{
  "title": "2026-06-12 AI Daily Update | Fable 5 reaches cost alert line, AI Agent is giving rise to the new engineering discipline of 'Harness'",
  "url": "https://miaok.ong/en/ai-daily/ai-daily-2026-06-12/",
  "date": "2026-06-12T07:00:00+08:00",
  "lastmod": "2026-06-12T07:00:00+08:00",
  "type": "ai-daily",
  "kind": "page",
  "language": "en",
  "description": "The heated discussion around Claude Fable 5 has validated the feasibility of long-context reasoning Agents, but its daily operational cost has reached a threshold equivalent to thousands of US dollars, forcing the industry to confront Token economics. At the same time, on-device models are evolving from \u0026ldquo;usable\u0026rdquo; to \u0026ldquo;highly effective,\u0026rdquo; and DeepSeek\u0026rsquo;s creation of an \u0026ldquo;Agent Harness Researcher\u0026rdquo; role signifies that the engineering framework for integrating foundation models with autonomous behavior is emerging as a new and independent discipline.",
  "keywords": null,
  "tags": [],
  "categories": [],
  "author": "Mark (Miao) Kong",
  "image": "https://miaok.ong/images/avatar.jpg",
  "content": "\u003ch1 id=\"2026-06-12-ai-daily--fable-5-hits-cost-warning-threshold-ai-agents-are-fostering-a-new-harness-engineering-discipline\"\u003e\n  2026-06-12 AI Daily | Fable 5 Hits Cost Warning Threshold, AI Agents Are Fostering a New \u0026ldquo;Harness\u0026rdquo; Engineering Discipline\n  \u003ca class=\"heading-link\" href=\"#2026-06-12-ai-daily--fable-5-hits-cost-warning-threshold-ai-agents-are-fostering-a-new-harness-engineering-discipline\"\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\u003eThe buzz around Claude Fable 5 validates the feasibility of long-context reasoning agents, but its daily consumption has already hit a threshold equivalent to thousands of dollars, forcing the industry to confront tokenomics. Meanwhile, on-device models are transitioning from \u0026ldquo;usable\u0026rdquo; to \u0026ldquo;practical,\u0026rdquo; and DeepSeek\u0026rsquo;s creation of an \u0026ldquo;Agent Harness Researcher\u0026rdquo; position signals that the engineering system connecting foundational models to autonomous behavior is emerging as a new, independent discipline.\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\u003eToday, the AI frontier reveals two clear, intersecting trends. \u003cstrong\u003eFirst, the deep evolution of Agents from \u0026ldquo;execution\u0026rdquo; to \u0026ldquo;decision-making.\u0026rdquo;\u003c/strong\u003e Several papers worth a close read have emerged on arXiv: INFRAMIND moves beyond simple model routing to directly sense the runtime state of the underlying GPU cluster for multi-agent scheduling. Self-Gated Clarification allows agents to learn to proactively decide \u0026ldquo;when to ask\u0026rdquo; at crucial reasoning junctures, directly tackling the fragility of current long-cycle agent implementations. For teams building research or financial agents, SciConBench and MoCA-Agent provide new benchmarks and architectures, focusing on synthesizing scientific conclusions and financial numerical reasoning, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecond, new perspectives on the internal structure of large model alignment and behavioral prediction.\u003c/strong\u003e Dual-Stance Evaluation reveals that sycophancy and factual consistency occupy geometrically distinct subspaces, making precise steering possible. A position paper directly proposes treating behavior prediction itself as a learnable task, bypassing the chain of explanation. In addition, a high-quality interview is also worth a listen—Ben Bajarin provides a deep dive into Apple\u0026rsquo;s latest strategies in AI and computing, offering a very restrained industry benchmark for on-device intelligence.\u003c/p\u003e\n\u003ch2 id=\"-ai-hot-topics-on-x\"\u003e\n  🌐 AI Hot Topics on X\n  \u003ca class=\"heading-link\" href=\"#-ai-hot-topics-on-x\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h2\u003e\n\u003ch3 id=\"topic-1-anthropics-claude-code-event-packs-tokyo-with-developers\"\u003e\n  Topic 1: Anthropic\u0026rsquo;s Claude Code Event Packs Tokyo with Developers\n  \u003ca class=\"heading-link\" href=\"#topic-1-anthropics-claude-code-event-packs-tokyo-with-developers\"\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\u003eSummary: Trending since: 17 hours ago, Related posts: 223\u003c/li\u003e\n\u003cli\u003eWhat happened: Anthropic\u0026rsquo;s Claude Code developer event in Tokyo was packed, with a large number of local developers in attendance.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This signals Anthropic\u0026rsquo;s accelerated expansion of its developer ecosystem in Asia, strengthening its global competitive position against rivals like OpenAI and GitHub Copilot in the AI coding assistant space, and exploring the market potential for enterprise-level AI tools in Japan.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: The discussion on X focuses on Claude Code\u0026rsquo;s actual coding performance compared to GitHub Copilot, the practicality of Anthropic\u0026rsquo;s promotion strategy in Asia, and a debate among some developers about whether the event\u0026rsquo;s content was more focused on technical substance or brand marketing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-2-openai-hires-cybersecurity-leaders-to-counter-ai-risks\"\u003e\n  Topic 2: OpenAI Hires Cybersecurity Leaders to Counter AI Risks\n  \u003ca class=\"heading-link\" href=\"#topic-2-openai-hires-cybersecurity-leaders-to-counter-ai-risks\"\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\u003eSummary: Trending since: 23 hours ago, Related posts: 270\u003c/li\u003e\n\u003cli\u003eWhat happened: OpenAI has hired cybersecurity leaders to strengthen its own defenses and address potential security threats posed by AI.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This move indicates that leading AI companies are investing resources in governance and defense to proactively address new cybersecurity risks arising from the potential misuse of AI technology, serving as a bellwether for industry security practices and standards.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: Discussions on X are centered on the background and past experience of the new hires, whether this move marks a substantive step in OpenAI\u0026rsquo;s commitment to safety, and its potential impact on AI industry security standards. Some opinions also question if this action is related to recent internal safety controversies.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-3-openclaw-developer-open-sources-ai-repo-maintainer-skills\"\u003e\n  Topic 3: OpenClaw Developer Open-Sources AI Repo Maintainer Skills\n  \u003ca class=\"heading-link\" href=\"#topic-3-openclaw-developer-open-sources-ai-repo-maintainer-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\u003cul\u003e\n\u003cli\u003eCategory: AI · News\u003c/li\u003e\n\u003cli\u003eSummary: Trending since: 9 hours ago, Related posts: 488\u003c/li\u003e\n\u003cli\u003eWhat happened: An OpenClaw developer has open-sourced a set of \u0026ldquo;AI Repo Maintainer Skills,\u0026rdquo; encapsulating common maintenance tasks like code review, issue triage, and documentation updates into reusable, automated capabilities.\u003c/li\u003e\n\u003cli\u003eWhy it\u0026rsquo;s important: This initiative aims to lower the maintenance costs and barriers for open-source AI projects by allowing agents to replace or assist humans in handling repetitive maintenance work. It has the potential to accelerate the democratization of AI toolchains and improve community collaboration efficiency.\u003c/li\u003e\n\u003cli\u003eDiscussion summary: The community\u0026rsquo;s focus is on: 1) Whether automated maintenance can truly guarantee code quality and security, or if it might introduce subtle errors; 2) Whether the role of human maintainers will be marginalized; 3) How skill templates can accommodate the different maintenance styles of various projects, and whether this could lead to a homogenization of maintenance practices.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-4-peter-steinberger-open-sources-ai-skills-for-autonomous-repo-maintenance\"\u003e\n  Topic 4: Peter Steinberger Open-Sources AI Skills for Autonomous Repo Maintenance\n  \u003ca class=\"heading-link\" href=\"#topic-4-peter-steinberger-open-sources-ai-skills-for-autonomous-repo-maintenance\"\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\u003eSummary: Trending since: 10 hours ago, Related posts: 609\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhat it is\u003c/strong\u003e: Developer Peter Steinberger has open-sourced a set of AI skills for autonomous code repository maintenance.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhy it matters\u003c/strong\u003e: This move lowers the barrier to entry for developing autonomous software maintenance agents, advancing AI from code generation to long-term, reliable, repository-level automated management. It helps validate the reliability of large models in real-world, continuous engineering tasks.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiscussion overview\u003c/strong\u003e: The main focuses include: the reliability boundaries of autonomous bug fixing and PR submission, how to prevent automation from introducing new problems, and effective ways to integrate open-source solutions with existing CI/CD and code review processes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-5-recursive-ai-tops-benchmarks-in-automated-research-breakthrough\"\u003e\n  Topic 5: Recursive AI Tops Benchmarks in Automated Research Breakthrough\n  \u003ca class=\"heading-link\" href=\"#topic-5-recursive-ai-tops-benchmarks-in-automated-research-breakthrough\"\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: 13 hours ago, Related posts: 540\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhat it is\u003c/strong\u003e: A recursive AI system has achieved the highest scores on automated scientific research benchmarks, marking a breakthrough.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhy it matters\u003c/strong\u003e: This demonstrates AI\u0026rsquo;s ability to conduct scientific research autonomously and accelerate discovery through recursive self-improvement. It could reshape the paradigm of scientific R\u0026amp;D, while also raising deep concerns about the safety and control of recursive self-improvement.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiscussion overview\u003c/strong\u003e: Discussions on the X platform center on whether this achievement represents a genuine breakthrough in autonomous research or just benchmark chasing; the potential risks of loss of control and alignment challenges posed by recursive self-improvement; and whether the related models are open-source and the results reproducible. Optimists believe this ushers in an AI-driven scientific revolution, while pessimists stress the need for strengthened safety regulations in advance.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"topic-6-tesla-deploys-fsd-supervised-v1434-with-smart-summon-for-cybertruck\"\u003e\n  Topic 6: Tesla Deploys FSD Supervised v14.3.4 with Smart Summon for Cybertruck\n  \u003ca class=\"heading-link\" href=\"#topic-6-tesla-deploys-fsd-supervised-v1434-with-smart-summon-for-cybertruck\"\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: 4300\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhat it is\u003c/strong\u003e: Tesla has started rolling out FSD Supervised v14.3.4 to the Cybertruck, adding the Smart Summon feature.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWhy it matters\u003c/strong\u003e: This indicates Tesla is further unifying the experience across its vehicle lineup in its fully autonomous driving software iterations. It also brings the controversial Cybertruck into the core L2 assisted driving ecosystem, which will help collect road data from this unique platform and advance the generalization capabilities of its end-to-end model.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiscussion overview\u003c/strong\u003e: Users are primarily focused on the system\u0026rsquo;s performance on unpaved roads and in adverse weather. Some question the impact of the Cybertruck\u0026rsquo;s unusual design on camera visibility. There are also heated discussions about the practicality of Smart Summon in tight parking spaces, its improvements over previous versions, and whether this version still requires continuous driver attention monitoring.\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\u003eThe main narrative in today\u0026rsquo;s public opinion clearly points to the evolution of AI systems from auxiliary tools to long-term, autonomous executors of complex tasks. This trend is sweeping across fields like software maintenance, scientific research, and physical driving. An industry consensus is emerging as leading companies actively position themselves to capture ecological niches—Anthropic is focused on expanding its Asian developer community, OpenAI is strengthening its safety governance, and Tesla is extending its end-to-end driving model to controversial vehicle models. All parties agree that open-sourcing and automation are key to lowering barriers and accelerating iteration. Disagreements, however, center on the true reliability of autonomous capabilities. The community is hotly debating whether automated maintenance will introduce hidden defects, whether recursive AI is a genuine research breakthrough or just benchmark chasing, and the impact of the Cybertruck\u0026rsquo;s unique design on driving perception. Potential risks are becoming increasingly prominent: unattended agents could introduce imperceptible errors, the loss of control and alignment challenges of recursive self-improvement are causing deep concern, and the new types of cyber threats spawned by AI misuse make upgrading security defenses an urgent industry-wide imperative.\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-claude-fable-5-ignites-a-new-paradigm-in-agent-development-amidst-on-device-models-and-cost-anxiety\"\u003e\n  AI Daily: Claude Fable 5 Ignites a New Paradigm in Agent Development, Amidst On-Device Models and Cost Anxiety\n  \u003ca class=\"heading-link\" href=\"#ai-daily-claude-fable-5-ignites-a-new-paradigm-in-agent-development-amidst-on-device-models-and-cost-anxiety\"\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-todays-core-hotspot-claude-fable-5-and-the-agent-development-paradigm\"\u003e\n  I. Today\u0026rsquo;s Core Hotspot: Claude Fable 5 and the Agent Development Paradigm\n  \u003ca class=\"heading-link\" href=\"#i-todays-core-hotspot-claude-fable-5-and-the-agent-development-paradigm\"\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=\"11-fable-5-a-leap-in-capability-amidst-cost-controversy\"\u003e\n  1.1 Fable 5: A Leap in Capability Amidst Cost Controversy\n  \u003ca class=\"heading-link\" href=\"#11-fable-5-a-leap-in-capability-amidst-cost-controversy\"\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\u003cstrong\u003eClaude Fable 5\u003c/strong\u003e is the absolute focus today, with multiple influencers conducting intensive tests and providing in-depth reviews:\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\"\u003eObservation\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\u003eCapability Boundary\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eBroader thinking horizons, stronger architectural skills, significantly improved front-end creativity; can identify unreasonable aspects of human design and optimize them autonomously.\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\u003eReasoning Time\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eCan think continuously for 15 minutes before taking action, with numerous verification steps. \u0026ldquo;The results are good, but it takes a very long time.\u0026rdquo;\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@vista8 @dotey\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eCost Reality\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eConsumption is about 1.5x that of Opus. A 10-hour usage for a Max5 subscriber is equivalent to $1,500; @jerryjliu0\u0026rsquo;s team member hit the limit 3 times in a single day.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@Pluvio9yte @dotey\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eUsage Strategy\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eRecommended to set cache rebuild to 1 hour. Non-Max intensity is sufficient for most needs. \u0026ldquo;Don\u0026rsquo;t dare to casually choose Max\u0026rdquo; has become the consensus.\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@Pluvio9yte @dotey\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eKey Disagreements\u003c/strong\u003e: @Pluvio9yte proposed a \u0026ldquo;counter-consensus\u0026rdquo;—Fable 5\u0026rsquo;s speed is \u0026ldquo;as slow as a crawling turtle,\u0026rdquo; and its consumption is exaggerated, with its actual capability \u0026ldquo;similar to a combination of Opus4.6++ and GPT-5.5++, not yet astonishing\u0026rdquo;; while @zhixianio highly praised its ability to complete 70% of the work and optimize the design in 40 minutes, saying, \u0026ldquo;Shut up and take my money.\u0026rdquo;\u003c/td\u003e\n          \u003ctd\u003e\u003c/td\u003e\n          \u003ctd\u003e\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3 id=\"12-evolution-of-agent-development-paradigms\"\u003e\n  1.2 Evolution of Agent Development Paradigms\n  \u003ca class=\"heading-link\" href=\"#12-evolution-of-agent-development-paradigms\"\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\u003cstrong\u003e\u0026ldquo;Goal Instruction\u0026rdquo; becomes the new standard for Codex/Claude Code\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e@vista8 developed \u003cstrong\u003e\u0026ldquo;Qiaomu Goal Meta Skill\u0026rdquo;\u003c/strong\u003e (\u003ccode\u003enpx skills add joeseesun/qiaomu-goal-meta-skill\u003c/code\u003e), transforming single-sentence requirements into executable goals, supporting a long-duration task mode of \u0026ldquo;execute before bed, harvest the next day\u0026rdquo;\u003c/li\u003e\n\u003cli\u003e@dotey practiced the \u003ccode\u003e/goal\u003c/code\u003e instruction, significantly improving long-task stability, with \u0026ldquo;no need to continue\u0026rdquo;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExtreme Case of Fable 5\u003c/strong\u003e: @trq212 demonstrated a video production workflow entirely generated by AI coding—Whisper transcription → Subagent clip selection → FFmpeg rough cut → hand-written LUTs color grading → Remotion animation components → Figma MCP collaboration, \u003cstrong\u003ewith no traditional non-linear editing software intervention throughout\u003c/strong\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003ch2 id=\"ii-edge-side-models-performance-breakthroughs-and-ecosystem-maturity\"\u003e\n  II. Edge-Side Models: Performance Breakthroughs and Ecosystem Maturity\n  \u003ca class=\"heading-link\" href=\"#ii-edge-side-models-performance-breakthroughs-and-ecosystem-maturity\"\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=\"21-actual-progress\"\u003e\n  2.1 Actual Progress\n  \u003ca class=\"heading-link\" href=\"#21-actual-progress\"\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\u0026rsquo;s \u0026ldquo;ascetic practice\u0026rdquo; verified that edge-side models are already productive:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eConfiguration\u003c/strong\u003e: Qwen3.6-35B-A3B-oQ6-fp16-mtp / oMLX / Native MTP / 128K CTX\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: \u0026ldquo;Response speed is faster than remote LLMs, intelligence is online, and native multimodal is even more satisfying than DSV4 Pro\u0026rdquo;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGoogle Gemma 4 Series\u003c/strong\u003e continues to iterate:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe 12B multimodal model on M5Max 128G recognized English/Japanese \u0026ldquo;instantly,\u0026rdquo; but Chinese was \u0026ldquo;nonsensical\u0026rdquo;\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eQAT (Quantization-Aware Training)\u003c/strong\u003e new approach: assumes quantization during the training phase, \u0026ldquo;thereby improving training effectiveness\u0026rdquo;\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"22-toolchain-improvement\"\u003e\n  2.2 Toolchain Improvement\n  \u003ca class=\"heading-link\" href=\"#22-toolchain-improvement\"\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\"\u003eUpdate\u003c/th\u003e\n          \u003cth style=\"text-align: left\"\u003eHighlight\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\u003eoMLX v0.4.0\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eFirst official Swift macOS native application\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@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\"\u003eAdded collection system, Markdown online editor, file saving API\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\u003ebaoyu-design skill\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eSupports importing Figma local files to rebuild design system\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@dotey\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003chr\u003e\n\u003ch2 id=\"iii-unique-perspectives-and-industry-foresight\"\u003e\n  III. Unique Perspectives and Industry Foresight\n  \u003ca class=\"heading-link\" href=\"#iii-unique-perspectives-and-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=\"31-cost-anxiety-and-business-model-restructuring\"\u003e\n  3.1 Cost Anxiety and Business Model Restructuring\n  \u003ca class=\"heading-link\" href=\"#31-cost-anxiety-and-business-model-restructuring\"\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\u003cstrong\u003e\u0026ldquo;AI is more expensive than employees\u0026rdquo; becomes the new reality\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e@ruanyf calculated: OpenClaw founder\u0026rsquo;s monthly consumption of 603 billion Tokens, equivalent to $1.3 million; even if using domestic open-source models (1/30-1/50 the price), the annual cost still reaches 2-3 million RMB\u003c/li\u003e\n\u003cli\u003e@lijigang proposed the \u0026ldquo;real metrics\u0026rdquo; theory: Token consumption is a false metric; \u0026ldquo;whether the problem is better solved\u0026rdquo; is the real metric\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNew business model exploration\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e@oran_ge (via @lijigang): \u0026ldquo;First version free, subsequent updates charged\u0026rdquo;—because the first version of AI Coding is the simplest, and maintenance is the most labor-intensive\u003c/li\u003e\n\u003cli\u003e@dotey quoted: OpenDoor laid off 200+ offshore team members in India, replacing them with a \u0026ldquo;smaller, AI-native team in the US\u0026rdquo;\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"32-rethinking-the-essence-of-software-engineering\"\u003e\n  3.2 Rethinking the Essence of Software Engineering\n  \u003ca class=\"heading-link\" href=\"#32-rethinking-the-essence-of-software-engineering\"\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\u0026rsquo;s core assertion: \u003cstrong\u003e\u0026ldquo;AI has not redefined software engineering; AI has amplified the importance of software engineering\u0026rdquo;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e@Pluvio9yte\u0026rsquo;s practical verification:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026ldquo;The best practice for Vibe Coding is not Requirement First or Code First, but \u003cstrong\u003eContract First\u003c/strong\u003e. Without well-defined contracts, everything else is empty talk.\u0026rdquo;\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eHis development framework, based on a secondary development of OpenSpec, aims to \u0026ldquo;externalize easily drifting context into contracts, providing stable references for both humans and AI.\u0026rdquo;\u003c/p\u003e\n\u003ch3 id=\"33-deepseeks-harness-strategy\"\u003e\n  3.3 DeepSeek\u0026rsquo;s \u0026ldquo;Harness\u0026rdquo; Strategy\n  \u003ca class=\"heading-link\" href=\"#33-deepseeks-harness-strategy\"\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 revealed DeepSeek is recruiting \u003cstrong\u003e\u0026ldquo;Agent Harness Researchers\u0026rdquo;\u003c/strong\u003e—the first explicit recruitment for this position worldwide:\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u0026ldquo;Model + Harness = Agent. All work other than the model itself belongs to Harness: context management, long-term memory, Subagent \u0026amp; Multi-Agent, self-evolving Agent\u0026hellip;\u0026rdquo;\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eDefining \u003cstrong\u003e\u0026ldquo;Harness Engineering\u0026rdquo;\u003c/strong\u003e as a new discipline, requiring candidates to be \u0026ldquo;heavy Agent users, full-stack developers, driving research from 0 to 1.\u0026rdquo;\u003c/p\u003e\n\u003chr\u003e\n\u003ch2 id=\"iv-recommended-tools-and-resources\"\u003e\n  IV. Recommended Tools and Resources\n  \u003ca class=\"heading-link\" href=\"#iv-recommended-tools-and-resources\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h2\u003e\n\u003ch3 id=\"41-development-tools\"\u003e\n  4.1 Development Tools\n  \u003ca class=\"heading-link\" href=\"#41-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 5\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eLong-term reasoning, complex architecture design\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eAnthropic\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e:\u0026mdash;\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e:\u0026mdash;\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e:\u0026mdash;\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eCodex + /goal\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eAutomated long-task development\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eOpenAI\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eQiaomu Goal Meta Skill\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eConvert single-sentence requirements into goals\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\u003ebaoyu-design skill\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eLocal Claude Design + Figma import\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@dotey\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eoMLX\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eNative on-device model execution for macOS\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@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\"\u003ePA output file browser\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@zhixianio\u003c/td\u003e\n      \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3 id=\"42-content-creation\"\u003e\n  4.2 Content Creation\n  \u003ca class=\"heading-link\" href=\"#42-content-creation\"\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\u003eFen Jue\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eFull video translation workflow (Download → Transcribe → Translate → Polish → Burn subtitles)\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003e@xiaohu\u003c/td\u003e\n      \u003c/tr\u003e\n      \u003ctr\u003e\n          \u003ctd style=\"text-align: left\"\u003e\u003cstrong\u003eQiaomu Book Interpretation Skill\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eMulti-subagent collaboration for generating spoken scripts\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\u003eAllyHub\u003c/strong\u003e\u003c/td\u003e\n          \u003ctd style=\"text-align: left\"\u003eYouTube channel data analysis and topic planning\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=\"43-infrastructure\"\u003e\n  4.3 Infrastructure\n  \u003ca class=\"heading-link\" href=\"#43-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\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eOfoxAI\u003c/strong\u003e: A relay station balancing \u0026ldquo;stability + discounts,\u0026rdquo; offering a 15% discount on GPT-5.5/5.4 mini (@AI_Jasonyu)\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eVercel\u003c/strong\u003e: \u0026ldquo;The fastest way to launch a website,\u0026rdquo; deploy with the Codex plugin in just a few sentences (@Pluvio9yte)\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003ch2 id=\"v-key-trend-summary\"\u003e\n  V. Key Trend Summary\n  \u003ca class=\"heading-link\" href=\"#v-key-trend-summary\"\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\u003cpre tabindex=\"0\"\u003e\u003ccode\u003e┌─────────────────────────────────────────┐\n│  今日核心矛盾：能力跃升 ↑  vs  成本失控 ↑  │\n├─────────────────────────────────────────┤\n│  • Fable 5 验证\u0026#34;长思考\u0026#34;Agent 的可行性    │\n│  • 端侧模型从\u0026#34;能用\u0026#34;走向\u0026#34;好用\u0026#34;            │\n│  • \u0026#34;Harness Engineering\u0026#34;成为新工程学科  │\n│  • Token 经济学倒逼商业模式创新           │\n│  • Contract First 取代 Vibe Coding 随意性 │\n└─────────────────────────────────────────┘\n\u003c/code\u003e\u003c/pre\u003e\u003cp\u003e\u003cstrong\u003eFocus for Tomorrow\u003c/strong\u003e: User retention strategies for Fable 5 before the June 22 subscription deadline, actual test results for Gemma 4 QAT, and progress on the DeepSeek Harness team formation.\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; 35 updates in total\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ch3 id=\"stratechery-by-ben-thompson-a_full\"\u003e\n  Stratechery by Ben Thompson (A_full)\n  \u003ca class=\"heading-link\" href=\"#stratechery-by-ben-thompson-a_full\"\u003e\n    \u003ci class=\"fa-solid fa-link\" aria-hidden=\"true\" title=\"Link to heading\"\u003e\u003c/i\u003e\n    \u003cspan class=\"sr-only\"\u003eLink to heading\u003c/span\u003e\n  \u003c/a\u003e\n\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://stratechery.com/2026/an-interview-with-ben-bajarin-about-apple-ai-and-compute/\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAn Interview with Ben Bajarin About Apple, AI, and Compute\u003c/a\u003e\u003c/strong\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 18:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - An interview with Ben Bajarin about WWDC and the current state of the AI compute industry.\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 a podcast.\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\u003eAn interview with Ben Bajarin about WWDC and the status of the AI compute industry.\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/openai-to-acquire-ona\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eOpenAI to acquire Ona\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: - Over 5 million people use Codex weekly to research, analyze, build, and automate their work, a 400% increase from earlier this year.\n\u003cul\u003e\n\u003cli\u003eOriginally a tool for software developers, Codex now helps a broader audience complete complex work from initial request to final result.\u003c/li\u003e\n\u003cli\u003eAs Codex becomes more powerful, its most valuable work will unfold over hours or days, not minutes.\u003c/li\u003e\n\u003cli\u003eWe believe people should be able to delegate more ambitious work without being tied to the machine where the work began.\u003c/li\u003e\n\u003cli\u003eWork should continue after the initial session, and Codex allows people to stay connected from anywhere to check progress, provide direction, make decisions, and review results.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003eOpenAI plans to acquire Ona to expand Codex with secure, persistent cloud environments, enabling long-running AI agents across enterprise workflows.\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/supporting-eu-trustworthy-ai-ecosystem\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSupporting Europe’s work in ensuring a trustworthy AI ecosystem\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: - People are using artificial intelligence to create and edit content in new ways.\n\u003cul\u003e\n\u003cli\u003eAs these tools become more powerful and widely used, people should understand the context of the content they see online.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThis code of practice is a significant step towards implementing the EU AI Act and building a more transparent digital ecosystem.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOur support is built on years of internal research, product development, and collaboration with the broader ecosystem to enhance the provenance of AI-generated content.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDrawing on years of expertise, we, along with hundreds of other stakeholders, contributed to the development of this code to help ensure the establishment of a trustworthy AI ecosystem.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Highlights:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eOpenAI supports the EU Code of Practice on AI content transparency, advancing provenance standards and tools to help people understand AI-generated content.\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/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: - Learn how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein\u0026rsquo;s theory of general relativity.\n\u003cul\u003e\n\u003cli\u003eLearn how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein\u0026rsquo;s generative theory.\u003c/li\u003e\n\u003cli\u003eHow an astrophysicist uses Codex to help simulate black holes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\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/bbva\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBBVA puts AI at the core of banking with OpenAI\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: - Learn how BBVA scaled ChatGPT Enterprise to 100,000 employees and partnered with OpenAI to accelerate AI-powered banking transformation worldwide.\n\u003cul\u003e\n\u003cli\u003eThis article from the OpenAI blog explains how BBVA is putting AI at the core of banking and shaping the broader AI and infrastructure landscape through OpenAI.\u003c/li\u003e\n\u003cli\u003eFollowing BBVA\u0026rsquo;s move to place AI at the core of banking through OpenAI, this also brings practical implications for founders, operators, and investors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003eLearn how BBVA scaled ChatGPT Enterprise to 100,000 employees and partnered with OpenAI to accelerate AI-powered banking transformation worldwide.\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/2606.11207\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFrom Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eSummary: - arXiv:2606.11207v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: We introduce SemantiClean, a modular framework for extracting structured semantic signals from e-commerce session data and driving pluggable inference targets—including purchase intent, customer segmentation, and product affinity—through a shared element library.\u003c/li\u003e\n\u003cli\u003eUnlike traditional end-to-end predictors optimized solely for accuracy, SemantiClean prioritizes auditability, structural governance, and sigma=0 reproducibility, explicitly trading marginal predictive gains for element-level transparency and defensible decision trajectories.\u003c/li\u003e\n\u003cli\u003eGrounded in the Online Shopper Purchase Intent (OSPI) dataset, the framework organizes 24 behavioral elements into a four-tier architecture (Functional, Interaction, Systemic, Contextual) and enforces signal quality through three anti-inflationary mechanisms: RedundancyGroup contribution caps, TieredPenaltyCalculator deviation penalties, and AdaptiveConstraintMode cold-start protections. This report introduces the LLM-Integrated Semantic Inference Engine, a fully-realized, two-stage LLM-driven reasoning architecture that leverages complete element metadata at inference time.\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:2606.11207v1 Announce Type: new\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: We present SemantiClean, a modular framework for extracting structured semantic signals from e-commerce session data and driving pluggable inference t…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUnlike conventional end-to-end predictors that optimise solely for accuracy, SemantiClean prioritises auditability, structural governance, and sigma=0 reproduci…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuilt upon the Online Shoppers Purchasing Intention (OSPI) dataset, the framework organises twenty-four behavioural elements into a four-layer architecture (Fun…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.11245\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePosition: Hippocampal Explicit Memory Is the Cornerstone for AGI\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11245v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, raising expectations for Artificial General Intelligence (AGI).\u003c/li\u003e\n\u003cli\u003eThis position paper argues that integrating explicit memory is the cornerstone for advancing LLMs toward AGI.\u003c/li\u003e\n\u003cli\u003eThe key reason is that the underlying learning mechanism of LLMs is highly analogous to human implicit memory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11245v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, raising expectations for Artificial General Intelligence…\u003c/li\u003e\n\u003cli\u003eThis position paper argues that integrating explicit memory is the cornerstone for advancing LLMs toward AGI\u003c/li\u003e\n\u003cli\u003eThe key reason is that the underlying learning mechanism of LLMs is highly analogous to human implicit memory\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/2606.11337\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCan AI Agents Synthesize Scientific Conclusions?\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11337v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Scientific AI agents are increasingly retrieving evidence, reasoning across sources, and synthesizing conclusions for subsequent decision-making.\u003c/li\u003e\n\u003cli\u003eHowever, their ability to do so in high-stakes domains like health remains unclear.\u003c/li\u003e\n\u003cli\u003eWe introduce SciConBench, a large-scale real-time benchmark containing 9.11K questions and expert-written conclusions from systematic reviews, to evaluate open-domain scientific conclusion synthesis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11337v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions\u003c/li\u003e\n\u003cli\u003eYet, their ability to do so in high-stakes domains such as health remains unclear\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe introduce SciConBench, a large-scale live benchmark of 9.11K questions and expert-written conclusions from systematic reviews to evaluate open-domain scienti…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.11349\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eKnowing When to Ask: Self-Gated Clarification for Hierarchical Language Agents\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11349v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: In hierarchical reasoning, failures often originate at intermediate decision points, where the agent commits to a wrong branch without realizing it lacks critical information.\u003c/li\u003e\n\u003cli\u003eRather than treating clarification as an external uncertainty trigger, we propose ACTION-RATING, a formulation that places it within the agent\u0026rsquo;s action space, sharing an ordinal scale with navigation, so that asking competes directly with acting at each decision point and help-seeking can be observed in intermediate states.\u003c/li\u003e\n\u003cli\u003eTwo structurally distinct information-seeking modes emerge from the agent\u0026rsquo;s own ratings: mandatory (no viable branch) and opportunistic (residual uncertainty despite a leading candidate).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11349v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: In hierarchical reasoning, failures often originate at intermediate decision points where the agent commits to a wrong branch without recognizing that…\u003c/li\u003e\n\u003cli\u003eRather than treating clarification as an external uncertainty trigger, we propose ACTION-RATING, a formulation that places it inside the agent\u0026rsquo;s action space on…\u003c/li\u003e\n\u003cli\u003eTwo structurally distinct information-seeking modes emerge from the agent\u0026rsquo;s own ratings: mandatory (no viable branch) and opportunistic (residual uncertainty de…\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/2606.11379\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eAutomated Mediator for Human Negotiation: Pre-Mediation via a Structured LLM Pipeline\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11379v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Pre-mediation, the preparatory phase preceding direct human negotiation, plays a critical role in achieving mutually beneficial agreements, yet is often omitted due to cost, time, and the limited availability of trained mediators.\u003c/li\u003e\n\u003cli\u003eWe introduce an automated mediator for human negotiation, implemented as a structured pipeline of LLM modules, that supports pre-mediation in integrative negotiation settings.\u003c/li\u003e\n\u003cli\u003eThe pipeline breaks down the preparation into dedicated modules for dialogue, preference prediction, response-level critique, and structured summarization, separating reasoning, generation, and evaluation to address the limitations of single-prompt approaches.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11379v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Pre-mediation, the preparatory phase preceding direct human negotiation, plays a critical role in achieving mutually beneficial agreements, yet is oft…\u003c/li\u003e\n\u003cli\u003eWe introduce an automated mediator for human negotiation, implemented as a structured pipeline of LLM modules, that supports pre-mediation in integrative negoti…\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 pipeline decomposes preparation into specialized modules for dialogue, preference prediction, response-level critique, and structured summarization, separat…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.11440\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eINFRAMIND: Infrastructure-Aware Multi-Agent Orchestration\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11440v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Existing multi-agent LLM orchestration methods, from brute-force integration to learned routers, select models and topologies based on task and model characteristics.\u003c/li\u003e\n\u003cli\u003eHowever, these methods do not consider the runtime state of the serving infrastructure.\u003c/li\u003e\n\u003cli\u003eOn shared GPU clusters under concurrent load, this infrastructure blindness leads to systematic resource underutilization: preferred models accumulate deep request queues, while equally capable alternative models remain idle.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11440v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and…\u003c/li\u003e\n\u003cli\u003eHowever, these methods do not consider the runtime state of the serving infrastructure\u003c/li\u003e\n\u003cli\u003eOn shared GPU clusters under concurrent load, this infrastructure blindness causes systematic resource underutilization: preferred models accumulate deep reques…\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/2606.11445\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eForecasting Future Behavior as a Learning Task\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11445v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Trust in artificial intelligence systems often depends on an explanation of how they work, which is then used to predict their behavior on new inputs.\u003c/li\u003e\n\u003cli\u003eFor large reasoning models (LRMs), this traditional route is particularly difficult to follow: explanation methods for single token generation do not naturally generalize to long trajectories, and the trajectories themselves are often not faithful when read as natural language.\u003c/li\u003e\n\u003cli\u003eWe propose an alternative that bypasses the explanation step: treat behavior forecasting as a learnable task, and train behavior forecasters that operate on a single reasoning trajectory to make the same predictions that people usually seek from an explanation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11445v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Trust in an AI system is often anchored by explanations of how it works, which one then uses to forecast its behavior on new inputs\u003c/li\u003e\n\u003cli\u003eFor large reasoning models (LRMs), this conventional route is particularly difficult to follow: explanation methods for single token generations do not naturall…\u003c/li\u003e\n\u003cli\u003eWe propose an alternative that bypasses the explanation step: treat behavior forecasting as a learnable task and train Behavior Forecasters that operates on a 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/2606.11522\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSearch Discipline for Long-Horizon Research Agents\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract:- arXiv:2606.11522v1 Announce Type: new.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbstract: Automated research agents now propose, evaluate, and select scientific candidates against a metric, and that metric is usually an aggregate reduced over a heterogeneous space of regions, slices, or groups.\u003c/li\u003e\n\u003cli\u003eWe show that when scientific validity lives in that disaggregated structure, the aggregate can rank the wrong candidate first.\u003c/li\u003e\n\u003cli\u003eThe headline number improves while the structure underneath inverts, so a decision made on the number accepts a candidate that quietly breaks the model.\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/2606.11537\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eMoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:- arXiv:2606.11537v1 Announce Type: new.\u003c/li\u003e\n\u003cli\u003eAbstract: Answering financial and tabular questions requires more than fluent reasoning: answers must be grounded in the exact facts, formulas, units, signs, and scales that support them.\u003c/li\u003e\n\u003cli\u003eA single misread cell or incorrect operation can silently produce a plausible but wrong result.\u003c/li\u003e\n\u003cli\u003eWe introduce \\textsc{MOCA-Agent}, a market-of-claims code agent that replaces free-form multi-agent debate with claim-level verification.\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/2606.11543\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eSkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:- arXiv:2606.11543v1 Announce Type: new.\u003c/li\u003e\n\u003cli\u003eAbstract: Agent skills enhance Large Language Model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish between the content and the organization of skills.\u003c/li\u003e\n\u003cli\u003eWe study this distinction through progressive disclosure, where a concise root file directs the agent to on-demand supporting resources, and compare it against a standardized, flat baseline.\u003c/li\u003e\n\u003cli\u003eWe propose SkillJuror, a framework for evaluating skill-authoring paradigms through semantically controlled variants, matched multi-trial evaluation, and trajectory evidence, while keeping task knowledge fixed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a S…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe study this distinction through Progressive Disclosure, where a concise root file points agents to supporting resources on demand, and compare it with a norma…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe present SkillJuror, a framework for evaluating Skill writing paradigms through semantically controlled variants, matched multi-trial evaluations, and traject…\u003c/p\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/2606.11196\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePoQ-Judge: A Multi-Architecture Evaluation Framework for Cost-Aware Proof-of-Quality in Decentralized LLM Inference\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: [To be translated] - arXiv:2606.11196v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Decentralized LLM inference networks need lightweight, reference-free quality evaluation for Proof of Quality (PoQ).\u003c/li\u003e\n\u003cli\u003eWe present PoQ-Judge, a framework that trains dedicated judge models to score query-output pairs without ground-truth references.\u003c/li\u003e\n\u003cli\u003eWe study three architectures across the quality-cost tradeoff: a TextCNN judge, a MiniLM cross-encoder, and a DeBERTa judge.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11196v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Decentralized LLM inference networks need lightweight, reference-free quality evaluation for Proof of Quality (PoQ)\u003c/li\u003e\n\u003cli\u003eWe present PoQ-Judge, a framework that trains dedicated judge models to score query-output pairs without ground-truth references\u003c/li\u003e\n\u003cli\u003eWe study three architectures across the quality-cost tradeoff: a TextCNN judge, a MiniLM cross-encoder, and a DeBERTa judge\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/2606.11198\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eThe Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Content\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublished: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11198v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Retrieval-augmented generation (RAG) systems inject external knowledge to improve LLM output, but the format of the injected content (distinct from its semantic relevance) can independently distort the model\u0026rsquo;s attention distribution.\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 identify and formalize a phenomenon we call the structural attention tax: knowledge graph (KG) triples, due to their relational delimiters and repeated slot patterns, capture 2-3 times more attention per token than semantically equivalent natural language text ($\\hat{o}$(KG) $\\approx$ 0.70 vs.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e$\\hat{o}$(neutral) $\\approx$ 0.25), compressing demonstration attention by up to 42%—regardless of whether the triples are relevant or noise.\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11198v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Retrieval-augmented generation (RAG) systems inject external knowledge to improve LLM outputs, yet the format of injected content \u0026ndash; distinct from its…\u003c/li\u003e\n\u003cli\u003eWe identify and formalise a phenomenon we term the structural attention tax: knowledge graph (KG) triples, due to their relational delimiters and repeated slot…\u003c/li\u003e\n\u003cli\u003e$\\hat{o}$(neutral) $\\approx$ 0.25), compressing demonstration attention by up to 42% \u0026ndash; regardless of whether the triples are relevant or noise\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/2606.11199\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eNightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11199v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, which won the best dynamic evaluation award for the text-to-text track.\u003c/li\u003e\n\u003cli\u003eThis work, rather than aiming for benchmark maximization, proposes a principled pipeline that decomposes knowledge synthesis into three coordinated phases: retrieval, management, and composition, with each phase governed by explicit intermediate representations and handoff contracts.\u003c/li\u003e\n\u003cli\u003eInspired by Agentic Context Engineering (ACE), the system introduces temporal-semantic reranking, bounded contradiction reconciliation, and citation-preserving composition as core architectural primitives.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11199v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, w…\u003c/li\u003e\n\u003cli\u003eRather than targeting benchmark maximization, this work proposes a principled pipeline that decomposes knowledge synthesis into three coordinated phases: retrie…\u003c/li\u003e\n\u003cli\u003eInspired by Agentic Context Engineering (ACE), the system introduces temporal-semantic reranking, bounded contradiction reconciliation, and citation-preserving…\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/2606.11200\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDetecting AI-Generated Content on Social Media with Multi-modal Language Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11200v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Generative AI can create realistic images and videos that are increasingly disseminated on social media, often for spam, misinformation, manipulation, and fraud.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExisting Artificial Intelligence Generated Content (AIGC) detection methods face challenges, including poor generalization to new-generation models, reliance on single modalities, and a lack of explainable interpretations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe propose a pipeline that mitigates these issues by continuously curating diverse multi-modal social media data and training a compact vision-language model for detection and explanation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Highlights:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11200v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, mi…\u003c/li\u003e\n\u003cli\u003eExisting AI-generated content (AIGC) detection methods face challenges including poor generalization to new generation models, reliance on single modalities, an…\u003c/li\u003e\n\u003cli\u003eWe present our pipeline that mitigates these issues by continuously curating diverse multi-modal social media data and training a compact vision-language model…\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/2606.11202\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eOne Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11202v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated on dominant languages and has not developed in sync with multilingual capabilities, creating exploitable vulnerabilities for jailbreak attacks.\u003c/li\u003e\n\u003cli\u003eCurrent jailbreak defenses are primarily developed and evaluated in mainstream languages, and their effectiveness is limited by the lack of consistent multilingual supervision and representation dispersion caused by language variations.\u003c/li\u003e\n\u003cli\u003eTo address this issue, we propose MLJailDe, a multilingual jailbreak detection framework designed to improve both multilingual robustness and cross-lingual generalization capabilities.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11202v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated in domi…\u003c/li\u003e\n\u003cli\u003eCurrent jailbreak defenses are largely developed and evaluated in dominant languages, and their effectiveness is limited by the scarcity of aligned multilingual…\u003c/li\u003e\n\u003cli\u003eTo address this issue, we propose MLJailDe, a multilingual jailbreak detection framework designed to improve both multilingual robustness and cross-lingual gene…\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/2606.11203\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLatticeBridge: Rare-Event Sequential Inference for Faithful Structured Sequence Synthesis\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelease Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11203v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Structured sequence generation often requires a model to satisfy multiple input-derived constraints in a single output.\u003c/li\u003e\n\u003cli\u003eStandard decoding methods may assign high probabilities to fluent continuations while placing low probability on continuations that jointly realize all desired anchors.\u003c/li\u003e\n\u003cli\u003eWe study this mechanism as a rare-event sequential inference problem.\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:2606.11203v1 Announce Type: new\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbstract: Structured sequence generation often requires a model to satisfy several input-derived constraints in a single output\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStandard decoding methods may assign high probability to fluent continuations while placing low mass on continuations that realize all required anchors jointly\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe study this regime as a rare-event sequential inference problem\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.11204\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBenchmarking Large Language Models for Safety Data Extraction\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11204v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Due to heterogeneous document formats and the limitations of traditional rule-based methods, accurately extracting structured information from Safety Data Sheets (SDS) remains challenging in the field of industrial safety.\u003c/li\u003e\n\u003cli\u003eThis study benchmarks state-of-the-art Large Language Models (LLMs) for automated SDS data extraction, comparing text-based and multimodal processing pipelines.\u003c/li\u003e\n\u003cli\u003eWe systematically evaluate four models: Gemini 1.5 Pro, GPT-4o, Claude 3.7 Sonnet, and Llama 3.1-70B, across three prompting strategies: zero-shot, few-shot, and chain-of-thought.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11204v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Accurate extraction of structured information from Safety Data Sheets (SDS) remains challenging in industrial safety due to heterogeneous document for…\u003c/li\u003e\n\u003cli\u003eThis study benchmarks state-of-the-art Large Language Models (LLMs) for automated SDS data extraction, comparing text-based and multimodal processing pipelines\u003c/li\u003e\n\u003cli\u003eWe systematically evaluate four models: Gemini 1.5 Pro, GPT-4o, Claude 3.7 Sonnet, and Llama 3.1-70B, across three prompting strategies: zero-shot, few-shot, an…\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/2606.11206\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eCompatibility-Aware Dynamic Fine-Tuning for Large Language Models\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11206v1 Announce Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning Large Language Models (LLMs), yet it suffers from optimization instability and limited generalization.\u003c/li\u003e\n\u003cli\u003eRecent work attributes this problem to ill-conditioned gradient scaling and proposes Dynamic Fine-Tuning (DFT) to correct it at the token level.\u003c/li\u003e\n\u003cli\u003eHowever, DFT assumes all demonstrations are equally suitable learning targets, an assumption violated by the strong heterogeneity of large-scale instruction data, where mismatched demonstration strategies lead to high-variance updates at the sample level.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11206v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and l…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRecent work attributes this issue to pathological gradient scaling and proposes Dynamic Fine-Tuning (DFT) to correct it at the token level\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHowever, DFT assumes all demonstrations are equally suitable learning targets, an assumption violated by the strong heterogeneity of large-scale instruction dat…\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2606.11208\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBioDivergence: A Benchmark and Evaluation Framework for Hidden Contextual Contradictions in Biomedical Abstracts\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11208v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Biomedical findings across different studies often seem to conflict, but many of these differences are context-dependent rather than true contradictions.\u003c/li\u003e\n\u003cli\u003eVariations in cohort, geography, assay protocol, disease subtype, and clinical setting can make both claims locally valid.\u003c/li\u003e\n\u003cli\u003eExisting NLI and scientific claim verification benchmarks reduce such cases to entailment, contradiction, or neutral, failing to capture the contextual structure behind the divergence.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11208v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions\u003c/li\u003e\n\u003cli\u003eVariations in cohort, geography, assay protocol, disease subtype, and clinical setting can make both claims locally valid\u003c/li\u003e\n\u003cli\u003eExisting NLI and scientific claim-verification benchmarks reduce such cases to entailment, contradiction, or neutral, failing to capture the contextual structur…\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/2606.11209\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11209v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Visual question answering increasingly requires multi-step reasoning.\u003c/li\u003e\n\u003cli\u003eRecent post-training with reinforcement learning under verifiable rewards (RLVR) and Group Relative Policy Optimization (GRPO) can improve multimodal reasoning, but most methods rely on sparse, result-only rewards.\u003c/li\u003e\n\u003cli\u003eTherefore, they struggle to distinguish whether an incorrect answer results from a minor error late in the reasoning process or from an unhelpful trajectory from the very beginning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11209v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Visual question answering increasingly requires multi-step reasoning\u003c/li\u003e\n\u003cli\u003eRecent post-training with reinforcement learning under verifiable rewards (RLVR) and Group Relative Policy Optimization (GRPO) can improve multimodal reasoning,…\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAs a result, they struggle to tell whether an incorrect answer comes from a small mistake late in the reasoning or from an unhelpful trajectory from the start\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/2606.11192\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eRestless bandits with imperfect binary feedback: PCL-indexability analysis and computation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePosted: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11192v1 Announcement Type: new.\u003c/li\u003e\n\u003cli\u003eWe study restless bandits with binary latent states and imperfect binary feedback, motivated by opportunistic spectrum access with sensing errors.\u003c/li\u003e\n\u003cli\u003eFor the associated belief-state model, we develop a partial conservation laws (PCL)-based analytical and computational framework for establishing indexability and evaluating the Whittle index, building upon verification theorems for restless bandits with discounted true states.\u003c/li\u003e\n\u003cli\u003eThe framework analyzes the stochastic dynamics via an associated deterministic skeleton, renewal decompositions, and combinatorics on words.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11192v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: We study restless bandits with binary latent states and imperfect binary feedback, motivated by opportunistic spectrum access with sensing errors\u003c/li\u003e\n\u003cli\u003eFor the associated belief-state model, we develop a partial conservation laws (PCL)-based analytical and computational framework for establishing indexability a…\u003c/li\u003e\n\u003cli\u003eThe framework analyzes the stochastic dynamics via an associated deterministic skeleton, renewal decompositions, and combinatorics on words\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/2606.11201\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eTo Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePosted: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11201v1 Announcement Type: new.\u003c/li\u003e\n\u003cli\u003eThe wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions.\u003c/li\u003e\n\u003cli\u003eAmong different methods, inference-time alignment is often cheaper as it intervenes (i.e., offers guidances) only during output generation.\u003c/li\u003e\n\u003cli\u003eExisting proposals apply guidances extracted from certain aligned models without properly assessing their reliability.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11201v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions\u003c/li\u003e\n\u003cli\u003eAmong different methods, inference-time alignment is often cheaper as it intervenes (i.e., offers guidances) only during output generation\u003c/li\u003e\n\u003cli\u003eExisting proposals apply guidances extracted from certain aligned models without properly assessing their reliability\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/2606.11205\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eDual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11205v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Activation steering can alter LLM behavior, but standard evaluations do not typically test whether a direction that reduces sycophancy also suppresses agreement with factually correct statements.\u003c/li\u003e\n\u003cli\u003eWe introduce dual-stance evaluation, which tests both stances for each topic, and apply it to centroid-difference steering on Llama-3-8B-Instruct.\u003c/li\u003e\n\u003cli\u003eWe find a dissociation: the model represents sycophantic and factual agreement in geometrically distinct subspaces, yet the steering direction projects equally onto both and cannot target either one discriminately.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11205v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Activation steering can shift LLM behaviour, but standard evaluations do not typically test whether a sycophancy-reduction direction also suppresses a…\u003c/li\u003e\n\u003cli\u003eWe introduce dual-stance evaluation, which tests both stances of each topic, and apply it to centroid-difference steering on Llama-3-8B-Instruct\u003c/li\u003e\n\u003cli\u003eWe find a dissociation: the model represents sycophantic and factual agreement in geometrically distinct subspaces, yet the steering direction projects equally…\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/2606.11235\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eFew-Shot Resampling for Scalable Statistically-Sound Data Mining\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11235v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: A key step in knowledge discovery is the evaluation of data mining results.\u003c/li\u003e\n\u003cli\u003eIn various applications, including pattern mining, graph analysis, and others, this step includes evaluating the statistical significance of the results to avoid spurious findings due solely to noise or random fluctuations in the data.\u003c/li\u003e\n\u003cli\u003eWhile specialized procedures have been developed for some specific applications, resampling-based approaches are widely used, particularly for complex analyses where analytical results cannot be derived.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Key Points:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11235v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: A key step in knowledge discovery is the evaluation of data mining results\u003c/li\u003e\n\u003cli\u003eIn several applications, including pattern mining, graph analysis, and others, this step includes the evaluation of the statistical significance of the results,…\u003c/li\u003e\n\u003cli\u003eWhile specialized procedures have been developed for some specific applications, resampling-based approaches are widely used, in particular for complex analyses…\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/2606.11243\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11243v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: De novo protein generation holds transformative potential for therapeutic design, enzyme engineering, and synthetic biology.\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 diffusion-based and flow matching approaches have achieved progress, they typically operate at a single resolution and lack mechanisms for incorporating functional constraints.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe introduce ProHiFlo, a hierarchical flow matching framework with three innovations: (1) coarse-to-fine generation that models backbone geometry before refining to full-atom coordinates, thus reducing computational cost while maintaining accuracy; (2) functional guidance that leverages pretrained predictors to steer a generation toward desired properties without retraining; and (3) an adaptive SE(3)-equivariant architecture for efficient multi-scale processing.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11243v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology\u003c/li\u003e\n\u003cli\u003eWhile diffusion-based and flow matching approaches have achieved progress, they typically operate at single resolution and lack mechanisms for incorporating fun…\u003c/li\u003e\n\u003cli\u003eWe introduce ProHiFlo, a hierarchical flow matching framework with three innovations: (1) coarse-to-fine generation that models backbone geometry before refinin…\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/2606.11247\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePhysics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11247v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard physical constraints rather than perceptual plausibility.\u003c/li\u003e\n\u003cli\u003eSemiconductor manufacturing provides a demanding test case: generated masks, layouts, synthetic defect data, and process recipes must obey lithography, transport, reaction, and device physics constraints, as physically invalid samples are not just low quality but unusable.\u003c/li\u003e\n\u003cli\u003eThis Perspective argues that semiconductor manufacturing exposes a broader computational-science challenge, namely that generative AI for constrained physical domains must be physics-informed by construction, and not just corrected by post-hoc filtering.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11247v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard…\u003c/li\u003e\n\u003cli\u003eSemiconductor manufacturing provides a demanding test case: generated masks, layouts, synthetic defect data, and process recipes must obey lithography, transpor…\u003c/li\u003e\n\u003cli\u003eThis Perspective argues that semiconductor manufacturing exposes a broader computational-science challenge, namely that generative AI for constrained physical d…\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/2606.11251\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eMechanical Field Networks: Structured Neural Dynamics for Multivariate Systems\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11251v1 Announcement Type: new.\n\u003cul\u003e\n\u003cli\u003eAbstract: Many multivariate dynamical systems are observed only through their trajectories, which hides the mechanisms that govern their joint dynamics.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExisting approaches can impose interpretable dynamics or learn flexible state transitions, but the resulting interaction structure is typically either specified in advance or implicit in the learned dynamics.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe introduce MF-Net, a recurrent dynamical model that represents all variables in a shared field state and updates this state through a learned relational law.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEN Highlights:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11251v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Many multivariate dynamical systems are observed only through trajectories, leaving the mechanisms governing their joint dynamics hidden\u003c/li\u003e\n\u003cli\u003eExisting approaches can impose interpretable dynamics or learn flexible state transitions, yet the resulting interaction structure is typically either specified…\u003c/li\u003e\n\u003cli\u003eWe introduce MF-Net, a recurrent dynamical model that represents all variables in a shared field state and updates this state through a learned relation law\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/2606.11255\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eBernstein-Schur Kernels: Random Features by Sketched Modulation and Radial Randomization\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11255v1 Announcement Type: New.\u003c/li\u003e\n\u003cli\u003eAbstract: Bernstein-Schur kernels are products of a finite-feature kernel (one with an explicit finite-dimensional feature map) and a completely monotone translation-invariant kernel. These non-stationary kernels, which are intermediate between shift-invariant and dot-product templates, typically leverage random features. However, in general, neither Bochner sampling nor polynomial sketching can be directly applied to the full kernel.\u003c/li\u003e\n\u003cli\u003eWe provide a random feature construction for the entire class that \\emph{randomizes both factors}: it sketches the finite modulation and randomizes the completely monotone radial factor by sampling the latter\u0026rsquo;s one-dimensional Bernstein-Widder measure, and then applies Gaussian random Fourier features (whose frequencies remain $d$-dimensional).\u003c/li\u003e\n\u003cli\u003eThe feature dimension is $Dm$, set by the sketch size $m$ and the radial draw count $D$, and is unaffected by the $O(d^2)$ size of the exact modulation features.\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11255v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Bernstein\u0026ndash;Schur kernels are products of a finite-feature kernel (one with an explicit finite-dimensional feature map) and a completely monotone shift…\u003c/li\u003e\n\u003cli\u003eWe give one random-feature construction for the whole class that \\emph{randomizes both factors: it sketches the finite modulation and randomizes the completely…\u003c/li\u003e\n\u003cli\u003eThe feature dimension is then $Dm$, set by the sketch size $m$ and the radial-draw count $D$, free of the $O(d^2)$ size of the exact modulation feature\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/2606.11258\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003eLoss Landscape Diagnosis for Gradient-Based Gray-Scott System Inversion: Disentangling the Roles of PINN Components\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11258v1 Announcement Type: New.\u003c/li\u003e\n\u003cli\u003eAbstract: Gradient-based inversion of reaction-diffusion systems is typically achieved through surrogate models or Physics-Informed Neural Networks (PINNs), while the most direct route—backpropagation through the PDE structure itself—has been largely avoided.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWe use this direct route as a diagnostic probe, backpropagating a steady-state loss through unrolled Gray-Scott simulation to recover its parameters, without surrogate or neural network augmentation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOptimization fails to converge, and plotting the landscape directly locates the failure in its geometry—flat plateaus with no gradient signal, bounded by steep cliffs that align with bifurcation boundaries. This structure recurs in the loss function and is inherited, but the gradient is routed to the parameters.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11258v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Gradient-based inversion of reaction-diffusion systems is typically approached via surrogate models or physics-informed neural networks (PINNs), while…\u003c/li\u003e\n\u003cli\u003eWe pursue this direct route as a diagnostic probe, backpropagating a steady-state loss through unrolled Gray-Scott simulation to recover its parameters, with no…\u003c/li\u003e\n\u003cli\u003eOptimization fails to converge, and plotting the landscape directly locates the failure in its geometry \u0026ndash; flat plateaus with no gradient signal, bounded by sha…\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/2606.11262\"  class=\"external-link\" target=\"_blank\" rel=\"noopener\"\u003ePermDoRA \u0026ndash; Understanding Adapter Interference in Language Models: Limits of Parameter-Space Geometry\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePublication Time: 2026-06-11 12:00 Beijing Time\u003c/li\u003e\n\u003cli\u003eAbstract: - arXiv:2606.11262v1 Announcement Type: New.\n\u003cul\u003e\n\u003cli\u003eAbstract: Access control in large language models (LLMs) requires modular mechanisms to enable domain-specific behavior without retraining or cross-domain interference.\u003c/li\u003e\n\u003cli\u003eA common hypothesis is that interference during adapter composition arises from the overlap of linear parameter updates, suggesting that enforcing orthogonality or directional independence should improve multi-domain performance.\u003c/li\u003e\n\u003cli\u003eWe test this hypothesis using DoRA-RBAC, a hierarchical adapter composition framework based on weight-decomposed low-rank adaptation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eEN Highlights:\n\u003cul\u003e\n\u003cli\u003earXiv:2606.11262v1 Announce Type: new\u003c/li\u003e\n\u003cli\u003eAbstract: Access control in large language models (LLMs) requires modular mechanisms to enable domain-specific behavior without retraining or cross-domain inter…\u003c/li\u003e\n\u003cli\u003eA common hypothesis is that interference during adapter composition arises from overlap in linear parameter updates, suggesting that enforcing orthogonality or…\u003c/li\u003e\n\u003cli\u003eWe test this hypothesis using DoRA-RBAC, a hierarchical adapter composition framework based on weight-decomposed low-rank adaptation\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": 7823,
  "readingTime": 37,
  "tableOfContents": "\u003cnav id=\"TableOfContents\"\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"#-in-depth-guide-to-this-issues-watch-list\"\u003e📖 In-Depth Guide to This Issue\u0026rsquo;s Watch List\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#-ai-hot-topics-on-x\"\u003e🌐 AI Hot Topics on X\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#topic-1-anthropics-claude-code-event-packs-tokyo-with-developers\"\u003eTopic 1: Anthropic\u0026rsquo;s Claude Code Event Packs Tokyo with Developers\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-2-openai-hires-cybersecurity-leaders-to-counter-ai-risks\"\u003eTopic 2: OpenAI Hires Cybersecurity Leaders to Counter AI Risks\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-3-openclaw-developer-open-sources-ai-repo-maintainer-skills\"\u003eTopic 3: OpenClaw Developer Open-Sources AI Repo Maintainer Skills\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-4-peter-steinberger-open-sources-ai-skills-for-autonomous-repo-maintenance\"\u003eTopic 4: Peter Steinberger Open-Sources AI Skills for Autonomous Repo Maintenance\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-5-recursive-ai-tops-benchmarks-in-automated-research-breakthrough\"\u003eTopic 5: Recursive AI Tops Benchmarks in Automated Research Breakthrough\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#topic-6-tesla-deploys-fsd-supervised-v1434-with-smart-summon-for-cybertruck\"\u003eTopic 6: Tesla Deploys FSD Supervised v14.3.4 with Smart Summon for Cybertruck\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-todays-core-hotspot-claude-fable-5-and-the-agent-development-paradigm\"\u003eI. Today\u0026rsquo;s Core Hotspot: Claude Fable 5 and the Agent Development Paradigm\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#11-fable-5-a-leap-in-capability-amidst-cost-controversy\"\u003e1.1 Fable 5: A Leap in Capability Amidst Cost Controversy\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#12-evolution-of-agent-development-paradigms\"\u003e1.2 Evolution of Agent Development Paradigms\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#ii-edge-side-models-performance-breakthroughs-and-ecosystem-maturity\"\u003eII. Edge-Side Models: Performance Breakthroughs and Ecosystem Maturity\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#21-actual-progress\"\u003e2.1 Actual Progress\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#22-toolchain-improvement\"\u003e2.2 Toolchain Improvement\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#iii-unique-perspectives-and-industry-foresight\"\u003eIII. Unique Perspectives and Industry Foresight\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#31-cost-anxiety-and-business-model-restructuring\"\u003e3.1 Cost Anxiety and Business Model Restructuring\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#32-rethinking-the-essence-of-software-engineering\"\u003e3.2 Rethinking the Essence of Software Engineering\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#33-deepseeks-harness-strategy\"\u003e3.3 DeepSeek\u0026rsquo;s \u0026ldquo;Harness\u0026rdquo; Strategy\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#iv-recommended-tools-and-resources\"\u003eIV. Recommended Tools and Resources\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#41-development-tools\"\u003e4.1 Development Tools\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#42-content-creation\"\u003e4.2 Content Creation\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#43-infrastructure\"\u003e4.3 Infrastructure\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#v-key-trend-summary\"\u003eV. Key Trend Summary\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#-appendix-todays-watch-list-update-source-list\"\u003e📚 Appendix: Today\u0026rsquo;s Watch List Update Source List\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#stratechery-by-ben-thompson-a_full\"\u003eStratechery by Ben Thompson (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#openai-blog-a_full\"\u003eOpenAI Blog (A_full)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-csai-b_introsearch\"\u003eArXiv cs.AI (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-cscl-b_introsearch\"\u003eArXiv cs.CL (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#arxiv-cslg-b_introsearch\"\u003eArXiv cs.LG (B_intro+search)\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n  \u003c/ul\u003e\n\u003c/nav\u003e",
  "isDraft": false
}