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✨EgoX: Egocentric Video Generation from a Single Exocentric Video
📝 Summary:
EgoX generates egocentric videos from single exocentric inputs. It uses video diffusion models with LoRA adaptation, unified conditioning, and geometry-guided self-attention for coherent and realistic results.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08269
• PDF: https://arxiv.org/pdf/2512.08269
• Project Page: https://keh0t0.github.io/EgoX/
• Github: https://github.com/KEH0T0/EgoX
==================================
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#EgocentricVideo #VideoGeneration #DiffusionModels #ComputerVision #DeepLearning
📝 Summary:
EgoX generates egocentric videos from single exocentric inputs. It uses video diffusion models with LoRA adaptation, unified conditioning, and geometry-guided self-attention for coherent and realistic results.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08269
• PDF: https://arxiv.org/pdf/2512.08269
• Project Page: https://keh0t0.github.io/EgoX/
• Github: https://github.com/KEH0T0/EgoX
==================================
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#EgocentricVideo #VideoGeneration #DiffusionModels #ComputerVision #DeepLearning
❤1
✨Sliding Window Attention Adaptation
📝 Summary:
Sliding Window Attention Adaptation SWAA allows pretrained LLMs to use efficient sliding window attention for long contexts without retraining. SWAA combines five adaptation methods, with specific synergistic combinations effectively recovering original long-context performance.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10411
• PDF: https://arxiv.org/pdf/2512.10411
🔹 Models citing this paper:
• https://huggingface.co/yuyijiong/Qwen3-SWA-adaptation
✨ Datasets citing this paper:
• https://huggingface.co/datasets/yuyijiong/LongMemEval_24k
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLMs #SlidingWindowAttention #LongContextAI #NLP #AIResearch
📝 Summary:
Sliding Window Attention Adaptation SWAA allows pretrained LLMs to use efficient sliding window attention for long contexts without retraining. SWAA combines five adaptation methods, with specific synergistic combinations effectively recovering original long-context performance.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10411
• PDF: https://arxiv.org/pdf/2512.10411
🔹 Models citing this paper:
• https://huggingface.co/yuyijiong/Qwen3-SWA-adaptation
✨ Datasets citing this paper:
• https://huggingface.co/datasets/yuyijiong/LongMemEval_24k
==================================
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#LLMs #SlidingWindowAttention #LongContextAI #NLP #AIResearch
❤2
✨MeshSplatting: Differentiable Rendering with Opaque Meshes
📝 Summary:
MeshSplatting is a novel mesh-based method for real-time novel view synthesis. It uses differentiable rendering to optimize geometry and appearance, producing high-quality meshes that integrate with AR/VR pipelines. It outperforms prior methods in quality, speed, and memory.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06818
• PDF: https://arxiv.org/pdf/2512.06818
• Project Page: https://meshsplatting.github.io/
• Github: https://github.com/meshsplatting/mesh-splatting
==================================
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#DifferentiableRendering #NovelViewSynthesis #ComputerGraphics #ARVR #3DRendering
📝 Summary:
MeshSplatting is a novel mesh-based method for real-time novel view synthesis. It uses differentiable rendering to optimize geometry and appearance, producing high-quality meshes that integrate with AR/VR pipelines. It outperforms prior methods in quality, speed, and memory.
🔹 Publication Date: Published on Dec 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06818
• PDF: https://arxiv.org/pdf/2512.06818
• Project Page: https://meshsplatting.github.io/
• Github: https://github.com/meshsplatting/mesh-splatting
==================================
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#DifferentiableRendering #NovelViewSynthesis #ComputerGraphics #ARVR #3DRendering
❤1
✨CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images
📝 Summary:
This work quantifies uncertainty in landmark-based chest X-ray segmentation using hybrid neural networks. It derives latent and predictive uncertainty measures, showing they identify unreliable predictions. The paper also releases CheXmask-U, a large dataset with per-node uncertainty estimates.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10715
• PDF: https://arxiv.org/pdf/2512.10715
✨ Datasets citing this paper:
• https://huggingface.co/datasets/mcosarinsky/CheXmask-U
✨ Spaces citing this paper:
• https://huggingface.co/spaces/mcosarinsky/CheXmask-U
==================================
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#CheXmaskU #MedicalImaging #UncertaintyQuantification #DeepLearning #XraySegmentation
📝 Summary:
This work quantifies uncertainty in landmark-based chest X-ray segmentation using hybrid neural networks. It derives latent and predictive uncertainty measures, showing they identify unreliable predictions. The paper also releases CheXmask-U, a large dataset with per-node uncertainty estimates.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10715
• PDF: https://arxiv.org/pdf/2512.10715
✨ Datasets citing this paper:
• https://huggingface.co/datasets/mcosarinsky/CheXmask-U
✨ Spaces citing this paper:
• https://huggingface.co/spaces/mcosarinsky/CheXmask-U
==================================
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#CheXmaskU #MedicalImaging #UncertaintyQuantification #DeepLearning #XraySegmentation
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✨LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator
📝 Summary:
LEO-RobotAgent is a general-purpose language-driven framework that uses large language models to enable various robot types to complete complex tasks. It enhances human-robot interaction and task planning, demonstrating strong generalization, robustness, and efficiency across different scenarios.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10605
• PDF: https://arxiv.org/pdf/2512.10605
• Github: https://github.com/LegendLeoChen/LEO-RobotAgent
==================================
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#Robotics #LLM #HumanRobotInteraction #EmbodiedAI #AI
📝 Summary:
LEO-RobotAgent is a general-purpose language-driven framework that uses large language models to enable various robot types to complete complex tasks. It enhances human-robot interaction and task planning, demonstrating strong generalization, robustness, and efficiency across different scenarios.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10605
• PDF: https://arxiv.org/pdf/2512.10605
• Github: https://github.com/LegendLeoChen/LEO-RobotAgent
==================================
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#Robotics #LLM #HumanRobotInteraction #EmbodiedAI #AI
❤1
✨SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications
📝 Summary:
SWE-SQL introduces BIRD-CRITIC, a new benchmark for SQL issue debugging, and Six-Gym, a training environment using f-Plan Boosting. Their open-source Bird-Fixer agent surpasses proprietary LLMs like GPT-4.1 in performance, democratizing advanced SQL-debugging capabilities.
🔹 Publication Date: Published on Jun 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.18951
• PDF: https://arxiv.org/pdf/2506.18951
• Project Page: https://bird-critic.github.io
• Github: https://github.com/bird-bench/BIRD-CRITIC-1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-flash-exp
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-open
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-postgresql
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#SQL #LLM #AI #Debugging #OpenSource
📝 Summary:
SWE-SQL introduces BIRD-CRITIC, a new benchmark for SQL issue debugging, and Six-Gym, a training environment using f-Plan Boosting. Their open-source Bird-Fixer agent surpasses proprietary LLMs like GPT-4.1 in performance, democratizing advanced SQL-debugging capabilities.
🔹 Publication Date: Published on Jun 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.18951
• PDF: https://arxiv.org/pdf/2506.18951
• Project Page: https://bird-critic.github.io
• Github: https://github.com/bird-bench/BIRD-CRITIC-1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-flash-exp
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-open
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-postgresql
==================================
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#SQL #LLM #AI #Debugging #OpenSource
❤1
✨Scaling Behavior of Discrete Diffusion Language Models
📝 Summary:
Research on discrete diffusion language models DLMs shows their scaling behavior depends on noise type. Uniform diffusion is more parameter and data efficient than masked diffusion, making it promising for data-bound settings. A 10B parameter model confirmed this.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10858
• PDF: https://arxiv.org/pdf/2512.10858
• Github: https://github.com/dvruette/gidd-easydel
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#DiffusionModels #LanguageModels #NLP #AIResearch #DeepLearning
📝 Summary:
Research on discrete diffusion language models DLMs shows their scaling behavior depends on noise type. Uniform diffusion is more parameter and data efficient than masked diffusion, making it promising for data-bound settings. A 10B parameter model confirmed this.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10858
• PDF: https://arxiv.org/pdf/2512.10858
• Github: https://github.com/dvruette/gidd-easydel
==================================
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#DiffusionModels #LanguageModels #NLP #AIResearch #DeepLearning
❤1
✨Sharp Monocular View Synthesis in Less Than a Second
📝 Summary:
SHARP synthesizes photorealistic 3D views from a single image using a 3D Gaussian representation. It achieves state-of-the-art quality with rapid processing, taking less than a second, and supports metric camera movements.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10685
• PDF: https://arxiv.org/pdf/2512.10685
• Project Page: https://apple.github.io/ml-sharp/
• Github: https://github.com/apple/ml-sharp
🔹 Models citing this paper:
• https://huggingface.co/apple/Sharp
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ronedgecomb/ml-sharp
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#ViewSynthesis #3DVision #ComputerVision #RealtimeAI #GaussianSplats
📝 Summary:
SHARP synthesizes photorealistic 3D views from a single image using a 3D Gaussian representation. It achieves state-of-the-art quality with rapid processing, taking less than a second, and supports metric camera movements.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10685
• PDF: https://arxiv.org/pdf/2512.10685
• Project Page: https://apple.github.io/ml-sharp/
• Github: https://github.com/apple/ml-sharp
🔹 Models citing this paper:
• https://huggingface.co/apple/Sharp
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ronedgecomb/ml-sharp
==================================
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#ViewSynthesis #3DVision #ComputerVision #RealtimeAI #GaussianSplats
❤1
✨Fairy2i: Training Complex LLMs from Real LLMs with All Parameters in {pm 1, pm i}
📝 Summary:
Fairy2i converts pre-trained real-valued LLMs to a complex form, enabling efficient low-bit quantization while reusing existing checkpoints. It achieves near full-precision performance for LLaMA-2 7B at 2-bit, significantly outperforming real-valued binary methods.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2512.02901
• PDF: https://arxiv.org/pdf/2512.02901
• Github: https://github.com/PKULab1806/Fairy2i-W2
🔹 Models citing this paper:
• https://huggingface.co/PKU-DS-LAB/Fairy2i-W2
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#LLM #Quantization #ModelCompression #DeepLearning #AIResearch
📝 Summary:
Fairy2i converts pre-trained real-valued LLMs to a complex form, enabling efficient low-bit quantization while reusing existing checkpoints. It achieves near full-precision performance for LLaMA-2 7B at 2-bit, significantly outperforming real-valued binary methods.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2512.02901
• PDF: https://arxiv.org/pdf/2512.02901
• Github: https://github.com/PKULab1806/Fairy2i-W2
🔹 Models citing this paper:
• https://huggingface.co/PKU-DS-LAB/Fairy2i-W2
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLM #Quantization #ModelCompression #DeepLearning #AIResearch
❤2
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✨CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare
📝 Summary:
CLINIC is a multilingual benchmark evaluating language model trustworthiness in healthcare across 15 languages and five dimensions. It finds that LMs struggle with factual correctness, demonstrate bias, and are vulnerable to privacy breaches and attacks. This work highlights shortcomings to impro...
🔹 Publication Date: Published on Dec 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11437
• PDF: https://arxiv.org/pdf/2512.11437
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#AI #HealthcareAI #LLM #AISafety #MultilingualAI
📝 Summary:
CLINIC is a multilingual benchmark evaluating language model trustworthiness in healthcare across 15 languages and five dimensions. It finds that LMs struggle with factual correctness, demonstrate bias, and are vulnerable to privacy breaches and attacks. This work highlights shortcomings to impro...
🔹 Publication Date: Published on Dec 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11437
• PDF: https://arxiv.org/pdf/2512.11437
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#AI #HealthcareAI #LLM #AISafety #MultilingualAI
❤1
✨Causal Judge Evaluation: Calibrated Surrogate Metrics for LLM Systems
📝 Summary:
CJE improves LLM-as-judge evaluation by fixing statistical issues like uncalibrated scores and poor confidence intervals. It achieves 99% ranking accuracy at 14x lower cost by calibrating a cheaper judge with 5% oracle labels.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11150
• PDF: https://arxiv.org/pdf/2512.11150
• Project Page: https://www.cimolabs.com/cje
• Github: https://github.com/cimo-labs/cje
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLMs #AIEvaluation #MachineLearning #DataScience #NLP
📝 Summary:
CJE improves LLM-as-judge evaluation by fixing statistical issues like uncalibrated scores and poor confidence intervals. It achieves 99% ranking accuracy at 14x lower cost by calibrating a cheaper judge with 5% oracle labels.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11150
• PDF: https://arxiv.org/pdf/2512.11150
• Project Page: https://www.cimolabs.com/cje
• Github: https://github.com/cimo-labs/cje
==================================
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#LLMs #AIEvaluation #MachineLearning #DataScience #NLP
✨Particulate: Feed-Forward 3D Object Articulation
📝 Summary:
Particulate is a feed-forward method using a transformer network to infer articulated 3D structures from single static meshes, achieving faster and more accurate results than prior approaches. AI-gene...
🔹 Publication Date: Published on Dec 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11798
• PDF: https://arxiv.org/pdf/2512.11798
🔹 Models citing this paper:
• https://huggingface.co/rayli/Particulate
✨ Spaces citing this paper:
• https://huggingface.co/spaces/rayli/particulate
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Particulate is a feed-forward method using a transformer network to infer articulated 3D structures from single static meshes, achieving faster and more accurate results than prior approaches. AI-gene...
🔹 Publication Date: Published on Dec 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11798
• PDF: https://arxiv.org/pdf/2512.11798
🔹 Models citing this paper:
• https://huggingface.co/rayli/Particulate
✨ Spaces citing this paper:
• https://huggingface.co/spaces/rayli/particulate
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching
📝 Summary:
Fast-FoundationStereo enables real-time zero-shot stereo matching. It uses knowledge distillation, neural architecture search, and structured pruning to achieve this. The model runs over 10x faster than previous models while maintaining accuracy, setting a new state-of-the-art.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11130
• PDF: https://arxiv.org/pdf/2512.11130
• Project Page: https://nvlabs.github.io/Fast-FoundationStereo/
• Github: https://github.com/NVlabs/Fast-FoundationStereo
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Fast-FoundationStereo enables real-time zero-shot stereo matching. It uses knowledge distillation, neural architecture search, and structured pruning to achieve this. The model runs over 10x faster than previous models while maintaining accuracy, setting a new state-of-the-art.
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11130
• PDF: https://arxiv.org/pdf/2512.11130
• Project Page: https://nvlabs.github.io/Fast-FoundationStereo/
• Github: https://github.com/NVlabs/Fast-FoundationStereo
==================================
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❤2
✨Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit
📝 Summary:
Sparse autoencoders SAEs create interpretable, cost-effective embeddings where dimensions map to concepts. These SAE embeddings outperform LLMs and dense embeddings for large-scale text analysis, offering better control for tasks like bias identification and dataset comparison.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10092
• PDF: https://arxiv.org/pdf/2512.10092
• Project Page: https://interp-embed.com
• Github: https://github.com/nickjiang2378/interp_embed
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Sparse autoencoders SAEs create interpretable, cost-effective embeddings where dimensions map to concepts. These SAE embeddings outperform LLMs and dense embeddings for large-scale text analysis, offering better control for tasks like bias identification and dataset comparison.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10092
• PDF: https://arxiv.org/pdf/2512.10092
• Project Page: https://interp-embed.com
• Github: https://github.com/nickjiang2378/interp_embed
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
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✨Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge
📝 Summary:
The Openpi Comet solution for the 2025 BEHAVIOR Challenge addresses household tasks using pre-training and post-training. It achieved a close second place, significantly outperforming other submissions, demonstrating the scaling power of these methods.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2512.10071
• PDF: https://arxiv.org/pdf/2512.10071
• Github: https://github.com/mli0603/openpi-comet
🔹 Models citing this paper:
• https://huggingface.co/sunshk/comet_submission
==================================
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📝 Summary:
The Openpi Comet solution for the 2025 BEHAVIOR Challenge addresses household tasks using pre-training and post-training. It achieved a close second place, significantly outperforming other submissions, demonstrating the scaling power of these methods.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2512.10071
• PDF: https://arxiv.org/pdf/2512.10071
• Github: https://github.com/mli0603/openpi-comet
🔹 Models citing this paper:
• https://huggingface.co/sunshk/comet_submission
==================================
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✨MentraSuite: Post-Training Large Language Models for Mental Health Reasoning and Assessment
📝 Summary:
MentraSuite, a unified framework, advances reliable mental health reasoning using Mindora, a post-trained model with hybrid SFT-RL, evaluated via MentraBench, a benchmark assessing task performance an...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09636
• PDF: https://arxiv.org/pdf/2512.09636
• Github: https://github.com/elsa66666/MentraSuite
==================================
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📝 Summary:
MentraSuite, a unified framework, advances reliable mental health reasoning using Mindora, a post-trained model with hybrid SFT-RL, evaluated via MentraBench, a benchmark assessing task performance an...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09636
• PDF: https://arxiv.org/pdf/2512.09636
• Github: https://github.com/elsa66666/MentraSuite
==================================
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✨Error-Free Linear Attention is a Free Lunch: Exact Solution from Continuous-Time Dynamics
📝 Summary:
Error-Free Linear Attention (EFLA) is a stable, parallelizable, and theoretically sound linear-time attention mechanism that outperforms DeltaNet in language modeling and downstream tasks. AI-generate...
🔹 Publication Date: Published on Dec 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12602
• PDF: https://arxiv.org/pdf/2512.12602
• Github: https://github.com/declare-lab/EFLA
==================================
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📝 Summary:
Error-Free Linear Attention (EFLA) is a stable, parallelizable, and theoretically sound linear-time attention mechanism that outperforms DeltaNet in language modeling and downstream tasks. AI-generate...
🔹 Publication Date: Published on Dec 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12602
• PDF: https://arxiv.org/pdf/2512.12602
• Github: https://github.com/declare-lab/EFLA
==================================
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✨NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
📝 Summary:
NL2Repo Bench evaluates long-horizon software development capabilities of coding agents by assessing their ability to generate complete Python libraries from natural-language requirements. AI-generate...
🔹 Publication Date: Published on Dec 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12730
• PDF: https://arxiv.org/pdf/2512.12730
• Project Page: https://github.com/multimodal-art-projection/NL2RepoBench
• Github: https://github.com/multimodal-art-projection/NL2RepoBench
==================================
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📝 Summary:
NL2Repo Bench evaluates long-horizon software development capabilities of coding agents by assessing their ability to generate complete Python libraries from natural-language requirements. AI-generate...
🔹 Publication Date: Published on Dec 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12730
• PDF: https://arxiv.org/pdf/2512.12730
• Project Page: https://github.com/multimodal-art-projection/NL2RepoBench
• Github: https://github.com/multimodal-art-projection/NL2RepoBench
==================================
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❤1
✨LongVie 2: Multimodal Controllable Ultra-Long Video World Model
📝 Summary:
LongVie 2, an end-to-end autoregressive framework, enhances controllability, visual quality, and temporal consistency in video world models through three progressive training stages. AI-generated summ...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13604
• PDF: https://arxiv.org/pdf/2512.13604
• Project Page: https://vchitect.github.io/LongVie2-project/
• Github: https://github.com/Vchitect/LongVie
==================================
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📝 Summary:
LongVie 2, an end-to-end autoregressive framework, enhances controllability, visual quality, and temporal consistency in video world models through three progressive training stages. AI-generated summ...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13604
• PDF: https://arxiv.org/pdf/2512.13604
• Project Page: https://vchitect.github.io/LongVie2-project/
• Github: https://github.com/Vchitect/LongVie
==================================
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❤1
✨V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions
📝 Summary:
The V-REX evaluation suite assesses vision-language models' multi-step reasoning and exploration capabilities through a Chain-of-Questions framework, revealing their strengths and weaknesses in planni...
🔹 Publication Date: Published on Dec 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11995
• PDF: https://arxiv.org/pdf/2512.11995
• Github: https://github.com/tianyi-lab/VREX
✨ Datasets citing this paper:
• https://huggingface.co/datasets/umd-zhou-lab/V-REX
==================================
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📝 Summary:
The V-REX evaluation suite assesses vision-language models' multi-step reasoning and exploration capabilities through a Chain-of-Questions framework, revealing their strengths and weaknesses in planni...
🔹 Publication Date: Published on Dec 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11995
• PDF: https://arxiv.org/pdf/2512.11995
• Github: https://github.com/tianyi-lab/VREX
✨ Datasets citing this paper:
• https://huggingface.co/datasets/umd-zhou-lab/V-REX
==================================
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