✨Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding

📝 Summary:
Omni-Weather is a new multimodal foundation model that unifies weather generation and understanding in a single architecture. It uses shared self-attention and a Chain-of-Thought dataset for interpretable, high-quality outputs, achieving state-of-the-art performance.

🔹 Publication Date: Published on Dec 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21643
• PDF: https://arxiv.org/pdf/2512.21643

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#WeatherGeneration #FoundationModels #MultimodalAI #AIResearch #DeepLearning

✨Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss

📝 Summary:
Expert-Router Coupling ERC loss aligns MoE router decisions with expert capabilities. It uses proxy tokens and activation constraints to ensure experts specialize, improving performance and computational efficiency. ERC also allows tracking expert specialization during training.

🔹 Publication Date: Published on Dec 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23447
• PDF: https://arxiv.org/pdf/2512.23447

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#MixtureOfExperts #DeepLearning #MachineLearning #AI #NeuralNetworks

✨Yume-1.5: A Text-Controlled Interactive World Generation Model

📝 Summary:
Yume-1.5 is a novel framework that generates realistic, interactive, and continuous worlds from a single image or text prompt. It overcomes prior limitations in real-time performance and text control by using unified context compression, streaming acceleration, and text-controlled world events.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22096
• PDF: https://arxiv.org/pdf/2512.22096
• Project Page: https://stdstu12.github.io/YUME-Project/
• Github: https://github.com/stdstu12/YUME

🔹 Models citing this paper:
• https://huggingface.co/stdstu123/Yume-5B-720P

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#AI #GenerativeAI #WorldGeneration #ComputerGraphics #DeepLearning

✨SpotEdit: Selective Region Editing in Diffusion Transformers

📝 Summary:
SpotEdit is a training-free framework for selective image editing in diffusion transformers. It avoids reprocessing stable regions by reusing their features, combining them with edited areas. This reduces computation and preserves unchanged regions, enhancing efficiency and precision.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22323
• PDF: https://arxiv.org/pdf/2512.22323
• Project Page: https://biangbiang0321.github.io/SpotEdit.github.io
• Github: https://biangbiang0321.github.io/SpotEdit.github.io

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#ImageEditing #DiffusionModels #ComputerVision #AIResearch #DeepLearning

✨Evaluating Parameter Efficient Methods for RLVR

📝 Summary:
This work evaluates 12 PEFT methods for RLVR in mathematical reasoning, challenging LoRAs default use. It finds that structural variants like DoRA outperform LoRA, while SVD-informed methods fail and extreme parameter reduction bottlenecks reasoning.

🔹 Publication Date: Published on Dec 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23165
• PDF: https://arxiv.org/pdf/2512.23165

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#PEFT #RLVR #MathematicalReasoning #LoRA #DeepLearning

✨UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement

📝 Summary:
UltraShape 1.0 is a 3D diffusion framework that generates high-fidelity shapes using a two-stage process: coarse then refined geometry. It includes a novel data pipeline improving dataset quality, enabling strong geometric results on public data.

🔹 Publication Date: Published on Dec 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21185
• PDF: https://arxiv.org/pdf/2512.21185
• Project Page: https://pku-yuangroup.github.io/UltraShape-1.0/
• Github: https://pku-yuangroup.github.io/UltraShape-1.0/

🔹 Models citing this paper:
• https://huggingface.co/infinith/UltraShape

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#3DGeneration #DiffusionModels #GenerativeAI #ComputerGraphics #DeepLearning

✨CosineGate: Semantic Dynamic Routing via Cosine Incompatibility in Residual Networks

📝 Summary:
CosineGate enables dynamic routing in residual networks using cosine incompatibility to skip redundant blocks. This reduces computation by up to 28.5 percent while matching or exceeding ResNet-20 accuracy, without auxiliary supervision.

🔹 Publication Date: Published on Dec 21, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22206
• PDF: https://arxiv.org/pdf/2512.22206
• Github: https://github.com/thotayogeswarreddy/CosineGate

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#DeepLearning #NeuralNetworks #DynamicRouting #ModelEfficiency #AIResearch

✨Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models

📝 Summary:
Youtu-LLM is a lightweight 1.96B LLM, pre-trained from scratch with a compact architecture and a multi-stage curriculum focused on commonsense, STEM, and agentic tasks. It achieves state-of-the-art performance for sub-2B models, demonstrating strong intrinsic agentic capabilities.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24618
• PDF: https://arxiv.org/pdf/2512.24618

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#LLM #AI #AgenticAI #LightweightLLM #DeepLearning

✨SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time

📝 Summary:
SpaceTimePilot is a video diffusion model for dynamic scene rendering, offering independent control over spatial viewpoint and temporal motion. It achieves precise space-time disentanglement via a time-embedding, temporal-warping training, and a synthetic dataset.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25075
• PDF: https://arxiv.org/pdf/2512.25075
• Project Page: https://zheninghuang.github.io/Space-Time-Pilot/

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#VideoDiffusion #GenerativeAI #DynamicScenes #ComputerGraphics #DeepLearning

✨Geometry-Aware Optimization for Respiratory Sound Classification: Enhancing Sensitivity with SAM-Optimized Audio Spectrogram Transformers

📝 Summary:
This paper improves respiratory sound classification using AST enhanced with SAM. It optimizes loss surface geometry for flatter minima, yielding state-of-the-art 68.10% score and crucial 68.31% sensitivity on ICBHI 2017.

🔹 Publication Date: Published on Dec 27, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22564
• PDF: https://arxiv.org/pdf/2512.22564

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#RespiratoryHealth #MedicalAI #DeepLearning #SoundClassification #AIHealthcare