https://huggingface.co/collections/moonshotai/kimi-vl-a3b-67f67b6ac91d3b03d382dd85
Space: https://huggingface.co/spaces/moonshotai/Kimi-VL-A3B-Thinking
GitHub: https://github.com/MoonshotAI/Kimi-VL

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This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

\n

The following papers were recommended by the Semantic Scholar API

\n

\n
• Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI (2025)
\n
• SmolVLM: Redefining small and efficient multimodal models (2025)
\n
• FCoT-VL:Advancing Text-oriented Large Vision-Language Models with Efficient Visual Token Compression (2025)
\n
• Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources (2025)
\n
• M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performance (2025)
\n
• JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse (2025)
\n
• MindGYM: Enhancing Vision-Language Models via Synthetic Self-Challenging Questions (2025)
\n

\n

Please give a thumbs up to this comment if you found it helpful!

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If you want recommendations for any Paper on Hugging Face checkout this Space

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You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: \n\n@librarian-bot\n\t recommend

\n","updatedAt":"2025-04-12T01:33:48.979Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7002951502799988},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}},{"id":"686171dea53e52fb923cde42","author":{"_id":"67fa89232b03c68dd26a0b7a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2jgTD4DDO9fFLrilYdyiP.png","fullname":"Seshathri","name":"Seshathri0007","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false},"createdAt":"2025-06-29T17:03:26.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"\n\n","html":"

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What is this

\n","updatedAt":"2025-06-29T17:03:37.014Z","author":{"_id":"67fa89232b03c68dd26a0b7a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2jgTD4DDO9fFLrilYdyiP.png","fullname":"Seshathri","name":"Seshathri0007","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9951521158218384},"editors":["Seshathri0007"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2jgTD4DDO9fFLrilYdyiP.png"],"reactions":[],"isReport":false},"replies":[{"id":"686382e602c9ff71d86de0ea","author":{"_id":"63047ed2412a1b9d381b09c9","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63047ed2412a1b9d381b09c9/2Ill5G0uSMyGstrawgmIb.jpeg","fullname":"Haoning Wu, Teo","name":"teowu","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":110,"isUserFollowing":false},"createdAt":"2025-07-01T06:40:38.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"Please try out our model's visual question answering ability on https://huggingface.co/spaces/moonshotai/Kimi-VL-A3B-Thinking.","html":"

Please try out our model's visual question answering ability on https://huggingface.co/spaces/moonshotai/Kimi-VL-A3B-Thinking.

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Kimi-VL\ndemonstrates strong performance across challenging domains: as a\ngeneral-purpose VLM, Kimi-VL excels in multi-turn agent tasks (e.g., OSWorld),\nmatching flagship models. Furthermore, it exhibits remarkable capabilities\nacross diverse challenging vision language tasks, including college-level image\nand video comprehension, OCR, mathematical reasoning, and multi-image\nunderstanding. In comparative evaluations, it effectively competes with\ncutting-edge efficient VLMs such as GPT-4o-mini, Qwen2.5-VL-7B, and\nGemma-3-12B-IT, while surpassing GPT-4o in several key domains. Kimi-VL also\nadvances in processing long contexts and perceiving clearly. With a 128K\nextended context window, Kimi-VL can process diverse long inputs, achieving\nimpressive scores of 64.5 on LongVideoBench and 35.1 on MMLongBench-Doc. Its\nnative-resolution vision encoder, MoonViT, further allows it to see and\nunderstand ultra-high-resolution visual inputs, achieving 83.2 on InfoVQA and\n34.5 on ScreenSpot-Pro, while maintaining lower computational cost for common\ntasks. Building upon Kimi-VL, we introduce an advanced long-thinking variant:\nKimi-VL-Thinking. Developed through long chain-of-thought (CoT) supervised\nfine-tuning (SFT) and reinforcement learning (RL), this model exhibits strong\nlong-horizon reasoning capabilities. It achieves scores of 61.7 on MMMU, 36.8\non MathVision, and 71.3 on MathVista while maintaining the compact 2.8B\nactivated LLM parameters, setting a new standard for efficient multimodal\nthinking models. Code and models are publicly accessible at\nhttps://github.com/MoonshotAI/Kimi-VL.","upvotes":137,"discussionId":"67f8a3de7de2391a06a3b420","projectPage":"https://huggingface.co/spaces/moonshotai/Kimi-VL-A3B-Thinking","githubRepo":"https://github.com/MoonshotAI/Kimi-VL","githubRepoAddedBy":"user","ai_summary":"Kimi-VL, an efficient Mixture-of-Experts vision-language model, excels in multimodal reasoning, long-context understanding, and diverse vision-language tasks, achieving competitive performance with reduced computational cost.","ai_keywords":["Mixture-of-Experts (MoE)","vision-language model (VLM)","multi-turn agent tasks","OSWorld","college-level image and video comprehension","OCR","mathematical reasoning","multi-image understanding","GPT-4o-mini","Qwen2.5-VL-7B","Gemma-3-12B-IT","long contexts","LongVideoBench","MMLongBench-Doc","MoonViT","InfoVQA","ScreenSpot-Pro","chain-of-thought (CoT) supervised fine-tuning (SFT)","reinforcement learning (RL)","MMMU","MathVision","MathVista"],"githubStars":1162,"organization":{"_id":"6425a114812813f8f4a9b02c","name":"moonshotai","fullname":"Moonshot AI","avatar":"https://cdn-uploads.huggingface.co/production/uploads/641c1e77c3983aa9490f8121/X1yT2rsaIbR9cdYGEVu0X.jpeg"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"63047ed2412a1b9d381b09c9","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63047ed2412a1b9d381b09c9/2Ill5G0uSMyGstrawgmIb.jpeg","isPro":true,"fullname":"Haoning Wu, Teo","user":"teowu","type":"user"},{"_id":"667a7cefdfe717c193e53f99","avatarUrl":"/avatars/94e4410f94e43e0c0e740730d108eace.svg","isPro":false,"fullname":"Yimin","user":"Maxwelldht","type":"user"},{"_id":"64ec364e7e2ec711a7601cde","avatarUrl":"/avatars/6ba47d496586de65df183f056d35982b.svg","isPro":false,"fullname":"Hao Yang","user":"hayayanghao","type":"user"},{"_id":"6365df6912188d67e65f5c5b","avatarUrl":"/avatars/59a1d2f30ba4faea0336bedf4df321a8.svg","isPro":false,"fullname":"Yanru Chen","user":"AChen-qaq","type":"user"},{"_id":"63dc7aa562dc193e6d467200","avatarUrl":"/avatars/56c81062b497ccede76a347a5b049485.svg","isPro":false,"fullname":"Zhiqi Huang","user":"googlebrain","type":"user"},{"_id":"64c21fb42426d683e56b42bf","avatarUrl":"/avatars/60359fe204e32af831d701d2975c4599.svg","isPro":false,"fullname":"Du","user":"DuChenZhuang","type":"user"},{"_id":"65c20ee58aedd6edd2b89000","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65c20ee58aedd6edd2b89000/LtS4YTbmxiCFqHSGHfdC8.png","isPro":false,"fullname":"Chmielewski","user":"Eryk-Chmielewski","type":"user"},{"_id":"6331606f18711776b4655e67","avatarUrl":"/avatars/1479c2ca743b9f92d845b0ed23fcd07b.svg","isPro":false,"fullname":"Enming Yuan","user":"EnmingYuan","type":"user"},{"_id":"642bcd9be8dfcc1fe4f4f853","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/642bcd9be8dfcc1fe4f4f853/M9Yqkyt66dnWWCwmBZ8l0.jpeg","isPro":false,"fullname":"Haotian Yao","user":"skylark-95","type":"user"},{"_id":"6656ae9485c7a647ebd3c407","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6656ae9485c7a647ebd3c407/h5bo91uLLQB_CrjctCcPl.jpeg","isPro":false,"fullname":"Theo Li","user":"bbtfr","type":"user"},{"_id":"657152eb12f162153b50ec9d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/657152eb12f162153b50ec9d/qnldHP35PclV0pDz_05q8.jpeg","isPro":false,"fullname":"Byung-Kwan Lee","user":"BK-Lee","type":"user"},{"_id":"6343d01a08c017b2c042305d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6343d01a08c017b2c042305d/cmJrYkGs9RjDAKfMYCqdW.jpeg","isPro":false,"fullname":"Flood Sung","user":"floodsung","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":1,"organization":{"_id":"6425a114812813f8f4a9b02c","name":"moonshotai","fullname":"Moonshot AI","avatar":"https://cdn-uploads.huggingface.co/production/uploads/641c1e77c3983aa9490f8121/X1yT2rsaIbR9cdYGEVu0X.jpeg"}}">

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