✨Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process
📝 Summary:
This paper introduces RISE, an unsupervised framework using sparse auto-encoders to discover and control LLM reasoning behaviors. It identifies interpretable reasoning vectors like reflection and backtracking, enabling targeted interventions and discovery of novel behaviors without retraining.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23988
• PDF: https://arxiv.org/pdf/2512.23988
==================================
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#LLM #AI #MachineLearning #AIReasoning #Interpretability
📝 Summary:
This paper introduces RISE, an unsupervised framework using sparse auto-encoders to discover and control LLM reasoning behaviors. It identifies interpretable reasoning vectors like reflection and backtracking, enabling targeted interventions and discovery of novel behaviors without retraining.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23988
• PDF: https://arxiv.org/pdf/2512.23988
==================================
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#LLM #AI #MachineLearning #AIReasoning #Interpretability
✨Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
📝 Summary:
The Agentic Learning Ecosystem ALE is a new infrastructure to streamline LLM agent development for real-world tasks. ALE comprises ROLL for optimization, ROCK for sandboxing, and iFlow CLI for context. Their agent ROME, built with ALE, shows strong benchmark performance.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24873
• PDF: https://arxiv.org/pdf/2512.24873
==================================
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#AIAgents #LLMDevelopment #AgenticLearning #AIArchitecture #MachineLearning
📝 Summary:
The Agentic Learning Ecosystem ALE is a new infrastructure to streamline LLM agent development for real-world tasks. ALE comprises ROLL for optimization, ROCK for sandboxing, and iFlow CLI for context. Their agent ROME, built with ALE, shows strong benchmark performance.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24873
• PDF: https://arxiv.org/pdf/2512.24873
==================================
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#AIAgents #LLMDevelopment #AgenticLearning #AIArchitecture #MachineLearning
✨Figure It Out: Improving the Frontier of Reasoning with Active Visual Thinking
📝 Summary:
Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performan...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24297
• PDF: https://arxiv.org/pdf/2512.24297
• Github: https://github.com/chenmeiqii/FIGR
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performan...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24297
• PDF: https://arxiv.org/pdf/2512.24297
• Github: https://github.com/chenmeiqii/FIGR
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Pretraining Frame Preservation in Autoregressive Video Memory Compression
📝 Summary:
We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temp...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23851
• PDF: https://arxiv.org/pdf/2512.23851
• Github: https://github.com/lllyasviel/PFP
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temp...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23851
• PDF: https://arxiv.org/pdf/2512.23851
• Github: https://github.com/lllyasviel/PFP
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Factorized Learning for Temporally Grounded Video-Language Models
📝 Summary:
Video-language models struggle with temporal grounding from coupled tasks. Our D^2VLM framework decouples grounding and textual response using evidence tokens. Factorized preference optimization explicitly optimizes temporal grounding for both tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24097
• PDF: https://arxiv.org/pdf/2512.24097
• Project Page: https://github.com/nusnlp/d2vlm
• Github: https://github.com/nusnlp/d2vlm
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Video-language models struggle with temporal grounding from coupled tasks. Our D^2VLM framework decouples grounding and textual response using evidence tokens. Factorized preference optimization explicitly optimizes temporal grounding for both tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24097
• PDF: https://arxiv.org/pdf/2512.24097
• Project Page: https://github.com/nusnlp/d2vlm
• Github: https://github.com/nusnlp/d2vlm
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
📝 Summary:
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for Joint Audio-Video (JAV) comprehension and generation. JavisGPT adopts a concise encoder-LLM-decoder architect...
🔹 Publication Date: Published on Dec 28, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.23377
• PDF: https://arxiv.org/pdf/2512.22905
• Project Page: https://javisverse.github.io/JavisGPT-page/
• Github: https://github.com/JavisVerse/JavisGPT
🔹 Models citing this paper:
• https://huggingface.co/JavisVerse/JavisGPT-v0.1-7B-Instruct
✨ Datasets citing this paper:
• https://huggingface.co/datasets/JavisVerse/MM-PreTrain
• https://huggingface.co/datasets/JavisVerse/JavisUnd-Eval
• https://huggingface.co/datasets/JavisVerse/AV-FineTune
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for Joint Audio-Video (JAV) comprehension and generation. JavisGPT adopts a concise encoder-LLM-decoder architect...
🔹 Publication Date: Published on Dec 28, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.23377
• PDF: https://arxiv.org/pdf/2512.22905
• Project Page: https://javisverse.github.io/JavisGPT-page/
• Github: https://github.com/JavisVerse/JavisGPT
🔹 Models citing this paper:
• https://huggingface.co/JavisVerse/JavisGPT-v0.1-7B-Instruct
✨ Datasets citing this paper:
• https://huggingface.co/datasets/JavisVerse/MM-PreTrain
• https://huggingface.co/datasets/JavisVerse/JavisUnd-Eval
• https://huggingface.co/datasets/JavisVerse/AV-FineTune
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
JavisDiT: Joint Audio-Video Diffusion Transformer with...
This paper introduces JavisDiT, a novel Joint Audio-Video Diffusion Transformer designed for synchronized audio-video generation (JAVG). Built upon the powerful Diffusion Transformer (DiT)...
✨Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems
📝 Summary:
The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundat...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24385
• PDF: https://arxiv.org/pdf/2512.24385
• Github: https://github.com/worldbench/awesome-spatial-intelligence
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundat...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24385
• PDF: https://arxiv.org/pdf/2512.24385
• Github: https://github.com/worldbench/awesome-spatial-intelligence
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Valori: A Deterministic Memory Substrate for AI Systems
📝 Summary:
Valori introduces a deterministic AI memory substrate using fixed-point arithmetic, ensuring bit-identical results across platforms. This eliminates non-determinism from floating-point operations in vector embeddings and search, making AI systems trustworthy and verifiable.
🔹 Publication Date: Published on Dec 25, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22280
• PDF: https://arxiv.org/pdf/2512.22280
• Project Page: https://valori.systems/
• Github: https://github.com/varshith-Git/Valori-Kernel
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Valori introduces a deterministic AI memory substrate using fixed-point arithmetic, ensuring bit-identical results across platforms. This eliminates non-determinism from floating-point operations in vector embeddings and search, making AI systems trustworthy and verifiable.
🔹 Publication Date: Published on Dec 25, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22280
• PDF: https://arxiv.org/pdf/2512.22280
• Project Page: https://valori.systems/
• Github: https://github.com/varshith-Git/Valori-Kernel
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts
📝 Summary:
A framework called BEDA uses probabilistic constraints on belief estimation to improve strategic dialogue through formalized adversarial and alignment acts, outperforming baselines across multiple tas...
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24885
• PDF: https://arxiv.org/pdf/2512.24885
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A framework called BEDA uses probabilistic constraints on belief estimation to improve strategic dialogue through formalized adversarial and alignment acts, outperforming baselines across multiple tas...
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24885
• PDF: https://arxiv.org/pdf/2512.24885
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
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✨GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstruction
📝 Summary:
GaMO improves sparse-view 3D reconstruction by using geometry-aware multi-view outpainting. It expands existing views to enhance scene coverage and consistency. This achieves state-of-the-art quality 25x faster than prior methods, with reduced computational cost.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25073
• PDF: https://arxiv.org/pdf/2512.25073
• Project Page: https://yichuanh.github.io/GaMO/
• Github: https://yichuanh.github.io/GaMO/
==================================
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#3DReconstruction #ComputerVision #DiffusionModels #GaMO #AI
📝 Summary:
GaMO improves sparse-view 3D reconstruction by using geometry-aware multi-view outpainting. It expands existing views to enhance scene coverage and consistency. This achieves state-of-the-art quality 25x faster than prior methods, with reduced computational cost.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25073
• PDF: https://arxiv.org/pdf/2512.25073
• Project Page: https://yichuanh.github.io/GaMO/
• Github: https://yichuanh.github.io/GaMO/
==================================
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#3DReconstruction #ComputerVision #DiffusionModels #GaMO #AI
✨Geometry-Aware Optimization for Respiratory Sound Classification: Enhancing Sensitivity with SAM-Optimized Audio Spectrogram Transformers
📝 Summary:
This paper improves respiratory sound classification using AST enhanced with SAM. It optimizes loss surface geometry for flatter minima, yielding state-of-the-art 68.10% score and crucial 68.31% sensitivity on ICBHI 2017.
🔹 Publication Date: Published on Dec 27, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22564
• PDF: https://arxiv.org/pdf/2512.22564
==================================
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#RespiratoryHealth #MedicalAI #DeepLearning #SoundClassification #AIHealthcare
📝 Summary:
This paper improves respiratory sound classification using AST enhanced with SAM. It optimizes loss surface geometry for flatter minima, yielding state-of-the-art 68.10% score and crucial 68.31% sensitivity on ICBHI 2017.
🔹 Publication Date: Published on Dec 27, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22564
• PDF: https://arxiv.org/pdf/2512.22564
==================================
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#RespiratoryHealth #MedicalAI #DeepLearning #SoundClassification #AIHealthcare
✨AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents
📝 Summary:
This paper bridges the gap between human memory systems and AI agent memory design. It synthesizes interdisciplinary knowledge, comparing biological and artificial memory mechanisms, reviewing benchmarks, and exploring security and future directions.
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23343
• PDF: https://arxiv.org/pdf/2512.23343
• Github: https://github.com/AgentMemory/Huaman-Agent-Memory
==================================
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#AI #CognitiveNeuroscience #MemorySystems #AutonomousAgents #BrainInspiredAI
📝 Summary:
This paper bridges the gap between human memory systems and AI agent memory design. It synthesizes interdisciplinary knowledge, comparing biological and artificial memory mechanisms, reviewing benchmarks, and exploring security and future directions.
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23343
• PDF: https://arxiv.org/pdf/2512.23343
• Github: https://github.com/AgentMemory/Huaman-Agent-Memory
==================================
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#AI #CognitiveNeuroscience #MemorySystems #AutonomousAgents #BrainInspiredAI
✨mHC: Manifold-Constrained Hyper-Connections
📝 Summary:
Manifold-Constrained Hyper-Connections mHC resolve training instability and scalability issues of Hyper-Connections HC. mHC restores identity mapping via manifold projection and infrastructure optimization, enabling effective large-scale training with improved performance.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24880
• PDF: https://arxiv.org/pdf/2512.24880
==================================
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#MachineLearning #DeepLearning #NeuralNetworks #ManifoldLearning #AI
📝 Summary:
Manifold-Constrained Hyper-Connections mHC resolve training instability and scalability issues of Hyper-Connections HC. mHC restores identity mapping via manifold projection and infrastructure optimization, enabling effective large-scale training with improved performance.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24880
• PDF: https://arxiv.org/pdf/2512.24880
==================================
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#MachineLearning #DeepLearning #NeuralNetworks #ManifoldLearning #AI
✨Kronos: A Foundation Model for the Language of Financial Markets
📝 Summary:
Kronos is a novel foundation model for financial K-line data. It uses a specialized tokenizer and autoregressive pre-training on a vast dataset to significantly outperform existing models in price and volatility forecasting, and synthetic data generation, establishing it as a versatile tool for f...
🔹 Publication Date: Published on Aug 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02739
• PDF: https://arxiv.org/pdf/2508.02739
• Github: https://github.com/shiyu-coder/Kronos
🔹 Models citing this paper:
• https://huggingface.co/NeoQuasar/Kronos-base
• https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base
• https://huggingface.co/NeoQuasar/Kronos-mini
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ByronWang2005/Kronos-CS2-Skins-Forecast-Demo
• https://huggingface.co/spaces/yangyang158/kronos
• https://huggingface.co/spaces/heyunfei/crypt
==================================
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#FoundationModel #FinancialAI #DeepLearning #QuantitativeFinance #Forecasting
📝 Summary:
Kronos is a novel foundation model for financial K-line data. It uses a specialized tokenizer and autoregressive pre-training on a vast dataset to significantly outperform existing models in price and volatility forecasting, and synthetic data generation, establishing it as a versatile tool for f...
🔹 Publication Date: Published on Aug 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02739
• PDF: https://arxiv.org/pdf/2508.02739
• Github: https://github.com/shiyu-coder/Kronos
🔹 Models citing this paper:
• https://huggingface.co/NeoQuasar/Kronos-base
• https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base
• https://huggingface.co/NeoQuasar/Kronos-mini
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ByronWang2005/Kronos-CS2-Skins-Forecast-Demo
• https://huggingface.co/spaces/yangyang158/kronos
• https://huggingface.co/spaces/heyunfei/crypt
==================================
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#FoundationModel #FinancialAI #DeepLearning #QuantitativeFinance #Forecasting
arXiv.org
Kronos: A Foundation Model for the Language of Financial Markets
The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to...
✨Guiding a Diffusion Transformer with the Internal Dynamics of Itself
📝 Summary:
This paper introduces Internal Guidance IG for diffusion models, which adds auxiliary supervision to intermediate layers during training and extrapolates outputs during sampling. This simple strategy significantly improves training efficiency and generation quality. IG achieves state-of-the-art F...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24176
• PDF: https://arxiv.org/pdf/2512.24176
• Project Page: https://zhouxingyu13.github.io/Internal-Guidance/
• Github: https://github.com/CVL-UESTC/Internal-Guidance
==================================
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#DiffusionModels #AI #DeepLearning #GenerativeAI #ComputerVision
📝 Summary:
This paper introduces Internal Guidance IG for diffusion models, which adds auxiliary supervision to intermediate layers during training and extrapolates outputs during sampling. This simple strategy significantly improves training efficiency and generation quality. IG achieves state-of-the-art F...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24176
• PDF: https://arxiv.org/pdf/2512.24176
• Project Page: https://zhouxingyu13.github.io/Internal-Guidance/
• Github: https://github.com/CVL-UESTC/Internal-Guidance
==================================
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#DiffusionModels #AI #DeepLearning #GenerativeAI #ComputerVision
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✨A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers
📝 Summary:
CoLog is a log anomaly detection framework using collaborative transformers and a modality adaptation layer to accurately detect both point and collective anomalies across diverse log data. It achieves high precision and recall over 99% on benchmark datasets, outperforming existing methods.
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23380
• PDF: https://arxiv.org/pdf/2512.23380
• Project Page: https://www.alarmif.com
• Github: https://github.com/NasirzadehMoh/CoLog
==================================
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#AnomalyDetection #LogAnalysis #Transformers #MachineLearning #Cybersecurity
📝 Summary:
CoLog is a log anomaly detection framework using collaborative transformers and a modality adaptation layer to accurately detect both point and collective anomalies across diverse log data. It achieves high precision and recall over 99% on benchmark datasets, outperforming existing methods.
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23380
• PDF: https://arxiv.org/pdf/2512.23380
• Project Page: https://www.alarmif.com
• Github: https://github.com/NasirzadehMoh/CoLog
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✓ https://t.iss.one/DataScienceT
#AnomalyDetection #LogAnalysis #Transformers #MachineLearning #Cybersecurity
❤2
✨Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation
📝 Summary:
DiffusionGS is a novel single-stage 3D diffusion model that directly generates 3D Gaussian point clouds from a single image. It ensures strong view consistency from any prompt view. This method achieves superior quality and is over 5x faster than state-of-the-art techniques.
🔹 Publication Date: Published on Nov 21, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2411.14384
• PDF: https://arxiv.org/pdf/2411.14384
• Project Page: https://caiyuanhao1998.github.io/project/DiffusionGS/
• Github: https://github.com/caiyuanhao1998/Open-DiffusionGS
🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/DiffusionGS
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/DiffusionGS
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#3DGeneration #DiffusionModels #GaussianSplatting #ComputerVision #AIResearch
📝 Summary:
DiffusionGS is a novel single-stage 3D diffusion model that directly generates 3D Gaussian point clouds from a single image. It ensures strong view consistency from any prompt view. This method achieves superior quality and is over 5x faster than state-of-the-art techniques.
🔹 Publication Date: Published on Nov 21, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2411.14384
• PDF: https://arxiv.org/pdf/2411.14384
• Project Page: https://caiyuanhao1998.github.io/project/DiffusionGS/
• Github: https://github.com/caiyuanhao1998/Open-DiffusionGS
🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/DiffusionGS
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/DiffusionGS
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#3DGeneration #DiffusionModels #GaussianSplatting #ComputerVision #AIResearch
arXiv.org
Baking Gaussian Splatting into Diffusion Denoiser for Fast and...
Existing feedforward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction...