✨Learning Eigenstructures of Unstructured Data Manifolds
📝 Summary:
This deep learning framework learns spectral bases directly from unstructured data, eliminating traditional operator selection and eigendecomposition. It provides a data-driven alternative for geometry processing, recovering spectral bases and eigenvalues unsupervised without explicit operator co...
🔹 Publication Date: Published on Nov 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01103
• PDF: https://arxiv.org/pdf/2512.01103
==================================
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#DeepLearning #DataScience #ManifoldLearning #GeometryProcessing #UnsupervisedLearning
📝 Summary:
This deep learning framework learns spectral bases directly from unstructured data, eliminating traditional operator selection and eigendecomposition. It provides a data-driven alternative for geometry processing, recovering spectral bases and eigenvalues unsupervised without explicit operator co...
🔹 Publication Date: Published on Nov 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01103
• PDF: https://arxiv.org/pdf/2512.01103
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#DeepLearning #DataScience #ManifoldLearning #GeometryProcessing #UnsupervisedLearning
✨Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models
📝 Summary:
Wikontic is a multi-stage pipeline that builds high-quality, ontology-consistent knowledge graphs from text. It achieves state-of-the-art performance in information retention and efficiency, providing structured grounding for LLMs.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00590
• PDF: https://arxiv.org/pdf/2512.00590
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#KnowledgeGraphs #LLMs #Ontologies #NLP #AI
📝 Summary:
Wikontic is a multi-stage pipeline that builds high-quality, ontology-consistent knowledge graphs from text. It achieves state-of-the-art performance in information retention and efficiency, providing structured grounding for LLMs.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00590
• PDF: https://arxiv.org/pdf/2512.00590
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#KnowledgeGraphs #LLMs #Ontologies #NLP #AI
✨SCALE: Selective Resource Allocation for Overcoming Performance Bottlenecks in Mathematical Test-time Scaling
📝 Summary:
SCALE improves LLM math reasoning by selectively allocating resources based on sub-problem difficulty. It addresses uniform allocation bottlenecks, boosting accuracy up to 13.75% and cutting costs by 33-53% compared to uniform scaling.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00466
• PDF: https://arxiv.org/pdf/2512.00466
• Github: https://github.com/XiaoYang66/DualThinking
✨ Datasets citing this paper:
• https://huggingface.co/datasets/YangXiao-nlp/DualThinking
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#LLM #AI #MachineLearning #PerformanceOptimization #MathReasoning
📝 Summary:
SCALE improves LLM math reasoning by selectively allocating resources based on sub-problem difficulty. It addresses uniform allocation bottlenecks, boosting accuracy up to 13.75% and cutting costs by 33-53% compared to uniform scaling.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00466
• PDF: https://arxiv.org/pdf/2512.00466
• Github: https://github.com/XiaoYang66/DualThinking
✨ Datasets citing this paper:
• https://huggingface.co/datasets/YangXiao-nlp/DualThinking
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLM #AI #MachineLearning #PerformanceOptimization #MathReasoning
✨Agentic Policy Optimization via Instruction-Policy Co-Evolution
📝 Summary:
INSPO introduces a novel framework dynamically optimizing instructions within the reinforcement learning loop for autonomous agents. It substantially outperforms static instruction methods in multi-turn reasoning by discovering innovative, strategic reasoning paths.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01945
• PDF: https://arxiv.org/pdf/2512.01945
• Github: https://github.com/cambridgeltl/inspo
==================================
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#ReinforcementLearning #AIAgents #PolicyOptimization #MachineLearning #AI
📝 Summary:
INSPO introduces a novel framework dynamically optimizing instructions within the reinforcement learning loop for autonomous agents. It substantially outperforms static instruction methods in multi-turn reasoning by discovering innovative, strategic reasoning paths.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01945
• PDF: https://arxiv.org/pdf/2512.01945
• Github: https://github.com/cambridgeltl/inspo
==================================
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#ReinforcementLearning #AIAgents #PolicyOptimization #MachineLearning #AI
✨Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation
📝 Summary:
Multilingual text-to-image models often generate culturally neutral images. This paper identifies specific neurons for cultural information and proposes two strategies: inference-time activation and layer-targeted enhancement. These methods improve cultural consistency while preserving image qual...
🔹 Publication Date: Published on Nov 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17282
• PDF: https://arxiv.org/pdf/2511.17282
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#TextToImage #CulturalAI #ResponsibleAI #DeepLearning #AIResearch
📝 Summary:
Multilingual text-to-image models often generate culturally neutral images. This paper identifies specific neurons for cultural information and proposes two strategies: inference-time activation and layer-targeted enhancement. These methods improve cultural consistency while preserving image qual...
🔹 Publication Date: Published on Nov 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17282
• PDF: https://arxiv.org/pdf/2511.17282
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#TextToImage #CulturalAI #ResponsibleAI #DeepLearning #AIResearch
✨DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models
📝 Summary:
DreamingComics improves story visualization with better layout control, character consistency, and style. It uses a video diffusion-transformer, regional positional encoding, and an LLM for comic-style layouts, significantly boosting visual quality.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01686
• PDF: https://arxiv.org/pdf/2512.01686
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#StoryVisualization #GenerativeAI #DiffusionModels #LLM #AIArt
📝 Summary:
DreamingComics improves story visualization with better layout control, character consistency, and style. It uses a video diffusion-transformer, regional positional encoding, and an LLM for comic-style layouts, significantly boosting visual quality.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01686
• PDF: https://arxiv.org/pdf/2512.01686
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#StoryVisualization #GenerativeAI #DiffusionModels #LLM #AIArt
❤1
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✨CauSight: Learning to Supersense for Visual Causal Discovery
📝 Summary:
CauSight is a novel vision-language model for visual causal discovery, inferring cause-effect relations in images. It uses the VCG-32K dataset and Tree-of-Causal-Thought, significantly outperforming GPT-4.1 with a threefold performance boost.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01827
• PDF: https://arxiv.org/pdf/2512.01827
• Github: https://github.com/OpenCausaLab/CauSight
🔹 Models citing this paper:
• https://huggingface.co/OpenCausaLab/CauSight
✨ Datasets citing this paper:
• https://huggingface.co/datasets/OpenCausaLab/VCG-32K
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#VisualCausalDiscovery #VisionLanguageModels #AI #DeepLearning #CausalInference
📝 Summary:
CauSight is a novel vision-language model for visual causal discovery, inferring cause-effect relations in images. It uses the VCG-32K dataset and Tree-of-Causal-Thought, significantly outperforming GPT-4.1 with a threefold performance boost.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01827
• PDF: https://arxiv.org/pdf/2512.01827
• Github: https://github.com/OpenCausaLab/CauSight
🔹 Models citing this paper:
• https://huggingface.co/OpenCausaLab/CauSight
✨ Datasets citing this paper:
• https://huggingface.co/datasets/OpenCausaLab/VCG-32K
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VisualCausalDiscovery #VisionLanguageModels #AI #DeepLearning #CausalInference
✨POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models
📝 Summary:
POLARIS minimizes approximate noise errors in diffusion models during image inversion. It robustly treats the guidance scale as a step-wise variable, significantly improving image editing and restoration accuracy by reducing errors at each step.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00369
• PDF: https://arxiv.org/pdf/2512.00369
• Project Page: https://polaris-code-official.github.io/
• Github: https://github.com/Chatonz/POLARIS
==================================
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#DiffusionModels #ImageProcessing #AI #MachineLearning #ComputerVision
📝 Summary:
POLARIS minimizes approximate noise errors in diffusion models during image inversion. It robustly treats the guidance scale as a step-wise variable, significantly improving image editing and restoration accuracy by reducing errors at each step.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00369
• PDF: https://arxiv.org/pdf/2512.00369
• Project Page: https://polaris-code-official.github.io/
• Github: https://github.com/Chatonz/POLARIS
==================================
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#DiffusionModels #ImageProcessing #AI #MachineLearning #ComputerVision
❤2
✨Flow Straighter and Faster: Efficient One-Step Generative Modeling via MeanFlow on Rectified Trajectories
📝 Summary:
Rectified MeanFlow enables efficient one-step generative modeling. It achieves this by modeling the mean velocity field on a single-step rectified trajectory with a truncation heuristic, improving both sample quality and training efficiency over prior methods.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23342
• PDF: https://arxiv.org/pdf/2511.23342
• Github: https://github.com/Xinxi-Zhang/Re-MeanFlow
==================================
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#GenerativeAI #MachineLearning #DeepLearning #AIResearch #MeanFlow
📝 Summary:
Rectified MeanFlow enables efficient one-step generative modeling. It achieves this by modeling the mean velocity field on a single-step rectified trajectory with a truncation heuristic, improving both sample quality and training efficiency over prior methods.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23342
• PDF: https://arxiv.org/pdf/2511.23342
• Github: https://github.com/Xinxi-Zhang/Re-MeanFlow
==================================
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#GenerativeAI #MachineLearning #DeepLearning #AIResearch #MeanFlow
👍1
✨MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification
📝 Summary:
Conformer-based decoders were adapted for MEG signals to perform Speech Detection and Phoneme Classification. Using MEG-oriented augmentations and normalization, their systems achieved high performance, surpassing competition baselines and ranking within the top-10 in both tasks.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01443
• PDF: https://arxiv.org/pdf/2512.01443
• Github: https://github.com/neural2speech/libribrain-experiments
🔹 Models citing this paper:
• https://huggingface.co/zuazo/megconformer-speech-detection
• https://huggingface.co/zuazo/megconformer-phoneme-classification
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#MEGConformer #MEG #SpeechProcessing #Neuroscience #AI
📝 Summary:
Conformer-based decoders were adapted for MEG signals to perform Speech Detection and Phoneme Classification. Using MEG-oriented augmentations and normalization, their systems achieved high performance, surpassing competition baselines and ranking within the top-10 in both tasks.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01443
• PDF: https://arxiv.org/pdf/2512.01443
• Github: https://github.com/neural2speech/libribrain-experiments
🔹 Models citing this paper:
• https://huggingface.co/zuazo/megconformer-speech-detection
• https://huggingface.co/zuazo/megconformer-phoneme-classification
==================================
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#MEGConformer #MEG #SpeechProcessing #Neuroscience #AI
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✨Generative Video Motion Editing with 3D Point Tracks
📝 Summary:
This paper presents a track-conditioned video-to-video framework for precise joint camera and object motion editing. It uses 3D point tracks to maintain spatiotemporal coherence and handle occlusions through explicit depth cues. This enables diverse motion edits.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02015
• PDF: https://arxiv.org/pdf/2512.02015
• Project Page: https://edit-by-track.github.io/
==================================
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#VideoEditing #GenerativeAI #ComputerVision #3DTracking #DeepLearning
📝 Summary:
This paper presents a track-conditioned video-to-video framework for precise joint camera and object motion editing. It uses 3D point tracks to maintain spatiotemporal coherence and handle occlusions through explicit depth cues. This enables diverse motion edits.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02015
• PDF: https://arxiv.org/pdf/2512.02015
• Project Page: https://edit-by-track.github.io/
==================================
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#VideoEditing #GenerativeAI #ComputerVision #3DTracking #DeepLearning
❤1👍1
✨ORION: Teaching Language Models to Reason Efficiently in the Language of Thought
📝 Summary:
ORION models compress reasoning into ultra-compressed structured tokens, inspired by Mentalese. This reduces reasoning steps by 4-16x, cuts inference latency by 5x, and training costs by 7-9x while maintaining high accuracy.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22891
• PDF: https://arxiv.org/pdf/2511.22891
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLM #AI #AIReasoning #CognitiveAI #DeepLearning
📝 Summary:
ORION models compress reasoning into ultra-compressed structured tokens, inspired by Mentalese. This reduces reasoning steps by 4-16x, cuts inference latency by 5x, and training costs by 7-9x while maintaining high accuracy.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22891
• PDF: https://arxiv.org/pdf/2511.22891
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLM #AI #AIReasoning #CognitiveAI #DeepLearning
✨A Hierarchical Framework for Humanoid Locomotion with Supernumerary Limbs
📝 Summary:
A hierarchical control framework enables stable humanoid locomotion with supernumerary limbs. It combines learning-based gait with model-based limb balancing, improving stability and reducing the CoM trajectory Dynamic Time Warping distance by 47%. This decoupled design effectively mitigates dyna...
🔹 Publication Date: Published on Nov 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00077
• PDF: https://arxiv.org/pdf/2512.00077
• Github: https://github.com/heyzbw/HuSLs
==================================
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#Robotics #HumanoidRobotics #Locomotion #ControlSystems #SupernumeraryLimbs
📝 Summary:
A hierarchical control framework enables stable humanoid locomotion with supernumerary limbs. It combines learning-based gait with model-based limb balancing, improving stability and reducing the CoM trajectory Dynamic Time Warping distance by 47%. This decoupled design effectively mitigates dyna...
🔹 Publication Date: Published on Nov 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00077
• PDF: https://arxiv.org/pdf/2512.00077
• Github: https://github.com/heyzbw/HuSLs
==================================
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#Robotics #HumanoidRobotics #Locomotion #ControlSystems #SupernumeraryLimbs
✨DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
📝 Summary:
DeepSeek-V3.2 introduces DeepSeek Sparse Attention and a scalable reinforcement learning framework. This allows it to achieve superior reasoning and agent performance, with its Speciale variant surpassing GPT-5 and matching Gemini-3.0-Pro in complex tasks.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02556
• PDF: https://arxiv.org/pdf/2512.02556
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LLM #AI #DeepLearning #ReinforcementLearning #GenerativeAI
📝 Summary:
DeepSeek-V3.2 introduces DeepSeek Sparse Attention and a scalable reinforcement learning framework. This allows it to achieve superior reasoning and agent performance, with its Speciale variant surpassing GPT-5 and matching Gemini-3.0-Pro in complex tasks.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02556
• PDF: https://arxiv.org/pdf/2512.02556
==================================
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#LLM #AI #DeepLearning #ReinforcementLearning #GenerativeAI
✨Does Hearing Help Seeing? Investigating Audio-Video Joint Denoising for Video Generation
📝 Summary:
This paper shows audio-video joint denoising significantly improves video generation quality. By using audio as a privileged signal, the AVFullDiT model regularizes video dynamics, leading to better video quality beyond just synchrony.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02457
• PDF: https://arxiv.org/pdf/2512.02457
• Project Page: https://jianzongwu.github.io/projects/does-hearing-help-seeing/
• Github: https://github.com/jianzongwu/Does-Hearing-Help-Seeing
✨ Datasets citing this paper:
• https://huggingface.co/datasets/jianzongwu/ALT-Merge
• https://huggingface.co/datasets/jianzongwu/VGGSound-T2AV
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VideoGeneration #MultimodalAI #DeepLearning #ComputerVision #AIResearch
📝 Summary:
This paper shows audio-video joint denoising significantly improves video generation quality. By using audio as a privileged signal, the AVFullDiT model regularizes video dynamics, leading to better video quality beyond just synchrony.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02457
• PDF: https://arxiv.org/pdf/2512.02457
• Project Page: https://jianzongwu.github.io/projects/does-hearing-help-seeing/
• Github: https://github.com/jianzongwu/Does-Hearing-Help-Seeing
✨ Datasets citing this paper:
• https://huggingface.co/datasets/jianzongwu/ALT-Merge
• https://huggingface.co/datasets/jianzongwu/VGGSound-T2AV
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#VideoGeneration #MultimodalAI #DeepLearning #ComputerVision #AIResearch
✨PAI-Bench: A Comprehensive Benchmark For Physical AI
📝 Summary:
PAI-Bench is a new benchmark evaluating multi-modal LLMs and video generative models for physical AI perception and prediction. It reveals current models struggle with physical coherence, forecasting, and causal reasoning in real-world dynamics. This highlights significant gaps for future physica...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01989
• PDF: https://arxiv.org/pdf/2512.01989
• Github: https://github.com/SHI-Labs/physical-ai-bench
✨ Spaces citing this paper:
• https://huggingface.co/spaces/shi-labs/physical-ai-bench-leaderboard
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#PhysicalAI #LLMs #Benchmarking #GenerativeAI #ComputerVision
📝 Summary:
PAI-Bench is a new benchmark evaluating multi-modal LLMs and video generative models for physical AI perception and prediction. It reveals current models struggle with physical coherence, forecasting, and causal reasoning in real-world dynamics. This highlights significant gaps for future physica...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01989
• PDF: https://arxiv.org/pdf/2512.01989
• Github: https://github.com/SHI-Labs/physical-ai-bench
✨ Spaces citing this paper:
• https://huggingface.co/spaces/shi-labs/physical-ai-bench-leaderboard
==================================
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#PhysicalAI #LLMs #Benchmarking #GenerativeAI #ComputerVision
✨Revisiting the Necessity of Lengthy Chain-of-Thought in Vision-centric Reasoning Generalization
📝 Summary:
Concise Chain-of-Thought steps, specifically minimal visual grounding, are most effective for achieving generalizable visual reasoning in vision-language models. Longer or visual CoT primarily accelerate training but do not improve final performance or generalization across tasks.
🔹 Publication Date: Published on Nov 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22586
• PDF: https://arxiv.org/pdf/2511.22586
==================================
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#ChainOfThought #VisionLanguageModels #VisualReasoning #AIGeneralization #DeepLearning
📝 Summary:
Concise Chain-of-Thought steps, specifically minimal visual grounding, are most effective for achieving generalizable visual reasoning in vision-language models. Longer or visual CoT primarily accelerate training but do not improve final performance or generalization across tasks.
🔹 Publication Date: Published on Nov 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22586
• PDF: https://arxiv.org/pdf/2511.22586
==================================
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#ChainOfThought #VisionLanguageModels #VisualReasoning #AIGeneralization #DeepLearning
✨GUI Exploration Lab: Enhancing Screen Navigation in Agents via Multi-Turn Reinforcement Learning
📝 Summary:
GUI Exploration Lab is a simulation environment to train GUI agents for screen navigation. It finds supervised fine-tuning establishes basics, single-turn reinforcement learning improves generalization, and multi-turn RL enhances exploration for superior navigation performance.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02423
• PDF: https://arxiv.org/pdf/2512.02423
==================================
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#ReinforcementLearning #GUIAgents #AINavigation #MachineLearning #AIResearch
📝 Summary:
GUI Exploration Lab is a simulation environment to train GUI agents for screen navigation. It finds supervised fine-tuning establishes basics, single-turn reinforcement learning improves generalization, and multi-turn RL enhances exploration for superior navigation performance.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02423
• PDF: https://arxiv.org/pdf/2512.02423
==================================
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#ReinforcementLearning #GUIAgents #AINavigation #MachineLearning #AIResearch
✨Benchmarking Scientific Understanding and Reasoning for Video Generation using VideoScience-Bench
📝 Summary:
VideoScience-Bench introduces a new benchmark evaluating video models scientific reasoning. It assesses their ability to generate phenomena consistent with undergraduate physics and chemistry, filling a critical gap. It is the first to evaluate models as scientific reasoners.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02942
• PDF: https://arxiv.org/pdf/2512.02942
==================================
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#VideoGeneration #AIResearch #ScientificReasoning #AIModels #Benchmarking
📝 Summary:
VideoScience-Bench introduces a new benchmark evaluating video models scientific reasoning. It assesses their ability to generate phenomena consistent with undergraduate physics and chemistry, filling a critical gap. It is the first to evaluate models as scientific reasoners.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02942
• PDF: https://arxiv.org/pdf/2512.02942
==================================
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#VideoGeneration #AIResearch #ScientificReasoning #AIModels #Benchmarking
✨UnicEdit-10M: A Dataset and Benchmark Breaking the Scale-Quality Barrier via Unified Verification for Reasoning-Enriched Edits
📝 Summary:
This paper tackles image editing model performance gaps due to data scarcity by introducing UnicEdit-10M, a 10M-scale high-quality dataset from a lightweight verified pipeline. It also proposes UnicBench, a new benchmark with novel metrics to diagnose reasoning limitations in models.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02790
• PDF: https://arxiv.org/pdf/2512.02790
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#ImageEditing #AI #Dataset #Benchmark #ComputerVision
📝 Summary:
This paper tackles image editing model performance gaps due to data scarcity by introducing UnicEdit-10M, a 10M-scale high-quality dataset from a lightweight verified pipeline. It also proposes UnicBench, a new benchmark with novel metrics to diagnose reasoning limitations in models.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02790
• PDF: https://arxiv.org/pdf/2512.02790
==================================
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#ImageEditing #AI #Dataset #Benchmark #ComputerVision