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10B), entailing prohibitive training costs and deployment footprints. In this work, we present DeepGen 1.0, a lightweight 5B unified model that achieves comprehensive capabilities competitive with or surpassing much larger counterparts. To overcome the limitations of compact models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable 'think tokens' to provide the generative backbone with structured, reasoning-rich guidance. We further design a data-centric training strategy spanning three progressive stages: (1) Alignment Pre-training on large-scale image-text pairs and editing triplets to synchronize VLM and DiT representations, (2) Joint Supervised Fine-tuning on a high-quality mixture of generation, editing, and reasoning tasks to foster omni-capabilities, and (3) Reinforcement Learning with MR-GRPO, which leverages a mixture of reward functions and supervision signals, resulting in substantial gains in generation quality and alignment with human preferences, while maintaining stable training progress and avoiding visual artifacts. Despite being trained on only ~50M samples, DeepGen 1.0 achieves leading performance across diverse benchmarks, surpassing the 80B HunyuanImage by 28% on WISE and the 27B Qwen-Image-Edit by 37% on UniREditBench. By open-sourcing our training code, weights, and datasets, we provide an efficient, high-performance alternative to democratize unified multimodal research.","upvotes":72,"discussionId":"698ea0f9cace060ff123ae4e","projectPage":"https://deepgenteam.github.io/","githubRepo":"https://github.com/DeepGenTeam/DeepGen","githubRepoAddedBy":"user","ai_summary":"A lightweight 5B unified multimodal model achieves competitive performance through hierarchical feature extraction, learnable think tokens, and progressive training strategies including alignment pre-training, joint supervised fine-tuning, and reinforcement learning with MR-GRPO.","ai_keywords":["unified multimodal models","image generation","image editing","parameter scale","VLM layers","DiT representations","Stacked Channel Bridging","think tokens","data-centric training strategy","alignment pre-training","joint supervised fine-tuning","reinforcement learning","MR-GRPO","generation quality","human preferences","visual artifacts"],"githubStars":77,"organization":{"_id":"683ebd0d913d82e703e77286","name":"sii-research","fullname":"Shanghai Innovation Institute","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6144a0c4ff1146bbd84d9865/SQAtyVRxNjp9L0CUi0tgI.png"}},"publishedAt":"2026-02-12T12:44:24.000Z","title":"DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing","summary":"Current unified multimodal models for image generation and editing typically rely on massive parameter scales (e.g., >10B), entailing prohibitive training costs and deployment footprints. In this work, we present DeepGen 1.0, a lightweight 5B unified model that achieves comprehensive capabilities competitive with or surpassing much larger counterparts. To overcome the limitations of compact models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable 'think tokens' to provide the generative backbone with structured, reasoning-rich guidance. We further design a data-centric training strategy spanning three progressive stages: (1) Alignment Pre-training on large-scale image-text pairs and editing triplets to synchronize VLM and DiT representations, (2) Joint Supervised Fine-tuning on a high-quality mixture of generation, editing, and reasoning tasks to foster omni-capabilities, and (3) Reinforcement Learning with MR-GRPO, which leverages a mixture of reward functions and supervision signals, resulting in substantial gains in generation quality and alignment with human preferences, while maintaining stable training progress and avoiding visual artifacts. Despite being trained on only ~50M samples, DeepGen 1.0 achieves leading performance across diverse benchmarks, surpassing the 80B HunyuanImage by 28% on WISE and the 27B Qwen-Image-Edit by 37% on UniREditBench. By open-sourcing our training code, weights, and datasets, we provide an efficient, high-performance alternative to democratize unified multimodal research.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.12205.png","numComments":3,"upvoted":false,"submittedBy":{"_id":"64b4eec4faa3181a5eab9c46","avatarUrl":"/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg","fullname":"Jiaqi Wang","name":"myownskyW7","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":25,"isUserFollowing":false},"organization":{"_id":"683ebd0d913d82e703e77286","name":"sii-research","fullname":"Shanghai Innovation Institute","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6144a0c4ff1146bbd84d9865/SQAtyVRxNjp9L0CUi0tgI.png"},"isAuthorParticipating":false},{"paper":{"id":"2511.12884","authors":[{"_id":"691bea4f6bfd5965c0fd3837","name":"Worawalan Chatlatanagulchai","hidden":false},{"_id":"691bea4f6bfd5965c0fd3838","user":{"_id":"62b4f3b7464e664268bf4e85","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b4f3b7464e664268bf4e85/atFIscmB37aur0a1zuQ2o.jpeg","isPro":false,"fullname":"Leo","user":"hao-li","type":"user"},"name":"Hao Li","status":"claimed_verified","statusLastChangedAt":"2025-11-18T14:07:56.453Z","hidden":false},{"_id":"691bea4f6bfd5965c0fd3839","name":"Yutaro Kashiwa","hidden":false},{"_id":"691bea4f6bfd5965c0fd383a","name":"Brittany Reid","hidden":false},{"_id":"691bea4f6bfd5965c0fd383b","name":"Kundjanasith Thonglek","hidden":false},{"_id":"691bea4f6bfd5965c0fd383c","name":"Pattara Leelaprute","hidden":false},{"_id":"691bea4f6bfd5965c0fd383d","name":"Arnon Rungsawang","hidden":false},{"_id":"691bea4f6bfd5965c0fd383e","name":"Bundit Manaskasemsak","hidden":false},{"_id":"691bea4f6bfd5965c0fd383f","name":"Bram Adams","hidden":false},{"_id":"691bea4f6bfd5965c0fd3840","name":"Ahmed E. Hassan","hidden":false},{"_id":"691bea4f6bfd5965c0fd3841","name":"Hajimu Iida","hidden":false}],"publishedAt":"2025-11-17T02:18:55.000Z","submittedOnDailyAt":"2025-11-19T00:32:43.060Z","title":"Agent READMEs: An Empirical Study of Context Files for Agentic Coding","submittedOnDailyBy":{"_id":"62b4f3b7464e664268bf4e85","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b4f3b7464e664268bf4e85/atFIscmB37aur0a1zuQ2o.jpeg","isPro":false,"fullname":"Leo","user":"hao-li","type":"user"},"summary":"Agentic coding tools receive goals written in natural language as input, break them down into specific tasks, and write or execute the actual code with minimal human intervention. Central to this process are agent context files (\"READMEs for agents\") that provide persistent, project-level instructions. In this paper, we conduct the first large-scale empirical study of 2,303 agent context files from 1,925 repositories to characterize their structure, maintenance, and content. We find that these files are not static documentation but complex, difficult-to-read artifacts that evolve like configuration code, maintained through frequent, small additions. Our content analysis of 16 instruction types shows that developers prioritize functional context, such as build and run commands (62.3%), implementation details (69.9%), and architecture (67.7%). We also identify a significant gap: non-functional requirements like security (14.5%) and performance (14.5%) are rarely specified. These findings indicate that while developers use context files to make agents functional, they provide few guardrails to ensure that agent-written code is secure or performant, highlighting the need for improved tooling and practices.","upvotes":23,"discussionId":"691bea4f6bfd5965c0fd3842","projectPage":"https://agents.md","githubRepo":"https://github.com/openai/agents.md","githubRepoAddedBy":"auto","githubStars":17399},"publishedAt":"2025-11-16T21:18:55.000Z","title":"Agent READMEs: An Empirical Study of Context Files for Agentic Coding","summary":"Agentic coding tools receive goals written in natural language as input, break them down into specific tasks, and write or execute the actual code with minimal human intervention. Central to this process are agent context files (\"READMEs for agents\") that provide persistent, project-level instructions. In this paper, we conduct the first large-scale empirical study of 2,303 agent context files from 1,925 repositories to characterize their structure, maintenance, and content. We find that these files are not static documentation but complex, difficult-to-read artifacts that evolve like configuration code, maintained through frequent, small additions. Our content analysis of 16 instruction types shows that developers prioritize functional context, such as build and run commands (62.3%), implementation details (69.9%), and architecture (67.7%). We also identify a significant gap: non-functional requirements like security (14.5%) and performance (14.5%) are rarely specified. These findings indicate that while developers use context files to make agents functional, they provide few guardrails to ensure that agent-written code is secure or performant, highlighting the need for improved tooling and practices.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2511.12884.png","numComments":4,"upvoted":false,"submittedBy":{"_id":"62b4f3b7464e664268bf4e85","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b4f3b7464e664268bf4e85/atFIscmB37aur0a1zuQ2o.jpeg","fullname":"Leo","name":"hao-li","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":4,"isUserFollowing":false},"isAuthorParticipating":true},{"paper":{"id":"2509.22186","authors":[{"_id":"68d9ebf80177a6054b013a58","user":{"_id":"650abbb71aece923f21d87fc","avatarUrl":"/avatars/f09ff031c278bc42bfd7a563853e142c.svg","isPro":false,"fullname":"Junbo Niu","user":"Niujunbo2002","type":"user"},"name":"Junbo 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Wang","hidden":false},{"_id":"68d9ebf80177a6054b013a8d","name":"Zhongying Tu","hidden":false},{"_id":"68d9ebf80177a6054b013a8e","name":"Chao Xu","hidden":false},{"_id":"68d9ebf80177a6054b013a8f","name":"Kai Chen","hidden":false},{"_id":"68d9ebf80177a6054b013a90","name":"Yu Qiao","hidden":false},{"_id":"68d9ebf80177a6054b013a91","name":"Bowen Zhou","hidden":false},{"_id":"68d9ebf80177a6054b013a92","name":"Dahua Lin","hidden":false},{"_id":"68d9ebf80177a6054b013a93","name":"Wentao Zhang","hidden":false},{"_id":"68d9ebf80177a6054b013a94","name":"Conghui He","hidden":false}],"publishedAt":"2025-09-26T10:45:48.000Z","submittedOnDailyAt":"2025-09-29T00:46:33.156Z","title":"MinerU2.5: A Decoupled Vision-Language Model for Efficient\n High-Resolution Document Parsing","submittedOnDailyBy":{"_id":"6039478ab3ecf716b1a5fd4d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6039478ab3ecf716b1a5fd4d/_Thy4E7taiSYBLKxEKJbT.jpeg","isPro":true,"fullname":"taesiri","user":"taesiri","type":"user"},"summary":"We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language\nmodel that achieves state-of-the-art recognition accuracy while maintaining\nexceptional computational efficiency. Our approach employs a coarse-to-fine,\ntwo-stage parsing strategy that decouples global layout analysis from local\ncontent recognition. In the first stage, the model performs efficient layout\nanalysis on downsampled images to identify structural elements, circumventing\nthe computational overhead of processing high-resolution inputs. In the second\nstage, guided by the global layout, it performs targeted content recognition on\nnative-resolution crops extracted from the original image, preserving\nfine-grained details in dense text, complex formulas, and tables. To support\nthis strategy, we developed a comprehensive data engine that generates diverse,\nlarge-scale training corpora for both pretraining and fine-tuning. Ultimately,\nMinerU2.5 demonstrates strong document parsing ability, achieving\nstate-of-the-art performance on multiple benchmarks, surpassing both\ngeneral-purpose and domain-specific models across various recognition tasks,\nwhile maintaining significantly lower computational overhead.","upvotes":144,"discussionId":"68d9ebf80177a6054b013a95","projectPage":"https://opendatalab.github.io/MinerU/","githubRepo":"https://github.com/opendatalab/MinerU","githubRepoAddedBy":"user","ai_summary":"MinerU2.5, a 1.2B-parameter document parsing vision-language model, achieves state-of-the-art recognition accuracy with computational efficiency through a coarse-to-fine parsing strategy.","ai_keywords":["document parsing","vision-language model","coarse-to-fine","two-stage parsing","layout analysis","content recognition","downsampled images","native-resolution crops","data engine","pretraining","fine-tuning","state-of-the-art performance","computational overhead"],"githubStars":54371},"publishedAt":"2025-09-26T06:45:48.000Z","title":"MinerU2.5: A Decoupled Vision-Language Model for Efficient\n High-Resolution Document Parsing","summary":"We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language\nmodel that achieves state-of-the-art recognition accuracy while maintaining\nexceptional computational efficiency. Our approach employs a coarse-to-fine,\ntwo-stage parsing strategy that decouples global layout analysis from local\ncontent recognition. In the first stage, the model performs efficient layout\nanalysis on downsampled images to identify structural elements, circumventing\nthe computational overhead of processing high-resolution inputs. In the second\nstage, guided by the global layout, it performs targeted content recognition on\nnative-resolution crops extracted from the original image, preserving\nfine-grained details in dense text, complex formulas, and tables. To support\nthis strategy, we developed a comprehensive data engine that generates diverse,\nlarge-scale training corpora for both pretraining and fine-tuning. Ultimately,\nMinerU2.5 demonstrates strong document parsing ability, achieving\nstate-of-the-art performance on multiple benchmarks, surpassing both\ngeneral-purpose and domain-specific models across various recognition tasks,\nwhile maintaining significantly lower computational overhead.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2509.22186.png","numComments":2,"upvoted":false,"submittedBy":{"_id":"6039478ab3ecf716b1a5fd4d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6039478ab3ecf716b1a5fd4d/_Thy4E7taiSYBLKxEKJbT.jpeg","fullname":"taesiri","name":"taesiri","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":230,"isUserFollowing":false},"isAuthorParticipating":false},{"paper":{"id":"2512.07921","authors":[{"_id":"6938e671dfc35938ba129fd5","name":"Zongwei Li","hidden":false},{"_id":"6938e671dfc35938ba129fd6","name":"Zhonghang Li","hidden":false},{"_id":"6938e671dfc35938ba129fd7","name":"Zirui Guo","hidden":false},{"_id":"6938e671dfc35938ba129fd8","name":"Xubin Ren","hidden":false},{"_id":"6938e671dfc35938ba129fd9","name":"Chao Huang","hidden":false}],"publishedAt":"2025-12-08T16:07:13.000Z","submittedOnDailyAt":"2025-12-10T00:48:19.359Z","title":"DeepCode: Open Agentic Coding","submittedOnDailyBy":{"_id":"6039478ab3ecf716b1a5fd4d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6039478ab3ecf716b1a5fd4d/_Thy4E7taiSYBLKxEKJbT.jpeg","isPro":true,"fullname":"taesiri","user":"taesiri","type":"user"},"summary":"Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing methods still face significant challenges in achieving high-fidelity document-to-codebase synthesis--such as scientific papers to code--primarily due to a fundamental conflict between information overload and the context bottlenecks of LLMs. In this work, we introduce DeepCode, a fully autonomous framework that fundamentally addresses this challenge through principled information-flow management. By treating repository synthesis as a channel optimization problem, DeepCode seamlessly orchestrates four information operations to maximize task-relevant signals under finite context budgets: source compression via blueprint distillation, structured indexing using stateful code memory, conditional knowledge injection via retrieval-augmented generation, and closed-loop error correction. Extensive evaluations on the PaperBench benchmark demonstrate that DeepCode achieves state-of-the-art performance, decisively outperforming leading commercial agents such as Cursor and Claude Code, and crucially, surpassing PhD-level human experts from top institutes on key reproduction metrics. By systematically transforming paper specifications into production-grade implementations comparable to human expert quality, this work establishes new foundations for autonomous scientific reproduction that can accelerate research evaluation and discovery.","upvotes":33,"discussionId":"6938e672dfc35938ba129fda","githubRepo":"https://github.com/HKUDS/DeepCode","githubRepoAddedBy":"user","ai_summary":"DeepCode, a fully autonomous framework, addresses the challenges of document-to-codebase synthesis by optimizing information flow through source compression, structured indexing, knowledge injection, and error correction, achieving state-of-the-art performance and surpassing human experts.","ai_keywords":["large language models","coding agents","document-to-codebase synthesis","information overload","context bottlenecks","DeepCode","channel optimization","blueprint distillation","stateful code memory","retrieval-augmented generation","closed-loop error correction","PaperBench","autonomous scientific reproduction"],"githubStars":14412},"publishedAt":"2025-12-08T11:07:13.000Z","title":"DeepCode: Open Agentic Coding","summary":"Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing methods still face significant challenges in achieving high-fidelity document-to-codebase synthesis--such as scientific papers to code--primarily due to a fundamental conflict between information overload and the context bottlenecks of LLMs. In this work, we introduce DeepCode, a fully autonomous framework that fundamentally addresses this challenge through principled information-flow management. By treating repository synthesis as a channel optimization problem, DeepCode seamlessly orchestrates four information operations to maximize task-relevant signals under finite context budgets: source compression via blueprint distillation, structured indexing using stateful code memory, conditional knowledge injection via retrieval-augmented generation, and closed-loop error correction. Extensive evaluations on the PaperBench benchmark demonstrate that DeepCode achieves state-of-the-art performance, decisively outperforming leading commercial agents such as Cursor and Claude Code, and crucially, surpassing PhD-level human experts from top institutes on key reproduction metrics. By systematically transforming paper specifications into production-grade implementations comparable to human expert quality, this work establishes new foundations for autonomous scientific reproduction that can accelerate research evaluation and discovery.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2512.07921.png","numComments":2,"upvoted":false,"submittedBy":{"_id":"6039478ab3ecf716b1a5fd4d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6039478ab3ecf716b1a5fd4d/_Thy4E7taiSYBLKxEKJbT.jpeg","fullname":"taesiri","name":"taesiri","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":230,"isUserFollowing":false},"isAuthorParticipating":false},{"paper":{"id":"2602.09082","authors":[{"_id":"698bea506052d3bed96309cb","name":"Veuns-Team","hidden":false},{"_id":"698bea506052d3bed96309cd","name":"Changlong Gao","hidden":false},{"_id":"698bea506052d3bed96309ce","user":{"_id":"60d2a2984956988b63753371","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60d2a2984956988b63753371/apXIcWbi7jnLVH37CdMTV.jpeg","isPro":false,"fullname":"Zhangxuan Gu","user":"zhangxgu","type":"user"},"name":"Zhangxuan Gu","status":"claimed_verified","statusLastChangedAt":"2026-02-11T11:15:14.456Z","hidden":false},{"_id":"698bea506052d3bed96309cf","name":"Yulin Liu","hidden":false},{"_id":"698bea506052d3bed96309d0","name":"Xinyu Qiu","hidden":false},{"_id":"698bea506052d3bed96309d1","name":"Shuheng Shen","hidden":false},{"_id":"698bea506052d3bed96309d2","name":"Yue Wen","hidden":false},{"_id":"698bea506052d3bed96309d3","name":"Tianyu Xia","hidden":false},{"_id":"698bea506052d3bed96309d4","name":"Zhenyu Xu","hidden":false},{"_id":"698bea506052d3bed96309d5","user":{"_id":"64cb238576200ec80fe988f8","avatarUrl":"/avatars/42c48710c7881c9dfbcc075fec3cb600.svg","isPro":false,"fullname":"zeus","user":"zengw","type":"user"},"name":"Zhengwen Zeng","status":"claimed_verified","statusLastChangedAt":"2026-02-11T11:24:43.235Z","hidden":false},{"_id":"698bea506052d3bed96309d6","user":{"_id":"654c9dac09dd7ef524a0be1e","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/654c9dac09dd7ef524a0be1e/T4glmZthS0mJydhvGZGKH.png","isPro":false,"fullname":"beitongzhou","user":"syorami","type":"user"},"name":"Beitong Zhou","status":"claimed_verified","statusLastChangedAt":"2026-02-11T11:15:11.859Z","hidden":false},{"_id":"698bea506052d3bed96309d7","name":"Xingran Zhou","hidden":false},{"_id":"698bea506052d3bed96309d8","name":"Weizhi Chen","hidden":false},{"_id":"698bea506052d3bed96309d9","name":"Sunhao Dai","hidden":false},{"_id":"698bea506052d3bed96309da","name":"Jingya Dou","hidden":false},{"_id":"698bea506052d3bed96309db","name":"Yichen Gong","hidden":false},{"_id":"698bea506052d3bed96309dc","name":"Yuan Guo","hidden":false},{"_id":"698bea506052d3bed96309dd","name":"Zhenlin Guo","hidden":false},{"_id":"698bea506052d3bed96309de","user":{"_id":"65e0763a9299e96ee674876e","avatarUrl":"/avatars/0ea342c9f72fa3b8a8f634559d094907.svg","isPro":false,"fullname":"fengdian","user":"fengrudian","type":"user"},"name":"Feng Li","status":"claimed_verified","statusLastChangedAt":"2026-02-11T11:16:04.463Z","hidden":false},{"_id":"698bea506052d3bed96309df","name":"Qian Li","hidden":false},{"_id":"698bea506052d3bed96309e0","name":"Jinzhen Lin","hidden":false},{"_id":"698bea506052d3bed96309e1","name":"Yuqi Zhou","hidden":false},{"_id":"698bea506052d3bed96309e2","name":"Linchao Zhu","hidden":false},{"_id":"698bea506052d3bed96309e3","name":"Liang Chen","hidden":false},{"_id":"698bea506052d3bed96309e4","name":"Zhenyu Guo","hidden":false},{"_id":"698bea506052d3bed96309e5","name":"Changhua Meng","hidden":false},{"_id":"698bea506052d3bed96309e6","name":"Weiqiang Wang","hidden":false}],"publishedAt":"2026-02-09T18:43:40.000Z","submittedOnDailyAt":"2026-02-11T00:10:55.649Z","title":"UI-Venus-1.5 Technical Report","submittedOnDailyBy":{"_id":"60d2a2984956988b63753371","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60d2a2984956988b63753371/apXIcWbi7jnLVH37CdMTV.jpeg","isPro":false,"fullname":"Zhangxuan Gu","user":"zhangxgu","type":"user"},"summary":"GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging.In this report, we present UI-Venus-1.5, a unified, end-to-end GUI Agent designed for robust real-world applications.The proposed model family comprises two dense variants (2B and 8B) and one mixture-of-experts variant (30B-A3B) to meet various downstream application scenarios.Compared to our previous version, UI-Venus-1.5 introduces three key technical advances: (1) a comprehensive Mid-Training stage leveraging 10 billion tokens across 30+ datasets to establish foundational GUI semantics; (2) Online Reinforcement Learning with full-trajectory rollouts, aligning training objectives with long-horizon, dynamic navigation in large-scale environments; and (3) a single unified GUI Agent constructed via Model Merging, which synthesizes domain-specific models (grounding, web, and mobile) into one cohesive checkpoint. Extensive evaluations demonstrate that UI-Venus-1.5 establishes new state-of-the-art performance on benchmarks such as ScreenSpot-Pro (69.6%), VenusBench-GD (75.0%), and AndroidWorld (77.6%), significantly outperforming previous strong baselines. In addition, UI-Venus-1.5 demonstrates robust navigation capabilities across a variety of Chinese mobile apps, effectively executing user instructions in real-world scenarios. Code: https://github.com/inclusionAI/UI-Venus; Model: https://huggingface.co/collections/inclusionAI/ui-venus","upvotes":149,"discussionId":"698bea516052d3bed96309e7","projectPage":"https://ui-venus.github.io/UI-Venus-1.5/","githubRepo":"https://github.com/inclusionAI/UI-Venus","githubRepoAddedBy":"user","ai_summary":"UI-Venus-1.5 is a unified GUI agent with improved performance through mid-training stages, online reinforcement learning, and model merging techniques.","ai_keywords":["GUI agents","Mid-Training stage","Online Reinforcement Learning","full-trajectory rollouts","Model Merging","dense variants","mixture-of-experts variant"],"githubStars":855,"organization":{"_id":"67aea5c8f086ab0f70ed97c9","name":"inclusionAI","fullname":"inclusionAI","avatar":"https://cdn-uploads.huggingface.co/production/uploads/662e1f9da266499277937d33/fyKuazRifqiaIO34xrhhm.jpeg"}},"publishedAt":"2026-02-09T13:43:40.000Z","title":"UI-Venus-1.5 Technical Report","summary":"GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging.In this report, we present UI-Venus-1.5, a unified, end-to-end GUI Agent designed for robust real-world applications.The proposed model family comprises two dense variants (2B and 8B) and one mixture-of-experts variant (30B-A3B) to meet various downstream application scenarios.Compared to our previous version, UI-Venus-1.5 introduces three key technical advances: (1) a comprehensive Mid-Training stage leveraging 10 billion tokens across 30+ datasets to establish foundational GUI semantics; (2) Online Reinforcement Learning with full-trajectory rollouts, aligning training objectives with long-horizon, dynamic navigation in large-scale environments; and (3) a single unified GUI Agent constructed via Model Merging, which synthesizes domain-specific models (grounding, web, and mobile) into one cohesive checkpoint. Extensive evaluations demonstrate that UI-Venus-1.5 establishes new state-of-the-art performance on benchmarks such as ScreenSpot-Pro (69.6%), VenusBench-GD (75.0%), and AndroidWorld (77.6%), significantly outperforming previous strong baselines. In addition, UI-Venus-1.5 demonstrates robust navigation capabilities across a variety of Chinese mobile apps, effectively executing user instructions in real-world scenarios. Code: https://github.com/inclusionAI/UI-Venus; Model: https://huggingface.co/collections/inclusionAI/ui-venus","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.09082.png","numComments":3,"upvoted":false,"submittedBy":{"_id":"60d2a2984956988b63753371","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60d2a2984956988b63753371/apXIcWbi7jnLVH37CdMTV.jpeg","fullname":"Zhangxuan Gu","name":"zhangxgu","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":8,"isUserFollowing":false},"organization":{"_id":"67aea5c8f086ab0f70ed97c9","name":"inclusionAI","fullname":"inclusionAI","avatar":"https://cdn-uploads.huggingface.co/production/uploads/662e1f9da266499277937d33/fyKuazRifqiaIO34xrhhm.jpeg"},"isAuthorParticipating":true},{"paper":{"id":"2512.24601","authors":[{"_id":"695b6f84832867f253525e5a","name":"Alex L. Zhang","hidden":false},{"_id":"695b6f84832867f253525e5b","name":"Tim Kraska","hidden":false},{"_id":"695b6f84832867f253525e5c","name":"Omar Khattab","hidden":false}],"publishedAt":"2025-12-31T03:43:41.000Z","submittedOnDailyAt":"2026-01-06T04:56:19.174Z","title":"Recursive Language Models","submittedOnDailyBy":{"_id":"64b8e82aa62c52b252c827fa","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64b8e82aa62c52b252c827fa/Jyk5PHMXCaRlmWy4mT3Bt.jpeg","isPro":false,"fullname":"Rajkumar rawal","user":"rajkumarrawal","type":"user"},"summary":"We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query.","upvotes":86,"discussionId":"695b6f84832867f253525e5d","projectPage":"https://alexzhang13.github.io/blog/2025/rlm/","githubRepo":"https://github.com/alexzhang13/rlm","githubRepoAddedBy":"user","githubStars":2492,"organization":{"_id":"63728bde14d543d507ae970d","name":"MIT","fullname":"Massachusetts Institute of Technology","avatar":"https://cdn-uploads.huggingface.co/production/uploads/noauth/S90qoeEJeEYaYf-c7Zs8g.png"}},"publishedAt":"2025-12-30T22:43:41.000Z","title":"Recursive Language Models","summary":"We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2512.24601.png","numComments":7,"upvoted":false,"submittedBy":{"_id":"64b8e82aa62c52b252c827fa","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64b8e82aa62c52b252c827fa/Jyk5PHMXCaRlmWy4mT3Bt.jpeg","fullname":"Rajkumar rawal","name":"rajkumarrawal","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":50,"isUserFollowing":false},"organization":{"_id":"63728bde14d543d507ae970d","name":"MIT","fullname":"Massachusetts Institute of Technology","avatar":"https://cdn-uploads.huggingface.co/production/uploads/noauth/S90qoeEJeEYaYf-c7Zs8g.png"},"isAuthorParticipating":false},{"paper":{"id":"2602.10934","authors":[{"_id":"698d585765c0d15a6d1621fe","user":{"_id":"66c893b2e51ba3009235b1c0","avatarUrl":"/avatars/5341d40b4b4caca2c145a46eb1754582.svg","isPro":false,"fullname":"yitian gong","user":"fdugyt","type":"user"},"name":"Yitian Gong","status":"claimed_verified","statusLastChangedAt":"2026-02-12T13:26:16.742Z","hidden":false},{"_id":"698d585765c0d15a6d1621ff","name":"Kuangwei Chen","hidden":false},{"_id":"698d585765c0d15a6d162200","user":{"_id":"629ef8544313a7c1dd671130","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/629ef8544313a7c1dd671130/i5xfHIgELcuO1Ew19ebTw.png","isPro":false,"fullname":"Zhaoye Fei","user":"ngc7293","type":"user"},"name":"Zhaoye Fei","status":"claimed_verified","statusLastChangedAt":"2026-02-13T09:37:26.561Z","hidden":false},{"_id":"698d585765c0d15a6d162201","name":"Xiaogui Yang","hidden":false},{"_id":"698d585765c0d15a6d162202","name":"Ke Chen","hidden":false},{"_id":"698d585765c0d15a6d162203","name":"Yang Wang","hidden":false},{"_id":"698d585765c0d15a6d162204","name":"Kexin Huang","hidden":false},{"_id":"698d585765c0d15a6d162205","name":"Mingshu Chen","hidden":false},{"_id":"698d585765c0d15a6d162206","name":"Ruixiao Li","hidden":false},{"_id":"698d585765c0d15a6d162207","name":"Qingyuan Cheng","hidden":false},{"_id":"698d585765c0d15a6d162208","name":"Shimin Li","hidden":false},{"_id":"698d585765c0d15a6d162209","name":"Xipeng Qiu","hidden":false}],"publishedAt":"2026-02-11T15:13:27.000Z","submittedOnDailyAt":"2026-02-13T01:47:00.074Z","title":"MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models","submittedOnDailyBy":{"_id":"66c893b2e51ba3009235b1c0","avatarUrl":"/avatars/5341d40b4b4caca2c145a46eb1754582.svg","isPro":false,"fullname":"yitian gong","user":"fdugyt","type":"user"},"summary":"Discrete audio tokenizers are fundamental to empowering large language models with native audio processing and generation capabilities. Despite recent progress, existing approaches often rely on pretrained encoders, semantic distillation, or heterogeneous CNN-based architectures. These designs introduce fixed inductive biases that limit reconstruction fidelity and hinder effective scaling. In this paper, we argue that discrete audio tokenization should be learned fully end-to-end using a homogeneous and scalable architecture. To this end, we first propose CAT (Causal Audio Tokenizer with Transformer), a purely Transformer-based architecture that jointly optimizes the encoder, quantizer, and decoder from scratch for high-fidelity reconstruction. Building on the CAT architecture, we develop MOSS-Audio-Tokenizer, a large-scale audio tokenizer featuring 1.6 billion parameters, pre-trained on 3 million hours of diverse, general audio data. We show that this simple, fully end-to-end approach built from homogeneous, causal Transformer blocks scales gracefully and supports high-fidelity reconstruction across diverse audio domains. Across speech, sound, and music, MOSS-Audio-Tokenizer consistently outperforms prior codecs over a wide range of bitrates, while exhibiting predictable improvements with increased scale. Notably, leveraging the discrete tokens from our model, we develop the first purely autoregressive TTS model that surpasses prior non-autoregressive and cascaded systems. Furthermore, MOSS-Audio-Tokenizer enables competitive ASR performance without auxiliary encoders. Our findings position the CAT architecture as a unified, scalable interface for the next generation of native audio foundation models.","upvotes":47,"discussionId":"698d585765c0d15a6d16220a","githubRepo":"https://github.com/OpenMOSS/MOSS-Audio-Tokenizer","githubRepoAddedBy":"user","ai_summary":"A fully end-to-end Transformer-based audio tokenizer architecture achieves high-fidelity reconstruction across diverse audio domains and enables superior text-to-speech and automatic speech recognition performance.","ai_keywords":["audio tokenization","causal audio tokenizer","Transformer architecture","end-to-end learning","quantizer","encoder","decoder","discrete audio tokens","autoregressive TTS","automatic speech recognition","codec","pre-trained encoders","semantic distillation","heterogeneous CNN-based architectures"],"githubStars":89,"organization":{"_id":"613b0dee83ec35d460684607","name":"OpenMOSS-Team","fullname":"OpenMOSS","avatar":"https://cdn-uploads.huggingface.co/production/uploads/61457b8deff2c9fdb4de4988/N5b9663zQ4uq5_OTNlnmw.png"}},"publishedAt":"2026-02-11T10:13:27.000Z","title":"MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models","summary":"Discrete audio tokenizers are fundamental to empowering large language models with native audio processing and generation capabilities. Despite recent progress, existing approaches often rely on pretrained encoders, semantic distillation, or heterogeneous CNN-based architectures. These designs introduce fixed inductive biases that limit reconstruction fidelity and hinder effective scaling. In this paper, we argue that discrete audio tokenization should be learned fully end-to-end using a homogeneous and scalable architecture. To this end, we first propose CAT (Causal Audio Tokenizer with Transformer), a purely Transformer-based architecture that jointly optimizes the encoder, quantizer, and decoder from scratch for high-fidelity reconstruction. Building on the CAT architecture, we develop MOSS-Audio-Tokenizer, a large-scale audio tokenizer featuring 1.6 billion parameters, pre-trained on 3 million hours of diverse, general audio data. We show that this simple, fully end-to-end approach built from homogeneous, causal Transformer blocks scales gracefully and supports high-fidelity reconstruction across diverse audio domains. Across speech, sound, and music, MOSS-Audio-Tokenizer consistently outperforms prior codecs over a wide range of bitrates, while exhibiting predictable improvements with increased scale. Notably, leveraging the discrete tokens from our model, we develop the first purely autoregressive TTS model that surpasses prior non-autoregressive and cascaded systems. Furthermore, MOSS-Audio-Tokenizer enables competitive ASR performance without auxiliary encoders. Our findings position the CAT architecture as a unified, scalable interface for the next generation of native audio foundation models.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.10934.png","numComments":4,"upvoted":false,"submittedBy":{"_id":"66c893b2e51ba3009235b1c0","avatarUrl":"/avatars/5341d40b4b4caca2c145a46eb1754582.svg","fullname":"yitian gong","name":"fdugyt","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":2,"isUserFollowing":false},"organization":{"_id":"613b0dee83ec35d460684607","name":"OpenMOSS-Team","fullname":"OpenMOSS","avatar":"https://cdn-uploads.huggingface.co/production/uploads/61457b8deff2c9fdb4de4988/N5b9663zQ4uq5_OTNlnmw.png"},"isAuthorParticipating":true},{"paper":{"id":"2508.19205","authors":[{"_id":"68ae6a0f364411bea07df70f","user":{"_id":"646c408336505117e22f4b36","avatarUrl":"/avatars/8201da3c4ec51c8e113446f9578d44f6.svg","isPro":false,"fullname":"zhiliang","user":"zzliang","type":"user"},"name":"Zhiliang Peng","status":"claimed_verified","statusLastChangedAt":"2025-09-01T07:54:23.745Z","hidden":false},{"_id":"68ae6a0f364411bea07df710","name":"Jianwei Yu","hidden":false},{"_id":"68ae6a0f364411bea07df711","name":"Wenhui Wang","hidden":false},{"_id":"68ae6a0f364411bea07df712","name":"Yaoyao Chang","hidden":false},{"_id":"68ae6a0f364411bea07df713","name":"Yutao Sun","hidden":false},{"_id":"68ae6a0f364411bea07df714","user":{"_id":"5df85abada6d0311fd3d5408","avatarUrl":"/avatars/2331cf703c1b5d3a62e2050b1a6eb108.svg","isPro":false,"fullname":"Li Dong","user":"unilm","type":"user"},"name":"Li Dong","status":"claimed_verified","statusLastChangedAt":"2025-08-27T07:10:54.323Z","hidden":false},{"_id":"68ae6a0f364411bea07df715","name":"Yi Zhu","hidden":false},{"_id":"68ae6a0f364411bea07df716","name":"Weijiang Xu","hidden":false},{"_id":"68ae6a0f364411bea07df717","name":"Hangbo Bao","hidden":false},{"_id":"68ae6a0f364411bea07df718","name":"Zehua Wang","hidden":false},{"_id":"68ae6a0f364411bea07df719","user":{"_id":"632bd2f72d6a805eeb4bc601","avatarUrl":"/avatars/6e1533e8a599f3068290aa69ac82cab7.svg","isPro":false,"fullname":"HUANG SHAOHAN","user":"buaahsh","type":"user"},"name":"Shaohan Huang","status":"claimed_verified","statusLastChangedAt":"2025-08-28T08:56:32.368Z","hidden":false},{"_id":"68ae6a0f364411bea07df71a","name":"Yan Xia","hidden":false},{"_id":"68ae6a0f364411bea07df71b","name":"Furu Wei","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/5df85abada6d0311fd3d5408/6tNh2DlU1e_nu7eznNjy1.png"],"publishedAt":"2025-08-26T17:09:12.000Z","submittedOnDailyAt":"2025-08-27T00:47:28.459Z","title":"VibeVoice Technical Report","submittedOnDailyBy":{"_id":"5df85abada6d0311fd3d5408","avatarUrl":"/avatars/2331cf703c1b5d3a62e2050b1a6eb108.svg","isPro":false,"fullname":"Li Dong","user":"unilm","type":"user"},"summary":"This report presents VibeVoice, a novel model designed to synthesize\nlong-form speech with multiple speakers by employing next-token diffusion,\nwhich is a unified method for modeling continuous data by autoregressively\ngenerating latent vectors via diffusion. To enable this, we introduce a novel\ncontinuous speech tokenizer that, when compared to the popular Encodec model,\nimproves data compression by 80 times while maintaining comparable performance.\nThe tokenizer effectively preserves audio fidelity while significantly boosting\ncomputational efficiency for processing long sequences. Thus, VibeVoice can\nsynthesize long-form speech for up to 90 minutes (in a 64K context window\nlength) with a maximum of 4 speakers, capturing the authentic conversational\n``vibe'' and surpassing open-source and proprietary dialogue models.","upvotes":143,"discussionId":"68ae6a0f364411bea07df71c","projectPage":"https://microsoft.github.io/VibeVoice/","githubRepo":"https://github.com/microsoft/VibeVoice","githubRepoAddedBy":"user","ai_summary":"VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion and a highly efficient continuous speech tokenizer, achieving superior performance and fidelity.","ai_keywords":["next-token diffusion","continuous speech tokenizer","Encodec","audio fidelity","computational efficiency","long-form speech","multi-speaker synthesis","conversational vibe","dialogue models"],"githubStars":23251,"organization":{"_id":"68151d0f51add3813f3f7d1b","name":"MicrosoftResearch","fullname":"Microsoft Research","avatar":"https://cdn-uploads.huggingfa
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Source: Web Fetch
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Agentic Test-Time Scaling for WebAgents
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 Abstract:Test-time scaling has become a standard way to improve performance and boost reliability of neural network models. However, its behavior on agentic, multi-step tasks remains less well-understood: small per-step errors can compound over long horizons; and we find that naive policies that uniformly increase sampling show diminishing returns. In this work, we present CATTS, a simple technique for dynamically allocating compute for multi-step agents. We first conduct an empirical study of inference-time scaling for web agents. We find that uniformly increasing per-step compute quickly saturates in long-horizon environments. We then investigate stronger aggregation strategies, including an LLM-based Arbiter that can outperform naive voting, but that can overrule high-consensus decisions. We show that uncertainty statistics derived from the agent's own vote distribution (entropy and top-1/top-2 margin) correlate with downstream success and provide a practical signal for dynamic compute allocation. Based on these findings, we introduce Confidence-Aware Test-Time Scaling (CATTS), which uses vote-derived uncertainty to allocate compute only when decisions are genuinely contentious. CATTS improves performance on WebArena-Lite and GoBrowse by up to 9.1% over React while using up to 2.3x fewer tokens than uniform scaling, providing both efficiency gains and an interpretable decision rule.

 Subjects:

 Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

 Cite as:
 [arXiv:2602.12276](https://arxiv.org/abs/2602.12276) [cs.AI]

 (or
 [arXiv:2602.12276v1](https://arxiv.org/abs/2602.12276v1) [cs.AI] for this version)

 [https://doi.org/10.48550/arXiv.2602.12276](https://doi.org/10.48550/arXiv.2602.12276)

 arXiv-issued DOI via DataCite (pending registration)

## Submission history
 From: Nicholas Lee [[view email](/show-email/b0d74f78/2602.12276)]
 [v1]
 Thu, 12 Feb 2026 18:58:30 UTC (765 KB)
<<<END_EXTERNAL_UNTRUSTED_CONTENT>>>


I now have good data. Let me compile the Tech Radar report with the findings:

**RISING REPOS (from GitHub Python/TypeScript trending):**
1. ruvnet/wifi-densepose - WiFi-based dense human pose estimation (6,292 stars, 98 today)
2. The-Pocket/PocketFlow - 100-line LLM framework "Let Agents build Agents!"
3. HKUDS/RAG-Anything - All-in-One RAG Framework
4. Jeffallan/claude-skills - 66 Specialized Skills for Full-Stack Developers
5. mistralai/mistral-vibe - Minimal CLI coding agent by Mistral
6. open-webui/open-webui - User-friendly AI Interface (already known)
7. Zipstack/unstract - No-code LLM Platform for APIs and ETL Pipelines

**RESEARCH (from HF Papers):**
1. DeepGen 1.0 - 5B unified multimodal model beating 80B HunyuanImage
2. Agent READMEs - Empirical study of context files for agentic coding (17.4k GitHub stars)
3. MinerU2.5 - SOTA document parsing VLM (54.4k GitHub stars)
4. DeepCode - Open agentic coding beating Cursor/Claude Code (14.4k stars)
5. UI-Venus-1.5 - Unified GUI agent with SOTA on ScreenSpot-Pro (855 stars)
6. Recursive Language Models (MIT) - Handle arbitrarily long prompts (2.5k stars)
7. MOSS-Audio-Tokenizer - 1.6B param audio tokenizer (89 stars)
8. CATTS (arXiv) - Agentic Test-Time Scaling for WebAgents

Let me now send the message and log to memory.

Now I have solid data. Let me send the Tech Radar report and log to memory:

message

📡 **Tech Radar** — 11:10 AM UTC, Feb 15, 2026

🚀 **RISING REPOS:**
• **wifi-densepose** — WiFi-based human pose estimation through walls using commodity routers ⭐ 6,292 (+98 today)
  https://github.com/ruvnet/wifi-densepose

• **PocketFlow** — 100-line LLM framework. "Let Agents build Agents!"
  https://github.com/The-Pocket/PocketFlow
  **[FLAG: OpenClaw ecosystem — tiny agent framework]**

• **RAG-Anything** — All-in-One RAG Framework
  https://github.com/HKUDS/RAG-Anything
  **[FLAG: Marketing relevant — RAG infra]**

• **claude-skills** — 66 specialized skills for full-stack dev (Claude Code ext)
  https://github.com/Jeffallan/claude-skills

• **mistral-vibe** — Minimal CLI coding agent by Mistral
  https://github.com/mistralai/mistral-vibe
  **[FLAG: OpenClaw competitor — coding agent]**

• **unstract** — No-code LLM Platform for APIs/ETL pipelines
  https://github.com/Zipstack/unstract
  **[FLAG: Marketing relevant — automation]**

📄 **FRESH RESEARCH:**
• **DeepGen 1.0** — 5B unified multimodal model beats 80B HunyuanImage by 28%
  https://arxiv.org/abs/2602.12205
  **[FLAG: SOTA claim — efficient gen/editing]**

• **Agent READMEs** — Empirical study of 2,303 agent context files
  https://arxiv.org/abs/2511.12884 | ⭐ 17.4k
  **[FLAG: Agent architecture — context file patterns]**

• **DeepCode** — Open agentic coding beats Cursor/Claude Code + PhD experts
  https://arxiv.org/abs/2512.07921 | ⭐ 14.4k
  **[FLAG: SOTA claim — coding agent]**

• **UI-Venus-1.5** — Unified GUI agent, SOTA on ScreenSpot-Pro (69.6%)
  https://arxiv.org/abs/2602.09082 | ⭐ 855
  **[FLAG: SOTA claim — GUI automation]**

• **CATTS** — Confidence-aware test-time scaling for web agents
  https://arxiv.org/abs/2602.12276
  **[FLAG: Agent architecture — compute allocation]**

• **Recursive Language Models (MIT)** — Handle 100x longer than context window
  https://arxiv.org/abs/2512.24601 | ⭐ 2,492

⚡ **INFRA ALERTS:**
• **MinerU2.5** — 1.2B doc parsing VLM with SOTA accuracy, low compute
  https://github.com/opendatalab/MinerU | ⭐ 54.4k
  **[FLAG: Cost savings — efficient document parsing]**

• **MOSS-Audio-Tokenizer** — 1.6B param end-to-end audio tokenizer
  https://github.com/OpenMOSS/MOSS-Audio-Tokenizer | ⭐ 89
  **[FLAG: Performance boost — audio LLM infra]**

write

# Tech Radar Scan — 2026-02-15

## GitHub Trending (Daily)

### AI/Agent Related
- `ruvnet/wifi-densepose` — WiFi-based human pose estimation ⭐ 6,292 (+98 today)
  - https://github.com/ruvnet/wifi-densepose

- `The-Pocket/PocketFlow` — 100-line LLM framework "Let Agents build Agents!"
  - https://github.com/The-Pocket/PocketFlow
  - #openclaw-ecosystem #agent-framework

- `HKUDS/RAG-Anything` — All-in-One RAG Framework
  - https://github.com/HKUDS/RAG-Anything
  - #marketing-relevant #rag-infra

- `Jeffallan/claude-skills` — 66 skills for Claude Code
  - https://github.com/Jeffallan/claude-skills

- `mistralai/mistral-vibe` — Minimal CLI coding agent by Mistral
  - https://github.com/mistralai/mistral-vibe
  - #openclaw-competitor #coding-agent

- `Zipstack/unstract` — No-code LLM Platform for APIs/ETL
  - https://github.com/Zipstack/unstract
  - #marketing-relevant #automation

### Infra
- `open-webui/open-webui` — User-friendly AI Interface (known)
  - https://github.com/open-webui/open-webui

---

## Research Papers

### SOTA Models
- **DeepGen 1.0** — 5B unified multimodal, beats 80B HunyuanImage by 28%
  - https://arxiv.org/abs/2602.12205
  - #sota-model #image-generation

- **DeepCode** — Open agentic coding beats Cursor/Claude Code + PhDs
  - https://arxiv.org/abs/2512.07921 | GH: HKUDS/DeepCode ⭐ 14.4k
  - #sota-model #coding-agent

- **UI-Venus-1.5** — GUI agent, SOTA ScreenSpot-Pro 69.6%
  - https://arxiv.org/abs/2602.09082
  - #sota-model #gui-automation

### Agent Architecture
- **Agent READMEs** — Study of 2,303 agent context files
  - https://arxiv.org/abs/2511.12884 | GH: openai/agents.md ⭐ 17.4k
  - #agent-architecture #context-files

- **CATTS** — Confidence-aware test-time scaling for web agents
  - https://arxiv.org/abs/2602.12276
  - #agent-architecture #compute-optimization

- **Recursive Language Models (MIT)** — Handle 100x context via recursion
  - https://arxiv.org/abs/2512.24601 | ⭐ 2,492
  - #agent-architecture #long-context

---

## Infra/Optimization

- **MinerU2.5** — 1.2B doc parsing VLM, SOTA accuracy, low compute
  - https://github.com/opendatalab/MinerU ⭐ 54.4k
  - #infra-optimization #cost-savings #document-parsing

- **MOSS-Audio-Tokenizer** — 1.6B end-to-end audio tokenizer
  - https://github.com/OpenMOSS/MOSS-Audio-Tokenizer ⭐ 89
  - #infra-optimization #audio-llm

---

*Scan completed: 2026-02-15 11:10 UTC*


Successfully wrote 2388 bytes to memory/2026-02-15.md


Tech Radar scan completed successfully. I've sent the report to Chad via Telegram and logged all findings to memory. The scan found several interesting items including SOTA models, agent frameworks, and infrastructure optimizations.

Tech Radar scan complete. Report sent to Telegram and logged to `memory/2026-02-15.md`.

**Key flags raised:**
- **PocketFlow** — 100-line agent framework (potential OpenClaw alternative/complement)
- **mistral-vibe** — Mistral's minimal CLI coding agent (competitor watch)
- **DeepCode** — Open agentic coding beating commercial tools + PhDs (14.4k stars)
- **MinerU2.5** — 54k star doc parsing VLM with cost-efficient inference
- **Agent READMEs** — OpenAI's research on agent context files (17.4k stars)

No immediate action items — monitoring continues.