✨Scaling Laws for Code: Every Programming Language Matters
📝 Summary:
This paper explores scaling laws for multilingual code pre-training, finding interpreted languages benefit more from scaling. It proposes an optimal token allocation strategy for programming languages based on utility and synergy, outperforming uniform distribution.
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13472
• PDF: https://arxiv.org/pdf/2512.13472
==================================
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#CodeAI #MachineLearning #ProgrammingLanguages #ScalingLaws #LLMs
📝 Summary:
This paper explores scaling laws for multilingual code pre-training, finding interpreted languages benefit more from scaling. It proposes an optimal token allocation strategy for programming languages based on utility and synergy, outperforming uniform distribution.
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13472
• PDF: https://arxiv.org/pdf/2512.13472
==================================
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#CodeAI #MachineLearning #ProgrammingLanguages #ScalingLaws #LLMs
✨FlipVQA-Miner: Cross-Page Visual Question-Answer Mining from Textbooks
📝 Summary:
FlipVQA-Miner automates high-quality QA and VQA extraction from textbooks. It combines layout-aware OCR with LLM-based semantic parsing. This provides accurate, real-world data for LLM training, avoiding synthetic samples and improving reasoning.
🔹 Publication Date: Published on Nov 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16216
• PDF: https://arxiv.org/pdf/2511.16216
• Github: https://github.com/OpenDCAI/DataFlow
==================================
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#VQA #LLM #OCR #DataExtraction #AIResearch
📝 Summary:
FlipVQA-Miner automates high-quality QA and VQA extraction from textbooks. It combines layout-aware OCR with LLM-based semantic parsing. This provides accurate, real-world data for LLM training, avoiding synthetic samples and improving reasoning.
🔹 Publication Date: Published on Nov 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16216
• PDF: https://arxiv.org/pdf/2511.16216
• Github: https://github.com/OpenDCAI/DataFlow
==================================
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❤1
✨Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents
📝 Summary:
Memory-T1 is an RL framework improving temporal reasoning in long dialogues by selecting relevant sessions. It uses rewards for accuracy, evidence, and temporal consistency to achieve state-of-the-art performance on Time-Dialog and robustness to extensive histories.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20092
• PDF: https://arxiv.org/pdf/2512.20092
• Github: https://github.com/Elvin-Yiming-Du/Memory-T1/
==================================
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#ReinforcementLearning #TemporalReasoning #NLP #DialogueSystems #AI
📝 Summary:
Memory-T1 is an RL framework improving temporal reasoning in long dialogues by selecting relevant sessions. It uses rewards for accuracy, evidence, and temporal consistency to achieve state-of-the-art performance on Time-Dialog and robustness to extensive histories.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20092
• PDF: https://arxiv.org/pdf/2512.20092
• Github: https://github.com/Elvin-Yiming-Du/Memory-T1/
==================================
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#ReinforcementLearning #TemporalReasoning #NLP #DialogueSystems #AI
❤1
✨Learning to Refocus with Video Diffusion Models
📝 Summary:
A novel method enables realistic post-capture refocusing from a single defocused image. It uses video diffusion models to generate a focal stack for interactive focus adjustment. This approach outperforms existing methods, improving photography focus-editing.
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19823
• PDF: https://arxiv.org/pdf/2512.19823
• Project Page: https://learn2refocus.github.io/
• Github: https://github.com/tedlasai/learn2refocus
🔹 Models citing this paper:
• https://huggingface.co/tedlasai/learn2refocus
==================================
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#VideoDiffusionModels #ComputationalPhotography #ImageRefocusing #DeepLearning #ComputerVision
📝 Summary:
A novel method enables realistic post-capture refocusing from a single defocused image. It uses video diffusion models to generate a focal stack for interactive focus adjustment. This approach outperforms existing methods, improving photography focus-editing.
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19823
• PDF: https://arxiv.org/pdf/2512.19823
• Project Page: https://learn2refocus.github.io/
• Github: https://github.com/tedlasai/learn2refocus
🔹 Models citing this paper:
• https://huggingface.co/tedlasai/learn2refocus
==================================
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#VideoDiffusionModels #ComputationalPhotography #ImageRefocusing #DeepLearning #ComputerVision
❤2
✨T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation
📝 Summary:
T2AV-Compass introduces a unified benchmark for text-to-audio-video generation evaluation. It features 500 diverse prompts and a dual-level framework. Evaluations reveal current T2AV models struggle significantly with realism and cross-modal consistency.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21094
• PDF: https://arxiv.org/pdf/2512.21094
• Project Page: https://nju-link.github.io/T2AV-Compass/
• Github: https://github.com/NJU-LINK/T2AV-Compass/
==================================
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#TextToAudioVideo #MultimodalAI #AIEvaluation #GenerativeAI #AIResearch
📝 Summary:
T2AV-Compass introduces a unified benchmark for text-to-audio-video generation evaluation. It features 500 diverse prompts and a dual-level framework. Evaluations reveal current T2AV models struggle significantly with realism and cross-modal consistency.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21094
• PDF: https://arxiv.org/pdf/2512.21094
• Project Page: https://nju-link.github.io/T2AV-Compass/
• Github: https://github.com/NJU-LINK/T2AV-Compass/
==================================
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✨Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models
📝 Summary:
DSR Suite improves vision language models weak dynamic spatial reasoning. It creates 4D training data from videos using an automated pipeline and integrates geometric priors via a Geometry Selection Module. This significantly enhances VLM dynamic spatial reasoning capability while maintaining gen...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20557
• PDF: https://arxiv.org/pdf/2512.20557
==================================
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#VisionLanguageModels #SpatialReasoning #4D #ComputerVision #AIResearch
📝 Summary:
DSR Suite improves vision language models weak dynamic spatial reasoning. It creates 4D training data from videos using an automated pipeline and integrates geometric priors via a Geometry Selection Module. This significantly enhances VLM dynamic spatial reasoning capability while maintaining gen...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20557
• PDF: https://arxiv.org/pdf/2512.20557
==================================
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✨NVIDIA Nemotron 3: Efficient and Open Intelligence
📝 Summary:
NVIDIA introduces Nemotron 3, a family of models with strong agentic, reasoning, and conversational capabilities. They feature a hybrid Mamba-Transformer MoE architecture for high throughput and long context, plus advanced post-training for tool use. The models will be openly released.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20856
• PDF: https://arxiv.org/pdf/2512.20856
==================================
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#AI #LLM #DeepLearning #NVIDIA #OpenSource
📝 Summary:
NVIDIA introduces Nemotron 3, a family of models with strong agentic, reasoning, and conversational capabilities. They feature a hybrid Mamba-Transformer MoE architecture for high throughput and long context, plus advanced post-training for tool use. The models will be openly released.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20856
• PDF: https://arxiv.org/pdf/2512.20856
==================================
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✨Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
📝 Summary:
We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 trillion new unique t...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20848
• PDF: https://arxiv.org/pdf/2512.20848
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 trillion new unique t...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20848
• PDF: https://arxiv.org/pdf/2512.20848
==================================
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✨Streaming Video Instruction Tuning
📝 Summary:
We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that focus narrowly on question answering or captioning,...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21334
• PDF: https://arxiv.org/pdf/2512.21334
==================================
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📝 Summary:
We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that focus narrowly on question answering or captioning,...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21334
• PDF: https://arxiv.org/pdf/2512.21334
==================================
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✨LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics
📝 Summary:
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ranking system that ef...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21010
• PDF: https://arxiv.org/pdf/2512.21010
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ranking system that ef...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21010
• PDF: https://arxiv.org/pdf/2512.21010
==================================
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✨TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times
📝 Summary:
TurboDiffusion significantly accelerates video generation by 100-200x while maintaining quality. It achieves this speedup through attention acceleration, step distillation, and W8A8 quantization. Experiments confirm the substantial speedup on a single GPU.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16093
• PDF: https://jt-zhang.github.io/files/TurboDiffusion_Technical_Report.pdf
• Project Page: https://github.com/thu-ml/TurboDiffusion
• Github: https://github.com/thu-ml/TurboDiffusion
🔹 Models citing this paper:
• https://huggingface.co/TurboDiffusion/TurboWan2.2-I2V-A14B-720P
• https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P
• https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-14B-720P
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
TurboDiffusion significantly accelerates video generation by 100-200x while maintaining quality. It achieves this speedup through attention acceleration, step distillation, and W8A8 quantization. Experiments confirm the substantial speedup on a single GPU.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16093
• PDF: https://jt-zhang.github.io/files/TurboDiffusion_Technical_Report.pdf
• Project Page: https://github.com/thu-ml/TurboDiffusion
• Github: https://github.com/thu-ml/TurboDiffusion
🔹 Models citing this paper:
• https://huggingface.co/TurboDiffusion/TurboWan2.2-I2V-A14B-720P
• https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P
• https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-14B-720P
==================================
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✨HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming
📝 Summary:
High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To a...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21338
• PDF: https://arxiv.org/pdf/2512.21338
• Project Page: http://haonanqiu.com/projects/HiStream.html
• Github: https://github.com/arthur-qiu/HiStream
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To a...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21338
• PDF: https://arxiv.org/pdf/2512.21338
• Project Page: http://haonanqiu.com/projects/HiStream.html
• Github: https://github.com/arthur-qiu/HiStream
==================================
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