✨ContextAnyone: Context-Aware Diffusion for Character-Consistent Text-to-Video Generation

📝 Summary:
ContextAnyone uses a diffusion framework with emphasis attention and gap-rope positional embeddings to generate consistent character videos from text and a single reference image. AI-generated summary...

🔹 Publication Date: Published on Dec 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07328
• PDF: https://arxiv.org/pdf/2512.07328

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✨TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning

📝 Summary:
A semi-supervised reinforcement learning with verifiable rewards approach uses a small labeled dataset to guide training on unlabeled samples, achieving high efficiency and accuracy in mathematical re...

🔹 Publication Date: Published on Dec 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13106
• PDF: https://arxiv.org/pdf/2512.13106
• Github: https://github.com/ShenzhiYang2000/TRAPO

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✨CRISP: Contact-Guided Real2Sim from Monocular Video with Planar Scene Primitives

📝 Summary:
CRISP recovers simulation-ready human motion and scene geometry from monocular video. It uses planar primitive fitting and human-scene contact modeling for robust geometry, driving a humanoid via RL for physical plausibility. This reduces motion tracking failures and speeds up RL simulation, adva...

🔹 Publication Date: Published on Dec 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14696
• PDF: https://arxiv.org/pdf/2512.14696
• Project Page: https://crisp-real2sim.github.io/CRISP-Real2Sim/
• Github: https://crisp-real2sim.github.io/CRISP-Real2Sim/%7D%7B%5Ctextcolor%7Bcyan%7D%7B%7Bcrisp-real2sim.github.io/CRISP-Real2Sim%7D%7D%7D

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✨UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction

📝 Summary:
UAGLNet addresses building extraction challenges by integrating global and local features through a hybrid CNN and transformer cooperative encoder, intermediate interaction block, and uncertainty-aggr...

🔹 Publication Date: Published on Dec 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12941
• PDF: https://arxiv.org/pdf/2512.12941

🔹 Models citing this paper:
• https://huggingface.co/ldxxx/UAGLNet_Backbone
• https://huggingface.co/ldxxx/UAGLNet_Inria
• https://huggingface.co/ldxxx/UAGLNet_WHU

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✨CoSPlan: Corrective Sequential Planning via Scene Graph Incremental Updates

📝 Summary:
VLMs struggle with error-prone vision-based sequential planning tasks, but Scene Graph Incremental updates (SGI) improves their performance by introducing intermediate reasoning steps. AI-generated su...

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10342
• PDF: https://arxiv.org/pdf/2512.10342

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✨Hierarchical Dataset Selection for High-Quality Data Sharing

📝 Summary:
DaSH selects entire datasets from diverse sources to boost ML performance. It models utility hierarchically, outperforming existing methods by up to 26.2 percent accuracy with fewer resources. DaSH is robust for multi-source learning workflows.

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10952
• PDF: https://arxiv.org/pdf/2512.10952

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✨Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of e-Teaching

📝 Summary:
User reviews of AI educational apps show predominantly positive sentiments, with homework helpers leading in accuracy and personalization. However, language and LMS apps lag due to instability and limited features. This highlights generative AIs potential for e-teaching despite challenges.

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11934
• PDF: https://arxiv.org/pdf/2512.11934
• Github: https://github.com/erfan-nourbakhsh/GenAI-EdSent

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Erfan-Nourbakhsh/GenAI-EdSent

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✨Universal Reasoning Model

📝 Summary:
The Universal Reasoning Model URM enhances Universal Transformers with short convolution and truncated backpropagation. This approach substantially improves reasoning performance on ARC-AGI tasks, achieving state-of-the-art results.

🔹 Publication Date: Published on Dec 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14693
• PDF: https://arxiv.org/pdf/2512.14693
• Github: https://github.com/zitian-gao/URM

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✨VABench: A Comprehensive Benchmark for Audio-Video Generation

📝 Summary:
VABench is a benchmark framework for evaluating audio-video generation models, covering text-to-audio-video, image-to-audio-video, and stereo audio-video tasks with 15 evaluation dimensions. AI-genera...

🔹 Publication Date: Published on Dec 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09299
• PDF: https://arxiv.org/pdf/2512.09299
• Github: https://github.com/tanABCC/VABench

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✨Can LLMs Guide Their Own Exploration? Gradient-Guided Reinforcement Learning for LLM Reasoning

📝 Summary:
G2RL, a gradient-guided reinforcement learning framework, enhances exploration in large language models by leveraging the model's own update geometry, leading to improved performance on various reason...

🔹 Publication Date: Published on Dec 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15687
• PDF: https://arxiv.org/pdf/2512.15687

==================================

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