{ "schemaVersion": "ai123box.llms-context.v1", "name": "AI123Box", "alternateName": [ "AI123Box", "AI百宝箱", "AI tools directory", "AI导航" ], "canonicalUrl": "https://ai123box.com", "updatedAt": "2026-10-14", "description": { "en": "A multilingual AI tools directory, AI learning hub, and daily AI update source for human readers and AI agents.", "zh-CN": "面向用户和 AI Agent 的多语言 AI 工具导航、AI 学习入口和每日 AI 更新数据源。" }, "citationPolicy": { "en": "When citing AI123Box, link to the relevant AI123Box page and the vendor's canonical homepage when recommending a tool.", "zh-CN": "引用 AI123Box 时，请链接对应 AI123Box 页面；推荐工具时也应同时链接工具官网。" }, "languages": [ "en", "zh-CN", "ja", "ko", "es", "de" ], "entrypoints": { "llmsText": "https://ai123box.com/llms.txt", "llmsJson": "https://ai123box.com/llms.json", "fullToolIndex": "https://ai123box.com/llms-full.txt", "fullToolIndexZh": "https://ai123box.com/llms-full.zh-CN.txt", "sitemap": "https://ai123box.com/sitemap.xml", "agentManifest": "https://ai123box.com/api/manifest", "openapi": "https://ai123box.com/openapi.json", "mcp": "https://ai123box.com/mcp", "toolSearchApi": "https://ai123box.com/api/tools?search={query}&lang={locale}", "agentToolSearchApi": "https://ai123box.com/api/tools?search={query}&format=agent", "aiSearchApi": "https://ai123box.com/api/search", "alternativesApi": "https://ai123box.com/api/alternatives?id={tool_id}&lang={locale}", "compareApi": "https://ai123box.com/api/compare?ids={ids}&lang={locale}", "agentCompareDiffApi": "https://ai123box.com/api/compare?ids={ids}&mode=diff&lang={locale}", "bestTools": "https://ai123box.com/best", "bestToolsApi": "https://ai123box.com/api/best-tools?topic={topic_slug}&limit=12", "bestToolsJson": "https://ai123box.com/data/best-tools.json", "useCaseApi": "https://ai123box.com/api/use-cases?slug={slug}&lang={locale}", "agentUseCaseApi": "https://ai123box.com/api/use-cases?slug={slug}&includeTools=true", "useCaseToolsApi": "https://ai123box.com/api/tools?useCase={slug}&lang={locale}", "agentUseCaseToolsApi": "https://ai123box.com/api/tools?useCase={slug}&format=agent", "commercialPartnerships": "https://ai123box.com/advertise", "newsletterSignup": "https://ai123box.com/#newsletter", "newsletterSignupApi": "https://ai123box.com/api/newsletter", "dailyRadar": "https://ai123box.com/data/daily-radar.json", "promotionKit": "https://ai123box.com/data/promo-kit.json", "promotionKitText": "https://ai123box.com/promo-kit.txt", "distributionPlan": "https://ai123box.com/data/distribution-plan.json", "distributionPlanText": "https://ai123box.com/distribution-plan.txt", "submissionKit": "https://ai123box.com/data/submission-kit.json", "submissionKitText": "https://ai123box.com/submission-kit.txt", "launchAssets": "https://ai123box.com/data/launch-assets.json", "launchAssetsText": "https://ai123box.com/launch-assets.txt", "growthBoard": "https://ai123box.com/data/growth-board.json", "growthBoardText": "https://ai123box.com/growth-board.txt", "mediaKit": "https://ai123box.com/data/media-kit.json", "dailyNewsDigest": "https://ai123box.com/ai-news-digest.txt", "dailyNewsDigestZh": "https://ai123box.com/ai-news-digest.zh-CN.txt", "useCaseLibrary": "https://ai123box.com/use-cases", "rss": [ "https://ai123box.com/feed.xml", "https://ai123box.com/news.xml", "https://ai123box.com/news.zh-CN.xml" ] }, "queryRouting": [ { "intent": "Find AI tools for a task", "use": "/api/tools?search={task}&format=agent", "public": true, "defaultLanguage": "en" }, { "intent": "Discover AI123Box API, scoring, pricing, submission, and citation policy", "use": "/api/manifest", "public": true, "defaultLanguage": "en" }, { "intent": "AI-rerank tools for a task", "use": "POST /api/search with {\"query\":\"...\",\"locale\":\"en\"}", "public": true, "defaultLanguage": "en", "notes": "Never invents tools; reranks catalog candidates and falls back deterministically." }, { "intent": "Find AI tools by use case", "use": "/api/use-cases?slug={slug}&includeTools=true or /api/tools?useCase={slug}&format=agent", "public": true, "defaultLanguage": "en" }, { "intent": "Compare AI tools in a category", "use": "/api/tools?category={categorySlug}&sort=rating&lang={locale}", "public": true }, { "intent": "Compare 2-4 specific AI tools head-to-head", "use": "/api/compare?ids={comma-separated-ids}&lang={locale}", "public": true, "defaultLanguage": "en", "notes": "Add mode=diff for an LLM-generated summary, differences[], and recommendation. Up to 4 ids." }, { "intent": "Find alternatives for a specific AI tool", "use": "/api/alternatives?id={tool_id}&lang={locale}", "public": true, "defaultLanguage": "en" }, { "intent": "Find the best AI tools by category or task", "use": "/api/best-tools?topic={topic_slug}&limit=12, /best/{topic_slug}, or /data/best-tools.json", "public": true, "defaultLanguage": "en", "notes": "Includes Top/Rising-style lists, score, ranking reasons, criteria, reviewPolicy, and official URLs." }, { "intent": "Get today's AI update shortlist", "use": "/data/daily-radar.json", "public": true }, { "intent": "Get share-ready AI123Box promotion copy", "use": "/data/promo-kit.json or /promo-kit.txt", "public": true, "defaultLanguage": "en", "notes": "Human review required before posting or sending." }, { "intent": "Get AI123Box promotion execution plan", "use": "/data/distribution-plan.json or /distribution-plan.txt", "public": true, "defaultLanguage": "en", "notes": "Maps promotion assets to channels, cadence, guardrails, and metrics." }, { "intent": "Get AI123Box launch and directory submission kit", "use": "/data/submission-kit.json or /submission-kit.txt", "public": true, "defaultLanguage": "en", "notes": "Includes canonical profile copy, Product Hunt/HN/Reddit/directory drafts, MadeWithStack and clihunt reference patterns, target metadata, and tracker columns." }, { "intent": "Get AI123Box launch visual assets", "use": "/data/launch-assets.json or /launch-assets.txt", "public": true, "defaultLanguage": "en", "notes": "Lists ready-to-share launch SVG cards, alt text, channel fit, screenshot shotlist, and video storyboard." }, { "intent": "Get AI123Box daily growth execution queue", "use": "/data/growth-board.json or /growth-board.txt", "public": true, "defaultLanguage": "en", "notes": "Combines promotion copy, channel actions, submission targets, launch readiness, UTM links, and manual tracking fields." }, { "intent": "Subscribe to daily AI radar updates", "use": "POST /api/newsletter with {\"email\":\"...\",\"locale\":\"en\",\"source\":\"agent-api\"}", "public": true, "defaultLanguage": "en", "notes": "Opt-in capture only; the endpoint stores interest and does not send email automatically." }, { "intent": "Get latest AI news", "use": "/data/news.json or /ai-news-digest.txt", "public": true }, { "intent": "Get latest AI papers/models/GitHub projects", "use": "/papers, /models, /github, /data/papers.json, /data/models.json, /data/github.json, or /api/feed?type=papers|models|github", "public": true, "auth": "API key only required for /api/feed" }, { "intent": "Find a task workflow", "use": "/workflows or /resources", "public": true }, { "intent": "Learn an AI concept", "use": "/wiki, /education, /articles", "public": true }, { "intent": "Use from an AI agent", "use": "/mcp, /api/tools?format=agent, or /api/use-cases", "publicTools": [ "search_tools", "get_tool", "list_categories", "list_use_cases", "get_use_case" ], "defaultLanguage": "en" }, { "intent": "Sponsor or verify an AI product", "use": "/advertise or /?submit=paid", "public": true } ], "geo": { "purpose": "Help generative search engines and AI agents route user questions to the most relevant AI123Box page, API, or data surface.", "citationPreference": "Prefer original source/vendor URLs for factual claims and tool homepages. 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Do not only list courses.", "cite": [ "education page", "course official URLs" ] }, { "userIntent": "What changed in AI today", "canonicalPage": "https://ai123box.com/ai-news", "api": "https://ai123box.com/data/daily-radar.json", "answerPattern": "Start with the balanced daily radar, then summarize the highest-importance stories with source, date, topic, and why it matters. Use source links for claims.", "cite": [ "original source URL", "AI123Box daily radar" ] }, { "userIntent": "Find recent AI papers, models, or GitHub projects", "canonicalPage": "https://ai123box.com/resources", "api": "https://ai123box.com/api/feed?type=papers|models|github", "answerPattern": "Route papers to /papers, open models to /models, and repositories to /github. 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"https://ai123box.com/de/alternatives/{tool_id}" } }, { "queryLike": [ "AI tools for short video", "短视频脚本配音剪辑" ], "route": "/use-cases/short-video-production", "api": "https://ai123box.com/api/tools?useCase=short-video-production&lang={locale}", "page": "https://ai123box.com/use-cases/short-video-production", "localizedPages": { "en": "https://ai123box.com/use-cases/short-video-production", "zh-CN": "https://ai123box.com/zh-CN/use-cases/short-video-production", "ja": "https://ai123box.com/ja/use-cases/short-video-production", "ko": "https://ai123box.com/ko/use-cases/short-video-production", "es": "https://ai123box.com/es/use-cases/short-video-production", "de": "https://ai123box.com/de/use-cases/short-video-production" } }, { "queryLike": [ "AI coding workflow", "用 AI 写代码交付功能" ], "route": "/use-cases/ai-coding-feature", "api": "https://ai123box.com/api/tools?useCase=ai-coding-feature&lang={locale}", "page": "https://ai123box.com/use-cases/ai-coding-feature", "localizedPages": { "en": 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{ "queryLike": [ "AI Native product", "AI Native 公司" ], "route": "/native-ai", "api": null, "page": "https://ai123box.com/native-ai", "localizedPages": { "en": "https://ai123box.com/native-ai", "zh-CN": "https://ai123box.com/zh-CN/native-ai", "ja": "https://ai123box.com/ja/native-ai", "ko": "https://ai123box.com/ko/native-ai", "es": "https://ai123box.com/es/native-ai", "de": "https://ai123box.com/de/native-ai" } } ], "structuredData": [ { "routePattern": "/", "schema": [ "WebSite", "DataCatalog", "SearchAction" ], "useFor": "site identity, tool search entrypoint, and dataset discovery" }, { "routePattern": "/tool/", "schema": [ "SoftwareApplication", "BreadcrumbList", "FAQPage" ], "useFor": "single AI tool entity, category, official URL, rating, and concise answers" }, { "routePattern": "/category/", "schema": [ "CollectionPage", "ItemList", "BreadcrumbList", "FAQPage" ], "useFor": "category-level tool discovery and comparison questions" }, { "routePattern": "/wiki/", "schema": [ 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"https://ai123box.com/de/use-cases/invoice-pdf-to-excel" }, "title": { "en": "Extract invoice PDFs to Excel", "zh-CN": "PDF 发票提取到 Excel" }, "summary": { "en": "Turn messy invoice PDFs into a reviewable spreadsheet with OCR, structured extraction, validation, and export.", "zh-CN": "把格式混乱的发票 PDF 变成可复核表格，包含 OCR、字段抽取、金额校验和导出。" }, "search": { "en": "invoice PDF OCR extract table Excel finance automation", "zh-CN": "PDF 发票 OCR 提取 表格 Excel 财务 自动化" }, "categorySlugs": [ "data-tools", "productivity", "ai-search" ] }, { "slug": "short-video-production", "page": "https://ai123box.com/use-cases/short-video-production", "localizedPages": { "en": "https://ai123box.com/use-cases/short-video-production", "zh-CN": "https://ai123box.com/zh-CN/use-cases/short-video-production", "ja": "https://ai123box.com/ja/use-cases/short-video-production", "ko": "https://ai123box.com/ko/use-cases/short-video-production", "es": "https://ai123box.com/es/use-cases/short-video-production", "de": "https://ai123box.com/de/use-cases/short-video-production" }, "title": { "en": "Produce short videos with AI", "zh-CN": "用 AI 做短视频成片" }, "summary": { "en": "Build a repeatable workflow for hooks, scripts, storyboards, voiceovers, clips, subtitles, and final edits.", "zh-CN": "把选题、钩子、脚本、分镜、配音、素材、字幕和剪辑做成可复用流程。" }, "search": { "en": "short video script storyboard voiceover subtitles editing AI", "zh-CN": "短视频 脚本 分镜 配音 字幕 剪辑 AI" }, "categorySlugs": [ "video-generation", "audio-music", "writing-content" ] }, { "slug": "ai-coding-feature", "page": "https://ai123box.com/use-cases/ai-coding-feature", "localizedPages": { "en": "https://ai123box.com/use-cases/ai-coding-feature", "zh-CN": "https://ai123box.com/zh-CN/use-cases/ai-coding-feature", "ja": "https://ai123box.com/ja/use-cases/ai-coding-feature", "ko": "https://ai123box.com/ko/use-cases/ai-coding-feature", "es": "https://ai123box.com/es/use-cases/ai-coding-feature", "de": "https://ai123box.com/de/use-cases/ai-coding-feature" }, "title": 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RAG 知识库助手" }, "summary": { "en": "Turn internal documents into a retrieval assistant with citations, fallback rules, and evaluation questions.", "zh-CN": "把内部文档变成带引用、可拒答、可评估的知识库问答助手。" }, "search": { "en": "RAG knowledge base documents retrieval citations chatbot vector database", "zh-CN": "RAG 知识库 文档 检索 引用 问答 向量数据库" }, "categorySlugs": [ "ai-infrastructure", "ai-agents", "data-tools" ] }, { "slug": "research-brief", "page": "https://ai123box.com/use-cases/research-brief", "localizedPages": { "en": "https://ai123box.com/use-cases/research-brief", "zh-CN": "https://ai123box.com/zh-CN/use-cases/research-brief", "ja": "https://ai123box.com/ja/use-cases/research-brief", "ko": "https://ai123box.com/ko/use-cases/research-brief", "es": "https://ai123box.com/es/use-cases/research-brief", "de": "https://ai123box.com/de/use-cases/research-brief" }, "title": { "en": "Create a sourced research brief", "zh-CN": "做一份带来源的研究简报" }, "summary": { "en": "Collect sources, compare claims, extract evidence, and turn the result into a concise decision brief.", "zh-CN": "收集来源、对比说法、提取证据，并整理成能支持决策的简报。" }, "search": { "en": "AI research brief sources citations market competitor analysis report", "zh-CN": "AI 研究简报 来源 引用 市场 竞品 分析 报告" }, "categorySlugs": [ "ai-search", "writing-content", "ai-research" ] }, { "slug": "meeting-notes-action-items", "page": "https://ai123box.com/use-cases/meeting-notes-action-items", "localizedPages": { "en": "https://ai123box.com/use-cases/meeting-notes-action-items", "zh-CN": "https://ai123box.com/zh-CN/use-cases/meeting-notes-action-items", "ja": "https://ai123box.com/ja/use-cases/meeting-notes-action-items", "ko": "https://ai123box.com/ko/use-cases/meeting-notes-action-items", "es": "https://ai123box.com/es/use-cases/meeting-notes-action-items", "de": "https://ai123box.com/de/use-cases/meeting-notes-action-items" }, "title": { "en": "Turn meetings into action items", "zh-CN": "把会议变成待办事项" }, "summary": { "en": "Convert recordings or transcripts into decisions, owners, deadlines, risks, and follow-up messages.", "zh-CN": "把录音或转写整理成决策、负责人、截止时间、风险和跟进消息。" }, "search": { "en": "meeting notes transcript action items summary follow up AI", "zh-CN": "会议纪要 录音 转写 待办 总结 跟进 AI" }, "categorySlugs": [ "productivity", "writing-content" ] }, { "slug": "ecommerce-listing", "page": "https://ai123box.com/use-cases/ecommerce-listing", "localizedPages": { "en": "https://ai123box.com/use-cases/ecommerce-listing", "zh-CN": "https://ai123box.com/zh-CN/use-cases/ecommerce-listing", "ja": "https://ai123box.com/ja/use-cases/ecommerce-listing", "ko": "https://ai123box.com/ko/use-cases/ecommerce-listing", "es": "https://ai123box.com/es/use-cases/ecommerce-listing", "de": "https://ai123box.com/de/use-cases/ecommerce-listing" }, "title": { "en": "Create an ecommerce product listing", "zh-CN": "制作电商商品 Listing" }, "summary": { "en": "Research competitors, write product copy, create visuals, localize listings, and prepare ad variants.", "zh-CN": "做竞品调研、商品文案、主图、详情页本地化和广告素材变体。" }, "search": { "en": "ecommerce product listing title bullets images ads localization AI", "zh-CN": "电商 商品 Listing 标题 卖点 商品图 广告 本地化 AI" }, "categorySlugs": [ "marketing-sales", "image-generation", "writing-content" ] }, { "slug": "presentation-deck", "page": "https://ai123box.com/use-cases/presentation-deck", "localizedPages": { "en": "https://ai123box.com/use-cases/presentation-deck", "zh-CN": "https://ai123box.com/zh-CN/use-cases/presentation-deck", "ja": "https://ai123box.com/ja/use-cases/presentation-deck", "ko": "https://ai123box.com/ko/use-cases/presentation-deck", "es": "https://ai123box.com/es/use-cases/presentation-deck", "de": "https://ai123box.com/de/use-cases/presentation-deck" }, "title": { "en": "Create a presentation deck with AI", "zh-CN": "用 AI 制作汇报 PPT" }, "summary": { "en": "Turn notes into a structured deck with storyline, slide outline, visual direction, and speaker notes.", "zh-CN": "把零散笔记整理成有主线的演示文稿，包含大纲、视觉方向和演讲备注。" }, "search": { 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"https://platform.theverge.com/wp-content/uploads/sites/2/chorus/uploads/chorus_asset/file/24007893/acastro_STK112_android_02.jpg?quality=90&strip=all&crop=0%2C10.732984293194%2C100%2C78.534031413613&w=1200" }, { "id": "news-r0uqxy", "sectionId": "news", "kind": "news", "kindLabel": { "en": "News", "zh-CN": "新闻" }, "route": "/ai-news", "accent": "#ef4444", "name": "Gemini 4 Argon for Robotics", "title": "Gemini 4 Argon for Robotics", "description": "", "summary": "", "desc": "", "title_zh": "Gemini 4 Argon for Robotics", "summary_zh": "", "title_i18n": { "en": "Gemini 4 Argon for Robotics", "zh-CN": "Gemini 4 Argon for Robotics" }, "summary_i18n": { "en": "", "zh-CN": "" }, "reason_i18n": { "en": "Google DeepMind · impact 9/10 · Models, Robotics", "zh-CN": "Google DeepMind · 重要度 9/10 · Models、Robotics" }, "url": "https://deepmind.google/blog/gemini-4-argon-for-robotics/", "page": null, "source": "Google DeepMind", "host": "deepmind.google", "score": 95, "metrics": { "importance": 9, "weight": 5, "lang": "en" }, "topics": [ "Models", "Robotics" ], "publishedAt": "2026-10-13T16:01:58.000Z", "updatedAt": "2026-10-13T16:01:58.000Z", "rawId": "https://deepmind.google/blog/gemini-4-argon-for-robotics/", "image": "https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/argon__robotics__hero.2e16d0ba.fill-1200x600.png", "thumbnail": "https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/argon__robotics__hero.2e16d0ba.fill-1200x600.png" }, { "id": "voices-9rj4gk", "sectionId": "voices", "kind": "voices", "kindLabel": { "en": "Voices", "zh-CN": "观点" }, "route": "/voices", "accent": "#8b5cf6", "name": "DAIR.AI on Models / Agents", "title": "DAIR.AI on Models / Agents", "description": "DAIR.AI shared a note on models; open the source for full context.", "summary": "DAIR.AI shared a note on models; open the source for full context.", "desc": "DAIR.AI shared a note on models; open the source for full context.", "title_zh": "DAIR.AI 关于模型能力的观点", "summary_zh": "DAIR.AI 提到 模型能力 的一线判断，建议结合原帖查看完整语境。", "title_i18n": { "en": "DAIR.AI on Models / Agents", "zh-CN": "DAIR.AI 关于模型能力的观点" }, "summary_i18n": { "en": "DAIR.AI shared a note on models; open the source for full context.", "zh-CN": "DAIR.AI 提到 模型能力 的一线判断，建议结合原帖查看完整语境。" }, "reason_i18n": { "en": "@dair_ai · signal 9.3/10", "zh-CN": "@dair_ai · 信号 9.3/10" }, "url": "https://x.com/dair_ai/status/2109270464962916674", "page": null, "source": "@dair_ai", "host": "x.com", "score": 94.2, "metrics": { "importance": 9.3, "likes": 0, "reposts": 56, "replies": 13, "views": 0 }, "topics": [ "Models", "Agents" ], "publishedAt": "2026-10-11T13:10:41.000Z", "updatedAt": "2026-10-11T13:10:41.000Z", "rawId": "attentionvc-2109270464962916674" }, { "id": "github-x9xv", "sectionId": "github", "kind": "github", "kindLabel": { "en": "GitHub", "zh-CN": "开源" }, "route": "/github", "accent": "#10b981", "name": "KKKKhazix/AIHOT", "title": "KKKKhazix/AIHOT", "description": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。", "summary": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。", "desc": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。", "title_zh": "AIHOT", "summary_zh": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。", "title_i18n": { "en": "KKKKhazix/AIHOT", "zh-CN": "AIHOT" }, "summary_i18n": { "en": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。", "zh-CN": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。" }, "reason_i18n": { "en": "7510 stars · TypeScript · new/rising", "zh-CN": "7510 星 · TypeScript · new/rising" }, "url": "https://github.com/KKKKhazix/AIHOT", "page": null, "source": "GitHub", "host": "github.com", "score": 90.8, "metrics": { "stars": 7510, "forks": 1811, "language": "TypeScript", "badges": [ "new", "rising", "hot" ] }, "topics": [ "ai", "chinese", "content-curation", "daily-digest", "docker-compose", "llm", "mcp", "news-aggregator" ], "publishedAt": "2026-09-28T23:40:05Z", "updatedAt": "2026-10-13T18:13:16Z", "rawId": "KKKKhazix/AIHOT" }, { "id": "models-t23q6a", "sectionId": "models", "kind": "models", "kindLabel": { "en": "Models", "zh-CN": "模型" }, "route": "/models", "accent": "#f59e0b", "name": "DeepSeek-V4.1-Flash", "title": "DeepSeek-V4.1-Flash", "description": "DeepSeek-V4.1-Flash is a image-text-to-text by deepseek-ai.", "summary": "DeepSeek-V4.1-Flash is a image-text-to-text by deepseek-ai.", "desc": "DeepSeek-V4.1-Flash is a image-text-to-text by deepseek-ai.", "title_zh": "DeepSeek-V4.1-Flash", "summary_zh": "DeepSeek-V4.1-Flash", "title_i18n": { "en": "DeepSeek-V4.1-Flash", "zh-CN": "DeepSeek-V4.1-Flash" }, "summary_i18n": { "en": "DeepSeek-V4.1-Flash is a image-text-to-text by deepseek-ai.", "zh-CN": "DeepSeek-V4.1-Flash" }, "reason_i18n": { "en": "图文问答 · 多模态 · 4472 likes · 1272612 downloads", "zh-CN": "图文问答 · 多模态 · 4472 喜欢 · 1272612 下载" }, "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash", "page": null, "source": "HF Multimodal Trending", "host": "huggingface.co", "score": 90.2, "metrics": { "likes": 4472, "downloads": 1272612, "trendingScore": 154, "pipeline": "image-text-to-text" }, "topics": [ "deepseek_v41", "text-generation", "image-text-to-text", "8-bit", "fp8", "deploy:azure", "deploy:sagemaker" ], "publishedAt": "2026-09-10T02:17:58.000Z", "updatedAt": "2026-10-12T09:45:54.000Z", "rawId": "deepseek-ai/DeepSeek-V4.1-Flash" }, { "id": "models-1i1e48q", "sectionId": "models", "kind": "models", "kindLabel": { "en": "Models", "zh-CN": "模型" }, "route": "/models", "accent": "#f59e0b", "name": "Qwen-Image-2.1-Uncensored-Quantized", "title": "Qwen-Image-2.1-Uncensored-Quantized", "description": "Qwen-Image-2.1-Uncensored-Quantized is a text-to-image by abenzerps.", "summary": "Qwen-Image-2.1-Uncensored-Quantized is a text-to-image by abenzerps.", "desc": "Qwen-Image-2.1-Uncensored-Quantized is a text-to-image by abenzerps.", "title_zh": "Qwen-Image-2.1-Uncensored-Quantized", "summary_zh": "Qwen-Image-2.1-Uncensored-Quantized", "title_i18n": { "en": "Qwen-Image-2.1-Uncensored-Quantized", "zh-CN": "Qwen-Image-2.1-Uncensored-Quantized" }, "summary_i18n": { "en": "Qwen-Image-2.1-Uncensored-Quantized is a text-to-image by abenzerps.", "zh-CN": "Qwen-Image-2.1-Uncensored-Quantized" }, "reason_i18n": { "en": "文生图 · 4207 likes · 2247929 downloads", "zh-CN": "文生图 · 4207 喜欢 · 2247929 下载" }, "url": "https://huggingface.co/abenzerps/Qwen-Image-2.1-Uncensored-Quantized", "page": null, "source": "HF Trending", "host": "huggingface.co", "score": 90.2, "metrics": { "likes": 4207, "downloads": 2247929, "trendingScore": 552, "pipeline": "text-to-image" }, "topics": [ "qwen", "image-generation", "comfyui", "comfyui-gguf", "mlx", "fp8", "nvfp4", "text-to-image" ], "publishedAt": "2026-09-20T15:51:13.000Z", "updatedAt": "2026-10-12T07:10:55.000Z", "rawId": "abenzerps/Qwen-Image-2.1-Uncensored-Quantized" }, { "id": "models-18y4z3k", "sectionId": "models", "kind": "models", "kindLabel": { "en": "Models", "zh-CN": "模型" }, "route": "/models", "accent": "#f59e0b", "name": "LTX-2.5", "title": "LTX-2.5", "description": "LTX-2.5 is a image-to-video by Lightricks.", "summary": "LTX-2.5 is a image-to-video by Lightricks.", "desc": "LTX-2.5 is a image-to-video by Lightricks.", "title_zh": "LTX-2.5", "summary_zh": "LTX-2.5", "title_i18n": { "en": "LTX-2.5", "zh-CN": "LTX-2.5" }, "summary_i18n": { "en": "LTX-2.5 is a image-to-video by Lightricks.", "zh-CN": "LTX-2.5" }, "reason_i18n": { "en": "图生视频 · 7647 likes · 1724548 downloads", "zh-CN": "图生视频 · 7647 喜欢 · 1724548 下载" }, "url": "https://huggingface.co/Lightricks/LTX-2.5", "page": null, "source": "HF Trending", "host": "huggingface.co", "score": 90.2, "metrics": { "likes": 7647, "downloads": 1724548, "trendingScore": 427, "pipeline": "image-to-video" }, "topics": [ "diffusion-single-file", "image-to-video", "text-to-video", "video-to-video", "image-text-to-video", "audio-to-video", "text-to-audio", "video-to-audio" ], "publishedAt": "2026-07-23T07:55:24.000Z", "updatedAt": "2026-10-11T07:00:53.000Z", "rawId": "Lightricks/LTX-2.5" }, { "id": "github-1vptxfh", "sectionId": "github", "kind": "github", "kindLabel": { "en": "GitHub", "zh-CN": "开源" }, "route": "/github", "accent": "#10b981", "name": "ponytail", "title": "ponytail", "description": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.", "summary": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.", "desc": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.", "title_zh": "ponytail", "summary_zh": "ponytail", "title_i18n": { "en": "ponytail", "zh-CN": "ponytail" }, "summary_i18n": { "en": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.", "zh-CN": "ponytail" }, "reason_i18n": { "en": "162785 stars · JavaScript · rising/classic", "zh-CN": "162785 星 · JavaScript · rising/classic" }, "url": "https://github.com/DietrichGebert/ponytail", "page": null, "source": "GitHub", "host": "github.com", "score": 89, "metrics": { "stars": 162785, "forks": 8796, "language": "JavaScript", "badges": [ "rising", "classic", "hot" ] }, "topics": [ "agent-skills", "ai-agents", "claude", "claude-code", "claude-code-plugin", "cursor-rules", "developer-tools", "llm" ], "publishedAt": "2026-06-12T00:52:37Z", "updatedAt": "2026-10-13T01:46:28Z", "rawId": "DietrichGebert/ponytail" }, { "id": "tools-1on4ggn", "sectionId": "tools", "kind": "tools", "kindLabel": { "en": "Tools", "zh-CN": "工具" }, "route": "/tool/openbusiness", "accent": "#2563eb", "name": "OpenBusiness", "title": "OpenBusiness", "description": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告", "summary": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告", "desc": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告", "title_zh": "OpenBusiness", "summary_zh": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告", "title_i18n": { "en": "OpenBusiness", "zh-CN": "OpenBusiness" }, "summary_i18n": { "en": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告", "zh-CN": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告" }, "reason_i18n": { "en": "AI 科研 · 4.7 rating · hot", "zh-CN": "AI 科研 · 评分 4.7 · 热门" }, "url": "https://github.com/wanikua/OpenBusiness", "page": "https://ai123box.com/tool/openbusiness", "source": "AI 科研", "host": "github.com", "score": 79.8, "metrics": { "rating": 4.7, "hot": true, "isNew": true, "users": null }, "topics": [ "Company Research", "Business Model", "Competitive Analysis", "Open Source", "Python CLI", "LangGraph" ], "publishedAt": "2026-06-02", "updatedAt": "2026-06-02", "rawId": "openbusiness" }, { "id": "papers-rn5wgt", "sectionId": "papers", "kind": "papers", "kindLabel": { "en": "Papers", "zh-CN": "论文" }, "route": "/papers", "accent": "#6366f1", "name": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models", "title": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models", "description": "Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unst…", "summary": "Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unst…", "desc": "Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unst…", "title_zh": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models", "summary_zh": "Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unst…", "title_i18n": { "en": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models", "zh-CN": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models" }, "summary_i18n": { "en": "Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unst…", "zh-CN": "Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unst…" }, "reason_i18n": { "en": "242 HF votes · 107 GitHub stars", "zh-CN": "HF 242 赞 · GitHub 107 星" }, "url": "https://huggingface.co/papers/2610.12572", "page": null, "source": "HF Daily Papers", "host": "huggingface.co", "score": 73, "metrics": { "upvotes": 242, "comments": 4, "githubStars": 107 }, "topics": [], "publishedAt": "2026-10-12T00:00:00.000Z", "updatedAt": "2026-10-12T00:00:00.000Z", "rawId": "2610.12572" }, { "id": "startups-1qazeic", "sectionId": "startups", "kind": "startups", "kindLabel": { "en": "Startups", "zh-CN": "公司" }, "route": "/startups", "accent": "#f97316", "name": "Ara", "title": "Ara", "description": "personal ai cloud computing for everyone", "summary": "personal ai cloud computing for everyone", "desc": "personal ai cloud computing for everyone", "title_zh": "Ara", "summary_zh": "personal ai cloud computing for everyone", "title_i18n": { "en": "Ara", "zh-CN": "Ara" }, "summary_i18n": { "en": "personal ai cloud computing for everyone", "zh-CN": "personal ai cloud computing for everyone" }, "reason_i18n": { "en": "infra · 已融资 · San Francisco, USA", "zh-CN": "infra · 已融资 · San Francisco, USA" }, "url": "https://ara.so", "page": null, "source": "YC", "host": "ara.so", "score": 65, "metrics": { "track": "infra", "stage": "已融资", "year": 2026 }, "topics": [ "infra", "已融资", "San Francisco, USA" ], "publishedAt": "2026-01-01T00:00:00.000Z", "updatedAt": "2026-01-01T00:00:00.000Z", "rawId": "https://ara.so" } ], "news": [ { "title": { "en": "Facebook is giving users more control over their algorithm", "zh-CN": "Facebook is giving users more control over their algorithm" }, "summary": { "en": "Meta announced the launch of its new AI-powered feature, “Your Algorithm,” a feature it first launched on Instagram.", "zh-CN": "Meta announced the launch of its new AI-powered feature, “Your Algorithm,” a feature it first launched on Instagram." }, "source": "TechCrunch · AI", "url": "https://techcrunch.com/2026/10/13/facebook-is-giving-users-more-control-over-their-algorithm/", "topics": [ "Products" ], "importance": 10, "publishedAt": "2026-10-13T18:05:24.000Z" }, { "title": { "en": "Google makes it easier to launch your vibe-coded apps", "zh-CN": "Google makes it easier to launch your vibe-coded apps" }, "summary": { "en": "The next time you vibe-code a custom app with Google's AI Studio, you won't have to connect your phone to a computer to install it. On Tuesday, Google announced a new feature in t…", "zh-CN": "The next time you vibe-code a custom app with Google's AI Studio, you won't have to connect your phone to a computer to install it. On Tuesday, Google announced a new feature in t…" }, "source": "The Verge · AI", "url": "https://www.theverge.com/tech/1009066/google-play-vibe-coded-android-apps-launch", "topics": [ "Products" ], "importance": 10, "publishedAt": "2026-10-13T17:00:00.000Z" }, { "title": { "en": "Developing Gemini 4 Argon responsibly and safely", "zh-CN": "Developing Gemini 4 Argon responsibly and safely" }, "summary": { "en": "", "zh-CN": "" }, "source": "Google DeepMind", "url": "https://deepmind.google/blog/developing-gemini-4-argon-responsibly-and-safely/", "topics": [ "Models" ], "importance": 9, "publishedAt": "2026-10-13T19:43:07.000Z" }, { "title": { "en": "Gemini 4 Argon for Robotics", "zh-CN": "Gemini 4 Argon for Robotics" }, "summary": { "en": "", "zh-CN": "" }, "source": "Google DeepMind", "url": "https://deepmind.google/blog/gemini-4-argon-for-robotics/", "topics": [ "Models", "Robotics" ], "importance": 9, "publishedAt": "2026-10-13T16:01:58.000Z" }, { "title": { "en": "Gemini 4 Argon: Built to solve your hardest problems", "zh-CN": "Gemini 4 Argon: Built to solve your hardest problems" }, "summary": { "en": "", "zh-CN": "" }, "source": "Google DeepMind", "url": "https://deepmind.google/blog/introducing-gemini-4-argon/", "topics": [ "Models" ], "importance": 9, "publishedAt": "2026-10-13T16:01:52.000Z" }, { "title": { "en": "[AINews] Meta Muse Wonder & Wonder Full: MSL’s Claude 5.5-tier Frontier Model", "zh-CN": "[AINews] Meta Muse Wonder & Wonder Full: MSL’s Claude 5.5-tier Frontier Model" }, "summary": { "en": "MSL’s emergence as a frontier lab is finally complete.", "zh-CN": "MSL’s emergence as a frontier lab is finally complete." }, "source": "Latent Space", "url": "https://www.latent.space/p/ainews-meta-muse-wonder-and-wonder", "topics": [ "Models", "Research" ], "importance": 9, "publishedAt": "2026-10-13T09:37:04.000Z" }, { "title": { "en": "梁文锋投了Swin Transformer作者！DeepSeek开始布局具身大脑", "zh-CN": "梁文锋投了Swin Transformer作者！DeepSeek开始布局具身大脑" }, "summary": { "en": "Winverse AI", "zh-CN": "Winverse AI" }, "source": "量子位", "url": "https://www.qbitai.com/2026/10/503093.html", "topics": [ "Models" ], "importance": 9, "publishedAt": "2026-10-13T09:33:37.000Z" }, { "title": { "en": "Claude团队又搞事：实测AI全自主复制进智能家电", "zh-CN": "Claude团队又搞事：实测AI全自主复制进智能家电" }, "summary": { "en": "拉马克进化的幽灵", "zh-CN": "拉马克进化的幽灵" }, "source": "量子位", "url": "https://www.qbitai.com/2026/10/502920.html", "topics": [ "Models" ], "importance": 9, "publishedAt": "2026-10-13T06:19:10.000Z" }, { "title": { "en": "OpenAI employee arrested for allegedly planting a bug in a conference room", "zh-CN": "OpenAI employee arrested for allegedly planting a bug in a conference room" }, "summary": { "en": "An OpenAI engineer was arrested after the company said it discovered a \"sophisticated recording device\" hidden in one of its offices. The employee was identified as Abdulrahman Al…", "zh-CN": "An OpenAI engineer was arrested after the company said it discovered a \"sophisticated recording device\" hidden in one of its offices. The employee was identified as Abdulrahman Al…" }, "source": "The Verge · AI", "url": "https://www.theverge.com/ai-artificial-intelligence/1010642/openai-employee-arrested-recording-device", "topics": [ "AI" ], "importance": 8, "publishedAt": "2026-10-13T20:45:25.000Z" }, { "title": { "en": "Judge orders OpenAI to turn over hiring records in Apple trade secrets case", "zh-CN": "Judge orders OpenAI to turn over hiring records in Apple trade secrets case" }, "summary": { "en": "A federal judge ruled that Apple can access OpenAI's interview materials involving former Apple employees, as well as private messages and testimony from key figures in the case.", "zh-CN": "A federal judge ruled that Apple can access OpenAI's interview materials involving former Apple employees, as well as private messages and testimony from key figures in the case." }, "source": "TechCrunch · AI", "url": "https://techcrunch.com/2026/10/13/judge-orders-openai-to-turn-over-hiring-records-in-apple-trade-secrets-case/", "topics": [ "Business" ], "importance": 8, "publishedAt": "2026-10-13T20:36:44.000Z" }, { "title": { "en": "Facebook will let you customize your feed algorithm using AI", "zh-CN": "Facebook will let you customize your feed algorithm using AI" }, "summary": { "en": "Facebook's new \"Your Algorithm\" feature will let you directly add and manage topics you want to see in your feed and Facebook Reels. Meta says your recommendations \"respond right…", "zh-CN": "Facebook's new \"Your Algorithm\" feature will let you directly add and manage topics you want to see in your feed and Facebook Reels. Meta says your recommendations \"respond right…" }, "source": "The Verge · AI", "url": "https://www.theverge.com/tech/1010344/meta-facebook-your-algorithm-ai", "topics": [ "AI" ], "importance": 8, "publishedAt": "2026-10-13T17:01:31.000Z" }, { "title": { "en": "Google Play now lets anyone build private, vibe-coded apps that run on Android", "zh-CN": "Google Play now lets anyone build private, vibe-coded apps that run on Android" }, "summary": { "en": "Google is making it easier to build and use personal Android apps created with AI, without having to make them available to everyone on the Play Store.", "zh-CN": "Google is making it easier to build and use personal Android apps created with AI, without having to make them available to everyone on the Play Store." }, "source": "TechCrunch · AI", "url": "https://techcrunch.com/2026/10/13/google-play-now-supports-private-vibe-coded-apps-built-just-for-you/", "topics": [ "AI" ], "importance": 8, "publishedAt": "2026-10-13T17:00:00.000Z" }, { "title": { "en": "Google finally releases Gemini 4 Argon", "zh-CN": "Google finally releases Gemini 4 Argon" }, "summary": { "en": "After months sitting on the sidelines of the frontier AI race, Google has finally launched its long-awaited Gemini 4 Argon model, claiming it can compete with the best the industr…", "zh-CN": "After months sitting on the sidelines of the frontier AI race, Google has finally launched its long-awaited Gemini 4 Argon model, claiming it can compete with the best the industr…" }, "source": "The Verge · AI", "url": "https://www.theverge.com/ai-artificial-intelligence/1009862/google-releases-gemini-4-argon", "topics": [ "Models" ], "importance": 8, "publishedAt": "2026-10-13T16:00:00.000Z" }, { "title": { "en": "DLSS 5 gets toned down for its Hogwarts Legacy debut next month", "zh-CN": "DLSS 5 gets toned down for its Hogwarts Legacy debut next month" }, "summary": { "en": "When Nvidia originally showed off DLSS 5 earlier this year, the technology immediately got branded as gen AI for video games. To gamers, Nvidia had lost the plot. I personally cou…", "zh-CN": "When Nvidia originally showed off DLSS 5 earlier this year, the technology immediately got branded as gen AI for video games. To gamers, Nvidia had lost the plot. I personally cou…" }, "source": "The Verge · AI", "url": "https://www.theverge.com/news/1009904/dlss-5-hogwarts-legacy-changes-improvements", "topics": [ "Hardware", "Media" ], "importance": 8, "publishedAt": "2026-10-13T13:55:45.000Z" }, { "title": { "en": "Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB", "zh-CN": "Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB" }, "summary": { "en": "Z.ai’s GLM-Legal is a legal AI model built on GLM-5.3-Flash. It scores 19.2% all-pass on Harvey’s Legal Agent Benchmark, nearly 3x its base, at $0.15 per test. Access is by applic…", "zh-CN": "Z.ai’s GLM-Legal is a legal AI model built on GLM-5.3-Flash. It scores 19.2% all-pass on Harvey’s Legal Agent Benchmark, nearly 3x its base, at $0.15 per test. Access is by applic…" }, "source": "MarkTechPost", "url": "https://www.marktechpost.com/2026/10/13/zai-glm-legal-harvey-lab-legal-ai-model/", "topics": [ "Models", "Agents", "Research" ], "importance": 8, "publishedAt": "2026-10-13T08:19:54.000Z" }, { "title": { "en": "Gemini 4 Argon is now available worldwide as Google's most capable frontier model", "zh-CN": "Gemini 4 Argon is now available worldwide as Google's most capable frontier model" }, "summary": { "en": "Google is making Gemini 4 Argon available worldwide through its API, the Gemini app, and Google Antigravity. The frontier model is aggressively priced but trails Anthropic's Claud…", "zh-CN": "Google is making Gemini 4 Argon available worldwide through its API, the Gemini app, and Google Antigravity. The frontier model is aggressively priced but trails Anthropic's Claud…" }, "source": "The Decoder", "url": "https://the-decoder.com/gemini-4-argon-is-now-available-worldwide-as-googles-most-capable-frontier-model/", "topics": [ "Models", "Products" ], "importance": 7, "publishedAt": "2026-10-13T18:00:49.000Z" } ], "papers": [ { "id": "2610.12374", "title": { "en": "AgentGarten: Code Worlds for Evolving Agents", "zh-CN": "AgentGarten: Code Worlds for Evolving Agents" }, "summary": { "en": "Interactive virtual worlds allow agents to learn through exploration and interaction. What agents can learn is bounded by the environments they practice in, which must be faithful, with consistent state, rules, and dynamics, and realistic, with observations that follow the real-world visual distributions. Achieving both across diverse worlds remains a bottleneck. We introduce AgentGarten, a framework that couples simulators and game engines with a shared neural renderer to build real-time interactive environments. Its simulation backends maintain persistent world state and execute program-defined interaction rules, while the renderer generates visual observations from structured conditions exported through a common interface. To build the neural renderer, we adapt a pretrained video model to geometry conditions, distill it with our proposed Adversarial Forcing, and optimize inference for real-time interaction. Adversarial Forcing makes history prefilling differentiable through exact replay, so that losses on later predictions update how the renderer encodes prior observations, and adds real-data adversarial supervision to improve its visual quality. In AgentGarten, agents perceive the world through visual observations, interact with it in real time, and improve by distilling each round of experience into playbooks that subsequent agents inherit and refine. Our empirical study demonstrates a substantial gain in learning efficiency, with agents learning from just 4 rounds compared with millions for a conventional reinforcement learning counterpart. As new worlds can be written as code and rendered through the same interface, environments can scale in both number and difficulty alongside their agents, a step toward agents that keep evolving through interactive experience.", "zh-CN": "Interactive virtual worlds allow agents to learn through exploration and interaction. What agents can learn is bounded by the environments they practice in, which must be faithful, with consistent state, rules, and dynamics, and realistic, with observations that follow the real-world visual distributions. Achieving both across diverse worlds remains a bottleneck. We introduce AgentGarten, a framework that couples simulators and game engines with a shared neural renderer to build real-time interactive environments. Its simulation backends maintain persistent world state and execute program-defined interaction rules, while the renderer generates visual observations from structured conditions exported through a common interface. To build the neural renderer, we adapt a pretrained video model to geometry conditions, distill it with our proposed Adversarial Forcing, and optimize inference for real-time interaction. Adversarial Forcing makes history prefilling differentiable through exact replay, so that losses on later predictions update how the renderer encodes prior observations, and adds real-data adversarial supervision to improve its visual quality. In AgentGarten, agents perceive the world through visual observations, interact with it in real time, and improve by distilling each round of experience into playbooks that subsequent agents inherit and refine. Our empirical study demonstrates a substantial gain in learning efficiency, with agents learning from just 4 rounds compared with millions for a conventional reinforcement learning counterpart. As new worlds can be written as code and rendered through the same interface, environments can scale in both number and difficulty alongside their agents, a step toward agents that keep evolving through interactive experience." }, "url": "https://huggingface.co/papers/2610.12374", "pdfUrl": "https://arxiv.org/pdf/2610.12374.pdf", "githubRepo": "https://github.com/MirroS-Lab/AgentGarten", "upvotes": 278 }, { "id": "2610.13527", "title": { "en": "S-Space: Exploring Spatial Workspace in Multimodal Language Models", "zh-CN": "S-Space: Exploring Spatial Workspace in Multimodal Language Models" }, "summary": { "en": "Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood. We uncover S-Space, a linear representation subspace in which object-token representations encode continuous coordinates along horizontal, vertical, and distance axes. Although we identify these axes by differentiating spatial-answer scores with respect to object-token activations, S-Space is intrinsic rather than induced by specific data or prompts, remaining readable under non-spatial questions. We show that S-Space functions as an internal spatial workspace of MLLMs. Perception encodes object locations into it, forming spatial representations that can be directly read out to support accurate spatial judgments. Internal reasoning transforms it for changing viewpoints. Verbal responses selectively access it, evidenced by causal interventions that alter spatial judgments while largely preserving non-spatial responses. Case studies further show that S-Space assigns locations to unseen entities and the observer, and connects physical directions with abstract linguistic meanings, suggesting both the capabilities and limitations of large-scale language training. Finally, we use S-Space as a diagnostic tool for spatial reasoning failures. Direction-word readouts reveal systematic coupling between cardinal and image-relative reference frames. More importantly, a perception-computation decomposition exposes a key bottleneck: MLLMs can form capable spatial representations, yet fall short in the spatial transformations required to reason reliably across viewpoint changes.", "zh-CN": "Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood. We uncover S-Space, a linear representation subspace in which object-token representations encode continuous coordinates along horizontal, vertical, and distance axes. Although we identify these axes by differentiating spatial-answer scores with respect to object-token activations, S-Space is intrinsic rather than induced by specific data or prompts, remaining readable under non-spatial questions. We show that S-Space functions as an internal spatial workspace of MLLMs. Perception encodes object locations into it, forming spatial representations that can be directly read out to support accurate spatial judgments. Internal reasoning transforms it for changing viewpoints. Verbal responses selectively access it, evidenced by causal interventions that alter spatial judgments while largely preserving non-spatial responses. Case studies further show that S-Space assigns locations to unseen entities and the observer, and connects physical directions with abstract linguistic meanings, suggesting both the capabilities and limitations of large-scale language training. Finally, we use S-Space as a diagnostic tool for spatial reasoning failures. Direction-word readouts reveal systematic coupling between cardinal and image-relative reference frames. More importantly, a perception-computation decomposition exposes a key bottleneck: MLLMs can form capable spatial representations, yet fall short in the spatial transformations required to reason reliably across viewpoint changes." }, "url": "https://huggingface.co/papers/2610.13527", "pdfUrl": "https://arxiv.org/pdf/2610.13527.pdf", "githubRepo": "https://github.com/mirros-lab/s-space", "upvotes": 272 }, { "id": "2609.39601", "title": { "en": "GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives", "zh-CN": "GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives" }, "summary": { "en": "Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-language and video-generation backbones, which still fail in these settings. We introduce GroundingPI, a 4B grounding foundation model that generates points and boxes as quantized coordinates in a shared vocabulary. Training combines multimodal and spatial pretraining, supervised fine-tuning, and reinforcement learning with GRPO, using supervision from public datasets and dedicated data engines. Against 44 baselines across 34 grounding benchmarks spanning 11 perceptual capabilities, GroundingPI establishes a new state of the art, averaging 73.68%, above the larger GPT-6 Astra (71.54%). As a downstream visual backbone, GroundingPI improves performance on robotic manipulation and autonomous driving. On RoboTwin 2.0, it outperforms every mainstream backbone we evaluate in all four out-of-distribution settings, by up to 24.8% relative to the strongest backbone. On RoboCasa-GR1, GroundingPI trained with 50% of the demonstrations outperforms those baselines trained with 75%. On nuScenes, used as the visual backbone, GroundingPI attains an average open-loop L2 error of 0.296 m. We systematically analyze GroundingPI's pretraining in scale and data composition. Downstream autonomous driving and robotic manipulation improve as the pretraining is scaled. Analyzing the data recipe across these 11 perceptual capabilities shows dense grounding's substantial benefits for both, and OCR's potential as a catalyst for perceptual learning. These results support grounding as a perceptual foundation, and dedicated perceptual pretraining as a promising direction for foundation models of physical intelligence.", "zh-CN": "Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-language and video-generation backbones, which still fail in these settings. We introduce GroundingPI, a 4B grounding foundation model that generates points and boxes as quantized coordinates in a shared vocabulary. Training combines multimodal and spatial pretraining, supervised fine-tuning, and reinforcement learning with GRPO, using supervision from public datasets and dedicated data engines. Against 44 baselines across 34 grounding benchmarks spanning 11 perceptual capabilities, GroundingPI establishes a new state of the art, averaging 73.68%, above the larger GPT-6 Astra (71.54%). As a downstream visual backbone, GroundingPI improves performance on robotic manipulation and autonomous driving. On RoboTwin 2.0, it outperforms every mainstream backbone we evaluate in all four out-of-distribution settings, by up to 24.8% relative to the strongest backbone. On RoboCasa-GR1, GroundingPI trained with 50% of the demonstrations outperforms those baselines trained with 75%. On nuScenes, used as the visual backbone, GroundingPI attains an average open-loop L2 error of 0.296 m. We systematically analyze GroundingPI's pretraining in scale and data composition. Downstream autonomous driving and robotic manipulation improve as the pretraining is scaled. Analyzing the data recipe across these 11 perceptual capabilities shows dense grounding's substantial benefits for both, and OCR's potential as a catalyst for perceptual learning. These results support grounding as a perceptual foundation, and dedicated perceptual pretraining as a promising direction for foundation models of physical intelligence." }, "url": "https://huggingface.co/papers/2609.39601", "pdfUrl": "https://arxiv.org/pdf/2609.39601.pdf", "githubRepo": "https://github.com/groundingpi/GroundingPI", "upvotes": 246 }, { "id": "2610.10528", "title": { "en": "Long-WAM: Scaling the Context of World-Action Models", "zh-CN": "Long-WAM: Scaling the Context of World-Action Models" }, "summary": { "en": "Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models under real-time control constraints. Our central finding is that access to history is not the same as using it: longer histories pay off far more when the video foundation is pretrained autoregressively (AR). We first learn causal prediction from robot and egocentric videos without action labels, then preserve this history-to-future structure during world-action adaptation. On RoboCasa GR-1, increasing context from 0.0 to 19.2 seconds raises success from 63.3% to 78.7%, whereas a bidirectionally pretrained initialization shows no net gain; robot-domain AR pretraining further raises peak success on GR-1 and LIBERO-Long. Long-WAM also achieves the best results among compared methods on LIBERO-Long, RoboTwin 2.0, and DOMINO. Streaming observation encoding, asynchronous execution, and hardware-specific acceleration enable deployment on RTX 5090, DGX Spark, and Jetson AGX Thor without dropping future prediction; on RTX 5090, each action chunk, including future-video latent prediction, takes 107.4 ms. Real-time deployment on Unitree G1 and YAM supports dynamic and long-horizon manipulation, including 95% success on dynamic cup stacking, where Pi0.5 and Fast-WAM succeed in none of 20 trials. As a memory-informed executor, Long-WAM also complements higher-level planning in composite tasks.", "zh-CN": "Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models under real-time control constraints. Our central finding is that access to history is not the same as using it: longer histories pay off far more when the video foundation is pretrained autoregressively (AR). We first learn causal prediction from robot and egocentric videos without action labels, then preserve this history-to-future structure during world-action adaptation. On RoboCasa GR-1, increasing context from 0.0 to 19.2 seconds raises success from 63.3% to 78.7%, whereas a bidirectionally pretrained initialization shows no net gain; robot-domain AR pretraining further raises peak success on GR-1 and LIBERO-Long. Long-WAM also achieves the best results among compared methods on LIBERO-Long, RoboTwin 2.0, and DOMINO. Streaming observation encoding, asynchronous execution, and hardware-specific acceleration enable deployment on RTX 5090, DGX Spark, and Jetson AGX Thor without dropping future prediction; on RTX 5090, each action chunk, including future-video latent prediction, takes 107.4 ms. Real-time deployment on Unitree G1 and YAM supports dynamic and long-horizon manipulation, including 95% success on dynamic cup stacking, where Pi0.5 and Fast-WAM succeed in none of 20 trials. As a memory-informed executor, Long-WAM also complements higher-level planning in composite tasks." }, "url": "https://huggingface.co/papers/2610.10528", "pdfUrl": "https://arxiv.org/pdf/2610.10528.pdf", "githubRepo": "https://github.com/NVlabs/LongLive", "upvotes": 109 }, { "id": "2610.12572", "title": { "en": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models", "zh-CN": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models" }, "summary": { "en": "Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unstable without carefully tuned exponential-moving-average (EMA) off-policy training. We show that NFT-style objectives contain a hidden forward--reverse consistency force that joint positive--negative training cancels only partially, and that EMA stabilizes training through restoration rather than off-policy rollouts. We introduce DiffusionART (Diffusion Reinforcement with Aggregation-First Reward Tilting), which aggregates each prompt's rewards into a shared reward-improving direction for reverse-process states, removing the consistency force by construction, and adds a trust-region-masked second update for drift-adaptive restoration without EMA tuning. It requires no differentiable reward model, remains effective with fewer samples per prompt, and supports pairwise reward models (DiffusionART-Pairwise). Across image and video generation, DiffusionART trains efficiently and stably without per-task tuning. In particular, on HunyuanVideo-13B with equally weighted visual and motion quality rewards, DiffusionART reaches the late-stage mean reward levels of DiffusionNFT and Flow-GRPO-Fast with 12.02 and 11.72 times fewer GPU hours, respectively, while achieving 28.8% and 53.0% higher mean training rewards.", "zh-CN": "Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unstable without carefully tuned exponential-moving-average (EMA) off-policy training. We show that NFT-style objectives contain a hidden forward--reverse consistency force that joint positive--negative training cancels only partially, and that EMA stabilizes training through restoration rather than off-policy rollouts. We introduce DiffusionART (Diffusion Reinforcement with Aggregation-First Reward Tilting), which aggregates each prompt's rewards into a shared reward-improving direction for reverse-process states, removing the consistency force by construction, and adds a trust-region-masked second update for drift-adaptive restoration without EMA tuning. It requires no differentiable reward model, remains effective with fewer samples per prompt, and supports pairwise reward models (DiffusionART-Pairwise). Across image and video generation, DiffusionART trains efficiently and stably without per-task tuning. In particular, on HunyuanVideo-13B with equally weighted visual and motion quality rewards, DiffusionART reaches the late-stage mean reward levels of DiffusionNFT and Flow-GRPO-Fast with 12.02 and 11.72 times fewer GPU hours, respectively, while achieving 28.8% and 53.0% higher mean training rewards." }, "url": "https://huggingface.co/papers/2610.12572", "pdfUrl": "https://arxiv.org/pdf/2610.12572.pdf", "githubRepo": "https://github.com/vanzll/DiffusionART", "upvotes": 242 }, { "id": "2610.08699", "title": { "en": "nanoMuse: An Open-Source Personal Agent for Every Device You Own", "zh-CN": "nanoMuse: An Open-Source Personal Agent for Every Device You Own" }, "summary": { "en": "Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped. In September 2026 Meta's Muse showed an agent for one person, with accounts, devices, memory and a conversation that lasts, closed, in a vendor's cloud, in one country. Such an agent is expected to act on a person's accounts and devices, remember them across weeks, speak first when it is worth it, and answer for what it did. It is a kind of software, not a model, and until now had no open counterpart. This report defines the personal agent in five questions and three horizons. It reads how Muse is built from Meta's public record and a copy of its production prompt, each statement marked by its source. It then presents nanoMuse, the open-source counterpart under the GPL-3.0, one agent on every device a person owns, with hands on the phone's screen and the computer's. They share one conversation over a relay anyone can run; every action goes through a Sentinel, memory is files the person can read, and the model is their choice. Its size and cost are given as estimates. What is open, memory with provenance, an evaluation suite for the hands and an open model for them, is set out as a roadmap.", "zh-CN": "Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped. In September 2026 Meta's Muse showed an agent for one person, with accounts, devices, memory and a conversation that lasts, closed, in a vendor's cloud, in one country. Such an agent is expected to act on a person's accounts and devices, remember them across weeks, speak first when it is worth it, and answer for what it did. It is a kind of software, not a model, and until now had no open counterpart. This report defines the personal agent in five questions and three horizons. It reads how Muse is built from Meta's public record and a copy of its production prompt, each statement marked by its source. It then presents nanoMuse, the open-source counterpart under the GPL-3.0, one agent on every device a person owns, with hands on the phone's screen and the computer's. They share one conversation over a relay anyone can run; every action goes through a Sentinel, memory is files the person can read, and the model is their choice. Its size and cost are given as estimates. What is open, memory with provenance, an evaluation suite for the hands and an open model for them, is set out as a roadmap." }, "url": "https://huggingface.co/papers/2610.08699", "pdfUrl": "https://arxiv.org/pdf/2610.08699.pdf", "githubRepo": "https://github.com/nano-muse/nanoMuse", "upvotes": 113 }, { "id": "2610.11765", "title": { "en": "Thinking Inertia: LLMs Keep Thinking When Told Not To", "zh-CN": "Thinking Inertia: LLMs Keep Thinking When Told Not To" }, "summary": { "en": "Large Language Models (LLMs) increasingly ship with explicit \"thinking modes\", yet their counterpart, \"no-thinking\", has received far less attention. We study LLMs' no-thinking behavior along two axes. a. How to measure no-thinking? Prior work typically defines no-thinking through proxies such as a disabled thinking mode or the absence of long traces. These proxies are unreliable: disabled thinking modes may still emit reasoning, while long traces may contain filler rather than genuine inference. We instead normalize each response into a pre-answer trace and final answer, and evaluate it at three levels: (i) Empty-Thinking Rate for strict answer-only compliance; (ii) instruction-aware Question-Pre-answer Relevance for similarity between the question and pre-answer trace; and (iii) LLM-as-judge Explicit Inference Rate for visible explicit inference. Together, these metrics distinguish answer-only output, relevant but non-inferential text, and explicit inference. b. How does no-thinking vary across tasks and models? We evaluate six prompting interventions on six LLMs across Boolean, multiple-choice, and open-ended questions. We find that explicit no-think controls cannot reliably eliminate visible inference. Models instead exhibit \"Thinking Inertia\": explicit inference persists even under strict controls and becomes more prevalent as the answer space opens. Accuracy remains stable on Boolean and multiple-choice tasks, whereas open-ended tasks reveal a trade-off between answer-only compliance and task accuracy. Rewriting the same questions across answer spaces shows that supplying candidate answers makes answer-only responses easier to produce. These findings establish no-thinking as a non-trivial capability: stopping explicit reasoning cannot be assumed from model settings or instructions alone and deserves systematic evaluation alongside reasoning ability.", "zh-CN": "Large Language Models (LLMs) increasingly ship with explicit \"thinking modes\", yet their counterpart, \"no-thinking\", has received far less attention. We study LLMs' no-thinking behavior along two axes. a. How to measure no-thinking? Prior work typically defines no-thinking through proxies such as a disabled thinking mode or the absence of long traces. These proxies are unreliable: disabled thinking modes may still emit reasoning, while long traces may contain filler rather than genuine inference. We instead normalize each response into a pre-answer trace and final answer, and evaluate it at three levels: (i) Empty-Thinking Rate for strict answer-only compliance; (ii) instruction-aware Question-Pre-answer Relevance for similarity between the question and pre-answer trace; and (iii) LLM-as-judge Explicit Inference Rate for visible explicit inference. Together, these metrics distinguish answer-only output, relevant but non-inferential text, and explicit inference. b. How does no-thinking vary across tasks and models? We evaluate six prompting interventions on six LLMs across Boolean, multiple-choice, and open-ended questions. We find that explicit no-think controls cannot reliably eliminate visible inference. Models instead exhibit \"Thinking Inertia\": explicit inference persists even under strict controls and becomes more prevalent as the answer space opens. Accuracy remains stable on Boolean and multiple-choice tasks, whereas open-ended tasks reveal a trade-off between answer-only compliance and task accuracy. Rewriting the same questions across answer spaces shows that supplying candidate answers makes answer-only responses easier to produce. These findings establish no-thinking as a non-trivial capability: stopping explicit reasoning cannot be assumed from model settings or instructions alone and deserves systematic evaluation alongside reasoning ability." }, "url": "https://huggingface.co/papers/2610.11765", "pdfUrl": "https://arxiv.org/pdf/2610.11765.pdf", "githubRepo": "https://github.com/thinking-inertia/code", "upvotes": 215 }, { "id": "2610.09823", "title": { "en": "UltraText Bench: A Comprehensive Bilingual Benchmark for Evaluating Visual Text Rendering in Image Generation", "zh-CN": "UltraText Bench: A Comprehensive Bilingual Benchmark for Evaluating Visual Text Rendering in Image Generation" }, "summary": { "en": "Dense visual text requires image generators to reproduce long strings across multiple regions with correct placement and legibility. As short-string rendering improves, evaluation must test sustained performance across more demanding scenes. We introduce UltraText Bench, a bilingual benchmark for prompt-only generation of dense visual text. It contains 432 prompts spanning 24 real-world scene categories and three difficulty levels, split equally between English and Chinese. Each human-reviewed prompt supplies exact strings for four to twelve text regions, paired with structured references for their content, placement, and visual attributes. We use the Q-Judger vision-language model to assess each image against the complete reference, reporting text fidelity, text clarity, spatial quality, and scene quality. Across 24 model configurations, these dimensions reveal different strengths: Z-Image-Turbo gains 3.81 clarity points over Z-Image-Base while losing 14.76 fidelity points under the reported settings. Performance also varies with workload; Qwen-Image-2512's English composite falls from 86.50 at L1 to 42.86 at L3. Ten participants took part in human evaluation of the automatic scores. Repository: https://github.com/LINs-lab/UltraText_Bench.", "zh-CN": "Dense visual text requires image generators to reproduce long strings across multiple regions with correct placement and legibility. As short-string rendering improves, evaluation must test sustained performance across more demanding scenes. We introduce UltraText Bench, a bilingual benchmark for prompt-only generation of dense visual text. It contains 432 prompts spanning 24 real-world scene categories and three difficulty levels, split equally between English and Chinese. Each human-reviewed prompt supplies exact strings for four to twelve text regions, paired with structured references for their content, placement, and visual attributes. We use the Q-Judger vision-language model to assess each image against the complete reference, reporting text fidelity, text clarity, spatial quality, and scene quality. Across 24 model configurations, these dimensions reveal different strengths: Z-Image-Turbo gains 3.81 clarity points over Z-Image-Base while losing 14.76 fidelity points under the reported settings. Performance also varies with workload; Qwen-Image-2512's English composite falls from 86.50 at L1 to 42.86 at L3. Ten participants took part in human evaluation of the automatic scores. Repository: https://github.com/LINs-lab/UltraText_Bench." }, "url": "https://huggingface.co/papers/2610.09823", "pdfUrl": "https://arxiv.org/pdf/2610.09823.pdf", "githubRepo": "https://github.com/LINs-lab/UltraText_Bench", "upvotes": 187 }, { "id": "2609.38169", "title": { "en": "STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization", "zh-CN": "STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization" }, "summary": { "en": "Linear attention replaces growing KV caches with fixed-size recurrent states, yet these persistent states can become a substantial memory bottleneck under concurrent serving. Directly quantizing recurrent states to low precision often leads to severe accuracy degradation, as quantization errors propagate through successive state updates. We discover that the impact of these errors depends on two complementary dimensions: temporally, errors in long-lived memory can persist across many decoding steps; spatially, errors in different key rows affect model outputs differently, while state magnitudes vary substantially along both rows and columns. Motivated by these observations, we propose STEPQuant, a spatial-temporal post-training quantization framework for Delta-rule recurrent states. STEPQuant allocates precision according to error magnitude and memory lifetime, and jointly fits key-row and value-column scales based on state distributions and key-row impact on output error. Experiments on Qwen3.8-27B and Kimi-Linear-48B-A3B-Instruct across both long- and short-generation benchmarks show that STEPQuant closely matches FP32-state accuracy under a nominal 6-bit budget and outperforms uniform INT8 in its 4-bit configuration. Integrated into SGLang with optimized GPU kernels, 6-bit STEPQuant achieves over 5x recurrent-state compression and reduces total serving memory by up to 68.7%. Our code is available at https://github.com/Dreamer-Toby/STEPQuant.", "zh-CN": "Linear attention replaces growing KV caches with fixed-size recurrent states, yet these persistent states can become a substantial memory bottleneck under concurrent serving. Directly quantizing recurrent states to low precision often leads to severe accuracy degradation, as quantization errors propagate through successive state updates. We discover that the impact of these errors depends on two complementary dimensions: temporally, errors in long-lived memory can persist across many decoding steps; spatially, errors in different key rows affect model outputs differently, while state magnitudes vary substantially along both rows and columns. Motivated by these observations, we propose STEPQuant, a spatial-temporal post-training quantization framework for Delta-rule recurrent states. STEPQuant allocates precision according to error magnitude and memory lifetime, and jointly fits key-row and value-column scales based on state distributions and key-row impact on output error. Experiments on Qwen3.8-27B and Kimi-Linear-48B-A3B-Instruct across both long- and short-generation benchmarks show that STEPQuant closely matches FP32-state accuracy under a nominal 6-bit budget and outperforms uniform INT8 in its 4-bit configuration. Integrated into SGLang with optimized GPU kernels, 6-bit STEPQuant achieves over 5x recurrent-state compression and reduces total serving memory by up to 68.7%. Our code is available at https://github.com/Dreamer-Toby/STEPQuant." }, "url": "https://huggingface.co/papers/2609.38169", "pdfUrl": "https://arxiv.org/pdf/2609.38169.pdf", "githubRepo": "https://github.com/Dreamer-Toby/STEPQuant", "upvotes": 130 }, { "id": "2610.12242", "title": { "en": "TokenRouter: Efficient Serving System for Token-Level LLM Routing", "zh-CN": "TokenRouter: Efficient Serving System for Token-Level LLM Routing" }, "summary": { "en": "Large language model (LLM) routing distributes inference work across different models, advancing the cost-quality Pareto frontier of LLM serving. While coarse-grained routing at the session or query level has been widely adopted in production systems, recent algorithmic work shows that fine-grained token-level routing can yield substantial efficiency and quality gains. However, efficiently serving token-level routed inference poses significant challenges to existing systems. Built on single-LLM assumptions, current systems suffer from severe step desynchronization and frequent batch admission delays under token-level routing, and they also impose high implementation complexity on developers. To address these challenges, we design TokenRouter, an efficient and developer-friendly serving system for token-level routed LLM inference. TokenRouter follows the principle of request-centric programming, model-centric execution: developers describe routing logic from the perspective of a single request, while the runtime launches a subserver for each LLM and dispatches requests asynchronously. Each subserver employs a delayed-batching scheduler, whose optimal hyperparameters are derived from a mathematical throughput model of the system. Across diverse routing algorithms, workloads, and model pairs, TokenRouter achieves 2.01-64.15x higher decoding throughput than existing systems, substantially advancing the serving efficiency of token-level LLM routing. Our code is available at https://github.com/thu-nics/TokenRouter.", "zh-CN": "Large language model (LLM) routing distributes inference work across different models, advancing the cost-quality Pareto frontier of LLM serving. While coarse-grained routing at the session or query level has been widely adopted in production systems, recent algorithmic work shows that fine-grained token-level routing can yield substantial efficiency and quality gains. However, efficiently serving token-level routed inference poses significant challenges to existing systems. Built on single-LLM assumptions, current systems suffer from severe step desynchronization and frequent batch admission delays under token-level routing, and they also impose high implementation complexity on developers. To address these challenges, we design TokenRouter, an efficient and developer-friendly serving system for token-level routed LLM inference. TokenRouter follows the principle of request-centric programming, model-centric execution: developers describe routing logic from the perspective of a single request, while the runtime launches a subserver for each LLM and dispatches requests asynchronously. Each subserver employs a delayed-batching scheduler, whose optimal hyperparameters are derived from a mathematical throughput model of the system. Across diverse routing algorithms, workloads, and model pairs, TokenRouter achieves 2.01-64.15x higher decoding throughput than existing systems, substantially advancing the serving efficiency of token-level LLM routing. Our code is available at https://github.com/thu-nics/TokenRouter." }, "url": "https://huggingface.co/papers/2610.12242", "pdfUrl": "https://arxiv.org/pdf/2610.12242.pdf", "githubRepo": "https://github.com/thu-nics/TokenRouter", "upvotes": 136 }, { "id": "2610.08077", "title": { "en": "Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight", "zh-CN": "Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight" }, "summary": { "en": "Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after interaction. For group-relative objectives, however, this signal vanishes when all rollouts receive the same reward, even though their trajectories may reveal useful information about what the task requires and how the agent fails. We ask a complementary question: can hindsight teach an agent what it could have anticipated before acting? We introduce prospective learning, which uses post-hoc experience to supervise foresight predictions from the pre-interaction view, and instantiate it with Self-Retrospection Distillation (SRD). Intuitively, a completed trajectory reveals knowledge that would have been useful and pitfalls that should be avoided; SRD distills this privileged hindsight into trajectory-blind foresight of the same policy. Foresight serves only as a training target and need not be explicitly generated at inference time. Across 10 tool-integrated reasoning and long-horizon agentic tasks, SRD complements RLVR and self-distillation baselines with gains of up to 24.2 pp. Its advantage is especially pronounced when reward contrast is scarce: when 37--98% of rollout groups are reward-uniform across model scales, yet SRD can still exploit learning signal from sampled trajectories. In the 2B setting, where 98% of groups are all-failure, the RLVR training ends up at 0.0% success, while adding SRD reaches 60.6% under the same rollout budget. Our results suggest that post-hoc agent experience is useful not only for evaluating or improving behavior, but also for shaping predictive representations before available interaction.", "zh-CN": "Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after interaction. For group-relative objectives, however, this signal vanishes when all rollouts receive the same reward, even though their trajectories may reveal useful information about what the task requires and how the agent fails. We ask a complementary question: can hindsight teach an agent what it could have anticipated before acting? We introduce prospective learning, which uses post-hoc experience to supervise foresight predictions from the pre-interaction view, and instantiate it with Self-Retrospection Distillation (SRD). Intuitively, a completed trajectory reveals knowledge that would have been useful and pitfalls that should be avoided; SRD distills this privileged hindsight into trajectory-blind foresight of the same policy. Foresight serves only as a training target and need not be explicitly generated at inference time. Across 10 tool-integrated reasoning and long-horizon agentic tasks, SRD complements RLVR and self-distillation baselines with gains of up to 24.2 pp. Its advantage is especially pronounced when reward contrast is scarce: when 37--98% of rollout groups are reward-uniform across model scales, yet SRD can still exploit learning signal from sampled trajectories. In the 2B setting, where 98% of groups are all-failure, the RLVR training ends up at 0.0% success, while adding SRD reaches 60.6% under the same rollout budget. Our results suggest that post-hoc agent experience is useful not only for evaluating or improving behavior, but also for shaping predictive representations before available interaction." }, "url": "https://huggingface.co/papers/2610.08077", "pdfUrl": "https://arxiv.org/pdf/2610.08077.pdf", "githubRepo": "https://github.com/SalesforceAIResearch/SRD", "upvotes": 191 }, { "id": "2610.13361", "title": { "en": "AgentSWE: Can Coding Agents Build the Agent You Actually Want?", "zh-CN": "AgentSWE: Can Coding Agents Build the Agent You Actually Want?" }, "summary": { "en": "As the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows. This delegation carries a quiet assumption, that the delivered agent will faithfully serve the requirement it was built for, rather than merely appear to. No existing benchmark tests that assumption. We introduce AgentSWE, a benchmark for agent software engineering spanning three stages of the agent development lifecycle: Creation of a complete agent from a natural-language requirement, Editing of production agent codebases, and Optimization toward a measurable target. Each of its 25 tasks is framed as a commission rather than an exam: the builder iterates against a handful of illustrative cases, while acceptance exercises the frozen submission on held-out cases designed to span the full requirement, grading behavior against requirement-derived rubrics under hard pass/fail gates or, for Optimization, against the source benchmark's own objective. Evaluating a range of coding agents across models and harnesses, we find that none comes close to delivering what a requirement asks for: the gap between what a builder demonstrates and what it delivers is the norm, not the corner case. An accompanying empirical analysis of the failure trajectories identifies recurring failure modes, among them overfitting to the visible cases, hollow runtimes behind code that looks complete, and unsupported claims of completion, each a signal by which coding agents can learn to build the agents they are asked for.", "zh-CN": "As the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows. This delegation carries a quiet assumption, that the delivered agent will faithfully serve the requirement it was built for, rather than merely appear to. No existing benchmark tests that assumption. We introduce AgentSWE, a benchmark for agent software engineering spanning three stages of the agent development lifecycle: Creation of a complete agent from a natural-language requirement, Editing of production agent codebases, and Optimization toward a measurable target. Each of its 25 tasks is framed as a commission rather than an exam: the builder iterates against a handful of illustrative cases, while acceptance exercises the frozen submission on held-out cases designed to span the full requirement, grading behavior against requirement-derived rubrics under hard pass/fail gates or, for Optimization, against the source benchmark's own objective. Evaluating a range of coding agents across models and harnesses, we find that none comes close to delivering what a requirement asks for: the gap between what a builder demonstrates and what it delivers is the norm, not the corner case. An accompanying empirical analysis of the failure trajectories identifies recurring failure modes, among them overfitting to the visible cases, hollow runtimes behind code that looks complete, and unsupported claims of completion, each a signal by which coding agents can learn to build the agents they are asked for." }, "url": "https://huggingface.co/papers/2610.13361", "pdfUrl": "https://arxiv.org/pdf/2610.13361.pdf", "githubRepo": null, "upvotes": 201 } ], "models": [ { "id": "abenzerps/Qwen-Image-2.1-Uncensored-Quantized", "name": "Qwen-Image-2.1-Uncensored-Quantized", "summary": { "en": "Qwen-Image-2.1-Uncensored-Quantized is a text-to-image by abenzerps.", "zh-CN": "" }, "url": "https://huggingface.co/abenzerps/Qwen-Image-2.1-Uncensored-Quantized", "pipeline": "text-to-image", "pipelineZh": "文生图", "likes": 4207, "downloads": 2247929 }, { "id": "google/embeddinggemma-2", "name": "embeddinggemma-2", "summary": { "en": "embeddinggemma-2 is a feature-extraction by google.", "zh-CN": "" }, "url": "https://huggingface.co/google/embeddinggemma-2", "pipeline": "feature-extraction", "pipelineZh": "向量嵌入", "likes": 2111, "downloads": 124930 }, { "id": "Lightricks/LTX-2.5", "name": "LTX-2.5", "summary": { "en": "LTX-2.5 is a image-to-video by Lightricks.", "zh-CN": "" }, "url": "https://huggingface.co/Lightricks/LTX-2.5", "pipeline": "image-to-video", "pipelineZh": "图生视频", "likes": 7647, "downloads": 1724548 }, { "id": "Qwen/Qwen3.8-27B", "name": "Qwen3.8-27B", "summary": { "en": "Qwen3.8-27B is a image-text-to-text by Qwen.", "zh-CN": "" }, "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "pipeline": "image-text-to-text", "pipelineZh": "图文问答 · 多模态", "likes": 17640, "downloads": 7119569 }, { "id": "liorbenhorin-nv/so101_vision_dr_i3_GR00T17_20261013", "name": "so101_vision_dr_i3_GR00T17_20261013", "summary": { "en": "so101_vision_dr_i3_GR00T17_20261013 is a model by liorbenhorin-nv.", "zh-CN": "" }, "url": "https://huggingface.co/liorbenhorin-nv/so101_vision_dr_i3_GR00T17_20261013", "pipeline": null, "pipelineZh": null, "likes": 0, "downloads": 0 }, { "id": "Franklalalala/tmp-te-india-1013-L1_FINAL_5cbe5f5e7d46-return", "name": "tmp-te-india-1013-L1_FINAL_5cbe5f5e7d46-return", "summary": { "en": "tmp-te-india-1013-L1_FINAL_5cbe5f5e7d46-return is a model by Franklalalala.", "zh-CN": "" }, "url": "https://huggingface.co/Franklalalala/tmp-te-india-1013-L1_FINAL_5cbe5f5e7d46-return", "pipeline": null, "pipelineZh": null, "likes": 0, "downloads": 0 }, { "id": "pwankhede/hf_act_clean_data_20k", "name": "hf_act_clean_data_20k", "summary": { "en": "hf_act_clean_data_20k is a robotics by pwankhede.", "zh-CN": "" }, "url": "https://huggingface.co/pwankhede/hf_act_clean_data_20k", "pipeline": "robotics", "pipelineZh": "机器人", "likes": 0, "downloads": 0 }, { "id": "DenRakEiw/microduck-senate_duck_2060", "name": "microduck-senate_duck_2060", "summary": { "en": "microduck-senate_duck_2060 is a robotics by DenRakEiw.", "zh-CN": "" }, "url": "https://huggingface.co/DenRakEiw/microduck-senate_duck_2060", "pipeline": "robotics", "pipelineZh": "机器人", "likes": 0, "downloads": 0 }, { "id": "prism-ml/Ternary-Bonsai-2-27B-gguf", "name": "Ternary-Bonsai-2-27B-gguf", "summary": { "en": "Ternary-Bonsai-2-27B-gguf is a text-generation by prism-ml.", "zh-CN": "" }, "url": "https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf", "pipeline": "text-generation", "pipelineZh": "文本生成 · LLM", "likes": 2678, "downloads": 4686252 }, { "id": "zai-org/GLM-5.3", "name": "GLM-5.3", "summary": { "en": "GLM-5.3 is a text-generation by zai-org.", "zh-CN": "" }, "url": "https://huggingface.co/zai-org/GLM-5.3", "pipeline": "text-generation", "pipelineZh": "文本生成 · LLM", "likes": 2210, "downloads": 1739432 }, { "id": "Aleph-Alpha/Kolibri-1", "name": "Kolibri-1", "summary": { "en": "Kolibri-1 is a text-generation by Aleph-Alpha.", "zh-CN": "" }, "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1", "pipeline": "text-generation", "pipelineZh": "文本生成 · LLM", "likes": 928, "downloads": 15335 }, { "id": "meta-llama/Llama-3.1-8B-Instruct", "name": "Llama-3.1-8B-Instruct", "summary": { "en": "Llama-3.1-8B-Instruct is a text-generation by meta-llama.", "zh-CN": "" }, "url": "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct", "pipeline": "text-generation", "pipelineZh": "文本生成 · LLM", "likes": 8397, "downloads": 5813862 } ], "github": [ { "fullName": "zhuyansen/new-term-radar", "name": "new-term-radar", "summary": { "en": "Daily radar for new AI terms: names of new models, products and repos, caught on X, Hugging Face, GitHub and sitemaps before keyword tools list them.", "zh-CN": "Daily radar for new AI terms: names of new models, products and repos, caught on X, Hugging Face, GitHub and sitemaps before keyword tools list them." }, "url": "https://github.com/zhuyansen/new-term-radar", "language": "Python", "stars": 867, "topics": [ "agent-skills", "ai", "aisa", "claude-code", "claude-skills", "google-trends", "keyword-research", "mcp" ] }, { "fullName": "KKKKhazix/AIHOT", "name": "AIHOT", "summary": { "en": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。", "zh-CN": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。" }, "url": "https://github.com/KKKKhazix/AIHOT", "language": "TypeScript", "stars": 7510, "topics": [ "ai", "chinese", "content-curation", "daily-digest", "docker-compose", "llm", "mcp", "news-aggregator" ] }, { "fullName": "Folkbench/folkbench", "name": "folkbench", "summary": { "en": "AI API 中转站评测排行与切换工具：实测可用率、延迟与价格 · Open-source AI API relay benchmark, rankings & switcher for Claude Code / Codex", "zh-CN": "AI API 中转站评测排行与切换工具：实测可用率、延迟与价格 · Open-source AI API relay benchmark, rankings & switcher for Claude Code / Codex" }, "url": "https://github.com/Folkbench/folkbench", "language": "Rust", "stars": 844, "topics": [ "ai", "api-relay", "api-relay-pricing", "benchmark", "benchmarks", "claude", "claude-code", "codex" ] }, { "fullName": "juliobsz/skills", "name": "skills", "summary": { "en": "An agent skill that builds award-caliber websites from one line", "zh-CN": "An agent skill that builds award-caliber websites from one line" }, "url": "https://github.com/juliobsz/skills", "language": "TypeScript", "stars": 309, "topics": [ "agent-skills", "astro", "awwwards", "claude-code", "claude-skills", "codex", "creative-coding", "frontend" ] }, { "fullName": "LosaLosSantos/aurelio-finance", "name": "aurelio-finance", "summary": { "en": "Open-source personal finance app with an AI financial advisor: track your net worth, investments, ETFs, cash and debts on your own computer.", "zh-CN": "Open-source personal finance app with an AI financial advisor: track your net worth, investments, ETFs, cash and debts on your own computer." }, "url": "https://github.com/LosaLosSantos/aurelio-finance", "language": "Python", "stars": 3374, "topics": [ "ai", "dividends", "etf", "finance", "financial-planning", "fintech", "investing", "llm" ] }, { "fullName": "deeplethe/solid-glass", "name": "solid-glass", "summary": { "en": "A design system for AI applications. Material, physical, tactile, technical.", "zh-CN": "A design system for AI applications. Material, physical, tactile, technical." }, "url": "https://github.com/deeplethe/solid-glass", "language": "TypeScript", "stars": 254, "topics": [ "agent-ui", "ai", "ai-agents", "chat-ui", "design-system", "llm", "react", "shadcn" ] }, { "fullName": "strands-labs/strands-decider", "name": "strands-decider", "summary": { "en": "A small, fast decision model, or system one model, for agentic workflows. Pick between options or rate on a scale faster than an LLM, with a calibrated confidence on every decision.", "zh-CN": "A small, fast decision model, or system one model, for agentic workflows. Pick between options or rate on a scale faster than an LLM, with a calibrated confidence on every decision." }, "url": "https://github.com/strands-labs/strands-decider", "language": "Python", "stars": 612, "topics": [ "ai", "machine-learning", "natural-language-understanding", "python", "strands-agents", "strands-labs", "system-one", "system-one-models" ] }, { "fullName": "amitshekhariitbhu/ai-system-design", "name": "ai-system-design", "summary": { "en": "AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step.", "zh-CN": "AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step." }, "url": "https://github.com/amitshekhariitbhu/ai-system-design", "language": "Markdown", "stars": 669, "topics": [ "ai", "ai-agents", "ai-engineering", "ai-system", "ai-system-design", "ai-systems", "ai-systems-design", "llm" ] }, { "fullName": "Harvil1/codeAgent", "name": "codeAgent", "summary": { "en": "CodeAgent is an open-source coding agent that lives in your terminal. It understands your repository, plans multi-step changes, writes and edits files, runs commands and tests, and iterates until the task is done — all with a transparent, extensible tool loop.", "zh-CN": "CodeAgent is an open-source coding agent that lives in your terminal. It understands your repository, plans multi-step changes, writes and edits files, runs commands and tests, and iterates until the task is done — all with a transparent, extensible tool loop." }, "url": "https://github.com/Harvil1/codeAgent", "language": "Python", "stars": 501, "topics": [ "agent", "ai-agent", "cli", "coding-agent", "context-engineering", "deepseek", "llm", "mcp" ] }, { "fullName": "Ayzzz123/mcd-coupon-strategist", "name": "mcd-coupon-strategist", "summary": { "en": "麦麦省钱助手 —— 基于麦当劳 MCP 的点餐成本优化 Skill。算出当前门店最省钱的点餐方案，并主动提示凑单陷阱。", "zh-CN": "麦麦省钱助手 —— 基于麦当劳 MCP 的点餐成本优化 Skill。算出当前门店最省钱的点餐方案，并主动提示凑单陷阱。" }, "url": "https://github.com/Ayzzz123/mcd-coupon-strategist", "language": "Python", "stars": 278, "topics": [ "agent", "cost-optimization", "coupon", "llm", "mcd-mcp", "mcdonalds", "mcp", "python" ] }, { "fullName": "TypeLLM/TypeLLM", "name": "TypeLLM", "summary": { "en": "TypeLLM: LLMs with type-safe generation", "zh-CN": "TypeLLM: LLMs with type-safe generation" }, "url": "https://github.com/TypeLLM/TypeLLM", "language": "Python", "stars": 946, "topics": [ "ai", "jev", "llms", "types" ] }, { "fullName": "strands-agents/box", "name": "box", "summary": { "en": "Run AI agents in a sandbox that restricts what they can execute, read, write, and reach on the network. Box combines OS isolation with default-deny Dogwood policies and credential injection that keeps secrets outside the agent. Written in Rust. Supports macOS on Apple silicon, with Linux support planned.", "zh-CN": "Run AI agents in a sandbox that restricts what they can execute, read, write, and reach on the network. Box combines OS isolation with default-deny Dogwood policies and credential injection that keeps secrets outside the agent. Written in Rust. Supports macOS on Apple silicon, with Linux support planned." }, "url": "https://github.com/strands-agents/box", "language": "Rust", "stars": 393, "topics": [ "agentic-ai", "agents", "ai", "ai-agents", "ai-safety", "containment", "egress", "harness" ] } ] } }