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an audio overview for learning on the go: https://youtu.be/h-l7jqKs-Xg

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

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The following papers were recommended by the Semantic Scholar API

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• Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models (2025)
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• Kimi-VL Technical Report (2025)
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• Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains (2025)
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• Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency (2025)
\n
• JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse (2025)
\n
• VGRP-Bench: Visual Grid Reasoning Puzzle Benchmark for Large Vision-Language Models (2025)
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• NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks (2025)
\n

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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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Seed1.5-VL is composed\nwith a 532M-parameter vision encoder and a Mixture-of-Experts (MoE) LLM of 20B\nactive parameters. Despite its relatively compact architecture, it delivers\nstrong performance across a wide spectrum of public VLM benchmarks and internal\nevaluation suites, achieving the state-of-the-art performance on 38 out of 60\npublic benchmarks. Moreover, in agent-centric tasks such as GUI control and\ngameplay, Seed1.5-VL outperforms leading multimodal systems, including OpenAI\nCUA and Claude 3.7. Beyond visual and video understanding, it also demonstrates\nstrong reasoning abilities, making it particularly effective for multimodal\nreasoning challenges such as visual puzzles. We believe these capabilities will\nempower broader applications across diverse tasks. In this report, we mainly\nprovide a comprehensive review of our experiences in building Seed1.5-VL across\nmodel design, data construction, and training at various stages, hoping that\nthis report can inspire further research. Seed1.5-VL is now accessible at\nhttps://www.volcengine.com/ (Volcano Engine Model ID:\ndoubao-1-5-thinking-vision-pro-250428)","upvotes":149,"discussionId":"6822ab59a79983c00c8166ff","projectPage":"https://seed.bytedance.com/en/tech/seed1_5_vl","githubRepo":"https://github.com/ByteDance-Seed/Seed1.5-VL","ai_summary":"Seed1.5-VL, a vision-language foundation model combining a vision encoder and a large MoE LLM, achieves state-of-the-art performance across various benchmarks and excels in multimodal reasoning tasks such as visual puzzles.","ai_keywords":["vision-language foundation model","vision encoder","Mixture-of-Experts (MoE)","LLM","multimodal understanding","multimodal reasoning","visual puzzles","GUI control","gameplay","VLM benchmarks","visual and video understanding"],"githubStars":1445},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"646b3db131968a60a01e4cf5","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/646b3db131968a60a01e4cf5/DhfdqUYQaD1Qa8Svw996J.jpeg","isPro":false,"fullname":"Tianheng Cheng","user":"wondervictor","type":"user"},{"_id":"649d69387bd538409c4616e7","avatarUrl":"/avatars/5486e9e54e92ed348c370ce33ec22be6.svg","isPro":false,"fullname":"V","user":"Liuv","type":"user"},{"_id":"643f37cce9d063936912048b","avatarUrl":"/avatars/25822ea5676a79b2e1ddf08d5fc2226c.svg","isPro":false,"fullname":"Yujia Qin","user":"YujiaHi","type":"user"},{"_id":"6822ac770adcf6f3e0784ff2","avatarUrl":"/avatars/652be5c8a9ad3f1badb596f8d3c15aba.svg","isPro":false,"fullname":"Xuehan Xiong","user":"xiong828","type":"user"},{"_id":"668506e3a3537a9154e98a7d","avatarUrl":"/avatars/3c8c1c1877baaa0dd4742797d9f9cea1.svg","isPro":true,"fullname":"omni-research","user":"omni-research","type":"user"},{"_id":"6453fa96ed6d7fede94408e0","avatarUrl":"/avatars/e8c9025ef24cec958c87a1008bb54fd7.svg","isPro":false,"fullname":"Keming Lu","user":"keminglu","type":"user"},{"_id":"64d592c28767727dffa1f002","avatarUrl":"/avatars/fe38bcac944a2742dc12c624e62d24ef.svg","isPro":false,"fullname":"WangKe","user":"scikkk","type":"user"},{"_id":"6305f643660f01f1509efec6","avatarUrl":"/avatars/bebea525ebb5bde43e411ef9bbe47760.svg","isPro":false,"fullname":"matrix-zxw","user":"zxw","type":"user"},{"_id":"6536187bd34e9f02b9df1c3b","avatarUrl":"/avatars/0b34d62868b93053b0a05062a018b5bd.svg","isPro":false,"fullname":"Hao Gao","user":"Hao605","type":"user"},{"_id":"65a4a180c8a09bd5e8e900b8","avatarUrl":"/avatars/c135db68f6ff2c40119acd2e9ddce968.svg","isPro":false,"fullname":"Bo Jiang","user":"rb93dett","type":"user"},{"_id":"6342796a0875f2c99cfd313b","avatarUrl":"/avatars/98575092404c4197b20c929a6499a015.svg","isPro":false,"fullname":"Yuseung \"Phillip\" Lee","user":"phillipinseoul","type":"user"},{"_id":"645f719aa3e00932c9ef2dc8","avatarUrl":"/avatars/a6436453cfccf0351ac83719051db415.svg","isPro":false,"fullname":"Doctor-James","user":"Doctor-James","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":1}">