e\n\u003cli\u003eValidation is therefore target-relative: evidence certifies deployment only in directions resolved by the interventions that produced it.\u003c/li\u003e\n\u003cli\u003eWe represent validation and deployment rules as kernels over a reliability surface.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21496v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: AI systems increasingly generate alternatives, inspect evidence, and deploy a selected output\u003c/li\u003e\n\u003cli\u003eValidation is therefore target-relative: evidence certifies deployment only in directions resolved by the interventions that produced it\u003c/li\u003e\n\u003cli\u003eWe represent validation and deployment rules as kernels over a reliability surface\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/2608.21499\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSelection of Heart Sound Segments for Synchronous Classification of Multi-channel Heart Sounds\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2608.21499v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Cardiac auscultation remains the most cost-effective screening procedure for cardiovascular diseases, and requires listening at the four main auscultation spots.\u003c/li\u003e\n\u003cli\u003eDespite this, automatic heart sound analysis algorithms mostly classify patients using a single heart sound (single-channel), or, when using more than one (multi-channel), analyze each channel individually.\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 our knowledge, no prior work classifies patients through the synchronous analysis of multi-channel heart sounds, following the procedure used by physicians.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21499v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Cardiac auscultation remains the most cost-effective screening procedure for cardiovascular diseases, and requires listening at the four main ausculta…\u003c/li\u003e\n\u003cli\u003eDespite this, automatic heart sound analysis algorithms mostly classify patients using a single heart sound (single-channel), or, when using more than one (mult…\u003c/li\u003e\n\u003cli\u003eTo our knowledge, no prior work classifies patients through the synchronous analysis of multi-channel heart sounds, following the procedure used by physicians\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/2608.21504\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eChemDIRT: A Diversified Instruction, Representation, and Task Benchmark for Robust Chemistry-LLM Evaluation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2608.21504v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: The rapid advancement of large language models (LLMs) has led to increasing interest in their application to scientific domains such as chemistry.\u003c/li\u003e\n\u003cli\u003eHowever, existing chemistry benchmarks often provide only a narrow view of model capability, focusing on limited task sets while overlooking robustness to variations in problem formulation and chemical representation.\u003c/li\u003e\n\u003cli\u003eAs a result, reported performance may overestimate a model\u0026rsquo;s true ability to reason consistently across realistic settings.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21504v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: The rapid advancement of large language models (LLMs) has led to increasing interest in their application to scientific domains such as chemistry\u003c/li\u003e\n\u003cli\u003eHowever, existing chemistry benchmarks often provide only a narrow view of model capability, focusing on limited task sets while overlooking robustness to varia…\u003c/li\u003e\n\u003cli\u003eAs a result, reported performance may overestimate a model\u0026rsquo;s true ability to reason consistently across realistic settings\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/2608.21530\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eMultimodal Injury Risk and Performance Prediction in Tennis Using Weighted Ensemble Learning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-08-25 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2608.21530v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Machine learning has had a positive impact on the sports industry, with one of its most promising applications being the prediction of athlete performance and injury risk.\u003c/li\u003e\n\u003cli\u003eRecent advances have employed state-of-the-art models to improve prediction accuracy, yet progress remains limited by data availability and the reliance on subjective observations or expert assessments.\u003c/li\u003e\n\u003cli\u003eTo address these limitations, researchers in sports such as soccer, basketball, and wrestling have begun integrating heterogeneous data sources, such as wearable device readings, with traditional subjective assessments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2608.21530v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Machine learning has had a positive impact on the sports industry, with one of its most promising applications being the prediction of athlete perform…\u003c/li\u003e\n\u003cli\u003eRecent advances have employed state-of-the-art models to improve prediction accuracy, yet progress remains limited by data availability and the reliance on subj…\u003c/li\u003e\n\u003cli\u003eTo address these limitations, researchers in sports such as soccer, basketball, and wrestling have begun integrating heterogeneous data sources, such as wearabl…\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": 6704,
  "readingTime": 32,
  "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=\"#-ai-hot-topics-on-x\"\u003e🌐 AI Hot Topics on X\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#topic-1-xai-and-cursor-boost-grok-model-usage-limits-again\"\u003eTopic 1: xAI and Cursor Boost Grok Model Usage Limits Again\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-2-shopify-ceo-pushes-for-claude-code-to-adopt-agentsmd-standard\"\u003eTopic 2: Shopify CEO Pushes for Claude Code to Adopt AGENTS.md Standard\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-3-openai-engineer-surprises-with-changed-appearance-in-interview\"\u003eTopic 3: OpenAI Engineer Surprises with Changed Appearance in Interview\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-4-openai-unveils-jalapeño-chip-outpacing-nvidia-in-speed-and-efficiency\"\u003eTopic 4: OpenAI Unveils Jalapeño Chip Outpacing Nvidia in Speed and Efficiency\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=\"#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
}