✨End-to-End Training for Autoregressive Video Diffusion via Self-Resampling
📝 Summary:
Resampling Forcing is a teacher-free framework to train autoregressive video diffusion models. It uses self-resampling to simulate inference errors and history routing for efficient long video generation. This approach improves temporal consistency and achieves comparable performance to teacher-b...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15702
• PDF: https://arxiv.org/pdf/2512.15702
==================================
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📝 Summary:
Resampling Forcing is a teacher-free framework to train autoregressive video diffusion models. It uses self-resampling to simulate inference errors and history routing for efficient long video generation. This approach improves temporal consistency and achieves comparable performance to teacher-b...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15702
• PDF: https://arxiv.org/pdf/2512.15702
==================================
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✨LikeBench: Evaluating Subjective Likability in LLMs for Personalization
📝 Summary:
LikeBench introduces a multi-session evaluation framework to measure the likability of LLMs by their ability to adapt to user preferences across multiple dimensions, demonstrating that strong memory p...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13077
• PDF: https://arxiv.org/pdf/2512.13077
==================================
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📝 Summary:
LikeBench introduces a multi-session evaluation framework to measure the likability of LLMs by their ability to adapt to user preferences across multiple dimensions, demonstrating that strong memory p...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13077
• PDF: https://arxiv.org/pdf/2512.13077
==================================
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✨IC-Effect: Precise and Efficient Video Effects Editing via In-Context Learning
📝 Summary:
IC-Effect is an instruction-guided DiT framework for precise video VFX editing. It synthesizes complex effects with spatial-temporal consistency by leveraging contextual learning, a two-stage training strategy, and sparse tokenization, outperforming existing models.
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15635
• PDF: https://arxiv.org/pdf/2512.15635
==================================
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📝 Summary:
IC-Effect is an instruction-guided DiT framework for precise video VFX editing. It synthesizes complex effects with spatial-temporal consistency by leveraging contextual learning, a two-stage training strategy, and sparse tokenization, outperforming existing models.
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15635
• PDF: https://arxiv.org/pdf/2512.15635
==================================
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✨WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory
📝 Summary:
A novel deep learning architecture, WAY, uses nested sequence structures and spatial grids for accurate long-term vessel destination estimation from AIS data, incorporating CASP blocks and Gradient Dr...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13190
• PDF: https://arxiv.org/pdf/2512.13190
• Github: https://github.com/sadPororo/WAY
==================================
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📝 Summary:
A novel deep learning architecture, WAY, uses nested sequence structures and spatial grids for accurate long-term vessel destination estimation from AIS data, incorporating CASP blocks and Gradient Dr...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13190
• PDF: https://arxiv.org/pdf/2512.13190
• Github: https://github.com/sadPororo/WAY
==================================
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✨MMSI-Video-Bench: A Holistic Benchmark for Video-Based Spatial Intelligence
📝 Summary:
MMSI-Video-Bench is a comprehensive benchmark for video-based spatial intelligence in MLLMs, revealing significant gaps between human and AI performance and highlighting challenges in geometric reason...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10863
• PDF: https://arxiv.org/pdf/2512.10863
• Github: https://github.com/InternRobotics/MMSI-Video-Bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/rbler/MMSI-Video-Bench
==================================
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📝 Summary:
MMSI-Video-Bench is a comprehensive benchmark for video-based spatial intelligence in MLLMs, revealing significant gaps between human and AI performance and highlighting challenges in geometric reason...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10863
• PDF: https://arxiv.org/pdf/2512.10863
• Github: https://github.com/InternRobotics/MMSI-Video-Bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/rbler/MMSI-Video-Bench
==================================
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✨Hybrid Attribution Priors for Explainable and Robust Model Training
📝 Summary:
A novel framework, Class-Aware Attribution Prior (CAP), enhances language model interpretability and robustness by guiding the model to capture fine-grained class distinctions and combining with exist...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14719
• PDF: https://arxiv.org/pdf/2512.14719
==================================
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📝 Summary:
A novel framework, Class-Aware Attribution Prior (CAP), enhances language model interpretability and robustness by guiding the model to capture fine-grained class distinctions and combining with exist...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14719
• PDF: https://arxiv.org/pdf/2512.14719
==================================
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✨SS4D: Native 4D Generative Model via Structured Spacetime Latents
📝 Summary:
SS4D synthesizes dynamic 3D objects from monocular video using a native 4D generative model with structured spacetime latents, ensuring high fidelity, temporal coherence, and structural consistency. A...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14284
• PDF: https://arxiv.org/pdf/2512.14284
• Project Page: https://lizb6626.github.io/SS4D/
• Github: https://github.com/Lizb6626/SS4D/
==================================
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📝 Summary:
SS4D synthesizes dynamic 3D objects from monocular video using a native 4D generative model with structured spacetime latents, ensuring high fidelity, temporal coherence, and structural consistency. A...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14284
• PDF: https://arxiv.org/pdf/2512.14284
• Project Page: https://lizb6626.github.io/SS4D/
• Github: https://github.com/Lizb6626/SS4D/
==================================
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✨VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs
📝 Summary:
Voyager is a novel, training-free method that iteratively generates diverse synthetic datasets from LLMs. It uses determinantal point processes to optimize diversity, significantly outperforming baselines with a 1.5-3x improvement.
🔹 Publication Date: Published on Dec 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12072
• PDF: https://arxiv.org/pdf/2512.12072
==================================
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#LLMs #SyntheticData #DataScience #MachineLearning #AI
📝 Summary:
Voyager is a novel, training-free method that iteratively generates diverse synthetic datasets from LLMs. It uses determinantal point processes to optimize diversity, significantly outperforming baselines with a 1.5-3x improvement.
🔹 Publication Date: Published on Dec 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12072
• PDF: https://arxiv.org/pdf/2512.12072
==================================
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#LLMs #SyntheticData #DataScience #MachineLearning #AI
❤2
✨HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices
📝 Summary:
HyperVL is an efficient multimodal large language model for edge devices. It uses image tiling, a Visual Resolution Compressor, and Dual Consistency Learning to reduce memory, latency, and power. HyperVL maintains performance, making it practical for on-device inference.
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14052
• PDF: https://arxiv.org/pdf/2512.14052
==================================
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#HyperVL #MLLM #EdgeAI #EfficientAI #OnDeviceAI
📝 Summary:
HyperVL is an efficient multimodal large language model for edge devices. It uses image tiling, a Visual Resolution Compressor, and Dual Consistency Learning to reduce memory, latency, and power. HyperVL maintains performance, making it practical for on-device inference.
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14052
• PDF: https://arxiv.org/pdf/2512.14052
==================================
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✨Towards Seamless Interaction: Causal Turn-Level Modeling of Interactive 3D Conversational Head Dynamics
📝 Summary:
TIMAR is a new causal framework for 3D conversational head generation. It models dialogue using interleaved audio-visual contexts to predict continuous head dynamics, improving coherence and expressive variability. Experiments show TIMAR significantly reduces errors and improves performance.
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15340
• PDF: https://arxiv.org/pdf/2512.15340
• Project Page: https://github.com/CoderChen01/towards-seamleass-interaction/blob/main/README.md
• Github: https://github.com/CoderChen01/towards-seamleass-interaction
==================================
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#ConversationalAI #3DAnimation #HumanComputerInteraction #CausalModeling #AI
📝 Summary:
TIMAR is a new causal framework for 3D conversational head generation. It models dialogue using interleaved audio-visual contexts to predict continuous head dynamics, improving coherence and expressive variability. Experiments show TIMAR significantly reduces errors and improves performance.
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15340
• PDF: https://arxiv.org/pdf/2512.15340
• Project Page: https://github.com/CoderChen01/towards-seamleass-interaction/blob/main/README.md
• Github: https://github.com/CoderChen01/towards-seamleass-interaction
==================================
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✨MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining
📝 Summary:
MiMo-7B is a 7B LLM optimized for reasoning through pre-training with data mixing and Multi-Token Prediction. Post-training uses reinforcement learning on math and programming problems. This approach enables MiMo-7B to achieve superior reasoning performance, outperforming larger models and OpenAI...
🔹 Publication Date: Published on May 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.07608
• PDF: https://arxiv.org/pdf/2505.07608
• Github: https://github.com/XiaomiMiMo/MiMo
🔹 Models citing this paper:
• https://huggingface.co/XiaomiMiMo/MiMo-7B-RL
• https://huggingface.co/XiaomiMiMo/MiMo-7B-Base
• https://huggingface.co/XiaomiMiMo/MiMo-7B-RL-0530
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ISEEKYAN/megatron_memory_estimator
• https://huggingface.co/spaces/ISEEKYAN/megatron_memory_estimator_old
• https://huggingface.co/spaces/sizzlebop/ZeroGPU-LLM-Inference
==================================
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#LLM #AI #ReinforcementLearning #MachineLearning #Reasoning
📝 Summary:
MiMo-7B is a 7B LLM optimized for reasoning through pre-training with data mixing and Multi-Token Prediction. Post-training uses reinforcement learning on math and programming problems. This approach enables MiMo-7B to achieve superior reasoning performance, outperforming larger models and OpenAI...
🔹 Publication Date: Published on May 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.07608
• PDF: https://arxiv.org/pdf/2505.07608
• Github: https://github.com/XiaomiMiMo/MiMo
🔹 Models citing this paper:
• https://huggingface.co/XiaomiMiMo/MiMo-7B-RL
• https://huggingface.co/XiaomiMiMo/MiMo-7B-Base
• https://huggingface.co/XiaomiMiMo/MiMo-7B-RL-0530
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ISEEKYAN/megatron_memory_estimator
• https://huggingface.co/spaces/ISEEKYAN/megatron_memory_estimator_old
• https://huggingface.co/spaces/sizzlebop/ZeroGPU-LLM-Inference
==================================
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arXiv.org
MiMo: Unlocking the Reasoning Potential of Language Model -- From...
We present MiMo-7B, a large language model born for reasoning tasks, with optimization across both pre-training and post-training stages. During pre-training, we enhance the data preprocessing...
✨FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition
📝 Summary:
FiNERweb is a new pipeline that scales multilingual Named Entity Recognition dataset creation to 91 languages using LLMs. It produces 225k high-quality passages, enabling models to achieve comparable or improved zero-shot performance with 19x less data.
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13884
• PDF: https://arxiv.org/pdf/2512.13884
• Github: https://github.com/whoisjones/FiNERweb
==================================
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#NER #NLP #LLMs #MultilingualAI #Datasets
📝 Summary:
FiNERweb is a new pipeline that scales multilingual Named Entity Recognition dataset creation to 91 languages using LLMs. It produces 225k high-quality passages, enabling models to achieve comparable or improved zero-shot performance with 19x less data.
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13884
• PDF: https://arxiv.org/pdf/2512.13884
• Github: https://github.com/whoisjones/FiNERweb
==================================
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✨Understanding and Improving Hyperbolic Deep Reinforcement Learning
📝 Summary:
Hyper++ is a hyperbolic deep RL agent that improves stability and performance by addressing gradient issues and norm constraints in hyperbolic feature spaces. AI-generated summary The performance of r...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14202
• PDF: https://arxiv.org/pdf/2512.14202
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Hyper++ is a hyperbolic deep RL agent that improves stability and performance by addressing gradient issues and norm constraints in hyperbolic feature spaces. AI-generated summary The performance of r...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14202
• PDF: https://arxiv.org/pdf/2512.14202
==================================
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✨Puzzle Curriculum GRPO for Vision-Centric Reasoning
📝 Summary:
Puzzle Curriculum GRPO PC-GRPO improves VLM visual reasoning without annotations. It uses self-supervised puzzle environments for verifiable rewards and a difficulty-aware curriculum to enhance consistency and accuracy.
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14944
• PDF: https://arxiv.org/pdf/2512.14944
• Project Page: https://pcgrpo.github.io/
==================================
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#VLM #VisualReasoning #SelfSupervisedLearning #ComputerVision #AI
📝 Summary:
Puzzle Curriculum GRPO PC-GRPO improves VLM visual reasoning without annotations. It uses self-supervised puzzle environments for verifiable rewards and a difficulty-aware curriculum to enhance consistency and accuracy.
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14944
• PDF: https://arxiv.org/pdf/2512.14944
• Project Page: https://pcgrpo.github.io/
==================================
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#VLM #VisualReasoning #SelfSupervisedLearning #ComputerVision #AI
❤1
✨FrontierCS: Evolving Challenges for Evolving Intelligence
📝 Summary:
FrontierCS is a new benchmark for evaluating models on 156 open-ended computer science problems with unknown optimal solutions. Models must implement executable programs for tasks like NP-hard algorithmic and research problems. Empirical results show models lag human experts and over-optimize for...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15699
• PDF: https://arxiv.org/pdf/2512.15699
• Github: https://github.com/FrontierCS/Frontier-CS
==================================
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📝 Summary:
FrontierCS is a new benchmark for evaluating models on 156 open-ended computer science problems with unknown optimal solutions. Models must implement executable programs for tasks like NP-hard algorithmic and research problems. Empirical results show models lag human experts and over-optimize for...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15699
• PDF: https://arxiv.org/pdf/2512.15699
• Github: https://github.com/FrontierCS/Frontier-CS
==================================
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