✨YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection
📝 Summary:
YOLO-Master proposes an Efficient Sparse Mixture-of-Experts ES-MoE block for real-time object detection. It adaptively allocates computational resources based on scene complexity using a dynamic routing network, overcoming static computation limits. This improves accuracy and speed, especially on...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23273
• PDF: https://arxiv.org/pdf/2512.23273
==================================
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#ObjectDetection #YOLO #MixtureOfExperts #Transformers #RealTimeAI
📝 Summary:
YOLO-Master proposes an Efficient Sparse Mixture-of-Experts ES-MoE block for real-time object detection. It adaptively allocates computational resources based on scene complexity using a dynamic routing network, overcoming static computation limits. This improves accuracy and speed, especially on...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23273
• PDF: https://arxiv.org/pdf/2512.23273
==================================
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#ObjectDetection #YOLO #MixtureOfExperts #Transformers #RealTimeAI
❤1
✨Introducing TrGLUE and SentiTurca: A Comprehensive Benchmark for Turkish General Language Understanding and Sentiment Analysis
📝 Summary:
Evaluating the performance of various model architectures, such as transformers, large language models (LLMs), and other NLP systems, requires comprehensive benchmarks that measure performance across ...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22100
• PDF: https://arxiv.org/pdf/2512.22100
✨ Datasets citing this paper:
• https://huggingface.co/datasets/turkish-nlp-suite/TrGLUE
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Evaluating the performance of various model architectures, such as transformers, large language models (LLMs), and other NLP systems, requires comprehensive benchmarks that measure performance across ...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22100
• PDF: https://arxiv.org/pdf/2512.22100
✨ Datasets citing this paper:
• https://huggingface.co/datasets/turkish-nlp-suite/TrGLUE
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation
📝 Summary:
The primary obstacle for applying reinforcement learning (RL) to real-world robotics is the design of effective reward functions. While recently learning-based Process Reward Models (PRMs) are a promi...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23703
• PDF: https://arxiv.org/pdf/2512.23703
• Project Page: https://robo-dopamine.github.io/
• Github: https://github.com/FlagOpen/Robo-Dopamine
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
The primary obstacle for applying reinforcement learning (RL) to real-world robotics is the design of effective reward functions. While recently learning-based Process Reward Models (PRMs) are a promi...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23703
• PDF: https://arxiv.org/pdf/2512.23703
• Project Page: https://robo-dopamine.github.io/
• Github: https://github.com/FlagOpen/Robo-Dopamine
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
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🔰 Machine Learning with Python
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✨KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
📝 Summary:
KernelEvolve is an agentic kernel coding framework for deep learning recommendation models. It automates kernel generation and optimization for diverse AI accelerators, significantly improving performance and reducing development time on heterogeneous hardware at scale.
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23236
• PDF: https://arxiv.org/pdf/2512.23236
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
KernelEvolve is an agentic kernel coding framework for deep learning recommendation models. It automates kernel generation and optimization for diverse AI accelerators, significantly improving performance and reducing development time on heterogeneous hardware at scale.
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23236
• PDF: https://arxiv.org/pdf/2512.23236
==================================
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✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨Shape of Thought: When Distribution Matters More than Correctness in Reasoning Tasks
📝 Summary:
We present the surprising finding that a language model's reasoning capabilities can be improved by training on synthetic datasets of chain-of-thought (CoT) traces from more capable models, even when ...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22255
• PDF: https://arxiv.org/pdf/2512.22255
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
We present the surprising finding that a language model's reasoning capabilities can be improved by training on synthetic datasets of chain-of-thought (CoT) traces from more capable models, even when ...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22255
• PDF: https://arxiv.org/pdf/2512.22255
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Self-Evaluation Unlocks Any-Step Text-to-Image Generation
📝 Summary:
Self-E is a novel self-evaluating text-to-image model trained from scratch that supports any-step generation and combines local learning with self-driven global matching to achieve high quality even a...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22374
• PDF: https://arxiv.org/pdf/2512.22374
• Project Page: https://xinyu-andy.github.io/SelfE-project/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Self-E is a novel self-evaluating text-to-image model trained from scratch that supports any-step generation and combines local learning with self-driven global matching to achieve high quality even a...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22374
• PDF: https://arxiv.org/pdf/2512.22374
• Project Page: https://xinyu-andy.github.io/SelfE-project/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨LeVo: High-Quality Song Generation with Multi-Preference Alignment
📝 Summary:
LeVo enhances lyrics-to-song generation. It uses an LM to parallelly model mixed and dual-track audio tokens for vocal-instrument harmony and sound quality. Direct Preference Optimization improves musicality and instruction following, outperforming existing methods.
🔹 Publication Date: Published on Jun 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.07520
• PDF: https://arxiv.org/pdf/2506.07520
• Project Page: https://levo-demo.github.io/
• Github: https://github.com/tencent-ailab/songgeneration
🔹 Models citing this paper:
• https://huggingface.co/tencent/SongGeneration
• https://huggingface.co/waytan22/SongGeneration-v1.5-beta
• https://huggingface.co/chaitnya26/SongGeneration-fork
✨ Spaces citing this paper:
• https://huggingface.co/spaces/tencent/SongGeneration
• https://huggingface.co/spaces/NeoPy/SongGeneration
• https://huggingface.co/spaces/Open-Hat-Lab/Song-Generator
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
LeVo enhances lyrics-to-song generation. It uses an LM to parallelly model mixed and dual-track audio tokens for vocal-instrument harmony and sound quality. Direct Preference Optimization improves musicality and instruction following, outperforming existing methods.
🔹 Publication Date: Published on Jun 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.07520
• PDF: https://arxiv.org/pdf/2506.07520
• Project Page: https://levo-demo.github.io/
• Github: https://github.com/tencent-ailab/songgeneration
🔹 Models citing this paper:
• https://huggingface.co/tencent/SongGeneration
• https://huggingface.co/waytan22/SongGeneration-v1.5-beta
• https://huggingface.co/chaitnya26/SongGeneration-fork
✨ Spaces citing this paper:
• https://huggingface.co/spaces/tencent/SongGeneration
• https://huggingface.co/spaces/NeoPy/SongGeneration
• https://huggingface.co/spaces/Open-Hat-Lab/Song-Generator
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
LeVo: High-Quality Song Generation with Multi-Preference Alignment
Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly in lyrics-to-song generation. However, existing approaches...
✨DreamOmni3: Scribble-based Editing and Generation
📝 Summary:
DreamOmni3 introduces scribble-based editing and generation for more flexible image creation beyond text prompts. It proposes new tasks, data synthesis, and a joint input scheme using colored scribbles on source images for precise localization and complex edits.
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22525
• PDF: https://arxiv.org/pdf/2512.22525
• Github: https://github.com/dvlab-research/DreamOmni3
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DreamOmni3 introduces scribble-based editing and generation for more flexible image creation beyond text prompts. It proposes new tasks, data synthesis, and a joint input scheme using colored scribbles on source images for precise localization and complex edits.
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22525
• PDF: https://arxiv.org/pdf/2512.22525
• Github: https://github.com/dvlab-research/DreamOmni3
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨End-to-End Test-Time Training for Long Context
📝 Summary:
This paper introduces End-to-End Test-Time Training TTT-E2E for long-context language models. It uses a standard Transformer that continually learns from context at test time, compressing information into its weights. TTT-E2E scales well with context length and offers constant inference latency, ...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23675
• PDF: https://arxiv.org/pdf/2512.23675
• Github: https://github.com/test-time-training/e2e
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper introduces End-to-End Test-Time Training TTT-E2E for long-context language models. It uses a standard Transformer that continually learns from context at test time, compressing information into its weights. TTT-E2E scales well with context length and offers constant inference latency, ...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23675
• PDF: https://arxiv.org/pdf/2512.23675
• Github: https://github.com/test-time-training/e2e
==================================
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✨GraphLocator: Graph-guided Causal Reasoning for Issue Localization
📝 Summary:
The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in a...
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22469
• PDF: https://arxiv.org/pdf/2512.22469
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in a...
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22469
• PDF: https://arxiv.org/pdf/2512.22469
==================================
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✨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
📝 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
📝 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
✨GateBreaker: Gate-Guided Attacks on Mixture-of-Expert LLMs
📝 Summary:
GateBreaker is the first framework to compromise MoE LLM safety by identifying and disabling ~3% of safety neurons in expert layers. This raises attack success rates from 7.4% to 64.9% across eight LLMs and generalizes to VLMs, showing concentrated and transferable safety vulnerabilities.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21008
• PDF: https://arxiv.org/pdf/2512.21008
==================================
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✓ https://t.iss.one/DataScienceT
#LLM #AIsecurity #MoELLMs #AIvulnerability #GateBreaker
📝 Summary:
GateBreaker is the first framework to compromise MoE LLM safety by identifying and disabling ~3% of safety neurons in expert layers. This raises attack success rates from 7.4% to 64.9% across eight LLMs and generalizes to VLMs, showing concentrated and transferable safety vulnerabilities.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21008
• PDF: https://arxiv.org/pdf/2512.21008
==================================
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#LLM #AIsecurity #MoELLMs #AIvulnerability #GateBreaker
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✨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
📝 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
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✨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
📝 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
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✨GR-Dexter Technical Report
📝 Summary:
GR-Dexter introduces a hardware-model-data framework for bimanual dexterous-hand robot manipulation using VLA models. It combines a new 21-DoF hand, teleoperation for data, and diverse datasets. This framework achieves strong performance and robust generalization in real-world manipulation tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24210
• PDF: https://arxiv.org/pdf/2512.24210
• Project Page: https://byte-dexter.github.io/gr-dexter/
==================================
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#Robotics #DexterousManipulation #VLA #RobotHardware #MachineLearning
📝 Summary:
GR-Dexter introduces a hardware-model-data framework for bimanual dexterous-hand robot manipulation using VLA models. It combines a new 21-DoF hand, teleoperation for data, and diverse datasets. This framework achieves strong performance and robust generalization in real-world manipulation tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24210
• PDF: https://arxiv.org/pdf/2512.24210
• Project Page: https://byte-dexter.github.io/gr-dexter/
==================================
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#Robotics #DexterousManipulation #VLA #RobotHardware #MachineLearning
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✨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
📝 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