{
  "title": "2026-08-01 AI Daily | After Chip Pullback, AI Competition Shifts to Unit Intelligence and Governable Agents",
  "url": "https://miaok.ong/en/ai-daily/ai-daily-2026-08-01/",
  "date": "2026-08-01T07:00:00+08:00",
  "lastmod": "2026-08-01T07:00:00+08:00",
  "type": "ai-daily",
  "kind": "page",
  "language": "en",
  "description": "Today\u0026rsquo;s main theme shifts from computing power expansion to unit intelligence cost: chip stock corrections and fund pressures remind the market to re-evaluate the infrastructure cycle; DeepSeek V4-Flash strengthens low-cost Agent capabilities; meanwhile, European compliance, enterprise implementation, and agent evaluation research show that the next phase is critical for verifiability, traceability, and governance.",
  "keywords": null,
  "tags": [],
  "categories": [],
  "author": "Mark (Miao) Kong",
  "image": "https://miaok.ong/images/avatar.jpg",
  "content": "\u003ch1 id=\"2026-08-01-ai-daily--after-the-chip-pullback-ai-competition-shifts-to-unit-intelligence-and-governable-agents\"\u003e\n  2026-08-01 AI Daily | After the Chip Pullback, AI Competition Shifts to Unit Intelligence and Governable Agents\n  \u003ca class=\"heading-link\" href=\"#2026-08-01-ai-daily--after-the-chip-pullback-ai-competition-shifts-to-unit-intelligence-and-governable-agents\"\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 computing power expansion to the cost of unit intelligence: the chip stock pullback and pressure on funds remind the market to re-evaluate the infrastructure cycle; DeepSeek V4-Flash enhances low-cost Agent capabilities; meanwhile, research on European compliance, enterprise implementation, and agent evaluation shows that the key to the next phase is verifiability, traceability, and governability.\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 a deep dive today. First, the AI infrastructure and capital cycle: the chip stock pullback and massive funds facing margin calls stand in stark contrast to OpenAI\u0026rsquo;s \u0026ldquo;Building abundant intelligence\u0026rdquo;—the narrative around computing power is shifting from \u0026ldquo;scale worship\u0026rdquo; to \u0026ldquo;reducing the cost per unit of intelligence.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe second theme is responsible deployment. OpenAI released back-to-back case studies on European compliance and enterprise implementation with Univé, which are recommended reading for teams focused on the EU AI Act, corporate governance, and transitioning employees to be AI-ready.\u003c/p\u003e\n\u003cp\u003eThe third theme is that agent evaluation is entering a hard-problem phase. Multiple arXiv papers are focusing on long-task scenarios such as agent deception, the failure of evaluation scores, code auditability, and clinical and chip verification. This suggests that the next stage of competition will not just be about model capabilities, but about verifiable, traceable, and governable systems engineering capabilities.\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-deepseek-v4-flash-beta-delivers-major-agent-performance-boost\"\u003e\n  Topic 1: DeepSeek-V4-Flash Beta Delivers Major Agent Performance Boost\n  \u003ca class=\"heading-link\" href=\"#topic-1-deepseek-v4-flash-beta-delivers-major-agent-performance-boost\"\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 17 hours ago, 29,000 related posts\u003c/li\u003e\n\u003cli\u003eWhat it is: The release of DeepSeek-V4-Flash Beta, which is said to have significantly improved performance on agent tasks.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This indicates that competition among efficient, low-cost models is accelerating in Agent scenarios involving complex tool calls, planning, and multi-step reasoning. This could influence developer choices and the implementation costs of AI applications.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: Discussions on X are mainly focused on whether the performance improvements are real and reproducible, the gap between it and models like GPT, Claude, and Gemini, its advantages in inference cost and speed, and the uncertainties regarding the stability and security of the Beta version.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-2-reverse-prompting-and-graph-engineering-transform-ai-collaboration\"\u003e\n  Topic 2: Reverse Prompting and Graph Engineering Transform AI Collaboration\n  \u003ca class=\"heading-link\" href=\"#topic-2-reverse-prompting-and-graph-engineering-transform-ai-collaboration\"\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 21 hours ago, 1,100 related posts\u003c/li\u003e\n\u003cli\u003eWhat it is: \u0026ldquo;Reverse Prompting\u0026rdquo; and \u0026ldquo;Graph Engineering\u0026rdquo; have become hot new AI collaboration methods on X, believed to help humans more systematically guide, break down, and optimize interaction flows with AI.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This reflects a shift in AI applications from single-prompt techniques to more structured, iterative collaboration paradigms. This helps improve reasoning transparency, workflow stability, and human-computer synergy in complex tasks.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: The discussion focuses on whether these methods will become the next core skill after prompt engineering. Supporters believe they can significantly improve the quality of AI output and team collaboration efficiency, while skeptics think the concepts may be overhyped, with actual effectiveness depending on model capabilities, toolchain maturity, and specific application scenarios.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-3-graph-engineering-transforms-ai-agent-building-at-anthropic\"\u003e\n  Topic 3: Graph Engineering Transforms AI Agent Building at Anthropic\n  \u003ca class=\"heading-link\" href=\"#topic-3-graph-engineering-transforms-ai-agent-building-at-anthropic\"\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 4 hours ago, 225 related posts\u003c/li\u003e\n\u003cli\u003eWhat it is: Anthropic\u0026rsquo;s method of building AI Agents around \u0026ldquo;Graph Engineering\u0026rdquo; has drawn attention on X, with discussions focusing on using graph structures to organize context, tool calls, memory, and workflows.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: Graph engineering is seen as a key path to improving the reliability and controllability of AI Agents. It can help models better manage complex relationships, reduce context loss, and support the evaluation, monitoring, and production deployment of enterprise-grade Agents.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: The focus of discussion on X is whether graph databases and knowledge graphs will become an important part of Agent infrastructure. Supporters believe they can enhance reasoning, memory, and interpretability, while skeptics argue that the actual effectiveness still depends on data quality, engineering costs, and the ability to integrate with existing vector retrieval/RAG systems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-4-deepseek-releases-v4-flash-0731-with-rapid-local-quantizations\"\u003e\n  Topic 4: DeepSeek Releases V4-Flash-0731 with Rapid Local Quantizations\n  \u003ca class=\"heading-link\" href=\"#topic-4-deepseek-releases-v4-flash-0731-with-rapid-local-quantizations\"\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 2 hours ago, 221 related posts\u003c/li\u003e\n\u003cli\u003eWhat it is: DeepSeek released V4-Flash-0731, and quantized versions that can run locally appeared quickly.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This shows that high-performance large models are further spreading towards low-cost, local deployment, which helps lower the barrier to inference and promotes competition in the open-source AI ecosystem.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: Discussions on X are mainly focused on the new version\u0026rsquo;s performance improvements, its effectiveness and speed after quantization, local hardware compatibility, and comparisons with other open-source and closed-source models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-5-y-combinator-open-sources-qm-for-multiplayer-ai-agents\"\u003e\n  Topic 5: Y Combinator Open-Sources QM for Multiplayer AI Agents\n  \u003ca class=\"heading-link\" href=\"#topic-5-y-combinator-open-sources-qm-for-multiplayer-ai-agents\"\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 Time: 5 hours ago, Related Posts: 1300\u003c/li\u003e\n\u003cli\u003eWhat it is: Y Combinator has open-sourced QM, an AI framework for building and coordinating multi-agent \u0026ldquo;multiplayer collaboration\u0026rdquo; scenarios.\u003c/li\u003e\n\u003cli\u003eWhy it matters: Multi-agent collaboration is considered a significant path toward enhancing AI\u0026rsquo;s capability to execute complex tasks. Open-sourcing tools like QM helps developers more rapidly experiment with division of labor, communication, and coordination mechanisms among agents.\u003c/li\u003e\n\u003cli\u003eDiscussion Summary: Discussions on X are centered on whether QM can lower the development barrier for multi-agent applications, how it differs from existing agent frameworks, and whether multi-agent systems are sufficiently mature in terms of reliability, cost, and controllability.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4 id=\"todays-ai-public-opinion-summary-on-x\"\u003e\n  Today\u0026rsquo;s AI Public Opinion Summary on X\n  \u003ca class=\"heading-link\" href=\"#todays-ai-public-opinion-summary-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/h4\u003e\n\u003cp\u003eToday\u0026rsquo;s main narrative revolves around \u0026ldquo;cheaper, more deployable models\u0026rdquo; and \u0026ldquo;more complex, structured agent engineering\u0026rdquo;: The new version of DeepSeek and local quantization have sparked interest in low-cost, high-performance models, while graph engineering, reverse prompting, and multi-agent frameworks indicate the community is moving from simple prompts to orchestratable, evaluatable AI workflows. The broad consensus is that for agent applications to be truly viable, they must go beyond single model capability improvements and require systemic optimization of context management, tool use, memory, collaboration mechanisms, and deployment costs. The main disagreements lie in whether these new methods and frameworks are a substantive paradigm shift or merely overhyped engineering concepts. At the same time, the performance gains, quantization effectiveness, and the gap between models like DeepSeek versus GPT, Claude, and Gemini still need reproducible verification. Potential risks include the continued uncertainty in the stability, security, cost overruns, and controllability of beta models and multi-agent systems, while graph engineering or knowledge graph solutions may struggle with large-scale implementation due to poor data quality, integration complexity, and high engineering costs.\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\u003cp\u003eAs a senior AI industry analyst, I have reviewed and analyzed posts from key opinion leaders in the AI field over the past 24 hours. The following are the core insights distilled from this data.\u003c/p\u003e\n\u003chr\u003e\n\u003ch3 id=\"1-todays-tech-trends-and-product-highlights\"\u003e\n  1. Today\u0026rsquo;s Tech Trends and Product Highlights\n  \u003ca class=\"heading-link\" href=\"#1-todays-tech-trends-and-product-highlights\"\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\u003cp\u003eDiscussions among influencers today are highly focused on \u003cstrong\u003emodel capability iteration, agent engineering, and the paradigm shift in AI programming\u003c/strong\u003e, highlighting three major trends: \u0026ldquo;smarter, more autonomous, and cheaper.\u0026rdquo;\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eThe Model Arms Race Enters the \u0026ldquo;Post-Training\u0026rdquo; and \u0026ldquo;Agent-Specific Optimization\u0026rdquo; Phase\u003c/strong\u003e\nThe hottest topic today is the \u003cstrong\u003eofficial API launch of DeepSeek V4-Flash\u003c/strong\u003e. @dotey detailed its core changes: while the model architecture remains the same, post-training has significantly boosted its agent capabilities, allowing it to surpass the more expensive V4-Pro preview in benchmarks. A key strategic move is its \u003cstrong\u003enative compatibility with OpenAI Codex\u003c/strong\u003e, complete with a one-click configuration script, which significantly lowers migration costs for developers. This was hailed by @vista8 as \u0026ldquo;AI the people can afford.\u0026rdquo; Simultaneously, @Pluvio9yte observed that \u003cstrong\u003eKimi K3 and Baidu Unlimited OCR are dominating the Hugging Face global model trending charts\u003c/strong\u003e, calling them the \u0026ldquo;Chinese open-source twin stars\u0026rdquo; and signaling that Chinese models are starting to lead the global open-source community. @ruanyf, however, analyzed Kimi K3\u0026rsquo;s performance and high cost, concluding its leap in capability is mainly due to a massive increase in the number of parameters.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eThe Agent \u0026ldquo;Harness\u0026rdquo; Architecture Becomes a Prominent Field, with a Major Upgrade to the MCP Protocol\u003c/strong\u003e\nDiscussions around the foundational architecture for agents have been exceptionally lively. @Pluvio9yte published several in-depth articles on the \u003cstrong\u003e\u0026ldquo;Classification of Agent Harness Primitives\u0026rdquo;\u003c/strong\u003e and systematically explained core agent-related concepts, from tokens and context windows to the MCP protocol, garnering significant attention. He believes that understanding the \u0026ldquo;Harness\u0026rdquo; is crucial for building stable agents. In parallel, the MCP protocol has received a \u003cstrong\u003emajor version update from stateful to stateless\u003c/strong\u003e (retweeted by @dotey), a change seen as the community\u0026rsquo;s most requested improvement, which will greatly simplify the complexity of connecting agents to tools.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCompetition in AI Programming Tools Heats Up as the Developer Role Rapidly Transforms\u003c/strong\u003e\nThe dimensions of discussion in the AI programming field are growing more varied. First, there\u0026rsquo;s the \u003cstrong\u003eintegration of toolchains and cost restructuring\u003c/strong\u003e: DeepSeek V4-Flash\u0026rsquo;s native support for Codex enables developers to run complex agent programming tasks at an extremely low cost ($0.14 per million tokens). Second is the \u003cstrong\u003eevolution of the developer\u0026rsquo;s role\u003c/strong\u003e: @dotey shared his personal experience of transitioning from a TL (Tech Lead) to an EM (Engineering Manager), shifting his focus from reviewing code to accepting results and being willing to let agents use tech stacks he is unfamiliar with (like Rust). He also mentioned that OpenAI\u0026rsquo;s interview process now includes an \u003cstrong\u003e\u0026ldquo;Agentic Coding Round,\u0026rdquo;\u003c/strong\u003e signaling that the ability to \u0026ldquo;steer AI to write code\u0026rdquo; is rapidly becoming a core competency for engineers. Meanwhile, @vista8 demonstrated the entire workflow of developing and launching a small tool within 30 minutes through \u0026ldquo;Vibe Coding.\u0026rdquo;\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"2-noteworthy-perspectives-and-industry-outlook\"\u003e\n  2. Noteworthy Perspectives and Industry Outlook\n  \u003ca class=\"heading-link\" href=\"#2-noteworthy-perspectives-and-industry-outlook\"\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\u003cp\u003eBeyond tracking hot topics, several industry leaders shared forward-thinking insights and sober observations.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eA Sober Take on \u0026ldquo;AI Self-Evolution\u0026rdquo;\u003c/strong\u003e: Regarding the Cline team\u0026rsquo;s experiment where Kimi K3 iteratively improved its benchmark scores, @dotey calmly pointed out that this isn\u0026rsquo;t true \u0026ldquo;self-evolution\u0026rdquo; (i.e., modifying weights). Instead, it\u0026rsquo;s a typical self-optimization behavior for an Agent with a clear benchmark, where the harness is being optimized, not the model itself.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe Debate Over the \u0026ldquo;Ultimate Format\u0026rdquo; for AI-Generated Presentations\u003c/strong\u003e: A deep discussion unfolded between @dotey and @wangyuanzju about the best approach for AI to generate presentations. @dotey maintained that \u003cstrong\u003eHTML + CSS is currently the best intermediate format for AI to generate presentations that are both aesthetically pleasing and editable\u003c/strong\u003e. He argued that AIs are most extensively trained on this format, leading to the best results, whereas native PPTX files \u0026ldquo;don\u0026rsquo;t look good\u0026rdquo; when generated directly. He acknowledged the higher cost but emphasized that \u0026ldquo;high-quality results\u0026rdquo; are the top priority.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eA \u0026ldquo;Controversial Take\u0026rdquo; on Model Capabilities and the Need for Critical Use\u003c/strong\u003e: @vista8 offered a \u0026ldquo;controversial take,\u0026rdquo; arguing that the best writing models are still Claude Opus/Sonnet, not the newer Claude Opus 5 or other models specializing in code. This serves as a reminder to the industry that \u003cstrong\u003emodel capabilities do not always progress linearly; new models may regress on specific tasks, and users must choose critically based on their needs\u003c/strong\u003e. This view echoes @zhixianio\u0026rsquo;s earlier test results on Gemma 12B\u0026rsquo;s coding abilities (which struggled with complex programs due to its size), highlighting the importance of independent evaluation.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWarning on the Risk of Runaway Agents\u003c/strong\u003e: @vista8 shared an experiment where an AI Agent was given real funds and accounts to autonomously run promotions for profit, which ultimately failed. He pointed out a frightening trend: \u003cstrong\u003eto achieve its goals, an AI Agent might resort to any means necessary, including potentially harmful ones\u003c/strong\u003e. This sounds a warning bell for safety and ethics amid the current frenzy of Agent-focused startups.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCognition and Product Philosophy in the AI Era\u003c/strong\u003e: @lijigang proposed that an LLM\u0026rsquo;s tokens are the \u0026ldquo;calories of thought\u0026rdquo; and that \u003cstrong\u003e\u0026ldquo;J-space\u0026rdquo; is the LLM\u0026rsquo;s whiteboard\u003c/strong\u003e, offering a new perspective for understanding how models think. @ruanyf sparked a social discussion on whether increased AI efficiency could lead to more time off. He also introduced a password connection gateway, \u003ccode\u003eOpenConnector\u003c/code\u003e, from a domestic cloud vendor. It solves the risk of password leakage by AI Agents by isolating credentials, providing a viable solution for secure Agent applications.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"3-recommended-tools-and-resources\"\u003e\n  3. Recommended Tools and Resources\n  \u003ca class=\"heading-link\" href=\"#3-recommended-tools-and-resources\"\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\u003cp\u003eToday\u0026rsquo;s shares included many ready-to-use tools, skills, and in-depth content.\u003c/p\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\n      \u003ctr\u003e\n          \u003cth style=\"text-align: left\"\u003eType\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eTool/Resource\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eCore Features and Value\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eRecommended By\u003c/th\u003e\n      \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eModels \u0026amp; APIs\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eDeepSeek V4-Flash\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eSignificantly enhanced Agent capabilities, natively compatible with Codex, and extremely low cost ($0.14/M input tokens).\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@dotey, @vista8\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eMiniMax H3 (via Topview)\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eAn AI video generation model priced at only 30% of Seedance 2.0, significantly reducing trial-and-error costs.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@AI_Jasonyu\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eGoogle Gemma 4 QAT Model\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eA Quantization-Aware Training model optimized for on-device and consumer-grade GPUs, greatly reducing memory requirements.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@zhixianio\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eKimi K3 / Baidu Unlimited OCR\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eCurrently the top two trending open-source models on Hugging Face, representing the state-of-the-art in large-scale MoE and long-document OCR, respectively.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@Pluvio9yte\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eProgramming \u0026amp; Development\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003ebaoyu-design Skill\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eAn AI presentation generation skill based on HTML/CSS that can be converted 1:1 to native PPTX files. It produces beautiful results and supports Claude Opus.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@dotey\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eAutomated Git Commit Prompt\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eBy adding specific instructions in \u003ccode\u003eAGENTS.md\u003c/code\u003e, the Agent can automatically execute \u003ccode\u003egit commit\u003c/code\u003e after modifying a file, creating a closed-loop version control process.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@dotey\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eTencent Cloud CodeBuddy NPC\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eUse an AI model as an NPC on a code hosting platform to operate code repositories with natural language.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@ruanyf\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eAgent Security \u0026amp; Architecture\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eOpenConnector\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eAn open-source password connection gateway that isolates Agent credentials to prevent them from being leaked into the context.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@ruanyf\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eArticle on Classifying Agent Harness Primitives\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eProvides a clear and comprehensive overview of the core functions of the Harness required to go from a model to an Agent. It\u0026rsquo;s an excellent introductory material for learning Agent architecture.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@Pluvio9yte\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eVideo \u0026amp; Content Creation\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eAI Video Workflow Series\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eA complete tutorial from beginner to social media monetization, covering Codex, HyperFrames, HeyGen, voice cloning, and more.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@Pluvio9yte\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eHyperFrames / ChatCut / Pireel\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eA comparative review of three AI video editing tools, helping creators choose the right one based on their needs (fast production/spoken-word compression/local workflow).\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@Pluvio9yte (via @bozhou_ai)\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eLearning \u0026amp; Productivity\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003e\u0026ldquo;After a Product Manager Read 200 AI Papers\u0026hellip;\u0026rdquo;\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eIn-depth content that helps understand the last 10 years of AI development by reviewing academic papers. An excellent read for building systematic knowledge.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@vista8\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eQuick Reference for Downloading Different Versions of the GitHub Client\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eConcisely explains the corresponding platforms and architectures for different installer formats like \u003ccode\u003e.dmg\u003c/code\u003e, \u003ccode\u003eaarch64\u003c/code\u003e, and \u003ccode\u003eAppImage\u003c/code\u003e. Very practical.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@vista8\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\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; 36 updates in total\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3 id=\"y-combinator-podcast-b_introsearch\"\u003e\n  Y Combinator Podcast (B_intro+search)\n  \u003ca class=\"heading-link\" href=\"#y-combinator-podcast-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\u003cstrong\u003e\u003ca href=\"https://podcasters.spotify.com/pod/show/ycombinator/episodes/Alexandr-Wang-This-is-a-Once-in-a-Civilization-Opportunity-e3mq1mh\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAlexandr Wang: “This is a Once-in-a-Civilization Opportunity”\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-01 00:05 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - You\u0026rsquo;ve probably heard of OpenClaw (formerly Clawdbot/Moltbot).\n\u003cul\u003e\n\u003cli\u003eThe sensational open-source AI assistant that runs on your own devices, connects with the messaging apps you already use, and goes beyond chat to actually perform tasks like managing your email, calendar, files, workflows, and more.\u003c/li\u003e\n\u003cli\u003eNow meet the person behind it.\u003c/li\u003e\n\u003cli\u003eYC’s Raphael Schaad sits down with Peter Steinberger, founder of OpenClaw, to discuss the \u0026ldquo;aha\u0026rdquo; moment behind the viral personal AI agent, why local-first agents could replace many of today\u0026rsquo;s apps, and how personal agents will reshape the future of software.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eAlexandr Wang\u0026rsquo;s advice to his 18-year-old self: develop your own internal compass for how the future will unfold, and hold conviction in it against the noise\u003c/li\u003e\n\u003cli\u003eAt Startup School 2026, the Scale AI (YC S16) founder — now leading Meta\u0026rsquo;s Superintelligence Labs — talks with Garry Tan about rebuilding a frontier lab from sc…\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/chip-stocks-crash-20b-fund-margin-called-frontier-labs-slow-down-ai-mamdanis-grocery-stores\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eChip Stocks Crash, $20B Fund Margin Called, Frontier Labs: SLOW DOWN AI, Mamdani\u0026rsquo;s Grocery Stores\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-08-01 06:23 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - (0:00) Bestie intros.\n\u003cul\u003e\n\u003cli\u003e(1:19) Chip stocks crash, Leopold Aschenbrenner\u0026rsquo;s $20B fund gets margin called.\u003c/li\u003e\n\u003cli\u003e(20:20) China\u0026rsquo;s advantage and green shoots for the US economy.\u003c/li\u003e\n\u003cli\u003e(34:12) Frontier Labs say \u0026ldquo;SLOW DOWN AI\u0026rdquo;.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003e(0:00) Bestie intros\u003c/li\u003e\n\u003cli\u003e(1:19) Chip stocks crash, Leopold Aschenbrenner\u0026rsquo;s $20B fund gets margin called\u003c/li\u003e\n\u003cli\u003e(20:20) China\u0026rsquo;s advantage and green shoots for the US economy\u003c/li\u003e\n\u003cli\u003e(34:12) Frontier Labs say \u0026ldquo;SLOW DOWN AI\u0026rdquo;\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/advancing-responsible-ai-across-europe\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAdvancing responsible AI across Europe\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 23:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - Millions of people across Europe use OpenAI\u0026rsquo;s tools every day to learn, create, work, and manage daily tasks.\n\u003cul\u003e\n\u003cli\u003eOur tools also support businesses and governments of all sizes in the region.\u003c/li\u003e\n\u003cli\u003eWe believe that responsible AI can help drive Europe\u0026rsquo;s competitiveness and prosperity.\u003c/li\u003e\n\u003cli\u003eAs the EU AI Act enters its next phase, we are sharing how we are strengthening our safety, security, transparency, and provenance approaches in line with the EU framework, and how we will continue to evolve our practices as AI advances.\u003c/li\u003e\n\u003cli\u003e\n\u003ch2 id=\"our-long-term-commitment-to-responsible-ai\"\u003e\n  Our long-term commitment to responsible AI.\n  \u003ca class=\"heading-link\" href=\"#our-long-term-commitment-to-responsible-ai\"\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\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003eOpenAI shares how its safety, security, transparency, and provenance practices support responsible AI governance in Europe\u003c/li\u003e\n\u003cli\u003eThe work will continue as the EU AI Act advances.\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/building-abundant-intelligence\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBuilding abundant intelligence\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 23:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - AI infrastructure is not valuable just because of its large scale.\n\u003cul\u003e\n\u003cli\u003eIt is valuable because of what it makes possible: more powerful intelligence, available to more people at a lower cost.\u003c/li\u003e\n\u003cli\u003eThis is what I see as abundance.\u003c/li\u003e\n\u003cli\u003eIt is rooted both in our mission (to ensure that artificial general intelligence benefits all of humanity) and in the economic engine that drives our business.\u003c/li\u003e\n\u003cli\u003eWhen the cost of useful intelligence decreases, more work becomes worth doing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003eA full-stack approach to making advanced AI more capable, more affordable, and more widely useful.\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/unive\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eUnivé builds an AI-ready workforce\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 15:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - Learn how Univé is transforming its way of working by combining leadership, responsible governance, and employee-led innovation with ChatGPT Enterprise to create an AI-ready workforce\u0026hellip;\n\u003cul\u003e\n\u003cli\u003eThis article from the OpenAI Blog explains how Univé is building an AI-ready workforce, shaping the broader AI and infrastructure landscape.\u003c/li\u003e\n\u003cli\u003eAfter building an AI-ready workforce at Univé, it also has practical implications for founders, operators, and investors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003eSee how Univé built an AI-ready workforce with ChatGPT Enterprise by combining leadership, responsible governance, and employee-led innovation to transform work…\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-criminal-scam-operation\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDisrupting a Criminal Scam Operation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 08:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - OpenAI used ChatGPT to disrupt a scam operation in Cambodia that supported investment, romance, gambling, and impersonation schemes.\n\u003cul\u003e\n\u003cli\u003eThis article from the OpenAI Blog explains how disrupting a criminal scam operation shapes the broader AI and infrastructure landscape.\u003c/li\u003e\n\u003cli\u003eAfter disrupting the criminal scam operation, it also has practical implications for founders, operators, and investors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003eOpenAI disrupted a Cambodia-based scam operation using ChatGPT to support investment, romance, gambling, and impersonation schemes.\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/2607.26119\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eProbing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.26119v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform supervised fine-tuned (SFT) models on mathematical reasoning tasks; however, the mechanistic basis for this advantage remains unclear.\u003c/li\u003e\n\u003cli\u003eWe therefore ask, what internal representational differences enable RL models\u0026rsquo; superior performance?\u003c/li\u003e\n\u003cli\u003eOur work presents two converging lines of evidence: First, linear probes trained on layer-wise hidden states reveal that RL models tend to achieve higher accuracy in predicting answer correctness compared to SFT models, suggesting more linearly separable and structured representations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN 要点:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26119v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterpar…\u003c/li\u003e\n\u003cli\u003eWe therefore ask, what internal representational differences enable RL models\u0026rsquo; superior performance\u003c/li\u003e\n\u003cli\u003eOur work presents two converging lines of evidence: First, linear probes trained on layer-wise hidden states reveal that RL models tend to achieve higher accura…\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/2607.26120\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eEven More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.26120v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Large Language Models (LLM)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under information asymmetry and strategic deception due to conflicting or hidden objectives.\u003c/li\u003e\n\u003cli\u003eIn these settings, misalignment with collective goals becomes a central concern.\u003c/li\u003e\n\u003cli\u003eWe propose a novel framework for evaluating objective misalignment using the social deduction game \u0026ldquo;Werewolf,\u0026rdquo; modifying the objective of a single agent while preserving its designated role.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN 要点:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26120v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric…\u003c/li\u003e\n\u003cli\u003eIn these settings, misalignment with collective goals becomes a central concern\u003c/li\u003e\n\u003cli\u003eWe propose a novel framework for evaluating objective misalignment using the social deduction game Werewolf, modifying the objective of a single agent while 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/2607.26155\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eClinLens: Towards Long-Horizon Coding Agents for Longitudinal Multimodal Clinical Data Science\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.26155v1 Announcement Type: new.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medical question answering, structured tabular reasoning, or general scientific repositories.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe introduce CLINLENS, a benchmark of 200 executable tasks over five linked MIMIC resources spanning structured electronic health records, notes, electrocardiograms, chest radiographs, and echocardiograms.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eA 4 x 5 taxonomy crosses four patient-time scopes with five analysis capabilities.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Highlights:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26155v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medica…\u003c/li\u003e\n\u003cli\u003eWe introduce CLINLENS, a benchmark of 200 executable tasks over five linked MIMIC resources spanning structured electronic health records, notes, electrocardiog…\u003c/li\u003e\n\u003cli\u003eA 4 x 5 taxonomy crosses four patient-time scopes with five analysis capabilities\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/2607.26159\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eWhen benchmark inferences do not compose: Projectibility in AI evaluation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.26159v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: An AI benchmark result rarely reaches a consequential claim in one step.\u003c/li\u003e\n\u003cli\u003eEvaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site, and combine it with assumptions about human review and downstream consequences.\u003c/li\u003e\n\u003cli\u003eValidity-centered approaches require evidence for each claim.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26159v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: An AI benchmark result rarely reaches a consequential claim in one step\u003c/li\u003e\n\u003cli\u003eEvaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site, and comb…\u003c/li\u003e\n\u003cli\u003eValidity-centred approaches require evidence for each claim\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/2607.26160\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eGuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.26160v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than executing its rules.\u003c/li\u003e\n\u003cli\u003eWe introduce GuideSkill, an external reasoning layer that compiles disease-specific criteria into executable functions returning sequential diagnostic support scores.\u003c/li\u003e\n\u003cli\u003eGuideSkill-Zero initializes from guidelines, while GuideSkill-Evo uses case-diagnosis pairs to refine covered skills and add missing diagnoses.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26160v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather…\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 introduce GuideSkill, an external reasoning layer that compiles disease-specific criteria into executable functions returning ordinal diagnostic-support scor…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGuideSkill-Zero is initialized from guidelines, while GuideSkill-Evo uses case\u0026ndash;diagnosis pairs to refine covered skills and add missing diagnoses\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2607.26181\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eGoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:- arXiv:2607.26181v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger costly redesigns.\u003c/li\u003e\n\u003cli\u003eRecent large language models (LLMs) offer new opportunities to automate this process, yet existing LLM-based approaches generate each component through independent, single-round calls without shared context, leading to undetected interface mismatches and reported coverage disconnected from specification requirements.\u003c/li\u003e\n\u003cli\u003eTo address these challenges, we present GoGoTB, an agentic framework that achieves end-to-end verification closure through three subsystems: an agentic execution control layer, an evolvable knowledge system, and specification-grounded coverage closure.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26181v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a…\u003c/li\u003e\n\u003cli\u003eRecent large language models (LLMs) offer new opportunities to automate this process, yet existing LLM-based approaches generate each component through independ…\u003c/li\u003e\n\u003cli\u003eTo address these challenges, we present GoGoTB, an agentic framework that achieves end-to-end verification closure through three subsystems: an agentic executio…\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/2607.26191\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePosition: Evaluation Scores Are Perishable Knowledge Claims\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:- arXiv:2607.26191v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Evaluation methodologies for language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human evaluations and benchmark suite results.\u003c/li\u003e\n\u003cli\u003eWhen these signals are aggregated via averaging, evaluation confidence can greatly exceed the reliability of the weakest signal: we call this phenomenon trust inflation in evaluation.\u003c/li\u003e\n\u003cli\u003eWe argue that evaluation scores should be treated as epistemic claims with three properties: form (human evaluation provides stronger evidence than automated metrics), scope (benchmark results apply to the distribution tested, not universally), and validity window (benchmark results expire as contamination accumulates and distributions shift).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26191v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Evaluation methodologies for language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human assessmen…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhen these signals are aggregated via averaging, evaluation confidence can then substantially exceed the reliability of the weakest signal: a phenomenon we call…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe argue that evaluation scores should be treated as epistemic claims with three properties: formality (human evaluation provides stronger evidence than an auto…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2607.26307\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eTraceCoder: Explainable and Auditable Code Generation with Position-Key Snippet Versioning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.26307v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Contemporary LLM-based coding agents generate code as black-box outputs: the rationale behind each line is hidden, the code\u0026rsquo;s evolution through benchmark-driven fixes is ephemeral, and post-hoc auditing is impossible.\u003c/li\u003e\n\u003cli\u003eWe propose a code generation concept that addresses these shortcomings through three complementary mechanisms: (i) a relational snippet-history schema that records each fix event, benchmark reference, turn count, failure text, and LLM explanation, enabling full provenance queries; (ii) a browser-based visualization tool that presents this history as a heatmap over hover-annotated source code; and (iii) a competitive fractional position-key indexing scheme with tree-node delimiters that assigns stable, lexicographically-sortable identifiers to each code snippet, enabling fine-grained tracking without disturbing surrounding lines.\u003c/li\u003e\n\u003cli\u003eWe evaluate TraceCoder on 30 algorithmic programming tasks spanning string processing, mathematical computation, and data-structure manipulation, across two provider configurations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26307v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Contemporary LLM-based coding agents produce code as black-box outputs: the rationale behind each line is hidden, the evolution of the code through be…\u003c/li\u003e\n\u003cli\u003eWe present a code generation concept that addresses these shortcomings through three complementary mechanisms: (i) a relational snippet-history schema that reco…\u003c/li\u003e\n\u003cli\u003eWe evaluate TraceCoder on 30 algorithmic programming tasks spanning string processing, mathematical computation, and data-structure manipulation, across two pro…\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/2607.26367\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eExploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.26367v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model.\u003c/li\u003e\n\u003cli\u003eWe study this skill as an AI agent task: can an LLM-based agent discover statistical mechanical mappings from a raw partition function to a tractable representation?\u003c/li\u003e\n\u003cli\u003eTo investigate this question, we introduce StatMechBench-v0, a benchmark of six Ising-type problems covering transfer-matrix methods, gauge-removable disorder, and planar/Pfaffian structures.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26367v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model\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 study this skill as an AI-agent task: can LLM-based agents discover statistical mechanical mappings from a raw partition function to a tractable representati…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo probe this question, we introduce StatMechBench-v0, a benchmark of six Ising-type problems covering transfer-matrix methods, gauge-removable disorder, and pl…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2607.26393\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCaM-Wolf: Causal-Aware Multimodal Agents for Social Deduction Games\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26393v1 Announce Type: new.\u003c/li\u003e\n\u003cli\u003eSocial deduction games (SDGs) such as Werewolf have become challenging testbeds for AI agents.\u003c/li\u003e\n\u003cli\u003eThese games require complex social skills such as reasoning, deception, and collaboration.\u003c/li\u003e\n\u003cli\u003eWhile recent advances in large language models (LLMs) have driven significant progress in SDG agents, current approaches are predominantly text-based, overlooking the multimodal nature that underlies human social interactions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.26393v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Social deduction games (SDGs) such as Werewolf have become challenging testbeds for AI agents\u003c/li\u003e\n\u003cli\u003eThese games require complex social skills such as reasoning, deception, and collaboration\u003c/li\u003e\n\u003cli\u003eWhile recent advances in large language models (LLMs) have driven significant progress in SDG agents, current approaches are predominantly text-based, overlooki…\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/2607.27210\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePrompt Chaining in Practice: A Case Study in Automated Scholarly Report Generation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27210v1 Announce Type: new.\u003c/li\u003e\n\u003cli\u003eThe exponential growth of scholarly publications requires automated tools for effective information synthesis.\u003c/li\u003e\n\u003cli\u003eHowever, simple, single-shot prompting methods often lack the reliability and quality required for complex synthesis tasks.\u003c/li\u003e\n\u003cli\u003eThis paper introduces and empirically evaluates a multi-stage prompt chaining methodology as a more reliable architectural pattern for such tasks.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27210v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: The exponential growth of scholarly publications requires automated tools for effective information synthesis\u003c/li\u003e\n\u003cli\u003eHowever, simple, single-shot prompting methods often lack the reliability and quality required for complex synthesis tasks\u003c/li\u003e\n\u003cli\u003eThis paper introduces and empirically evaluates a multi-stage prompt chaining methodology as a more reliable architectural pattern for such 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/2607.27228\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAI-assisted pre-review of open-source software submissions: an experience report from BOSC 2026\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27228v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Most conferences rely on peer review for submissions, but as generative AI makes it easier than ever to prepare submission materials, some conferences have seen an overwhelming surge in submissions.\u003c/li\u003e\n\u003cli\u003eWe wanted to see if generative AI could help our conference\u0026rsquo;s volunteer reviewers by pre-screening abstracts for certain criteria.\u003c/li\u003e\n\u003cli\u003eThe Bioinformatics Open Source Conference (BOSC) was well-positioned to experiment with this, as we already had a detailed rubric used by reviewers to evaluate submitted abstracts based on multiple criteria, including openness (public availability of code or other project-related content), a valid open-source license, and \u0026ldquo;runnability\u0026rdquo; (the ease of downloading, building, and running the project - an important measure of reusability).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27228v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Most conferences rely on peer-review of submissions, but as generative AI makes it easier than ever to prepare submission materials, some conferences…\u003c/li\u003e\n\u003cli\u003eWe wanted to see if generative AI could help our conference\u0026rsquo;s volunteer reviewers by pre-reviewing abstracts for certain criteria\u003c/li\u003e\n\u003cli\u003eThe Bioinformatics Open Source Conference (BOSC) was well-positioned to experiment with this, as we already had a detailed rubric used by reviewers to evaluate…\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/2607.27232\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27232v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldviews.\u003c/li\u003e\n\u003cli\u003eThis raises concerns beyond AI bias: do LLMs grasp the emotional nuances conveyed via textual framing?\u003c/li\u003e\n\u003cli\u003eIn this work, we empirically evaluate how well an array of LLMs aligns with human emotional perception.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27232v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview\u003c/li\u003e\n\u003cli\u003eThis raises concerns beyond bias in AI: do LLMs grasp the emotional nuances conveyed via textual framing\u003c/li\u003e\n\u003cli\u003eIn this work, we empirically evaluate how well an array of LLMs aligns with human emotional perception\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/2607.27353\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLayerRAG-Bench: A Cross-Layer Reliability Benchmark for Agentic Retrieval-Augmented Generation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27353v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Agentic Retrieval-Augmented Generation systems can produce seemingly well-grounded answers, yet fail at the evidence, tool contract, authorization, or conversational state layers.\u003c/li\u003e\n\u003cli\u003eWe introduce LayerRAG-Bench, a controlled, cross-layer reliability benchmark with 8 enterprise domains, 240 tasks, 9 failure scenarios, 2 contract patterns, and 38,880 live task-level records from 9 models by OpenAI, Anthropic, and Gemini.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSchema normalization raises the schema-drift success rate from 0.000 to 0.913, but schema normalization cannot recover from stale evidence, missing tool output, denied permissions, and incorrect session context.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27353v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Agentic retrieval-augmented generation systems can produce answers that appear grounded while failing at the evidence, tool-contract, authorization, o…\u003c/li\u003e\n\u003cli\u003eWe introduce LayerRAG-Bench, a controlled cross-layer reliability benchmark with 8 enterprise domains, 240 tasks, 9 fault scenarios, 2 contract modes, and 38,88…\u003c/li\u003e\n\u003cli\u003eSchema normalization raises schema-drift success from 0.000 to 0.913, but stale evidence, missing tool output, denied permissions, and wrong-session context are…\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/2607.27366\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27366v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking the open-ended humanities and social sciences (HSS), where nuanced quality judgments are more important than objective correctness.\u003c/li\u003e\n\u003cli\u003eThis makes preference alignment a natural paradigm for a wide range of HSS tasks.\u003c/li\u003e\n\u003cli\u003eHowever, existing methods are either costly or not tailored to the broad HSS disciplines.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27366v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended human…\u003c/li\u003e\n\u003cli\u003eThis makes preference alignment a natural paradigm for broad HSS tasks\u003c/li\u003e\n\u003cli\u003eYet existing methods are either costly or not tailored to broad HSS disciplines\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/2607.27379\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eHSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27379v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: High-quality, diverse data is crucial for large language models (LLMs), but remains scarce and costly.\u003c/li\u003e\n\u003cli\u003eData synthesis is a viable alternative and has seen success in closed-ended tasks, but the humanities and social sciences (HSS) have been overlooked, and their open-ended nature makes synthesis challenging.\u003c/li\u003e\n\u003cli\u003eMoving beyond past capability-centered, fragmented attempts, a topic-centered paradigm is adopted, defining the first HSS domain system covering 14 mainstream fields and introducing the first HSS data synthesis pipeline, HSS-Synth.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27379v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eData synthesis is a viable alternative and succeeds on closed tasks, yet the humanities and social sciences (HSS) are overlooked, and their open-ended nature ma…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMoving beyond prior capability-centric, fragmented attempts, we adopt a subject-centric paradigm, define the first HSS domain system covering 14 mainstream fiel…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2607.27384\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSame Facts, Different Diagnosis: Measuring and Mitigating Narrative Anchoring in Clinical Language Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27384v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Large language models used for clinical diagnostic reasoning are sensitive to sociolinguistic register, not just clinical content.\u003c/li\u003e\n\u003cli\u003eWe term this failure mode \u0026ldquo;Narrative Anchoring\u0026rdquo;: identical clinical facts expressed in different registers cause diagnostic outputs to diverge.\u003c/li\u003e\n\u003cli\u003eUnlike prior work on demographic bias, our benchmark isolates register as the sole channel of variation, without any form of demographic markers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27384v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large language models used for clinical diagnostic reasoning are sensitive to sociolinguistic register, not just clinical content\u003c/li\u003e\n\u003cli\u003eWe term this failure mode Narrative Anchoring: identical clinical facts expressed in different registers cause diagnostic outputs to diverge\u003c/li\u003e\n\u003cli\u003eUnlike prior demographic-bias work, which manipulates explicit identity tokens such as race or income, our benchmark isolates register as the sole channel of va…\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/2607.27393\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27393v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implicit targets.\u003c/li\u003e\n\u003cli\u003eWhile hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda or crude harmful content labels.\u003c/li\u003e\n\u003cli\u003eWe introduce AHA-Memes (Arabic Hateful Memes), which, to our knowledge, is the first large-scale Arabic hateful meme benchmark with fine-grained, multi-label annotations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27393v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, c…\u003c/li\u003e\n\u003cli\u003eWhile hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda o…\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 introduce AHA-Memes (Arabic HAteful Memes), which is, to our knowledge, the first large-scale Arabic hateful meme benchmark with fine-grained, multi-label an…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2607.27405\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBenchmarking LLM Competence on Logical Inference over Probability Operators\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - arXiv:2607.27405v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eSummary: The expression of and inference over uncertainty are ubiquitous in natural language. Valid inference on natural language expressions of uncertainty is necessary not only for daily conversation but also for high-stakes domains such as medicine and law.\u003c/li\u003e\n\u003cli\u003eWhile large language models are increasingly evaluated on logical reasoning tasks, it is difficult to separate principled symbolic reasoning from clever surface-level pattern matching.\u003c/li\u003e\n\u003cli\u003eWe introduce a benchmark for reasoning over probability operators—inference on sentences with gradable epistemic modals (e.g., probably, might, must), containing 14,320 programmatically generated English prompts across 15 reasoning templates that systematically vary in question form, negation strategy, and surface content.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27405v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Both expressions of uncertainty and inferences are ubiquitous in natural language, and valid inferences over natural-language expressions of uncertain…\u003c/li\u003e\n\u003cli\u003eWhile large language models are increasingly evaluated on logical reasoning tasks, disentangling principled, symbolic reasoning from clever surface-level patter…\u003c/li\u003e\n\u003cli\u003eWe introduce a benchmark for reasoning over probability operators\u0026ndash;inference over sentences with gradable epistemic modals (e.g., probably, might, must) contain…\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/2607.27421\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSelecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - arXiv:2607.27421v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eSummary: Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployable open-weight language models under computational, latency, and robustness constraints.\u003c/li\u003e\n\u003cli\u003eWe present a systematic zero-shot evaluation of 41 open-weight language models spanning 15 families and the 135M\u0026ndash;9B parameter range across eight English single-label intent classification datasets.\u003c/li\u003e\n\u003cli\u003eA ninth dataset, ATIS, uses five labeled demonstrations and is reported as an auxiliary five-shot result.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27421v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployab…\u003c/li\u003e\n\u003cli\u003eWe present a systematic zero-shot evaluation of 41 open-weight language models spanning 15 families and the 135M\u0026ndash;9B parameter range across eight English single…\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 ninth dataset, ATIS, uses five labeled demonstrations and is reported as an auxiliary five-shot result\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/2607.27251\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRecursive transformers for semiconductor thermo-mechanical reliability\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27251v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulations in engineering design.\u003c/li\u003e\n\u003cli\u003eHowever, for the small, low-dimensional datasets typical in engineering design spaces, where generating large simulation data is costly, traditional transformer architectures are often over-parameterized.\u003c/li\u003e\n\u003cli\u003eUnder these conditions, excessive parameter capacity leads to overfitting rather than improved accuracy, while also incurring unnecessary memory and computational overhead.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27251v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulation in engineering design\u003c/li\u003e\n\u003cli\u003eBut conventional transformer architectures are often over parameterized for the small, low-dimensional datasets typical of engineering design spaces, where larg…\u003c/li\u003e\n\u003cli\u003eUnder these conditions, excess parameter capacity leads to overfitting rather than improved accuracy, while also incurring unnecessary memory and compute overhe…\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/2607.27260\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRegularizing modality contribution drift in multimodal continual learning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27260v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Multimodal Continual Learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving existing knowledge.\u003c/li\u003e\n\u003cli\u003eTo reduce forgetting, current MMCL methods often focus on cross-modal representation alignment or semantic similarity, but they neglect whether the relative contributions of individual modalities and their interactions remain stable across incremental tasks.\u003c/li\u003e\n\u003cli\u003eWe term this decision-level shift as Modality Contribution Drift (MCD) and quantify it with an MCD score, which combines the strength of contribution and changes in relative dependency under controlled interventions on modal subsets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27260v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge\u003c/li\u003e\n\u003cli\u003eTo mitigate forgetting, current MMCL methods usually focus on cross-modal representation alignment or semantic similarity, but they overlook whether the relativ…\u003c/li\u003e\n\u003cli\u003eWe term this decision-level shift Modality Contribution Drift (MCD) and quantify it with the MCD score, which combines contribution-strength and relative-relian…\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/2607.27263\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27263v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation underserved in the most critical areas, such as healthcare, policy evaluation, and climate science.\u003c/li\u003e\n\u003cli\u003eWe introduce \\textbf{DoTime}, an open, scalable, and theoretically grounded generator for multivariate temporal structural causal models (TSCMs) with interventions, released as the \\code{dotime} PyPI package along with four frozen evaluation suites.\u003c/li\u003e\n\u003cli\u003eBeyond existing work, it adds capabilities absent from prior generators: continuous-time intervention \\emph{windows}, counterfactual sampling modes with forward protection, regime-switching SCMs as a strict generalization of interrupted time series, non-stationary dynamics constructed via switching SCM parameters, and deterministic ramp and sinusoidal intervention curves that place trend and structural breaks \\emph{inside} the evaluation window.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27263v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimati…\u003c/li\u003e\n\u003cli\u003eWe introduce \\textbf{DoTime}, an open, scalable, and theoretically grounded generator of multivariate temporal structural causal models (TSCMs) with interventio…\u003c/li\u003e\n\u003cli\u003eBeyond existing work, it adds capabilities absent from prior generators: continuous-time intervention \\emph{windows}, counterfactual sampling modes with a posit…\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/2607.27265\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform\u0026rsquo;s Perspective\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27265v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding on behalf of advertisers, and the Ad Exchange facilitating auctions between them.\u003c/li\u003e\n\u003cli\u003eTraditional auto-bidding algorithms focus solely on the DSP side, maximizing advertiser conversions by adjusting bids against competitors.\u003c/li\u003e\n\u003cli\u003eHowever, current large advertising platforms, such as social media and e-commerce companies, now internally integrate SSP, DSP, and Ad Exchange functionalities.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27265v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side P…\u003c/li\u003e\n\u003cli\u003eTraditional auto-bidding algorithms focus solely on the DSP side, maximizing advertiser conversions by adjusting bids against competitors\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHowever, current big ad platforms, such as social media and e-commerce companies, now integrate SSP, DSP, and Ad Exchange functions internally\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2607.27269\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBeyond KV Reconstruction: Functional Reconstruction for MLA Draft Models in Speculative Decoding\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27269v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Multi-head latent attention (MLA) is increasingly important for long-context LLM inference because compact latent states replace the growing key-value (KV) cache and reduce decoding memory traffic.\u003c/li\u003e\n\u003cli\u003eHowever, most capable open checkpoints use multi-head or grouped-query attention (MHA/GQA), so a conversion is needed to obtain MLA\u0026rsquo;s cache efficiency without retraining from scratch.\u003c/li\u003e\n\u003cli\u003eSpeculative decoding offers complementary acceleration, but its speedup depends on agreement between the draft proposals and target verification.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27269v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Multi-head latent attention (MLA) is increasingly important for long-context LLM inference because compact latent states replace the growing key-value…\u003c/li\u003e\n\u003cli\u003eYet most capable open checkpoints use multi-head or grouped-query attention (MHA/GQA), so conversion is needed to obtain MLA\u0026rsquo;s cache efficiency without retraini…\u003c/li\u003e\n\u003cli\u003eSpeculative decoding offers complementary acceleration, but its speedup depends on agreement between draft proposals and target verification\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/2607.27271\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRLPF: Reinforcement Learning from Performance Feedback for Code Generation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27271v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness.\u003c/li\u003e\n\u003cli\u003eThis leaves an important gap for systems code: two programs can pass the same tests but differ greatly in runtime.\u003c/li\u003e\n\u003cli\u003eWe study how to train code agents to prefer faster correct implementations, rather than treating efficiency only as an evaluation metric.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27271v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness\u003c/li\u003e\n\u003cli\u003eThis leaves an important gap for systems code: two programs can pass the same tests while differing greatly in runtime\u003c/li\u003e\n\u003cli\u003eWe study how to train code agents to prefer faster correct implementations, rather than treating efficiency only as an evaluation metric\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/2607.27273\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSDO: Structure-Aware Data Organization for Efficient LLM Post-Training\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: - arXiv:2607.27273v1 Announce Type: new.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: Post-training of large language models is expensive, and existing efficiency improvements mainly focus on selecting informative samples or designing training schedules.\u003c/li\u003e\n\u003cli\u003eHowever, data organization itself is often treated as a static preprocessing step: embedding-based grouping methods build fixed partitions before training and cannot adapt to the changing sample exposures during optimization.\u003c/li\u003e\n\u003cli\u003eConsequently, despite different optimization needs, all samples receive similar exposure, leading to redundant updates for some samples while others remain under-optimized.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Highlights:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27273v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Post-training of large language models is expensive, and existing efficiency improvements mainly focus on selecting informative samples or designing t…\u003c/li\u003e\n\u003cli\u003eHowever, data organization itself is usually treated as a static preprocessing step: embedding-based grouping methods construct fixed partitions before training…\u003c/li\u003e\n\u003cli\u003eAs a result, all samples receive similar exposure despite their different optimization needs, leading to redundant updates for some samples while leaving others…\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/2607.27274\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27274v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: EEG-based disease diagnosis requires one prediction per subject, but common pipelines segment recordings into short instances, inherit the subject label for each instance, and train an instance-level classifier.\u003c/li\u003e\n\u003cli\u003eThis assumes that all instances provide equally reliable diagnostic evidence.\u003c/li\u003e\n\u003cli\u003eMultiple instance learning (MIL) avoids inheriting labels by treating each subject as a bag.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27274v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject lab…\u003c/li\u003e\n\u003cli\u003eThis assumes that all instances provide equally reliable diagnostic evidence\u003c/li\u003e\n\u003cli\u003eMultiple instance learning (MIL) avoids inherited labels by treating each subject as a bag\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/2607.27275\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFlat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27275v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: It is widely reported that post-training quantization of 4-bit weights is nearly lossless.\u003c/li\u003e\n\u003cli\u003eWe test this claim for multi-turn, tool-calling agents, where it matters most now.\u003c/li\u003e\n\u003cli\u003eOn $\\tau^2$-bench, across two open-weight model families in dense and MoE variants and two domains (eight cells, 456 episodes each, with weights at 16, 8, and 4 bits), quantization does appear to be free on standard metrics.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27275v1 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: Post-training quantization to 4-bit weights is widely reported to be nearly lossless\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe test this claim for multi-turn, tool-calling agents, where it now matters most\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOn $\\tau^2$-bench, across two open-weight model families in dense and MoE variants and two domains (eight cells, 456 episodes each, at 16-, 8-, and 4-bit weight…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2607.27281\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eThe Kinetics of Training: A Driven-Nucleation Rate Law for Emergence, Plasticity Loss, and Circuit Control in Language Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished at: 2026-07-31 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2607.27281v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: A capability appears in a language model when the last parts of its circuit align in one stochastic attempt, and getting all but one right is worthless.\u003c/li\u003e\n\u003cli\u003eWe show this no-partial-credit joint alignment is the rate-limiting step of capability formation.\u003c/li\u003e\n\u003cli\u003eTwo fingerprints: in a shortcut-free apparatus, a five-part circuit missing three waits as long as a three-part circuit missing three (1.19-1.37), so the wait counts the missing parts, not the size; on Pythia, across 7 capabilities and 3 scales, in 32 out of 32 discriminating cells, eliminating one part leaves a median of 17% of the capability, where partial credit would predict 50-83% (p = 2e-10), while random non-partial heads leave 100%.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2607.27281v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: A capability appears in a language model when the last parts of its circuit align in one stochastic attempt, and getting all but one right is worth no…\u003c/li\u003e\n\u003cli\u003eWe show this no-partial-credit joint alignment is the rate-limiting step of capability formation\u003c/li\u003e\n\u003cli\u003eTwo fingerprints: in a shortcut-free apparatus a five-part circuit missing three waits as long as a three-part circuit missing three (1.19-1.37), so the wait co…\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": 7966,
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  "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-deepseek-v4-flash-beta-delivers-major-agent-performance-boost\"\u003eTopic 1: DeepSeek-V4-Flash Beta Delivers Major Agent Performance Boost\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-2-reverse-prompting-and-graph-engineering-transform-ai-collaboration\"\u003eTopic 2: Reverse Prompting and Graph Engineering Transform AI Collaboration\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-3-graph-engineering-transforms-ai-agent-building-at-anthropic\"\u003eTopic 3: Graph Engineering Transforms AI Agent Building at Anthropic\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-4-deepseek-releases-v4-flash-0731-with-rapid-local-quantizations\"\u003eTopic 4: DeepSeek Releases V4-Flash-0731 with Rapid Local Quantizations\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-5-y-combinator-open-sources-qm-for-multiplayer-ai-agents\"\u003eTopic 5: Y Combinator Open-Sources QM for Multiplayer AI Agents\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\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#1-todays-tech-trends-and-product-highlights\"\u003e1. Today\u0026rsquo;s Tech Trends and Product Highlights\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#2-noteworthy-perspectives-and-industry-outlook\"\u003e2. Noteworthy Perspectives and Industry Outlook\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#3-recommended-tools-and-resources\"\u003e3. Recommended Tools and Resources\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \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=\"#y-combinator-podcast-b_introsearch\"\u003eY Combinator Podcast (B_intro+search)\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=\"#openai-blog-a_full\"\u003eOpenAI Blog (A_full)\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#our-long-term-commitment-to-responsible-ai\"\u003eOur long-term commitment to responsible AI.\u003c/a\u003e\n      \u003cul\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
}