✨ReHyAt: Recurrent Hybrid Attention for Video Diffusion Transformers
📝 Summary:
ReHyAt presents a recurrent hybrid attention mechanism, merging softmax fidelity with linear efficiency. This enables scalable, high-quality video generation by reducing computational cost from quadratic to linear, with significantly lower training costs.
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04342
• PDF: https://arxiv.org/pdf/2601.04342
• Project Page: https://qualcomm-ai-research.github.io/rehyat
==================================
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📝 Summary:
ReHyAt presents a recurrent hybrid attention mechanism, merging softmax fidelity with linear efficiency. This enables scalable, high-quality video generation by reducing computational cost from quadratic to linear, with significantly lower training costs.
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04342
• PDF: https://arxiv.org/pdf/2601.04342
• Project Page: https://qualcomm-ai-research.github.io/rehyat
==================================
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✨Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Views
📝 Summary:
HairGuard is a framework for recovering fine-grained soft boundary details in 3D vision tasks through specialized depth refinement and view synthesis techniques. AI-generated summary Soft boundaries, ...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03362
• PDF: https://arxiv.org/pdf/2601.03362
==================================
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📝 Summary:
HairGuard is a framework for recovering fine-grained soft boundary details in 3D vision tasks through specialized depth refinement and view synthesis techniques. AI-generated summary Soft boundaries, ...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03362
• PDF: https://arxiv.org/pdf/2601.03362
==================================
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✨Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset
📝 Summary:
A large-scale industrial multimodal defect dataset with 1 million image-text pairs enables efficient foundation model adaptation for manufacturing quality inspection and generation tasks. AI-generated...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24160
• PDF: https://arxiv.org/pdf/2512.24160
• Project Page: https://ninaneon.github.io/projectpage/
• Github: https://github.com/NinaNeon/IMDD-1M-Towards-Open-Vocabulary-Industrial-Defect-
==================================
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📝 Summary:
A large-scale industrial multimodal defect dataset with 1 million image-text pairs enables efficient foundation model adaptation for manufacturing quality inspection and generation tasks. AI-generated...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24160
• PDF: https://arxiv.org/pdf/2512.24160
• Project Page: https://ninaneon.github.io/projectpage/
• Github: https://github.com/NinaNeon/IMDD-1M-Towards-Open-Vocabulary-Industrial-Defect-
==================================
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✨Memorization in 3D Shape Generation: An Empirical Study
📝 Summary:
Researchers develop a framework to measure memorization in 3D generative models and identify factors affecting it, finding that data modality and model design parameters influence how much training da...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23628
• PDF: https://arxiv.org/pdf/2512.23628
• Github: https://github.com/zlab-princeton/3d-gen-mem
🔹 Models citing this paper:
• https://huggingface.co/pudashi/3DGenMem
==================================
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📝 Summary:
Researchers develop a framework to measure memorization in 3D generative models and identify factors affecting it, finding that data modality and model design parameters influence how much training da...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23628
• PDF: https://arxiv.org/pdf/2512.23628
• Github: https://github.com/zlab-princeton/3d-gen-mem
🔹 Models citing this paper:
• https://huggingface.co/pudashi/3DGenMem
==================================
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✨AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering
📝 Summary:
AgentDevel presents a release engineering approach for large language model agents that treats them as shippable artifacts and emphasizes stable, auditable improvements through externalized testing an...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04620
• PDF: https://arxiv.org/pdf/2601.04620
• Project Page: https://trotsky1997.github.io/agentdevel-dashboard/
==================================
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📝 Summary:
AgentDevel presents a release engineering approach for large language model agents that treats them as shippable artifacts and emphasizes stable, auditable improvements through externalized testing an...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04620
• PDF: https://arxiv.org/pdf/2601.04620
• Project Page: https://trotsky1997.github.io/agentdevel-dashboard/
==================================
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✨Beyond Binary Preference: Aligning Diffusion Models to Fine-grained Criteria by Decoupling Attributes
📝 Summary:
A two-stage framework for diffusion model alignment using hierarchical evaluation criteria and complex preference optimization demonstrates improved generation quality and expert alignment. AI-generat...
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04300
• PDF: https://arxiv.org/pdf/2601.04300
==================================
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📝 Summary:
A two-stage framework for diffusion model alignment using hierarchical evaluation criteria and complex preference optimization demonstrates improved generation quality and expert alignment. AI-generat...
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04300
• PDF: https://arxiv.org/pdf/2601.04300
==================================
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✨Learning User Preferences Through Interaction for Long-Term Collaboration
📝 Summary:
MultiSessionCollab benchmark evaluates agents' ability to learn and adapt to user preferences through persistent memory systems that enhance long-term collaboration quality. AI-generated summary As co...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02702
• PDF: https://arxiv.org/pdf/2601.02702
==================================
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📝 Summary:
MultiSessionCollab benchmark evaluates agents' ability to learn and adapt to user preferences through persistent memory systems that enhance long-term collaboration quality. AI-generated summary As co...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02702
• PDF: https://arxiv.org/pdf/2601.02702
==================================
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✨Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach
📝 Summary:
Learning Using Privileged Information paradigm enhances object detection accuracy by integrating additional training-time information through teacher-student architectures without increasing inference...
🔹 Publication Date: Published on Jan 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02016
• PDF: https://arxiv.org/pdf/2601.02016
• Github: https://github.com/mbar0075/lupi-for-object-detection
==================================
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📝 Summary:
Learning Using Privileged Information paradigm enhances object detection accuracy by integrating additional training-time information through teacher-student architectures without increasing inference...
🔹 Publication Date: Published on Jan 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02016
• PDF: https://arxiv.org/pdf/2601.02016
• Github: https://github.com/mbar0075/lupi-for-object-detection
==================================
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✨LEMAS: Large A 150K-Hour Large-scale Extensible Multilingual Audio Suite with Generative Speech Models
📝 Summary:
The LEMAS-Dataset enables high-quality multilingual speech synthesis and editing through specialized models leveraging flow-matching and autoregressive architectures with novel training techniques. AI...
🔹 Publication Date: Published on Jan 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04233
• PDF: https://arxiv.org/pdf/2601.04233
• Project Page: https://huggingface.co/spaces/LEMAS-Project/LEMAS-Edit
🔹 Models citing this paper:
• https://huggingface.co/LEMAS-Project/LEMAS-TTS
• https://huggingface.co/LEMAS-Project/LEMAS-Edit
✨ Datasets citing this paper:
• https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-train
• https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-eval
✨ Spaces citing this paper:
• https://huggingface.co/spaces/LEMAS-Project/LEMAS-TTS
• https://huggingface.co/spaces/LEMAS-Project/LEMAS-Edit
• https://huggingface.co/spaces/Kaiden423/LEMAS-TTS
==================================
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📝 Summary:
The LEMAS-Dataset enables high-quality multilingual speech synthesis and editing through specialized models leveraging flow-matching and autoregressive architectures with novel training techniques. AI...
🔹 Publication Date: Published on Jan 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04233
• PDF: https://arxiv.org/pdf/2601.04233
• Project Page: https://huggingface.co/spaces/LEMAS-Project/LEMAS-Edit
🔹 Models citing this paper:
• https://huggingface.co/LEMAS-Project/LEMAS-TTS
• https://huggingface.co/LEMAS-Project/LEMAS-Edit
✨ Datasets citing this paper:
• https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-train
• https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-eval
✨ Spaces citing this paper:
• https://huggingface.co/spaces/LEMAS-Project/LEMAS-TTS
• https://huggingface.co/spaces/LEMAS-Project/LEMAS-Edit
• https://huggingface.co/spaces/Kaiden423/LEMAS-TTS
==================================
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arXiv.org
LEMAS: Large A 150K-Hour Large-scale Extensible Multilingual Audio...
We present the LEMAS-Dataset, which, to our knowledge, is currently the largest open-source multilingual speech corpus with word-level timestamps. Covering over 150,000 hours across 10 major...
✨VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding
📝 Summary:
VERSE is a methodology for analyzing and improving Vision-Language Models in document understanding by visualizing latent representations and generating synthetic data to enhance performance in error-...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05125
• PDF: https://arxiv.org/pdf/2601.05125
• Project Page: https://huggingface.co/spaces/de-Rodrigo/Embeddings
• Github: https://github.com/nachoDRT/VrDU-Doctor
✨ Datasets citing this paper:
• https://huggingface.co/datasets/de-Rodrigo/merit
✨ Spaces citing this paper:
• https://huggingface.co/spaces/de-Rodrigo/Embeddings
• https://huggingface.co/spaces/de-Rodrigo/saliencies
==================================
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📝 Summary:
VERSE is a methodology for analyzing and improving Vision-Language Models in document understanding by visualizing latent representations and generating synthetic data to enhance performance in error-...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05125
• PDF: https://arxiv.org/pdf/2601.05125
• Project Page: https://huggingface.co/spaces/de-Rodrigo/Embeddings
• Github: https://github.com/nachoDRT/VrDU-Doctor
✨ Datasets citing this paper:
• https://huggingface.co/datasets/de-Rodrigo/merit
✨ Spaces citing this paper:
• https://huggingface.co/spaces/de-Rodrigo/Embeddings
• https://huggingface.co/spaces/de-Rodrigo/saliencies
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✨Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance
📝 Summary:
Safety alignment of large language models can be fully recovered with a single safety example, maintaining utility and achieving convergence in few epochs through identified low-rank gradient structur...
🔹 Publication Date: Published on Jan 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01887
• PDF: https://arxiv.org/pdf/2601.01887
==================================
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📝 Summary:
Safety alignment of large language models can be fully recovered with a single safety example, maintaining utility and achieving convergence in few epochs through identified low-rank gradient structur...
🔹 Publication Date: Published on Jan 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01887
• PDF: https://arxiv.org/pdf/2601.01887
==================================
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✨MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling
📝 Summary:
We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale up model size or co...
🔹 Publication Date: Published on Nov 14, 2025
🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/mirothinker-pushing-the-performance-boundaries-of-open-source-research-agents-via-model-context-and-interactive-scaling-9611-0f2289e7
• PDF: https://arxiv.org/pdf/2511.11793
• Project Page: https://dr.miromind.ai/
• Github: https://github.com/MiroMindAI/MiroThinker
🔹 Models citing this paper:
• https://huggingface.co/miromind-ai/MiroThinker-v1.5-235B
• https://huggingface.co/miromind-ai/MiroThinker-v1.5-30B
• https://huggingface.co/miromind-ai/MiroThinker-v1.0-72B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/miromind-ai/MiroVerse-v0.1
✨ Spaces citing this paper:
• https://huggingface.co/spaces/zoom-ai/hle-leaderboard
• https://huggingface.co/spaces/miromind-ai/MiroMind-Open-Source-Deep-Research
==================================
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📝 Summary:
We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale up model size or co...
🔹 Publication Date: Published on Nov 14, 2025
🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/mirothinker-pushing-the-performance-boundaries-of-open-source-research-agents-via-model-context-and-interactive-scaling-9611-0f2289e7
• PDF: https://arxiv.org/pdf/2511.11793
• Project Page: https://dr.miromind.ai/
• Github: https://github.com/MiroMindAI/MiroThinker
🔹 Models citing this paper:
• https://huggingface.co/miromind-ai/MiroThinker-v1.5-235B
• https://huggingface.co/miromind-ai/MiroThinker-v1.5-30B
• https://huggingface.co/miromind-ai/MiroThinker-v1.0-72B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/miromind-ai/MiroVerse-v0.1
✨ Spaces citing this paper:
• https://huggingface.co/spaces/zoom-ai/hle-leaderboard
• https://huggingface.co/spaces/miromind-ai/MiroMind-Open-Source-Deep-Research
==================================
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Arxivlens
MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling - AI…
AI-powered analysis of 'MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling'. We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and…
✨LTX-2: Efficient Joint Audio-Visual Foundation Model
📝 Summary:
LTX-2 is an open-source audiovisual diffusion model that generates synchronized video and audio content using a dual-stream transformer architecture with cross-modal attention and classifier-free guid...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03233
• PDF: https://arxiv.org/pdf/2601.03233
• Project Page: https://huggingface.co/papers/2511.12072
• Github: https://github.com/Lightricks/LTX-2
🔹 Models citing this paper:
• https://huggingface.co/Lightricks/LTX-2
• https://huggingface.co/unsloth/LTX-2-GGUF
• https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Canny-Control
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Lightricks/ltx-2-distilled
• https://huggingface.co/spaces/Lightricks/ltx-2
• https://huggingface.co/spaces/alexnasa/ltx-2-TURBO
==================================
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📝 Summary:
LTX-2 is an open-source audiovisual diffusion model that generates synchronized video and audio content using a dual-stream transformer architecture with cross-modal attention and classifier-free guid...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03233
• PDF: https://arxiv.org/pdf/2601.03233
• Project Page: https://huggingface.co/papers/2511.12072
• Github: https://github.com/Lightricks/LTX-2
🔹 Models citing this paper:
• https://huggingface.co/Lightricks/LTX-2
• https://huggingface.co/unsloth/LTX-2-GGUF
• https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Canny-Control
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Lightricks/ltx-2-distilled
• https://huggingface.co/spaces/Lightricks/ltx-2
• https://huggingface.co/spaces/alexnasa/ltx-2-TURBO
==================================
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arXiv.org
LTX-2: Efficient Joint Audio-Visual Foundation Model
Recent text-to-video diffusion models can generate compelling video sequences, yet they remain silent -- missing the semantic, emotional, and atmospheric cues that audio provides. We introduce...
✨SimpleMem: Efficient Lifelong Memory for LLM Agents
📝 Summary:
To support reliable long-term interaction in complex environments, LLM agents require memory systems that efficiently manage historical experiences. Existing approaches either retain full interaction ...
🔹 Publication Date: Published on Jan 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02553
• PDF: https://arxiv.org/pdf/2601.02553
• Project Page: https://aiming-lab.github.io/SimpleMem-Page/
• Github: https://aiming-lab.github.io/SimpleMem-Page/
==================================
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📝 Summary:
To support reliable long-term interaction in complex environments, LLM agents require memory systems that efficiently manage historical experiences. Existing approaches either retain full interaction ...
🔹 Publication Date: Published on Jan 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02553
• PDF: https://arxiv.org/pdf/2601.02553
• Project Page: https://aiming-lab.github.io/SimpleMem-Page/
• Github: https://aiming-lab.github.io/SimpleMem-Page/
==================================
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✨SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion
📝 Summary:
SmolDocling is a compact vision-language model that performs end-to-end document conversion with robust performance across various document types using 256M parameters and a new markup format. AI-gene...
🔹 Publication Date: Published on Mar 14, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.11576
• PDF: https://huggingface.co/papers/2502.18443
• Project Page: https://huggingface.co/spaces/docling-project/SmolDocling-256M-Demo
• Github: https://github.com/docling-project/docling
🔹 Models citing this paper:
• https://huggingface.co/docling-project/SmolDocling-256M-preview
• https://huggingface.co/ibm-granite/granite-docling-258M
• https://huggingface.co/prithivMLmods/granite-docling-258M-f32-GGUF
✨ Datasets citing this paper:
• https://huggingface.co/datasets/HuggingFaceM4/DoclingMatix
• https://huggingface.co/datasets/docling-project/SynthCodeNet
• https://huggingface.co/datasets/docling-project/SynthFormulaNet
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ibm-granite/granite-docling-258m-demo
• https://huggingface.co/spaces/ibm-granite/granite-docling-258M-WebGPU
• https://huggingface.co/spaces/docling-project/SmolDocling-256M-Demo
==================================
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📝 Summary:
SmolDocling is a compact vision-language model that performs end-to-end document conversion with robust performance across various document types using 256M parameters and a new markup format. AI-gene...
🔹 Publication Date: Published on Mar 14, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.11576
• PDF: https://huggingface.co/papers/2502.18443
• Project Page: https://huggingface.co/spaces/docling-project/SmolDocling-256M-Demo
• Github: https://github.com/docling-project/docling
🔹 Models citing this paper:
• https://huggingface.co/docling-project/SmolDocling-256M-preview
• https://huggingface.co/ibm-granite/granite-docling-258M
• https://huggingface.co/prithivMLmods/granite-docling-258M-f32-GGUF
✨ Datasets citing this paper:
• https://huggingface.co/datasets/HuggingFaceM4/DoclingMatix
• https://huggingface.co/datasets/docling-project/SynthCodeNet
• https://huggingface.co/datasets/docling-project/SynthFormulaNet
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ibm-granite/granite-docling-258m-demo
• https://huggingface.co/spaces/ibm-granite/granite-docling-258M-WebGPU
• https://huggingface.co/spaces/docling-project/SmolDocling-256M-Demo
==================================
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arXiv.org
SmolDocling: An ultra-compact vision-language model for end-to-end...
We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a new universal...
✨VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
📝 Summary:
VideoRAG enhances large language models for multi-modal video processing with a dual-channel architecture that integrates textual knowledge grounding and multi-modal context encoding. AI-generated sum...
🔹 Publication Date: Published on Feb 3, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.01549
• PDF: https://arxiv.org/pdf/2502.01549
• Github: https://github.com/hkuds/videorag
==================================
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📝 Summary:
VideoRAG enhances large language models for multi-modal video processing with a dual-channel architecture that integrates textual knowledge grounding and multi-modal context encoding. AI-generated sum...
🔹 Publication Date: Published on Feb 3, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.01549
• PDF: https://arxiv.org/pdf/2502.01549
• Github: https://github.com/hkuds/videorag
==================================
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✨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 proc...
🔹 Publication Date: Published on Nov 17, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12884
• PDF: https://arxiv.org/pdf/2511.12884
• Project Page: https://huggingface.co/papers/2511.03404
• Github: https://github.com/openai/agents.md
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For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 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 proc...
🔹 Publication Date: Published on Nov 17, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12884
• PDF: https://arxiv.org/pdf/2511.12884
• Project Page: https://huggingface.co/papers/2511.03404
• Github: https://github.com/openai/agents.md
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research