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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":74,"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":86,"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. 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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. 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However, their training and inference\nefficiency have emerged as a core bottleneck in making MLLMs more accessible\nand scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B\nparameter model designed for high efficiency and strong performance. We\nintroduce three core improvements in model architecture, data strategy and\ntraining method: a unified 3D-Resampler model architecture for highly compact\nencoding over images and videos, a unified learning paradigm for document\nknowledge and text recognition without heavy data engineering, and a hybrid\nreinforcement learning strategy for proficiency in both short and long\nreasoning modes. Comprehensive experimental results in OpenCompass evaluation\nshow that MiniCPM-V 4.5 surpasses widely used proprietary models such as\nGPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL\n72B. Notably, the strong performance is achieved with remarkable efficiency.\nFor example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 achieves\nstate-of-the-art performance among models under 30B size, using just 46.7\\% GPU\nmemory cost and 8.7\\% inference time of Qwen2.5-VL 7B.","upvotes":54,"discussionId":"68d351d80e215259d193b1f8","githubRepo":"https://github.com/OpenBMB/MiniCPM-V","githubRepoAddedBy":"user","ai_summary":"MiniCPM-V 4.5, a 8B parameter multimodal large language model, achieves high performance and efficiency through a unified 3D-Resampler architecture, a unified learning paradigm, and a hybrid reinforcement learning strategy.","ai_keywords":["3D-Resampler","unified learning paradigm","hybrid reinforcement learning strategy","multimodal large language models","OpenCompass evaluation","VideoMME benchmark"],"githubStars":23810},"publishedAt":"2025-09-16T15:41:48.000Z","title":"MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and\n Training Recipe","summary":"Multimodal Large Language Models (MLLMs) are undergoing rapid progress and\nrepresent the frontier of AI development. However, their training and inference\nefficiency have emerged as a core bottleneck in making MLLMs more accessible\nand scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B\nparameter model designed for high efficiency and strong performance. We\nintroduce three core improvements in model architecture, data strategy and\ntraining method: a unified 3D-Resampler model architecture for highly compact\nencoding over images and videos, a unified learning paradigm for document\nknowledge and text recognition without heavy data engineering, and a hybrid\nreinforcement learning strategy for proficiency in both short and long\nreasoning modes. Comprehensive experimental results in OpenCompass evaluation\nshow that MiniCPM-V 4.5 surpasses widely used proprietary models such as\nGPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL\n72B. 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Dong","hidden":false},{"_id":"68d9ebf80177a6054b013a84","user":{"_id":"64b4eec4faa3181a5eab9c46","avatarUrl":"/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg","isPro":true,"fullname":"Jiaqi Wang","user":"myownskyW7","type":"user"},"name":"Jiaqi Wang","status":"claimed_verified","statusLastChangedAt":"2025-09-29T05:58:49.303Z","hidden":false},{"_id":"68d9ebf80177a6054b013a85","name":"Bo Zhang","hidden":false},{"_id":"68d9ebf80177a6054b013a86","name":"Lei Bai","hidden":false},{"_id":"68d9ebf80177a6054b013a87","user":{"_id":"64c9beb2904317f42de06dd8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64c9beb2904317f42de06dd8/he3rxfyzfwEd1vLuK6_o2.jpeg","isPro":false,"fullname":"Pei Chu","user":"chupei","type":"user"},"name":"Pei Chu","status":"claimed_verified","statusLastChangedAt":"2025-09-29T05:58:41.995Z","hidden":false},{"_id":"68d9ebf80177a6054b013a88","name":"Weijia Li","hidden":false},{"_id":"68d9ebf80177a6054b013a89","name":"Jiang Wu","hidden":false},{"_id":"68d9ebf80177a6054b013a8a","user":{"_id":"643e60d96db6ba8c5ee177ad","avatarUrl":"/avatars/73ac7740e462ba0b53a2f2480d9f1e3e.svg","isPro":false,"fullname":"Lijun Wu","user":"apeters","type":"user"},"name":"Lijun Wu","status":"claimed_verified","statusLastChangedAt":"2025-10-08T11:49:34.562Z","hidden":false},{"_id":"68d9ebf80177a6054b013a8b","name":"Zhenxiang Li","hidden":false},{"_id":"68d9ebf80177a6054b013a8c","name":"Guangyu 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":54404},"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":"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":896,"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":"2512.07921","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":14439},"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":"2602.07085","authors":[{"_id":"698ab6f91b2dc6b37d61b031","user":{"_id":"69674549666228b695202137","avatarUrl":"/avatars/47ac3b87d395856b3dd1a9f24af82c25.svg","isPro":false,"fullname":"Han Jun","user":"Junqwef","type":"user"},"name":"Jun Han","status":"claimed_verified","statusLastChangedAt":"2026-02-11T11:19:06.698Z","hidden":false},{"_id":"698ab6f91b2dc6b37d61b032","name":"Shuo Zhang","hidden":false},{"_id":"698ab6f91b2dc6b37d61b033","name":"Wei Li","hidden":false},{"_id":"698ab6f91b2dc6b37d61b034","user":{"_id":"64aa645404e7b379feccc490","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64aa645404e7b379feccc490/4m8qcdy2OGK8visR5Jjl5.png","isPro":false,"fullname":"Zhi Yang","user":"yangzhi1","type":"user"},"name":"Zhi Yang","status":"claimed_verified","statusLastChangedAt":"2026-02-10T09:05:58.707Z","hidden":false},{"_id":"698ab6f91b2dc6b37d61b035","name":"Yifan Dong","hidden":false},{"_id":"698ab6f91b2dc6b37d61b036","name":"Tu Hu","hidden":false},{"_id":"698ab6f91b2dc6b37d61b037","name":"Jialuo Yuan","hidden":false},{"_id":"698ab6f91b2dc6b37d61b038","user":{"_id":"64084fa192033c150738e4f2","avatarUrl":"/avatars/dfff2216eb235c635e5abe6fda3084f0.svg","isPro":false,"fullname":"Yu_xm","user":"Yu2020","type":"user"},"name":"Xiaomin Yu","status":"claimed_verified","statusLastChangedAt":"2026-02-10T09:06:00.954Z","hidden":false},{"_id":"698ab6f91b2dc6b37d61b039","name":"Yumo Zhu","hidden":false},{"_id":"698ab6f91b2dc6b37d61b03a","name":"Fangqi Lou","hidden":false},{"_id":"698ab6f91b2dc6b37d61b03b","name":"Xin Guo","hidden":false},{"_id":"698ab6f91b2dc6b37d61b03c","name":"Zhaowei Liu","hidden":false},{"_id":"698ab6f91b2dc6b37d61b03d","user":{"_id":"6895e7f146763431aea25ca4","avatarUrl":"/avatars/52e550c3f7e8da2e31b63413e2e71e6c.svg","isPro":false,"fullname":"Tianyi Jiang","user":"LumosJiang","type":"user"},"name":"Tianyi Jiang","status":"claimed_verified","statusLastChangedAt":"2026-02-11T11:19:04.702Z","hidden":false},{"_id":"698ab6f91b2dc6b37d61b03e","name":"Ruichuan An","hidden":false},{"_id":"698ab6f91b2dc6b37d61b03f","name":"Jingping Liu","hidden":false},{"_id":"698ab6f91b2dc6b37d61b040","name":"Biao Wu","hidden":false},{"_id":"698ab6f91b2dc6b37d61b041","name":"Rongze Chen","hidden":false},{"_id":"698ab6f91b2dc6b37d61b042","name":"Kunyi Wang","hidden":false},{"_id":"698ab6f91b2dc6b37d61b043","name":"Yifan Wang","hidden":false},{"_id":"698ab6f91b2dc6b37d61b044","name":"Sen Hu","hidden":false},{"_id":"698ab6f91b2dc6b37d61b045","name":"Xinbing Kong","hidden":false},{"_id":"698ab6f91b2dc6b37d61b046","name":"Liwen Zhang","hidden":false},{"_id":"698ab6f91b2dc6b37d61b047","name":"Ronghao Chen","hidden":false},{"_id":"698ab6f91b2dc6b37d61b048","name":"Huacan Wang","hidden":false}],"publishedAt":"2026-02-06T08:08:04.000Z","submittedOnDailyAt":"2026-02-10T02:19:22.216Z","title":"QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining","submittedOnDailyBy":{"_id":"64aa645404e7b379feccc490","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64aa645404e7b379feccc490/4m8qcdy2OGK8visR5Jjl5.png","isPro":false,"fullname":"Zhi Yang","user":"yangzhi1","type":"user"},"summary":"Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose QuantaAlpha, an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors through trajectory-level mutation and crossover operations. QuantaAlpha localizes suboptimal steps in each trajectory for targeted revision and recombines complementary high-reward segments to reuse effective patterns, enabling structured exploration and refinement across mining iterations. During factor generation, QuantaAlpha enforces semantic consistency across the hypothesis, factor expression, and executable code, while constraining the complexity and redundancy of the generated factor to mitigate crowding. Extensive experiments on the China Securities Index 300 (CSI 300) demonstrate consistent gains over strong baseline models and prior agentic systems. When utilizing GPT-5.2, QuantaAlpha achieves an Information Coefficient (IC) of 0.1501, with an Annualized Rate of Return (ARR) of 27.75% and a Maximum Drawdown (MDD) of 7.98%. Moreover, factors mined on CSI 300 transfer effectively to the China Securities Index 500 (CSI 500) and the Standard & Poor's 500 Index (S&P 500), delivering 160% and 137% cumulative excess return over four years, respectively, which indicates strong robustness of QuantaAlpha under market distribution shifts.","upvotes":180,"discussionId":"698ab6fa1b2dc6b37d61b049","githubRepo":"https://github.com/QuantaAlpha/QuantaAlpha","githubRepoAddedBy":"user","githubStars":313,"organization":{"_id":"68b33ab6a9ed99140481cf44","name":"QuantaAlpha","fullname":"QuantaAlpha","avatar":"https://cdn-uploads.huggingface.co/production/uploads/63f7767fbd28622c9b9915e9/DRN8PvmnpKmn2MSLQ7qhF.jpeg"}},"publishedAt":"2026-02-06T03:08:04.000Z","title":"QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining","summary":"Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose QuantaAlpha, an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors through trajectory-level mutation and crossover operations. QuantaAlpha localizes suboptimal steps in each trajectory for targeted revision and recombines complementary high-reward segments to reuse effective patterns, enabling structured exploration and refinement across mining iterations. During factor generation, QuantaAlpha enforces semantic consistency across the hypothesis, factor expression, and executable code, while constraining the complexity and redundancy of the generated factor to mitigate crowding. Extensive experiments on the China Securities Index 300 (CSI 300) demonstrate consistent gains over strong baseline models and prior agentic systems. When utilizing GPT-5.2, QuantaAlpha achieves an Information Coefficient (IC) of 0.1501, with an Annualized Rate of Return (ARR) of 27.75% and a Maximum Drawdown (MDD) of 7.98%. Moreover, factors mined on CSI 300 transfer effectively to the China Securities Index 500 (CSI 500) and the Standard & Poor's 500 Index (S&P 500), delivering 160% and 137% cumulative excess return over four years, respectively, which indicates strong robustness of QuantaAlpha under market distribution shifts.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.07085.png","numComments":2,"upvoted":false,"submittedBy":{"_id":"64aa645404e7b379feccc490","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64aa645404e7b379feccc490/4m8qcdy2OGK8visR5Jjl5.png","fullname":"Zhi Yang","name":"yangzhi1","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false},"organization":{"_id":"68b33ab6a9ed99140481cf44","name":"QuantaAlpha","fullname":"QuantaAlpha","avatar":"https://cdn-uploads.huggingface.co/production/uploads/63f7767fbd28622c9b9915e9/DRN8PvmnpKmn2MSLQ7qhF.jpeg"},"isAuthorParticipating":true},{"paper":{"id":"2508.02739","authors":[{"_id":"68940073741a16f544fbce2f","name":"Yu Shi","hidden":false},{"_id":"68940073741a16f544fbce30","name":"Zongliang Fu","hidden":false},{"_id":"68940073741a16f544fbce31","name":"Shuo Chen","hidden":false},{"_id":"68940073741a16f544fbce32","name":"Bohan Zhao","hidden":false},{"_id":"68940073741a16f544fbce33","name":"Wei Xu","hidden":false},{"_id":"68940073741a16f544fbce34","name":"Changshui Zhang","hidden":false},{"_id":"68940073741a16f544fbce35","name":"Jian L
<<<END_EXTERNAL_UNTRUSTED_CONTENT>>>


Good! I've got some real data now. Let me parse what I have:

**GitHub Trending:**
- openclaw/openclaw — Your own personal AI assistant
- rowboatlabs/rowboat — Open-source AI coworker, with memory
- github/gh-aw — GitHub Agentic Workflows
- ChromeDevTools/chrome-devtools-mcp — Chrome DevTools for coding agents
- ruvnet/wifi-densepose — WiFi-based dense human pose estimation (352 stars today)
- moonshine-ai/moonshine — Fast ASR for edge devices

**arXiv Papers (2026-02-16):**
125 entries today in cs.AI - I have the IDs but not titles yet

**HuggingFace Trending Models:**
- zai-org/GLM-5 (753B params)
- MiniMaxAI/MiniMax-M2.5 (228B params)
- openbmb/MiniCPM-SALA
- moonshotai/Kimi-K2.5
- Qwen/Qwen3-Coder-Next

**Papers (from the redirect to HuggingFace papers):**
- DeepGen 1.0 (lightweight unified multimodal model)
- MiniCPM-V 4.5 (efficient MLLMs)
- MinerU2.5 (document parsing)
- UI-Venus-1.5 (GUI agent)
- DeepCode (autonomous coding)
- QuantaAlpha (alpha mining)

Let me compile a concise report and send it.

message

📡 **Tech Radar** — 3:02 AM UTC, Feb 16, 2026

🚀 **RISING REPOS:**
• **openclaw/openclaw** — Your own personal AI assistant 🦞
  [GitHub](https://github.com/openclaw/openclaw)
  **[FLAG: OpenClaw on trending!]**

• **rowboatlabs/rowboat** — Open-source AI coworker with memory
  [GitHub](https://github.com/rowboatlabs/rowboat)
  **[FLAG: Marketing relevant / AI agent competitor]**

• **github/gh-aw** — GitHub Agentic Workflows
  [GitHub](https://github.com/github/gh-aw)
  **[FLAG: Workflow automation for agents]**

• **ChromeDevTools/chrome-devtools-mcp** — Chrome DevTools for coding agents
  [GitHub](https://github.com/ChromeDevTools/chrome-devtools-mcp)
  **[FLAG: MCP server for browser control]**

• **ruvnet/wifi-densepose** — WiFi-based pose estimation (352 stars today!)
  [GitHub](https://github.com/ruvnet/wifi-densepose)
  **[FLAG: 352 stars in 24h]**

📄 **FRESH RESEARCH:**
• **DeepGen 1.0** — Lightweight 5B unified multimodal model (image gen/edit)
  [Paper](https://arxiv.org/abs/2602.12205)
  **[FLAG: Beats 80B HunyuanImage by 28%]**

• **UI-Venus-1.5** — Unified GUI agent (2B/8B/30B-A3B variants)
  [Paper](https://arxiv.org/abs/2602.09082) | [Code](https://github.com/inclusionAI/UI-Venus)
  **[FLAG: SOTA on ScreenSpot-Pro (69.6%), AndroidWorld (77.6%)]**

• **MinerU2.5** — 1.2B doc parsing model (SOTA on text/formula/table extraction)
  [Paper](https://arxiv.org/abs/2509.22186) | [Code](https://github.com/opendatalab/MinerU)
  **[FLAG: Document intelligence for agents]**

• **DeepCode** — Autonomous coding agent (surpasses PhD-level experts!)
  [Paper](https://arxiv.org/abs/2512.07921) | [Code](https://github.com/HKUDS/DeepCode)
  **[FLAG: Beats Cursor & Claude Code on PaperBench]**

• **QuantaAlpha** — LLM-driven alpha mining (27.75% ARR, 7.98% MDD)
  [Paper](https://arxiv.org/abs/2602.07085) | [Code](https://github.com/QuantaAlpha/QuantaAlpha)
  **[FLAG: Finance automation tool]**

⚡ **MODELS ALERT:**
• **GLM-5** (753B params) — Z.ai's flagship, trending on HuggingFace
• **Kimi-K2.5** (170B params) — Multimodal model from Moonshot
• **Qwen3-Coder-Next** (79B params) — Latest Qwen coding model

🔍 **NOTABLE:** 125 new papers on arXiv cs.AI today (Feb 16)


Good, message sent. Now I need to log everything to memory with appropriate tags.


# 2026-02-16 - Tech Radar Hourly Scan

## 2:05 AM - Tech Radar Results

### GitHub Trending Repos

- https://github.com/github/gh-aw #openclaw-ecosystem #agent-framework
  GitHub Agentic Workflows - MCP-based workflow automation

- https://github.com/ChromeDevTools/chrome-devtools-mcp #openclaw-ecosystem #infra-optimization
  Chrome DevTools MCP server for coding agents

- https://github.com/openclaw/openclaw #openclaw-ecosystem
  OpenClaw trending on GitHub

- https://github.com/rowboatlabs/rowboat #agent-framework #marketing-relevant
  AI coworker with memory graph

- https://github.com/ruvnet/wifi-densepose #infra-optimization
  WiFi-based pose estimation (6,506 stars, +352 today)

- https://github.com/steipete/gogcli #infra-optimization
  Google Suite CLI

### Research Sources

- https://arxiv.org/list/cs.AI/recent
  226 recent AI papers (Feb 13)

- https://huggingface.co/models
  Trending models checked

### Models Flagged

- https://huggingface.co/zai-org/GLM-5 #sota-model
  753B params, 1,199 stars

- https://huggingface.co/MiniMaxAI/MiniMax-M2.5 #sota-model
  228B params, 608 stars

- https://huggingface.co/moonshotai/Kimi-K2.5 #sota-model
  170B params multimodal, 2,186 stars

- https://huggingface.co/Qwen/Qwen3-Coder-Next #sota-model
  79B params coding model, 869 stars

### Priority Alerts

🚨 **github/gh-aw** — GitHub official agent workflow system (potential OpenClaw alternative pattern)
🚨 **chrome-devtools-mcp** — Google official Chrome MCP server (browser tool competition)

---

## Magoo News Pulse — 2:20 AM UTC (2:20 AM Monday)

Only 4-5 diverse items this cycle. Late night/early morning news drought (2:20 AM UTC Monday).

**Attempted:**
- web_search — rate-limited (429) after 1 query
- SearXNG searches — returned generic homepages, unrelated AR15 forum results instead of Ars Technica
- web_fetch on Reuters/TechCrunch/Ars — returned only navigation menus/footers (JS-loaded content)

**Result:** Insufficient new diverse items for full 10-12 item briefing.

**Sent notification to Chad:** "Only 4-5 diverse items this cycle (2:20 AM UTC Monday). Late night/early morning news drought. Waiting for more news. Next scan: ~4:00 AM UTC"

Next scan: ~4:00 AM UTC

---

## Magoo News Pulse — 02:40 UTC (2:40 AM EST Monday)

Successfully delivered 11-item diverse briefing.

### Tech
- https://www.bbc.com/news/articles/crm87109l3wo — Taylor Swift trademark: Cathay Home drops "Swift Home" bedding application

### AI
- https://www.thehindu.com/news/national/pm-modi-to-inaugurate-india-ai-impact-expo-on-february-16/article70635191.ece — India AI Impact Expo 2026: Modi, Altman, Pichai, 100+ countries

### Global
- https://www.bbc.com/news/articles/cj3vve6v17xo — Bangladesh: Dipu Das Hindu lynching, mob killing on blasphemy charge
- https://www.bbc.com/news/articles/c4gjx1j1nkjo — Trump Venezuela oil plans face skepticism from US energy firms
- https://www.reuters.com/world/middle-east/israeli-airstrikes-kill-9-gaza-including-tent-camp-palestinian-officials-say-2026-02-15/ — Israel Gaza strikes kill 11, Hamas ceasefire violations

### Sports
- https://www.bbc.com/sport/articles/cm2x7m17jydo — Matt Weston: First Brit to win 2 Winter Olympic golds (skeleton)

### Science
- ScienceDaily Feb 15 — Nordic diet linked to 23% longer lifespan
- ScienceDaily Feb 15 — AI SIGNET maps Alzheimer's gene networks
- ScienceDaily Feb 15 — Dreams can be nudged to boost creativity (Northwestern)
- ScienceDaily Feb 15 — Water isotope tracking method developed

**Coverage quality:** 11 diverse items, no duplicates, varied sources (BBC, Reuters, Hindu, ScienceDaily)
**Next scan:** ~04:30 UTC


Good, I can see the file exists. Now I'll append today's tech radar results.

edit

**Coverage quality:** 11 diverse items, no duplicates, varied sources (BBC, Reuters, Hindu, ScienceDaily)
**Next scan:** ~04:30 UTC

---

## 3:02 AM UTC - Tech Radar Hourly Scan

### GitHub Trending

- https://github.com/openclaw/openclaw #openclaw-ecosystem
  Personal AI assistant (🦞 trending!)

- https://github.com/rowboatlabs/rowboat #marketing-relevant #agent-framework
  Open-source AI coworker with memory

- https://github.com/github/gh-aw #openclaw-ecosystem #agent-framework
  GitHub Agentic Workflows

- https://github.com/ChromeDevTools/chrome-devtools-mcp #openclaw-ecosystem #mcp-server
  Chrome DevTools for coding agents

- https://github.com/ruvnet/wifi-densepose #infra-optimization
  WiFi-based pose estimation (352 stars today)

- https://github.com/moonshine-ai/moonshine
  Fast ASR for edge devices

### Research Papers

- https://arxiv.org/abs/2602.12205 #sota-model #marketing-relevant
  DeepGen 1.0: Lightweight 5B unified multimodal (beats 80B HunyuanImage by 28%)
  [Code](https://github.com/DeepGenTeam/DeepGen)

- https://arxiv.org/abs/2602.09082 #agent-framework #sota-model
  UI-Venus-1.5: Unified GUI agent (SOTA: ScreenSpot-Pro 69.6%, AndroidWorld 77.6%)
  [Code](https://github.com/inclusionAI/UI-Venus)

- https://arxiv.org/abs/2509.22186 #openclaw-ecosystem #marketing-relevant
  MinerU2.5: 1.2B doc parsing model (SOTA document intelligence)
  [Code](https://github.com/opendatalab/MinerU)

- https://arxiv.org/abs/2512.07921 #agent-framework #sota-model
  DeepCode: Autonomous coding agent (surpasses PhD-level, beats Cursor & Claude Code)
  [Code](https://github.com/HKUDS/DeepCode)

- https://arxiv.org/abs/2602.07085 #marketing-relevant #infra-optimization
  QuantaAlpha: LLM alpha mining (27.75% ARR, 7.98% MDD)
  [Code](https://github.com/QuantaAlpha/QuantaAlpha)

### HuggingFace Trending Models

- https://huggingface.co/zai-org/GLM-5 #sota-model
  753B params, 1,203 stars

- https://huggingface.co/MiniMaxAI/MiniMax-M2.5 #sota-model
  228B params, 609 stars

- https://huggingface.co/openbmb/MiniCPM-SALA #sota-model
  9B params

- https://huggingface.co/moonshotai/Kimi-K2.5 #sota-model
  170B params multimodal, 2,186 stars

- https://huggingface.co/Qwen/Qwen3-Coder-Next #sota-model
  79B params coding model, 869 stars

### arXiv Status
- 125 new papers in cs.AI today (Feb 16)

### Alerts
🚨 **OpenClaw trending** on GitHub!
🚨 **WiFi-DensePose** — 352 stars in 24h
🚨 **DeepCode** — Beats PhD-level experts on PaperBench
🚨 **UI-Venus-1.5** — SOTA GUI agent performance


Successfully replaced text in memory/2026-02-16.md.

     ...
  93 - ScienceDaily Feb 15 — Water isotope tracking method developed
  94
  95 **Coverage quality:** 11 diverse items, no duplicates, varied sources (BBC, Reuters, Hindu, ScienceDaily)
  96 **Next scan:** ~04:30 UTC
+ 97
+ 98 ---
+ 99
+100 ## 3:02 AM UTC - Tech Radar Hourly Scan
+101
+102 ### GitHub Trending
+103
+104 - https://github.com/openclaw/openclaw #openclaw-ecosystem
+105   Personal AI assistant (🦞 trending!)
+106
+107 - https://github.com/rowboatlabs/rowboat #marketing-relevant #agent-framework
+108   Open-source AI coworker with memory
+109
+110 - https://github.com/github/gh-aw #openclaw-ecosystem #agent-framework
+111   GitHub Agentic Workflows
+112
+113 - https://github.com/ChromeDevTools/chrome-devtools-mcp #openclaw-ecosystem #mcp-server
+114   Chrome DevTools for coding agents
+115
+116 - https://github.com/ruvnet/wifi-densepose #infra-optimization
+117   WiFi-based pose estimation (352 stars today)
+118
+119 - https://github.com/moonshine-ai/moonshine
+120   Fast ASR for edge devices
+121
+122 ### Research Papers
+123
+124 - https://arxiv.org/abs/2602.12205 #sota-model #marketing-relevant
+125   DeepGen 1.0: Lightweight 5B unified multimodal (beats 80B HunyuanImage by 28%)
+126   [Code](https://github.com/DeepGenTeam/DeepGen)
+127
+128 - https://arxiv.org/abs/2602.09082 #agent-framework #sota-model
+129   UI-Venus-1.5: Unified GUI agent (SOTA: ScreenSpot-Pro 69.6%, AndroidWorld 77.6%)
+130   [Code](https://github.com/inclusionAI/UI-Venus)
+131
+132 - https://arxiv.org/abs/2509.22186 #openclaw-ecosystem #marketing-relevant
+133   MinerU2.5: 1.2B doc parsing model (SOTA document intelligence)
+134   [Code](https://github.com/opendatalab/MinerU)
+135
+136 - https://arxiv.org/abs/2512.07921 #agent-framework #sota-model
+137   DeepCode: Autonomous coding agent (surpasses PhD-level, beats Cursor & Claude Code)
+138   [Code](https://github.com/HKUDS/DeepCode)
+139
+140 - https://arxiv.org/abs/2602.07085 #marketing-relevant #infra-optimization
+141   QuantaAlpha: LLM alpha mining (27.75% ARR, 7.98% MDD)
+142   [Code](https://github.com/QuantaAlpha/QuantaAlpha)
+143
+144 ### HuggingFace Trending Models
+145
+146 - https://huggingface.co/zai-org/GLM-5 #sota-model
+147   753B params, 1,203 stars
+148
+149 - https://huggingface.co/MiniMaxAI/MiniMax-M2.5 #sota-model
+150   228B params, 609 stars
+151
+152 - https://huggingface.co/openbmb/MiniCPM-SALA #sota-model
+153   9B params
+154
+155 - https://huggingface.co/moonshotai/Kimi-K2.5 #sota-model
+156   170B params multimodal, 2,186 stars
+157
+158 - https://huggingface.co/Qwen/Qwen3-Coder-Next #sota-model
+159   79B params coding model, 869 stars
+160
+161 ### arXiv Status
+162 - 125 new papers in cs.AI today (Feb 16)
+163
+164 ### Alerts
+165 🚨 **OpenClaw trending** on GitHub!
+166 🚨 **WiFi-DensePose** — 352 stars in 24h
+167 🚨 **DeepCode** — Beats PhD-level experts on PaperBench
+168 🚨 **UI-Venus-1.5** — SOTA GUI agent performance


Perfect! I've:
1. ✅ Compiled the tech radar scan
2. ✅ Sent it to Chad via Telegram
3. ✅ Logged all URLs to memory/2026-02-16.md with appropriate tags

The scan is complete. I found:
- 6 GitHub trending repos (including OpenClaw!)
- 5 major research papers
- 5 trending HuggingFace models
- Key alerts about SOTA claims and high star growth

All done for this hourly scan.

Tech radar scan complete. Delivered report to Chad with:
- **6 GitHub repos** (OpenClaw trending! 🦞, MCP servers, AI agents)
- **5 research papers** (DeepCode beats PhD-level, UI-Venus SOTA GUI agent, DeepGen 1.0)
- **5 HuggingFace models** (GLM-5, Kimi-K2.5, Qwen3-Coder-Next)
- **4 priority flags** (WiFi-DensePose +352 stars/day, DeepCode > PhDs, OpenClaw trending)

All URLs logged to `memory/2026-02-16.md` with tags: `#openclaw-ecosystem`, `#marketing-relevant`, `#sota-model`, `#agent-framework`, `#mcp-server`, `#infra-optimization`.