{
  "title": "2026-06-11 AI Daily Update | When Top AI Programming Costs More Than Humans, Edge Models Accelerate Taking Over Daily Tasks",
  "url": "https://miaok.ong/en/ai-daily/ai-daily-2026-06-11/",
  "date": "2026-06-11T07:00:00+08:00",
  "lastmod": "2026-06-11T07:00:00+08:00",
  "type": "ai-daily",
  "kind": "page",
  "language": "en",
  "description": "After the release of Anthropic\u0026rsquo;s strongest model, Claude Fable 5, actual tests show that the cost of high-frequency usage has exceeded hiring a human programmer, indicating that the engineering implementation of AI is hitting a cost wall. At the same time, edge-side models (Gemma 4, Qwen, etc.) have achieved breakthroughs in local inference usability, offering rapid response but still exhibiting weak Chinese language capabilities. Industry methodologies are also shifting from \u0026ldquo;atmospheric coding\u0026rdquo; to \u0026ldquo;contract-first,\u0026rdquo; emphasizing testable and auditable engineering standards. Cost inversion and the rise of edge-side solutions are reshaping enterprise deployment strategies.",
  "keywords": null,
  "tags": [],
  "categories": [],
  "author": "Mark (Miao) Kong",
  "image": "https://miaok.ong/images/avatar.jpg",
  "content": "\u003ch1 id=\"2026-06-11-ai-daily--when-top-tier-ai-programming-costs-more-than-a-human-on-device-models-are-accelerating-their-takeover-of-everyday-tasks\"\u003e\n  2026-06-11 AI Daily | When Top-Tier AI Programming Costs More Than a Human, On-Device Models Are Accelerating Their Takeover of Everyday Tasks\n  \u003ca class=\"heading-link\" href=\"#2026-06-11-ai-daily--when-top-tier-ai-programming-costs-more-than-a-human-on-device-models-are-accelerating-their-takeover-of-everyday-tasks\"\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\u003eAfter the release of Anthropic\u0026rsquo;s most powerful model, Claude Fable 5, real-world tests show that the cost of high-frequency use has already surpassed hiring a human programmer, as the engineering implementation of AI hits a cost wall. At the same time, on-device models (Gemma 4, Qwen, etc.) have achieved a usability breakthrough in local inference, offering rapid responses, though their Chinese capabilities remain weak. Industry methodology is also shifting from \u0026ldquo;vibe-driven coding\u0026rdquo; to a \u0026ldquo;contract-first\u0026rdquo; approach, emphasizing testable and auditable engineering standards. This cost inversion and the rise of on-device models are reshaping enterprise deployment strategies.\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\u003e\u003cstrong\u003eIn-depth Guide to This Issue\u0026rsquo;s Watch List\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eToday, there are several threads worth examining together in depth.\u003c/p\u003e\n\u003cp\u003eThe first is about \u003cstrong\u003ethe intersection of enterprise-grade AI and cutting-edge scientific research\u003c/strong\u003e. OpenAI released two pieces of information simultaneously: astrophysicists using Codex to simulate black hole gravity, and the model being offered for direct delivery via Oracle Cloud. This directly addresses a trend—leading-edge models are both pushing the boundaries of fundamental science (like testing general relativity) and accelerating their implementation through mature procurement frameworks. Engineering teams should pay attention to this deployment logic of \u0026ldquo;one model, two trust pathways.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe second is \u003cstrong\u003ethe deep evolution of agent engineering\u003c/strong\u003e. We can see a concentrated discussion on \u0026ldquo;context engineering\u0026rdquo; and \u0026ldquo;deployment-time memorization\u0026rdquo; across multiple papers. \u0026ldquo;Less Context, Better Agents\u0026rdquo; directly confronts the context overflow brought by enterprise-grade tools, while \u0026ldquo;Deployment-Time Memorization\u0026rdquo; defines the privacy-utility boundary for memory design. I highly recommend that AI engineering leads read these in conjunction with the auditable autonomous improvement loop proposed in \u0026ldquo;Regimes.\u0026rdquo; Together, these three point to the core challenges of long-term agent implementation.\u003c/p\u003e\n\u003cp\u003eFinally, regarding \u003cstrong\u003ecognitive security from a geopolitical perspective\u003c/strong\u003e, OpenAI\u0026rsquo;s report on influence operations related to China targeting the US AI debate requires attention. It reveals how technical debates are being deliberately co-opted by external narratives, a warning for all technology decision-makers.\u003c/p\u003e\n\u003ch2 id=\"-ai-hotspots-on-x\"\u003e\n  🌐 AI Hotspots on X\n  \u003ca class=\"heading-link\" href=\"#-ai-hotspots-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-anthropic-releases-claude-fable-5-as-most-capable-public-ai-model\"\u003e\n  Topic 1: Anthropic Releases Claude Fable 5 as Most Capable Public AI Model\n  \u003ca class=\"heading-link\" href=\"#topic-1-anthropic-releases-claude-fable-5-as-most-capable-public-ai-model\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Trending for: 2 days ago, Related posts: 223,000\u003c/li\u003e\n\u003cli\u003eWhat it is: Anthropic has released Claude Fable 5, its most powerful AI model available to the public.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This marks a shift in the focus of AI development from purely performance improvement to safety governance and responsible deployment, reflecting the industry\u0026rsquo;s heightened emphasis on compliance and risk control after significant advancements in model capabilities.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: The community is focused on the balance between the model\u0026rsquo;s high-level reasoning capabilities and its strict safety guardrails. Some users placed bets on its release in prediction markets, and it has also sparked discussions on the differentiation between Anthropic and its competitors in terms of performance and deployment strategies.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-2-anthropic-ceo-calls-for-urgent-ai-policy-overhaul\"\u003e\n  Topic 2: Anthropic CEO Calls for Urgent AI Policy Overhaul\n  \u003ca class=\"heading-link\" href=\"#topic-2-anthropic-ceo-calls-for-urgent-ai-policy-overhaul\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Trending for: 4 hours ago, Related posts: 5,800\u003c/li\u003e\n\u003cli\u003eWhat it is: The CEO of Anthropic has publicly called for an urgent and thorough overhaul of current US AI policy.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This move highlights the deep anxiety of leading AI labs that the existing regulatory framework is severely lagging behind technological iteration. It could compel major global economies to accelerate the tightening of AI governance, thereby reshaping the industry\u0026rsquo;s R\u0026amp;D landscape and safety standards.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: There is significant disagreement on X regarding this call. Supporters believe that preventing catastrophic risks is now urgent, while opponents criticize it as a lobbying effort by Anthropic to solidify its competitive advantage by promoting strict regulations. Both sides are focused on whether \u0026ldquo;safety advocacy has become a commercial tool.\u0026rdquo;\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-3-google-ai-studio-hits-12-million-apps-per-week-milestone\"\u003e\n  Topic 3: Google AI Studio Hits 1.2 Million Apps Per Week Milestone\n  \u003ca class=\"heading-link\" href=\"#topic-3-google-ai-studio-hits-12-million-apps-per-week-milestone\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Trending for: 22 hours ago, Related posts: 342\u003c/li\u003e\n\u003cli\u003eWhat it is: The Google AI Studio platform has reached a milestone of 1.2 million applications built per week.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This figure signifies the accelerating democratization of AI development tools. Low-code and no-code models have significantly lowered the barrier to application creation, reflecting strong market demand for the rapid deployment of AI features.\u003c/li\u003e\n\u003cli\u003eDiscussion overview: The discussion centers on whether the 1.2 million figure includes a large number of one-time, disposable, or debugging projects, and whether this statistic accurately reflects the scale of the active developer community. The conversation also extends to comparisons with competitors like OpenAI\u0026rsquo;s GPTs and the sustainability of this explosive growth within the developer ecosystem.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-4-leaked-system-prompt-reveals-claude-fable-5s-inner-workings\"\u003e\n  Topic 4: Leaked System Prompt Reveals Claude Fable 5\u0026rsquo;s Inner Workings\n  \u003ca class=\"heading-link\" href=\"#topic-4-leaked-system-prompt-reveals-claude-fable-5s-inner-workings\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eOverview: Trending for: 18 hours ago, Related posts: 695\u003c/li\u003e\n\u003cli\u003eWhat it is: The System Prompt for Anthropic\u0026rsquo;s Claude Fable 5 conversational model was leaked, revealing its internal behavioral rules and persona-setting details.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhy it matters\u003c/strong\u003e: The system prompt defines the model\u0026rsquo;s alignment and constraints. Its leak reveals how Anthropic constructs boundaries for safety, personality, and functionality. This helps external parties evaluate its security mechanisms and potential prompt injection risks, providing valuable insights for research on transparency and interpretability.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiscussion overview\u003c/strong\u003e: Discussions on the X platform primarily revolve around the authenticity of the leak, the degree of restraint in the prompt\u0026rsquo;s content, and whether it contains hidden biases or tendencies toward content censorship. Some users worry that such disclosures could be exploited for jailbreak attacks, while others believe the exposure will help standardize alignment practices for open-source models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-5-anthropic-boosts-claude-with-autonomous-agent-tools\"\u003e\n  Topic 5: Anthropic Boosts Claude with Autonomous Agent Tools\n  \u003ca class=\"heading-link\" href=\"#topic-5-anthropic-boosts-claude-with-autonomous-agent-tools\"\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\u003eCategory\u003c/strong\u003e: AI · News\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOverview\u003c/strong\u003e: Trending time: 6 hours ago, Related posts: 884\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhat happened\u003c/strong\u003e: Anthropic demonstrated autonomous agent tools for Claude, enabling it to handle complex, long-running, multi-step tasks such as programming and workflow automation.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhy it matters\u003c/strong\u003e: This marks a shift for AI from conversational assistants to agents capable of autonomous task execution. It could reshape human-computer collaboration in fields like software development and business processes, and intensify competition in model capabilities, extending it from pure text generation to real-world automation.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiscussion overview\u003c/strong\u003e: Discussions on X focus on the practicality and reliability of these agents. Supporters believe such tools will significantly boost development efficiency, evolving it from \u0026ldquo;vibe coding\u0026rdquo; to rigorous operational practices. Skeptics, on the other hand, are concerned about permission control, fault tolerance, and the potential unintended consequences of autonomous agents, fearing risks associated with deployment without sufficient human oversight.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-6-ai-powers-faceless-youtube-channels-to-thousands-in-monthly-earnings\"\u003e\n  Topic 6: AI Powers Faceless YouTube Channels to Thousands in Monthly Earnings\n  \u003ca class=\"heading-link\" href=\"#topic-6-ai-powers-faceless-youtube-channels-to-thousands-in-monthly-earnings\"\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\u003eCategory\u003c/strong\u003e: AI · News\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOverview\u003c/strong\u003e: Trending time: , Related posts: 28\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhat happened\u003c/strong\u003e: AI tools are being widely used to automatically generate content, driving \u0026ldquo;faceless\u0026rdquo; YouTube channels to earn thousands of dollars per month.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhy it matters\u003c/strong\u003e: This highlights AI\u0026rsquo;s potential to lower the barrier to content creation and reshape the creator economy. At the same time, it raises deep industry questions about the proliferation of low-quality content, copyright ambiguity, and the health of the platform ecosystem.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiscussion overview\u003c/strong\u003e: Discussions on X are centered on: the praise and criticism of entrepreneurial opportunities enabled by these tools; whether algorithms will deteriorate due to AI content overload; whether human creators face unfair competition; and the ethical controversies surrounding such channels regarding disclosure of AI use and content originality.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4 id=\"ai-public-opinion-summary-on-x-today\"\u003e\n  AI Public Opinion Summary on X Today\n  \u003ca class=\"heading-link\" href=\"#ai-public-opinion-summary-on-x-today\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h4\u003e\n\u003cp\u003eToday\u0026rsquo;s main narrative revolves around Anthropic\u0026rsquo;s flurry of activities, from the release of its most powerful model, Claude Fable 5, to the system prompt leak, the debut of autonomous agents, and its CEO\u0026rsquo;s call for urgent policy reforms. This reflects a collective shift at the forefront of the industry from a performance race to a focus on safety governance and responsible deployment. The consensus is that AI is rapidly transitioning into a mass-market tool and an autonomous task executor, while the lag in safety guardrails, transparency, and regulatory frameworks has become an unavoidable concern. Disagreements are concentrated on questioning the motives behind Anthropic\u0026rsquo;s safety claims, with many fearing that its push for strict regulation is a way to build commercial barriers under the guise of security. The statistical methods for counting Google AI Studio users and the impact of AI-generated content on the creator economy have also sparked debates where truth is hard to discern. Potential risks include the system prompt leak being exploited for jailbreak attacks, autonomous agents causing unintended consequences without adequate supervision, and the proliferation of low-quality AI content leading to platform ecosystem degradation and unfair competition for human creators.\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\u003ch1 id=\"ai-daily-24-hour-hotspot-analysis\"\u003e\n  AI Daily: 24-Hour Hotspot Analysis\n  \u003ca class=\"heading-link\" href=\"#ai-daily-24-hour-hotspot-analysis\"\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\u003ch2 id=\"i-key-tech-trends-and-product-hotspots\"\u003e\n  I. Key Tech Trends and Product Hotspots\n  \u003ca class=\"heading-link\" href=\"#i-key-tech-trends-and-product-hotspots\"\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=\"1-claude-fable-5-release-the-most-powerful-general-model-sparks-heated-discussion\"\u003e\n  1. \u003cstrong\u003eClaude Fable 5 Release: The Most Powerful General Model Sparks Heated Discussion\u003c/strong\u003e\n  \u003ca class=\"heading-link\" href=\"#1-claude-fable-5-release-the-most-powerful-general-model-sparks-heated-discussion\"\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\u003eAnthropic\u0026rsquo;s release of \u003cstrong\u003eClaude Fable 5\u003c/strong\u003e has become the absolute focus. It is the first \u0026ldquo;Mythos-class\u0026rdquo; model available to the general public.\u003c/p\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\n      \u003ctr\u003e\n          \u003cth style=\"text-align: left\"\u003eDimension\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eKey Information\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\u003ePricing\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e$10/million input tokens, $50/million output tokens (60% cheaper than Mythos Preview)\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eSafety Mechanism\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eAutomatically downgrades to Opus 4.8 when the classifier is triggered; \u0026gt;95% of conversations do not trigger it\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eData Policy\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eTraffic data is mandatorily retained for 30 days (a major policy change)\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eLimited-Time Free Access\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eFree for subscribers from June 10-22\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eHands-on Feedback\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e@zhixianio: \u003cem\u003e\u0026ldquo;After 40 minutes, not only did it finish the job, but it also pointed out flaws in my original design and implemented a better solution on its own.\u0026rdquo;\u003c/em\u003e — Amazingly efficient\u003c/li\u003e\n\u003cli\u003e@dotey: \u003cem\u003e\u0026ldquo;Fable 5 consumes tokens incredibly fast. The $200 plan I just upgraded to is not nearly enough.\u0026rdquo;\u003c/em\u003e — Cost-sensitive\u003c/li\u003e\n\u003cli\u003e@Pluvio9yte: \u003cem\u003e\u0026ldquo;The list price is twice that of Opus, but the actual consumption isn\u0026rsquo;t double.\u0026rdquo;\u003c/em\u003e Recommends setting the strength to \u003ccode\u003e/effort max\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e@dotey\u0026rsquo;s comparative test conclusion: \u003cstrong\u003eFor UI/UX design, Claude 4.8 is good enough; Fable 5 does not show a significant advantage.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"2-on-device-models-are-accelerating-implementation\"\u003e\n  2. \u003cstrong\u003eOn-device Models Are Accelerating Implementation\u003c/strong\u003e\n  \u003ca class=\"heading-link\" href=\"#2-on-device-models-are-accelerating-implementation\"\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\u003e@zhixianio continues to delve deep into on-device scenarios, verifying feasibility through multiple threads:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eGemma 4 Series\u003c/strong\u003e: The 12B multimodal model on an M5Max 128G has \u0026ldquo;perfectly OK English accuracy and is very fast,\u0026rdquo; but its Chinese output is \u0026ldquo;completely nonsensical.\u0026rdquo;\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eQwen3.6-35B-A3B\u003c/strong\u003e: Running locally with oMLX, the \u0026ldquo;response speed is faster than remote LLMs, and its intelligence is on point.\u0026rdquo;\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eQAT (Quantization Aware Training)\u003c/strong\u003e: A new approach from Google, where the model \u0026ldquo;assumes during training that it will inevitably be quantized.\u0026rdquo;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExpanding Application Scenarios\u003c/strong\u003e: From code assistants (OpenClaw/PI-Mono) to life assistants (\u0026ldquo;defrosting a rice ball 🍙\u0026rdquo;), on-device models are penetrating daily life.\u003c/p\u003e\n\u003ch3 id=\"3-ai-agent-browsers-the-leap-from-tool-to-gateway\"\u003e\n  3. \u003cstrong\u003eAI Agent Browsers: The Leap from Tool to Gateway\u003c/strong\u003e\n  \u003ca class=\"heading-link\" href=\"#3-ai-agent-browsers-the-leap-from-tool-to-gateway\"\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\u003e@vista8 highly recommends the \u003cstrong\u003eAye Browser\u003c/strong\u003e as a representative of this new form:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBased on Chromium, it fully simulates human operations with AI (not a CLI/plugin, bypassing account detection).\u003c/li\u003e\n\u003cli\u003eBuilt-in Skill recording and scheduled execution: automatically block spam replies on X, reply to Xiaohongshu comments, and transcribe articles to multiple platforms.\u003c/li\u003e\n\u003cli\u003eIntegrates an RSS reader, ad blocker, and video translation/download.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eProduct Suggestions\u003c/strong\u003e: Needs to support Chrome account migration, a plugin ecosystem, and a clear payment plan.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2 id=\"ii-unique-perspectives--industry-foresight\"\u003e\n  II. Unique Perspectives \u0026amp; Industry Foresight\n  \u003ca class=\"heading-link\" href=\"#ii-unique-perspectives--industry-foresight\"\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=\"1-\"\u003e\n  1. \u003cstrong\u003e\u0026ldquo;Contract First\u0026rdquo; — The Evolution of Vibe Coding\u003c/strong\u003e\n  \u003ca class=\"heading-link\" href=\"#1-\"\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\u003e@Pluvio9yte proposes a key methodological shift:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The best practice for Vibe Coding is not Requirement First or Code First, but \u003cstrong\u003eContract First\u003c/strong\u003e. Without a well-defined contract, everything else is just empty talk.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eA development framework based on a customized OpenSpec externalizes \u0026ldquo;easily drifting context into contracts,\u0026rdquo; giving both humans and AI a stable reference point. This marks the evolution of AI programming from \u0026ldquo;savage growth\u0026rdquo; to engineered standards.\u003c/p\u003e\n\u003ch3 id=\"2-wechat-ai\"\u003e\n  2. \u003cstrong\u003eWeChat AI\u0026rsquo;s Strategic Predicament: The Innovator\u0026rsquo;s Dilemma\u003c/strong\u003e\n  \u003ca class=\"heading-link\" href=\"#2-wechat-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/h3\u003e\n\u003cp\u003e@dotey sharply points out:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;WeChat always thinks of itself as an OS, but it\u0026rsquo;s just a behemoth living parasitically on the phone\u0026rsquo;s operating system\u0026hellip; In the future, WeChat\u0026rsquo;s role as a gateway will diminish. The younger generation won\u0026rsquo;t open WeChat; they\u0026rsquo;ll just ask their Agent.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThe core conflict: WeChat\u0026rsquo;s RPV (Resource-Process-Value) is locked in by its existing ecosystem, making it difficult to create a truly AI-native independent product.\u003c/p\u003e\n\u003ch3 id=\"3-ai-cost-restructuring-from\"\u003e\n  3. \u003cstrong\u003eAI Cost Restructuring: From \u0026ldquo;Cheaper Than Humans\u0026rdquo; to \u0026ldquo;More Expensive Than Humans\u0026rdquo;\u003c/strong\u003e\n  \u003ca class=\"heading-link\" href=\"#3-ai-cost-restructuring-from\"\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\u003e@ruanyf cites data from the founder of OpenClaw: a monthly consumption of 603 billion tokens, valued at $1.3 million (at commercial pricing). Even when switching to domestic open-source models (at 1/30th to 1/50th the price), the annual cost still reaches 2-3 million RMB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Unlimited use of top-tier AI for programming is far more expensive than human programmers. Cost optimization will become a core competency in AI engineering.\u003c/p\u003e\n\u003ch3 id=\"4-\"\u003e\n  4. \u003cstrong\u003e\u0026ldquo;Shadow Book\u0026rdquo; Reading Method: A Cognitive Upgrade for the AI Era\u003c/strong\u003e\n  \u003ca class=\"heading-link\" href=\"#4-\"\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\u003e@lijigang proposes an AI-native reading paradigm:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;In the age of print, we could only read the one book the author wrote. In the AI era, we can read the \u0026lsquo;shadow books\u0026rsquo;—upon encountering any assertion, immediately use AI to analyze its three opposing schools of thought, its overlooked premises, its intellectual lineage, the boundaries of its reasoning\u0026hellip;\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eReading transforms from \u0026ldquo;unidirectional reception\u0026rdquo; to \u0026ldquo;multidimensional inquiry,\u0026rdquo; with AI becoming a cognitive amplifier, not a replacement.\u003c/p\u003e\n\u003ch3 id=\"5-testing-is-the-moat-the-code-moat-has-collapsed\"\u003e\n  5. \u003cstrong\u003eTesting is the Moat: The Code Moat Has Collapsed\u003c/strong\u003e\n  \u003ca class=\"heading-link\" href=\"#5-testing-is-the-moat-the-code-moat-has-collapsed\"\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\u003e@ruanyf cites the case of a Cloudflare engineer who rewrote Next.js using AI for only $1100 in token fees.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The key to preventing replication is the test cases.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eWhen AI can replicate large-scale software at a low cost, the \u003cstrong\u003etesting system\u003c/strong\u003e becomes the core barrier distinguishing \u0026ldquo;usable\u0026rdquo; from \u0026ldquo;reliable.\u0026rdquo;\u003c/p\u003e\n\u003chr\u003e\n\u003ch2 id=\"iii-recommended-tools--resources\"\u003e\n  III. Recommended Tools \u0026amp; Resources\n  \u003ca class=\"heading-link\" href=\"#iii-recommended-tools--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/h2\u003e\n\u003ch3 id=\"-development-tools\"\u003e\n  🔧 Development Tools\n  \u003ca class=\"heading-link\" href=\"#-development-tools\"\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\u003ctable\u003e\n  \u003cthead\u003e\n      \u003ctr\u003e\n          \u003cth style=\"text-align: left\"\u003eTool\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003ePurpose\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eSource\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\u003eFable\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eAI design/development Agent, \u0026ldquo;Shut up and take my money\u0026rdquo; level of efficiency\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\u003cstrong\u003eoMLX\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eNative MLX inference framework for macOS, supports MTP, multimodal\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@zhixianio via @jundotkim\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eOwlia Nest\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eOn-device model file browser, works with Tailscale for intranet access\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\u003cstrong\u003eAye Browser\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eDedicated AI Agent browser for automating web operations\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\u003cstrong\u003eQiaoMu Teleprompter\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eOpen-source teleprompter for streaming, developed in 5 hours with Codex\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\u003cstrong\u003ePerculia\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eQuick-switch tool for Mac Bluetooth devices (Free)\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\u003cstrong\u003eBartender 6\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eMac menu bar organizer ($20 one-time purchase)\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\u003eMaccy\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eOpen-source clipboard manager\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\u003eScreen Studio\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eTop-tier screen recording tool (note the price on Xianyu)\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@Pluvio9yte\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3 id=\"-skill-packs-skills\"\u003e\n  📚 Skill Packs (Skills)\n  \u003ca class=\"heading-link\" href=\"#-skill-packs-skills\"\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\u003ctable\u003e\n  \u003cthead\u003e\n      \u003ctr\u003e\n          \u003cth style=\"text-align: left\"\u003eSkill\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eFunction\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eInstallation Instructions\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\u003ebaoyu-design\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eClaude Design enhancement that supports importing Design Systems\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003ccode\u003enpx skills add JimLiu/baoyu-design\u003c/code\u003e\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eqiaomu-book-script\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eGenerates broadcast scripts for book interpretations (multi-subagent collaboration)\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003ccode\u003enpx skills add joeseesun/qiaomu-book-script\u003c/code\u003e\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eProduct Manager Skill Pack\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e13k stars in 5 days, covers daily PM workflows\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@vista8 comments section\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eChinese Creators Skills Collection\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eWriting, editing, reducing AI-like tone, creating illustrations, covers, and Xiaohongshu cards\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@wsl8297 (recommended by @AI_Jasonyu)\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3 id=\"-learning-resources\"\u003e\n  🎓 Learning Resources\n  \u003ca class=\"heading-link\" href=\"#-learning-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\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u0026ldquo;Cognition County\u0026rdquo; E5\u003c/strong\u003e: @zhixianio podcast, hosted by Owlia (on-device TTS), officially discussing on-device models\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u0026ldquo;A Thousand Brains\u0026rdquo;\u003c/strong\u003e: Recommended by @lijigang, inspiration from the \u0026ldquo;Reference Frames\u0026rdquo; theory for AI cognitive architecture\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u0026ldquo;The Inevitable,\u0026rdquo; Chapter 2 \u0026ldquo;Cognifying\u0026rdquo;\u003c/strong\u003e: Kevin Kelly, a classic on cognitive upgrading in the AI era\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"-infrastructure\"\u003e\n  💡 Infrastructure\n  \u003ca class=\"heading-link\" href=\"#-infrastructure\"\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\u003ctable\u003e\n  \u003cthead\u003e\n      \u003ctr\u003e\n          \u003cth style=\"text-align: left\"\u003eService\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eFeatures\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eSource\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\u003eOfoxAI\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eOfficial direct connection, 15% off promotion (gpt-image-2/GPT-5.5/o3)\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\u003cstrong\u003eGiffgaff\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eZero monthly fee, permanent overseas mobile number (\u0026ldquo;hardcore\u0026rdquo; choice)\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\u003cstrong\u003eVercel\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u0026ldquo;Fastest way to launch a website,\u0026rdquo; deploy in minutes with Codex + plugins\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@Pluvio9yte\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003chr\u003e\n\u003ch2 id=\"iv-one-sentence-insight\"\u003e\n  IV. One-Sentence Insight\n  \u003ca class=\"heading-link\" href=\"#iv-one-sentence-insight\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The world no longer rewards those who only do things themselves.\u0026rdquo;\u003c/em\u003e — @Pluvio9yte\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThe AI industry is moving from a \u0026ldquo;model race\u0026rdquo; into the deep waters of \u0026ldquo;engineering implementation\u0026rdquo; and \u0026ldquo;cost optimization,\u0026rdquo; with on-device deployment, agentification, and contract-based interaction becoming three definite trends.\u003c/p\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; covers 22 sources; 39 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/The-CEO-Must-Be-the-Chief-AI-Officer-e3kju57\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003e\u0026ldquo;The CEO Must Be the Chief AI Officer\u0026rdquo;\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-10 23:27 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - You\u0026rsquo;ve probably heard of OpenClaw (formerly Clawdbot/Moltbot).\n\u003cul\u003e\n\u003cli\u003eThe open-source AI assistant that\u0026rsquo;s causing a stir runs on your own device, connects with the messaging apps you already use, and goes beyond chat to actually do things like manage your email, calendar, files, workflows, and more.\u003c/li\u003e\n\u003cli\u003eNow, meet the man behind it.\u003c/li\u003e\n\u003cli\u003eYC\u0026rsquo;s Raphael Schaad sits down with OpenClaw founder Peter Steinberger to talk about 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\u003eBrex co-founder and CEO Pedro Franceschi believes most people still underestimate how much AI will change the way companies are built\u003c/li\u003e\n\u003cli\u003eAI isn\u0026rsquo;t just another tool, it\u0026rsquo;s a new foundation for building products, teams, and companies.In this episode of Lightcone, Pedro shares why he thinks we\u0026rsquo;re onl…\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\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://allinchamathjason.libsyn.com/senators-john-fetterman-and-dave-mccormick-bipartisanship-money-in-dc-datacenters-graham-platner\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSenators John Fetterman and Dave McCormick: Bipartisanship, Money in DC, Datacenters, Graham Platner\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 02:05 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - EY - EY helps private equity firms turn market insights into action, navigate complexity and carve new paths to growth and long-term value.\n\u003cul\u003e\n\u003cli\u003eNYSE - Thanks to our partners at the New York Stock Exchange - a modern marketplace and exchange dedicated to building the future.\u003c/li\u003e\n\u003cli\u003ePlaud, our official wearable AI note-taking partner at the All-In Liquidity Summit, captured every insight.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSenators John Fetterman and Dave McCormick: Bipartisanship, D.C. Money, Data Centers, with Graham Plataner.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003e(0:00) PA Senators Fetterman and McCormick join the Besties\u003c/li\u003e\n\u003cli\u003e(0:33) Bipartisanship in 2026, rejecting extremism\u003c/li\u003e\n\u003cli\u003e(6:37) All-time unpopularity in the Senate, the filibuster question, tribalism\u003c/li\u003e\n\u003cli\u003e(13:33) Fixing wealth concentration in the US\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://allinchamathjason.libsyn.com/dan-dreyfus-americas-critical-minerals-crisis-is-here\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDan Dreyfus: America\u0026rsquo;s Critical Minerals Crisis is Here\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-10 11:04 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - EY - Liquidity, growth, and what\u0026rsquo;s next for organizations are the focus of the summit.\n\u003cul\u003e\n\u003cli\u003eEY helps transform liquidity challenges into sustainable value.\u003c/li\u003e\n\u003cli\u003eNYSE - Thanks to our partner, the New York Stock Exchange - a modern marketplace and exchange dedicated to building the future.\u003c/li\u003e\n\u003cli\u003ePlaud, our official wearable AI note-taking partner at the All-In Liquidity Summit, captured every insight.\u003c/li\u003e\n\u003cli\u003eDan Dreyfus: America\u0026rsquo;s critical minerals crisis is here.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003e(0:00) Dan Dreyfus Presents: The Future of Critical Minerals\u003c/li\u003e\n\u003cli\u003e(0:33) America\u0026rsquo;s \u0026ldquo;Capital Light Era\u0026rdquo; is over, rapid supply/demand shocks\u003c/li\u003e\n\u003cli\u003e(5:40) Impact of China cutting off the US from critical minerals\u003c/li\u003e\n\u003cli\u003e(8:18) Copper\u0026rsquo;s Rise: The next 18 years need as much as the last 10,000\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"stratechery-by-ben-thompson-a_full\"\u003e\n  Stratechery by Ben Thompson (A_full)\n  \u003ca class=\"heading-link\" href=\"#stratechery-by-ben-thompson-a_full\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://stratechery.com/2026/fable-5-anthropic-alignment-ai-tiers/\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFable 5, Anthropic Alignment, AI Tiers\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-10 18:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - Fable 5 is the public version of Mythos, and while it is very capable, it sets some troubling new precedents.\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e$15\u003c/strong\u003e/month* or *\u003cstrong\u003e$150\u003c/strong\u003e/year.\u003c/li\u003e\n\u003cli\u003eSubstantive analysis of the day\u0026rsquo;s news via three weekly emails or podcasts.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eStrategy Interviews\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eInterviews with leading public company CEOs, private company founders, and discussions with fellow analysts.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eFable 5 is the public version of Mythos, and while it is very capable it sets some troubling new precedents.\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/using-codex-to-simulate-black-holes\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eHow an astrophysicist uses Codex to help simulate black holes\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 08:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - The gravity around a black hole is so immense that once close enough, nothing—not even light—can escape.\n\u003cul\u003e\n\u003cli\u003eAstrophysicists like Chi-kwan Chan study black holes through computer simulations and observations.\u003c/li\u003e\n\u003cli\u003eBut current algorithms and computational power limit how realistic these simulations can be.\u003c/li\u003e\n\u003cli\u003eChan, a researcher at the University of Arizona and Steward Observatory, is addressing this problem with Codex.\u003c/li\u003e\n\u003cli\u003eHe says black holes are one of the best places to test Einstein\u0026rsquo;s theory of general relativity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eDiscover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein’s theory of gen…\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/openai-on-oracle-cloud\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAccess OpenAI models and Codex through your Oracle cloud commitment\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 04:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - Leverage existing Oracle cloud commitments to give teams access to OpenAI\u0026rsquo;s most advanced models and Codex without creating new purchasing paths.\n\u003cul\u003e\n\u003cli\u003eEnterprises often want to deploy AI through the procurement processes and governance frameworks they already trust.\u003c/li\u003e\n\u003cli\u003eTo help achieve this, OpenAI and Oracle are collaborating to make it easier for Oracle Cloud Infrastructure (OCI) customers to access OpenAI\u0026rsquo;s cutting-edge models and Codex.\u003c/li\u003e\n\u003cli\u003eIn the coming weeks, Oracle customers will be able to apply eligible Oracle Customer Hub (UCM) credits to OpenAI models and Codex through OCI.\u003c/li\u003e\n\u003cli\u003eThis provides customers with a way to access OpenAI models under their existing procurement workflows and cloud commitments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eAccess OpenAI models and Codex through Oracle Cloud, using existing commitments to build and deploy AI with enterprise security and governance.\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/prc-linked-influence-operations-ai-debates\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePRC-linked influence operations are targeting AI debates in the US\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 20:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - A new report from OpenAI details PRC-linked influence operations using AI to target the United States.\n\u003cul\u003e\n\u003cli\u003eThe operations focus on technical debates about ChatGPT, data center narratives, tariffs, and false claims.\u003c/li\u003e\n\u003cli\u003eA new report from OpenAI details PRC-linked influence operations using AI to target U.S. tech debates, data center narratives, tariffs, and false claims about ChatGPT.\u003c/li\u003e\n\u003cli\u003ePRC-linked influence operations are targeting AI debates in the U.S.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eA new report from OpenAI details PRC-linked influence operations using AI to target U.S\u003c/li\u003e\n\u003cli\u003etech debates, data center narratives, tariffs, and false claims about ChatGPT.\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/lseg\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFrom data to decisions: how LSEG is scaling trusted AI\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 08:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - Learn how LSEG is using OpenAI to scale trusted AI across its global business, accelerating insights, shortening release cycles, and empowering 4,000 employees.\n\u003cul\u003e\n\u003cli\u003eThis article from the OpenAI blog explains how \u0026ldquo;From data to decisions: how LSEG is scaling trusted AI\u0026rdquo; is shaping the broader AI and infrastructure landscape.\u003c/li\u003e\n\u003cli\u003eIt also reveals the practical implications of \u0026ldquo;From data to decisions: how the London Stock Exchange Group is scaling trusted AI\u0026rdquo; for founders, operators, and investors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eSee how LSEG uses OpenAI to scale trusted AI across its global business, accelerating insights, shrinking release cycles, and empowering 4,000 employees.\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=\"google-deepmind-blog-a_full\"\u003e\n  Google DeepMind Blog (A_full)\n  \u003ca class=\"heading-link\" href=\"#google-deepmind-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\u003cstrong\u003e\u003ca href=\"https://deepmind.google/blog/diffusiongemma-4x-faster-text-generation/\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDiffusionGemma: 4x faster text generation\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 00:24 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - DiffusionGemma: 4x faster text generation.\n\u003cul\u003e\n\u003cli\u003eThis article from the Google DeepMind blog explains how \u0026ldquo;DiffusionGemma: 4x faster text generation\u0026rdquo; is shaping the broader AI and infrastructure landscape.\u003c/li\u003e\n\u003cli\u003eIt also reveals the practical implications of \u0026ldquo;DiffusionGemma: 4x faster text generation\u0026rdquo; for founders, operators, and investors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eDiffusionGemma: 4x faster text generation\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/2606.10044\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBusiness World Model\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10044v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Businesses are increasingly adopting artificial intelligence tools to improve productivity, reduce costs, and enhance products and services.\u003c/li\u003e\n\u003cli\u003eHowever, the transformative potential of AI is not just limited to automating predefined tasks: it also lies in enabling intelligent systems to plan, optimize, and execute business plans based on high-level strategic goals.\u003c/li\u003e\n\u003cli\u003eThis paper introduces the concept and architecture of the Business World Model (BWM), a world model specialized for business and organizational environments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10044v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Businesses are increasingly adopting AI-enabled tools to improve productivity, reduce costs, and enhance products and services\u003c/li\u003e\n\u003cli\u003eHowever, the transformative potential of AI extends beyond automating predefined tasks: it lies in enabling intelligent systems to plan, optimize, and execute b…\u003c/li\u003e\n\u003cli\u003eThis paper introduces the concept and architecture of a business world model (BWM), a world model specialized for business and organizational environments\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/2606.10062\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDeployment-Time Memorization in Foundation-Model Agents\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10062v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Foundation model agents are increasingly long-lived systems that can remember users during interactions, making memory an explicit deployment-time function, rather than just a property of model weights.\u003c/li\u003e\n\u003cli\u003eExisting work addresses parametric memory or audits fixed memory configurations, but does not describe how memory design choices jointly shape personalization utility, extraction risks, and deletion fidelity.\u003c/li\u003e\n\u003cli\u003eWe study this surface as deployment-time memorization, formulating agent memory as a privacy-utility frontier measured by Personalization Recall (PR) and Adversarial Extraction Rate (AER), and sweep across three memory design knobs: summarization aggressiveness, retrieval breadth (k), and deletion mode.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10062v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Foundation-model agents are increasingly long-lived systems that remember users across interactions, making memorization an explicit deployment-time f…\u003c/li\u003e\n\u003cli\u003eExisting work addresses parametric memorization or audits fixed memory configurations, but does not characterize how memory-design choices jointly shape persona…\u003c/li\u003e\n\u003cli\u003eWe study this surface as deployment-time memorization, formulating agent memory as a privacy-utility frontier measured by Personalization Recall (PR) and Advers…\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/2606.10086\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eExploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10086v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: This paper proposes a theory of exploratory adaptation under AI-assisted optimization.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe central argument is that the long-term adaptive effects of artificial intelligence systems critically depend on how predictive assistance interacts with the exploratory response itself.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe use a dynamic framework to formalize this mechanism, in which cognitive, institutional, and technological systems evolve over a rugged cognitive landscape characterized by multiple local reinforcement configurations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Highlights:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10086v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: This paper develops a theory of exploratory adaptation under AI-assisted optimization\u003c/li\u003e\n\u003cli\u003eThe central argument is that the long-run adaptive effects of AI systems depend critically on how predictive assistance interacts with exploratory responsivenes…\u003c/li\u003e\n\u003cli\u003eWe formalize this mechanism using a dynamical framework in which cognitive, institutional, and technological systems evolve over rugged epistemic landscapes cha…\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/2606.10094\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePredictive Assistance and the Temporal Dynamics of Exploratory Compression\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10094v1 Announce Type: new.\u003c/li\u003e\n\u003cli\u003eAbstract: Classical cognitive theories describe problem-solving as an exploratory search through a structured problem space, where repeated interactions gradually compress the search into an effective representational structure.\u003c/li\u003e\n\u003cli\u003ePredictive artificial intelligence systems introduce a unique mechanism where stability may occur before exploratory diversification unfolds, providing solutions and decision trajectories before an internal search is generated.\u003c/li\u003e\n\u003cli\u003eThis paper develops a geometric dynamic framework in which attention evolves over a landscape of strategies shaped by stabilizing drift, endogenous exploratory perturbations, and responsive gated learning.\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10094v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Classical theories of cognition describe problem solving as exploratory search through structured problem spaces in which repeated interaction gradual…\u003c/li\u003e\n\u003cli\u003ePredictive artificial intelligence systems introduce a distinct regime in which stabilization may occur before exploratory diversification unfolds, supplying so…\u003c/li\u003e\n\u003cli\u003eThis paper develops a geometric dynamical framework in which attention evolves over a landscape of strategies shaped by stabilizing drift, endogenous explorator…\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/2606.10147\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFrom Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10147v1 Announce Type: new.\u003c/li\u003e\n\u003cli\u003eAbstract: Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to form an answer?\u003c/li\u003e\n\u003cli\u003eAlthough audio and visual tokens play an increasingly important role in research and real-world applications, little is known about the internal pathways through which they affect final predictions.\u003c/li\u003e\n\u003cli\u003eIn this study, we examine the audiovisual information flow within Audiovisual Large Language Models (AVLLMs), tracking how they route, utilize, and integrate audio and video information across two input configurations: audiovisual videos and multiple interleaved audiovisual items.\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10147v1 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: Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answe…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDespite their growing role in research and real-world applications, the internal pathways through which audio and visual tokens influence the final prediction r…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn this study, we examine audio-visual information flow inside Audio-Visual Large Language Models (AVLLMs), tracing how AVLLMs route, utilize, and integrate aud…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.10209\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLess Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10209v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Large language models deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can lead to context overflow, stale state errors, and high inference costs.\u003c/li\u003e\n\u003cli\u003eWe study this problem in automated expense itemization in Microsoft Dynamics 365 Finance and Operations using Model Context Protocol tools.\u003c/li\u003e\n\u003cli\u003eWe evaluate four GPT-5 configurations on a 50-task hotel expense benchmark: no user model, full conversation history, context pruned to the last 5 tool call/response pairs, and pruning using automatic summarization.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10209v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large language models deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can…\u003c/li\u003e\n\u003cli\u003eWe study this problem in automated expense itemization in Microsoft Dynamics 365 Finance and Operations using Model Context Protocol tools\u003c/li\u003e\n\u003cli\u003eWe evaluate four GPT-5 configurations on a 50-task hotel expense benchmark: no user model, full conversation history, context pruned to the last 5 tool call/res…\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/2606.10237\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eMinimalist Genetic Programming\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10237v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Genetic programming (GP) is based on two important insights.\u003c/li\u003e\n\u003cli\u003eFirst, any learning task can be fundamentally viewed as a program induction problem, with the goal of constructing a symbolic hierarchical model represented as a syntax tree.\u003c/li\u003e\n\u003cli\u003eSecond, this task is treated as a search problem, and evolution is used to locate the desired model.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10237v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Genetic programming (GP) is based on two important insights\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFirst, that any learning task can fundamentally be posed as a program induction problem, where the goal is to construct a symbolic hierarchical model that is ex…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSecond, to pose this task as a search problem, and use evolution to locate the desired model\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.10241\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRegimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10241v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unrecorded, diagnostics cannot be replayed, and upgrade or abandonment decisions are stored in auxiliary databases rather than in the agent\u0026rsquo;s own history.\u003c/li\u003e\n\u003cli\u003eWe show that an event-sourced agent runtime eliminates this friction and transforms controlled improvement into a first-class workflow.\u003c/li\u003e\n\u003cli\u003eWhen the agent\u0026rsquo;s state is a deterministic projection of an append-only event log, failures are recorded, a run can be replayed accurately from its log, candidate patches are scoped to typed pipeline seams, gates are auditable, and every upgrade or discard is itself an event.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10241v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlo…\u003c/li\u003e\n\u003cli\u003eWe show that an event-sourced agent runtime removes that friction and turns controlled improvement into a first-class workflow\u003c/li\u003e\n\u003cli\u003eWhen the agent\u0026rsquo;s state is a deterministic projection of an append-only event log, failures are recorded, a run replays exactly from its log, candidate patches s…\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/2606.10254\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRealMath-Eval: Why SOTA Judges Struggle with Real Human Reasoning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10254v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: While Large Language Models (LLMs) have achieved near-perfect performance in solving high school mathematics, their ability to evaluate the diverse reasoning processes of real human students remains underexamined.\u003c/li\u003e\n\u003cli\u003eTo bridge this gap, we introduce \\textbf{RealMath-Eval}, a rigorously annotated benchmark containing 224 real exam answers from high schools.\u003c/li\u003e\n\u003cli\u003eOur preliminary evaluation shows that even the most advanced LLM judges encounter significant difficulties with this task, exhibiting a high mean squared error ($\\sim$2.96) compared to expert human scoring.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10254v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: While Large Language Models (LLMs) have achieved near-perfect performance in \\emph{solving} high-school mathematics, their ability to \\emph{evaluate}…\u003c/li\u003e\n\u003cli\u003eTo bridge this gap, we introduce \\textbf{RealMath-Eval}, a rigorously annotated benchmark of 224 real-world exam responses from high schools\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOur initial evaluation reveals that even state-of-the-art LLM judges struggle significantly on this task, exhibiting a high Mean Squared Error ($\\sim$2.96) agai…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.10279\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSupervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10279v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: It is widely believed that supervised fine-tuning with synthetic rationale data improves the performance of language models on clinical prediction tasks by teaching the model not only what to predict, but also why.\u003c/li\u003e\n\u003cli\u003eWe tested this hypothesis on five-year Alzheimer\u0026rsquo;s disease and related dementias (ADRD) prediction from longitudinal health histories.\u003c/li\u003e\n\u003cli\u003eIn a large-scale controlled experiment across 504 configurations, we find that rationale-based SFT consistently and substantially hurts prediction performance relative to label-only fine-tuning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10279v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching…\u003c/li\u003e\n\u003cli\u003eWe test this assumption on five-year Alzheimer\u0026rsquo;s disease and related dementias (ADRD) prediction from longitudinal health histories\u003c/li\u003e\n\u003cli\u003eAcross a large-scale controlled experiment of 504 configurations, we find that rationale-based SFT consistently and substantially hurts prediction performance r…\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/2606.09830\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAutomated Scoring of Arabic Text Using Large Language Models: A Literature Review\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09830v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: In modern educational systems, Automatic Text Scoring (ATS) plays a central role, enabling scalable and consistent evaluation of learner responses without human intervention.\u003c/li\u003e\n\u003cli\u003eRecently, the increased accessibility of LLMs and Arabic-specific datasets has sparked renewed interest in this area.\u003c/li\u003e\n\u003cli\u003eIn this work, we investigate LLM-Based approaches for the automated evaluation of Arabic texts, focusing on both short answer grading (ASAG) and essay scoring (AES).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09830v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: In modern educational systems, Automatic Text Scoring (ATS) plays a central role by enabling scalable and consistent evaluation of learner responses w…\u003c/li\u003e\n\u003cli\u003eRecently, the increased accessibility of LLMs and Arabic-specific datasets has sparked renewed interest in this area\u003c/li\u003e\n\u003cli\u003eIn this work, we investigate LLM-Based approaches for the automated evaluation of Arabic texts, focusing on both short answer grading (ASAG) and essay scoring (…\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/2606.09854\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCan Multi-Agent LLMs Identify Their Peers? Stylometric Fingerprinting in Role-Constrained Political Analysis\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09854v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Multi-agent large language model (LLM) pipelines for political statement analysis are vulnerable to peer-preservation bias: models tend to protect peer models from deactivation and show identity-related scoring distortions.\u003c/li\u003e\n\u003cli\u003ePrompt-level anonymization was proposed as a mitigation measure, but prior work simultaneously documented that stylometric fingerprints can survive anonymization in role-constrained output\u0026ndash;raising the question of whether this mitigation is sufficient.\u003c/li\u003e\n\u003cli\u003eThis paper provides the first systematic investigation of whether LLMs can identify the model family behind political analysis texts under anonymized conditions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09854v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Multi-agent large language model (LLM) pipelines for political statement analysis are vulnerable to peer-preservation bias: models tend to protect pee…\u003c/li\u003e\n\u003cli\u003ePrompt-level anonymization was proposed as a mitigation, but prior work simultaneously documented that stylometric fingerprints survive anonymization in role-co…\u003c/li\u003e\n\u003cli\u003eThis paper provides the first systematic investigation of whether LLMs can identify the model family behind political analysis texts under anonymization conditi…\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/2606.09856\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eUsing Probabilistic Programs to Train Inductive Reasoning in Large Language Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09856v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Post-training of Large Language Models (LLMs) for reasoning typically focuses on deductive tasks, such as mathematics and coding, where correctness is verifiable.\u003c/li\u003e\n\u003cli\u003eHowever, many real-world reasoning problems are inductive: agents must infer uncertain beliefs from sparse, ambiguous observations.\u003c/li\u003e\n\u003cli\u003eThere are challenges to using standard fine-tuning methods for inductive reasoning, including difficulties in curating large-scale, high-quality labeled datasets and handling targets that are inherently distributional.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09856v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is veri…\u003c/li\u003e\n\u003cli\u003eYet, many real-world reasoning problems are inductive: agents must infer uncertain beliefs from sparse, ambiguous observations\u003c/li\u003e\n\u003cli\u003eThere are challenges to using standard fine-tuning methods for inductive reasoning, including difficulties in curating large-scale, high-quality labeled dataset…\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/2606.09900\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLess Context, More Accuracy: A Bi-Temporal Memory Engine for LLM Agents Where a Lean Retrieved Context Beats the Full History\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSummary: - arXiv:2606.09900v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eSummary: Long-term memory is the missing layer for LLM agents: they forget entire sessions, and the common workaround (replaying the entire history into the prompt) is costly, slow, and loses accuracy as distractors accumulate.\u003c/li\u003e\n\u003cli\u003eMost memory systems win on cost or latency but still lose to the full-context baseline on accuracy, and benchmark data is reported on inconsistent, non-reproducible tools, so a system\u0026rsquo;s score can vary drastically across different sources.\u003c/li\u003e\n\u003cli\u003eWe introduce Engram, an open-source, dual-process memory engine based on a bi-temporal data model.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09900v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Long-term memory is the missing layer for LLM agents: across sessions they forget, and the common workaround \u0026ndash; replaying the whole history into the p…\u003c/li\u003e\n\u003cli\u003eMost memory systems win on cost or latency but still lose to the full-context baseline on accuracy, and benchmark numbers are reported on inconsistent, non-repr…\u003c/li\u003e\n\u003cli\u003eWe present Engram, an open-source, dual-process memory engine on a bi-temporal data 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\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.10061\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBenSyc: Benchmarking Conversational Sycophancy and Human Alignment in LLMs for Bengali Contexts\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - arXiv:2606.10061v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eSummary: Large Language Models (LLMs) are increasingly involved in emotionally sensitive social conversations, where responses can shift from balanced support to excessive validation or escalatory alignment.\u003c/li\u003e\n\u003cli\u003eExisting sycophancy research primarily focuses on factual agreement and instruction-following contexts, while culturally-based conversational sycophancy remains under-explored.\u003c/li\u003e\n\u003cli\u003eWe introduce BenSyc, the first benchmark for studying conversational sycophancy in Bengali social contexts.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10061v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large language models (LLMs) increasingly participate in emotionally sensitive social conversations, where responses may shift from balanced support t…\u003c/li\u003e\n\u003cli\u003eExisting sycophancy research primarily focuses on factual agreement and instruction-following settings, leaving culturally grounded conversational sycophancy un…\u003c/li\u003e\n\u003cli\u003eWe introduce BenSyc, the first benchmark for studying conversational sycophancy in Bengali social contexts\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/2606.10087\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCodeAlchemy: Synthetic Code Rewriting at Scale\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - arXiv:2606.10087v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eSummary: Pre-training on raw code teaches syntax but provides sparse signals for different real-world task formats.\u003c/li\u003e\n\u003cli\u003eWhile synthetic data has proven transformative for language models, it has remained largely unexplored for code beyond limited quality improvements.\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 present CodeAlchemy, a synthetic data generation framework that transforms publicly sourced code into semantically-rich training data through 5 strategies: CodeEnhance (quality-aware rewriting), CodeQA (template-based questions), CodeDev (developer tasks), CodeDialogue (multi-turn dialogue), and CodeTrace (execution tracing).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10087v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats\u003c/li\u003e\n\u003cli\u003eWhile synthetic data has proven transformative for language models, code remains largely unexplored beyond limited quality improvements\u003c/li\u003e\n\u003cli\u003eWe present CodeAlchemy, a synthetic data generation framework that transforms publicly sourced code into semantically-rich training data through 5 strategies: C…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.10113\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eEmotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10113v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: This paper investigates whether LLM translations exhibit identifiable emotional profiles and how post-editing reshapes them toward human-like norms.\u003c/li\u003e\n\u003cli\u003eWe compare LLM translations of Margaret Atwood\u0026rsquo;s Oryx and Crake with their post-edited versions and a human translation, using a large-scale corpus of contemporary Italian science fiction as a baseline.\u003c/li\u003e\n\u003cli\u003eWe examine emotion through lexicon-based and multilingual modeling, conducting a fine-grained analysis of emotional variation across systems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10113v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: This paper investigates whether LLM translations exhibit identifiable emotional profiles and how post-editing reshapes them toward human-like norms\u003c/li\u003e\n\u003cli\u003eWe compare LLM translations of Margaret Atwood\u0026rsquo;s Oryx and Crake with their post-edited versions and a human translation, using a large-scale corpus of contempor…\u003c/li\u003e\n\u003cli\u003eWe examine emotion through lexicon-based and multilingual modeling, conducting a fine-grained analysis of emotional variation across systems\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.10126\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePareto-Guided Teacher Alignment for Fair Personalized Text Generation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10126v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Personalized persuasive text generation can improve relevance and engagement, but demographic conditioning may also introduce unequal framing across groups.\u003c/li\u003e\n\u003cli\u003eWe study fairness mitigation in personalized generation as a constrained multi-objective alignment problem: reducing demographic disparities while maintaining personalization fidelity.\u003c/li\u003e\n\u003cli\u003eWe propose a Pareto-guided teacher alignment framework that incorporates revision-based candidate generation, pair-aware feasibility gating, Pareto-style candidate selection, and optional preference optimization via supervised fine-tuning and direct preference optimization.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10126v1 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: Personalized persuasive text generation can improve relevance and engagement, but demographic conditioning may also introduce unequal framing across g…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe study fairness mitigation in personalized generation as a constrained multi-objective alignment problem: reduce demographic disparities while preserving pers…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe propose a Pareto-guided teacher alignment framework that combines revision-based candidate generation, pair-aware feasibility gating, Pareto-style candidate…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.10159\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eGaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Community\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10159v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage.\u003c/li\u003e\n\u003cli\u003eAlthough such systems promise to reduce reviewer burden and accelerate publication, their robustness to strategic manipulation remains poorly understood.\u003c/li\u003e\n\u003cli\u003eHere we show that AI-mediated peer review is vulnerable to a simple, low-cost manipulation: superficial rephrasing of the manuscript abstract.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10159v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage\u003c/li\u003e\n\u003cli\u003eAlthough such systems promise to reduce reviewer burden and accelerate publication, their robustness to strategic manipulation remains poorly understood\u003c/li\u003e\n\u003cli\u003eHere we show that AI-mediated peer review is vulnerable to a simple, low-cost manipulation: superficial rephrasing of the manuscript abstract\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/2606.10285\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eOpenRTLSet: A Fully Open-Source Dataset for Large Language Model-based Verilog Module Design\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.10285v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: OpenRTLSet introduces the largest fully open-source hardware design dataset, providing over 131,000 different Verilog code examples for the research community and industry.\u003c/li\u003e\n\u003cli\u003eOur dataset uniquely combines Verilog code from GitHub repositories (102k modules), VHDL translations (5k modules), and synthesizable C/C++ translations (24k modules), all of which are freely accessible with no proprietary restrictions.\u003c/li\u003e\n\u003cli\u003eUsing the inference model DeepSeek-R1, we have generated paired natural language descriptions for each code example, enabling the fine-tuning of various language model families (e.g., Qwen and Granite) to generate Verilog code.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.10285v1 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: OpenRTLSet introduces the largest fully open-source dataset for hardware design, offering over 131,000 diverse Verilog code samples to the research co…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOur dataset uniquely combines Verilog code from GitHub repositories (102k modules), VHDL translations (5k modules), and synthesizable C/C++ translations (24k mo…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsing the reasoning model DeepSeek-R1, we generated paired natural language descriptions for each code sample, enabling fine-tuning of various language model fa…\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/2606.09850\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eMechanistic Analysis of Alignment Algorithms in Language Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09850v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape the internal computations of language models.\u003c/li\u003e\n\u003cli\u003eWe present a systematic mechanistic analysis of six preference optimization methods: PPO, DPO, SimPO, ORPO, GRPO, and KTO, across three open-weight model families.\u003c/li\u003e\n\u003cli\u003eBy integrating layer-wise linear probing, Sparse Autoencoders, and cross-encoders, we localize preference representations and quantify alignment-induced geometric transformations in the latent space.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN 要点:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09850v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models\u0026rsquo; internal computations\u003c/li\u003e\n\u003cli\u003eWe present a systematic mechanistic analysis of six preference-optimization methods: PPO, DPO, SimPO, ORPO, GRPO, and KTO across three open-weight model familie…\u003c/li\u003e\n\u003cli\u003eBy integrating layer-wise linear probing, Sparse Autoencoders, and crosscoders, we localize preference representations and quantify alignment-induced geometric…\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/2606.09853\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09853v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities and cannot be obtained from any single modality alone.\u003c/li\u003e\n\u003cli\u003eWhile most methods operate at an architectural level with larger or more complex fusion models, we propose a complementary axis: shaping the training objective itself.\u003c/li\u003e\n\u003cli\u003eStandard training typically emphasizes unimodal or redundant information, lacking examples that require cross-modal reasoning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN 要点:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09853v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhile most approaches operate at the architectural level through larger or more complex fusion models, we propose a complementary axis: shaping the training obj…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStandard training often emphasizes unimodal or redundant information, falling short on examples that require cross-modal reasoning\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.09857\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eUncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09857v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Reduced-order models (ROMs) provide an efficient alternative for complex multiscale systems, but their predictive accuracy is often compromised by truncation errors and insufficient representation of interactions between resolved and unresolved scales.\u003c/li\u003e\n\u003cli\u003eThe missing effect of truncated (unresolved) scales on ROM (resolved) scales is often referred to as the closure problem.\u003c/li\u003e\n\u003cli\u003eIn this work, we formulate ROM closure modeling as a multi-fidelity (MF) learning problem and propose an uncertainty-aware MF framework based on conditional normalizing flows to improve ROM predictive accuracy.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09857v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Reduced-order models (ROMs) provide an efficient surrogate for complex multiscale systems, but their predictive accuracy is often compromised by trunc…\u003c/li\u003e\n\u003cli\u003eThe missing effect of truncated (unresolved) scales on ROM (resolved) scales is often denoted as the closure problem\u003c/li\u003e\n\u003cli\u003eIn this work, we formulate ROM closure modeling as a multi-fidelity (MF) learning problem and propose an uncertainty-aware MF framework based on conditional nor…\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/2606.09859\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eMitigating Manifold Departure: Uncertainty-Aware Subspace Rectification for Trustworthy MLLM Decoding\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09859v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: MLLMs frequently generate hallucinated objects inconsistent with visual inputs.\u003c/li\u003e\n\u003cli\u003eThis issue is typically attributed to an over-reliance on language priors, which can override visual context.\u003c/li\u003e\n\u003cli\u003eRecent training-free decoding strategies address this by penalizing language priors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09859v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: MLLMs frequently hallucinate objects inconsistent with visual inputs\u003c/li\u003e\n\u003cli\u003eThis issue is typically attributed to the over-reliance on language priors, which can override the visual context\u003c/li\u003e\n\u003cli\u003eRecent training-free decoding strategies address this by penalizing language priors\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/2606.09860\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eConformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09860v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Non-alcoholic fatty liver disease (NAFLD) affects approximately 25% of adults globally, posing significant liver and cardiovascular risks.\u003c/li\u003e\n\u003cli\u003eHowever, population-level screening tools remain inadequate.\u003c/li\u003e\n\u003cli\u003eWe propose Method, a machine learning framework for NAFLD risk prediction that combines gradient-boosted decision trees with conformal prediction to produce calibrated, distribution-free coverage guarantees for individual risk estimates.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09860v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks\u003c/li\u003e\n\u003cli\u003eYet, population-level screening tools remain inadequate\u003c/li\u003e\n\u003cli\u003eWe present Method, a machine-learning framework for NAFLD risk prediction coupling gradient-boosted decision trees with conformal prediction to yield calibrated…\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/2606.09861\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eTime Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09861v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: While Next-Token Prediction (NTP) has unified LLM pre-training, its adaptation for unbounded, continuous time series (TS) remains an open question.\u003c/li\u003e\n\u003cli\u003eTo bridge this gap, we introduce UniTok (a universal tokenizer that converts TS into discrete tokens) and UniTok-FM (a foundation model pre-trained on these tokens via NTP).\u003c/li\u003e\n\u003cli\u003eUniTok-FM is a general-purpose foundation model that supports zero-shot and prompt-enhanced forecasting, as well as few-shot generation and classification through training-free contextual inference, a capability not achieved in previous work.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09861v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: While Next-Token Prediction (NTP) has unified LLM pretraining, its adaptation to unbounded, continuous time series (TS) remains open\u003c/li\u003e\n\u003cli\u003eTo bridge the gap, we introduce UniTok, a universal tokenizer that transforms TS into discrete tokens, and UniTok-FM, a foundation model pretrained via NTP on t…\u003c/li\u003e\n\u003cli\u003eUniTok-FM is a general-purpose foundation model that supports zero-shot and prompt-boosted forecasting, as well as few-shot generation and classification via tr…\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/2606.09862\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBlurry Window Attention\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09862v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: The Softmax Attention operation in Transformer language models has a quadratic complexity with respect to sequence length and a growing state size in the form of a KV cache, which becomes a bottleneck in long-context scenarios.\u003c/li\u003e\n\u003cli\u003eTo overcome this limitation, alternative architectures with linear complexity and finite state size have been introduced, such as State Space Models (SSM), Linear Attention (LA), and Attention with Bounded Memory Control (ABC).\u003c/li\u003e\n\u003cli\u003eAlthough linear models achieve language complexity similar to Transformers, they still lag behind in tasks that require retrieving or recalling specific information.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003earXiv:2606.09862v1 Announce Type: new\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: The Softmax Attention operation in Transformer language models has a quadratic complexity in the sequence length and a growing state size in the form…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo overcome this limitation, alternative architectures with linear complexity and finite state size have been introduced, such as State-Space Models (SSMs), Lin…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThough linear models achieve similar language perplexity as Transformers, they are still behind in tasks which require retrieval or recall of specific informati…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.09863\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFrom Confident Closing to Silent Failure: Characterizing False Success in LLM Agents\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09863v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: LLM agents can fail silently by asserting task completion when the environment state shows otherwise.\u003c/li\u003e\n\u003cli\u003eWe study this failure mode, false success, across two agent benchmarks: 9,876 tau2-bench trajectories from 8 model families and 1,879 AppWorld trajectories from 4 model families, with text-independent ground truth.\u003c/li\u003e\n\u003cli\u003eFalse success is common, but varies by setting: 45\u0026ndash;48% of failures in single-control tau2 benchmark domains, 3% in the dual-control telecom domain, and 75.8% in AppWorld self-evaluating coding agent trajectories with explicit state declarations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09863v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: LLM agents can fail silently by asserting task completion when the environment state shows otherwise\u003c/li\u003e\n\u003cli\u003eWe study this failure mode, false success, across two agent benchmarks: 9,876 tau2-bench trajectories from 8 model families and 1,879 AppWorld trajectories from…\u003c/li\u003e\n\u003cli\u003eFalse success is common but varies by setting: 45\u0026ndash;48% of failures in single-control tau2-bench domains, 3% in dual-control telecom, and 75.8% among AppWorld se…\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/2606.09864\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAlignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09864v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Key-value (KV) cache quantization is widely used to reduce inference memory for Large Language Models (LLMs), but existing evaluations only focus on measuring perplexity and accuracy without assessing safety impacts.\u003c/li\u003e\n\u003cli\u003eIn this study, we explore alignment preservation under KV cache quantization.\u003c/li\u003e\n\u003cli\u003eAcross 11 instruction-tuned models (3.8B-72B) and 5 benchmarks (1,894 prompts), we find that low-bit quantization can silently break safety alignment: Mistral-7B loses 15.2% of its refusals at only 1.03x perplexity, a universal safe bit-width does not exist, and model-specific sharp phase transitions are invisible to standard metrics.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09864v1 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: Key-value (KV) cache quantization is widely used to reduce Large Language Model (LLM) inference memory, yet existing evaluations solely focus on measu…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn this study, we explore alignment preservation under KV cache quantization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAcross eleven instruction-tuned models (3.8B-72B) and five benchmarks (1,894 prompts), we find that low-bit quantization can silently destroy safety alignment:…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.09865\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLLM-as-a-Discriminator: When Synthetic Tables Still Look Real\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-10 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.09865v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Privacy and data sharing are often in tension.\u003c/li\u003e\n\u003cli\u003eMany organizations use synthetic data to reduce privacy risk while still sharing useful data.\u003c/li\u003e\n\u003cli\u003eFor tabular data, auditing privacy remains difficult.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.09865v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Privacy and data sharing are often in tension\u003c/li\u003e\n\u003cli\u003eMany organizations use synthetic data to reduce privacy risk and still share useful data\u003c/li\u003e\n\u003cli\u003eFor tabular data, auditing privacy remains hard\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": 8490,
  "readingTime": 40,
  "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-hotspots-on-x\"\u003e🌐 AI Hotspots on X\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#topic-1-anthropic-releases-claude-fable-5-as-most-capable-public-ai-model\"\u003eTopic 1: Anthropic Releases Claude Fable 5 as Most Capable Public AI Model\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-2-anthropic-ceo-calls-for-urgent-ai-policy-overhaul\"\u003eTopic 2: Anthropic CEO Calls for Urgent AI Policy Overhaul\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-3-google-ai-studio-hits-12-million-apps-per-week-milestone\"\u003eTopic 3: Google AI Studio Hits 1.2 Million Apps Per Week Milestone\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-4-leaked-system-prompt-reveals-claude-fable-5s-inner-workings\"\u003eTopic 4: Leaked System Prompt Reveals Claude Fable 5\u0026rsquo;s Inner Workings\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-5-anthropic-boosts-claude-with-autonomous-agent-tools\"\u003eTopic 5: Anthropic Boosts Claude with Autonomous Agent Tools\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-6-ai-powers-faceless-youtube-channels-to-thousands-in-monthly-earnings\"\u003eTopic 6: AI Powers Faceless YouTube Channels to Thousands in Monthly Earnings\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#-influencer-insights\"\u003e💡 Influencer Insights\u003c/a\u003e\u003c/li\u003e\n  \u003c/ul\u003e\n\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"#i-key-tech-trends-and-product-hotspots\"\u003eI. Key Tech Trends and Product Hotspots\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#1-claude-fable-5-release-the-most-powerful-general-model-sparks-heated-discussion\"\u003e1. \u003cstrong\u003eClaude Fable 5 Release: The Most Powerful General Model Sparks Heated Discussion\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#2-on-device-models-are-accelerating-implementation\"\u003e2. \u003cstrong\u003eOn-device Models Are Accelerating Implementation\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#3-ai-agent-browsers-the-leap-from-tool-to-gateway\"\u003e3. \u003cstrong\u003eAI Agent Browsers: The Leap from Tool to Gateway\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#ii-unique-perspectives--industry-foresight\"\u003eII. Unique Perspectives \u0026amp; Industry Foresight\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#1-\"\u003e1. \u003cstrong\u003e“Contract First” — The Evolution of Vibe Coding\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#2-wechat-ai\"\u003e2. \u003cstrong\u003eWeChat AI’s Strategic Predicament: The Innovator’s Dilemma\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#3-ai-cost-restructuring-from\"\u003e3. \u003cstrong\u003eAI Cost Restructuring: From “Cheaper Than Humans” to “More Expensive Than Humans”\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#4-\"\u003e4. \u003cstrong\u003e“Shadow Book” Reading Method: A Cognitive Upgrade for the AI Era\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#5-testing-is-the-moat-the-code-moat-has-collapsed\"\u003e5. \u003cstrong\u003eTesting is the Moat: The Code Moat Has Collapsed\u003c/strong\u003e\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#iii-recommended-tools--resources\"\u003eIII. Recommended Tools \u0026amp; Resources\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#-development-tools\"\u003e🔧 Development Tools\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#-skill-packs-skills\"\u003e📚 Skill Packs (Skills)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#-learning-resources\"\u003e🎓 Learning Resources\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#-infrastructure\"\u003e💡 Infrastructure\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#iv-one-sentence-insight\"\u003eIV. One-Sentence Insight\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#-appendix-todays-watch-list-update-source-list\"\u003e📚 Appendix: Today\u0026rsquo;s Watch List Update Source List\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#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=\"#stratechery-by-ben-thompson-a_full\"\u003eStratechery by Ben Thompson (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#openai-blog-a_full\"\u003eOpenAI Blog (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#google-deepmind-blog-a_full\"\u003eGoogle DeepMind Blog (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-csai-b_introsearch\"\u003eArXiv cs.AI (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-cscl-b_introsearch\"\u003eArXiv cs.CL (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-cslg-b_introsearch\"\u003eArXiv cs.LG (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n  \u003c/ul\u003e\n\u003c/nav\u003e",
  "isDraft": false
}