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✨StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation
📝 Summary:
StereoWorld generates high-quality stereo video from monocular input using a pretrained video generator with geometry-aware regularization and spatio-temporal tiling. AI-generated summary The growing ...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09363
• PDF: https://arxiv.org/pdf/2512.09363
==================================
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📝 Summary:
StereoWorld generates high-quality stereo video from monocular input using a pretrained video generator with geometry-aware regularization and spatio-temporal tiling. AI-generated summary The growing ...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09363
• PDF: https://arxiv.org/pdf/2512.09363
==================================
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✨OmniPSD: Layered PSD Generation with Diffusion Transformer
📝 Summary:
OmniPSD, a diffusion framework within the Flux ecosystem, enables text-to-PSD generation and image-to-PSD decomposition, achieving high-fidelity results with transparency awareness. AI-generated summa...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09247
• PDF: https://arxiv.org/pdf/2512.09247
==================================
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📝 Summary:
OmniPSD, a diffusion framework within the Flux ecosystem, enables text-to-PSD generation and image-to-PSD decomposition, achieving high-fidelity results with transparency awareness. AI-generated summa...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09247
• PDF: https://arxiv.org/pdf/2512.09247
==================================
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✨WonderZoom: Multi-Scale 3D World Generation
📝 Summary:
WonderZoom generates multi-scale 3D scenes from a single image using scale-adaptive Gaussian surfels and a progressive detail synthesizer, outperforming existing models in quality and alignment. AI-ge...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09164
• PDF: https://arxiv.org/pdf/2512.09164
==================================
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📝 Summary:
WonderZoom generates multi-scale 3D scenes from a single image using scale-adaptive Gaussian surfels and a progressive detail synthesizer, outperforming existing models in quality and alignment. AI-ge...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09164
• PDF: https://arxiv.org/pdf/2512.09164
==================================
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✨Learning Unmasking Policies for Diffusion Language Models
📝 Summary:
Reinforcement learning is used to train sampling procedures for masked discrete diffusion language models, improving token throughput and quality compared to heuristic strategies. AI-generated summary...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09106
• PDF: https://arxiv.org/pdf/2512.09106
==================================
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📝 Summary:
Reinforcement learning is used to train sampling procedures for masked discrete diffusion language models, improving token throughput and quality compared to heuristic strategies. AI-generated summary...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09106
• PDF: https://arxiv.org/pdf/2512.09106
==================================
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✨Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication
📝 Summary:
The survey analyzes the cognitive architecture of medical AI systems, focusing on the shift from generative text prediction to agentic autonomy, and categorizes methods into four archetypes based on k...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01453
• PDF: https://arxiv.org/pdf/2512.01453
==================================
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📝 Summary:
The survey analyzes the cognitive architecture of medical AI systems, focusing on the shift from generative text prediction to agentic autonomy, and categorizes methods into four archetypes based on k...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01453
• PDF: https://arxiv.org/pdf/2512.01453
==================================
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✨HiF-VLA: Hindsight, Insight and Foresight through Motion Representation for Vision-Language-Action Models
📝 Summary:
HiF-VLA improves long-horizon robotic manipulation by using motion for bidirectional temporal reasoning. It addresses VLA model temporal myopia by integrating past dynamics hindsight and anticipating future motion foresight. This framework significantly outperforms baselines with negligible latency.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09928
• PDF: https://arxiv.org/pdf/2512.09928
• Project Page: https://github.com/OpenHelix-Team/HiF-VLA
• Github: https://hifvla.github.io/
==================================
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📝 Summary:
HiF-VLA improves long-horizon robotic manipulation by using motion for bidirectional temporal reasoning. It addresses VLA model temporal myopia by integrating past dynamics hindsight and anticipating future motion foresight. This framework significantly outperforms baselines with negligible latency.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09928
• PDF: https://arxiv.org/pdf/2512.09928
• Project Page: https://github.com/OpenHelix-Team/HiF-VLA
• Github: https://hifvla.github.io/
==================================
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✨Pay Less Attention to Function Words for Free Robustness of Vision-Language Models
📝 Summary:
Function-word De-Attention (FDA) mitigates adversarial attacks on robust VLMs by differentially subtracting function-word cross-attention, improving robustness with minimal performance trade-offs. AI-...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07222
• PDF: https://arxiv.org/pdf/2512.07222
• Github: https://github.com/michaeltian108/FDA
==================================
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📝 Summary:
Function-word De-Attention (FDA) mitigates adversarial attacks on robust VLMs by differentially subtracting function-word cross-attention, improving robustness with minimal performance trade-offs. AI-...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07222
• PDF: https://arxiv.org/pdf/2512.07222
• Github: https://github.com/michaeltian108/FDA
==================================
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✨Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules
📝 Summary:
SchED, a training-free early-exit algorithm, accelerates diffusion large language model decoding with minimal performance loss across various tasks. AI-generated summary Diffusion large language model...
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02892
• PDF: https://arxiv.org/pdf/2512.02892
==================================
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📝 Summary:
SchED, a training-free early-exit algorithm, accelerates diffusion large language model decoding with minimal performance loss across various tasks. AI-generated summary Diffusion large language model...
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02892
• PDF: https://arxiv.org/pdf/2512.02892
==================================
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✨InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models
📝 Summary:
InfiniteVL is a linear-complexity VLM architecture combining sliding window attention and Gated DeltaNet. It surpasses prior linear models and matches leading Transformers with less data, achieving over 3.6 times faster inference and robust long-term memory.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08829
• PDF: https://arxiv.org/pdf/2512.08829
• Project Page: https://github.com/hustvl/InfiniteVL
• Github: https://github.com/hustvl/InfiniteVL
==================================
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📝 Summary:
InfiniteVL is a linear-complexity VLM architecture combining sliding window attention and Gated DeltaNet. It surpasses prior linear models and matches leading Transformers with less data, achieving over 3.6 times faster inference and robust long-term memory.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08829
• PDF: https://arxiv.org/pdf/2512.08829
• Project Page: https://github.com/hustvl/InfiniteVL
• Github: https://github.com/hustvl/InfiniteVL
==================================
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✨EtCon: Edit-then-Consolidate for Reliable Knowledge Editing
📝 Summary:
A novel knowledge editing framework, Edit-then-Consolidate, addresses overfitting and lack of knowledge integration in large language models through targeted fine-tuning and policy optimization, enhan...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04753
• PDF: https://arxiv.org/pdf/2512.04753
• Github: https://github.com/RlinL/EtCon
==================================
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📝 Summary:
A novel knowledge editing framework, Edit-then-Consolidate, addresses overfitting and lack of knowledge integration in large language models through targeted fine-tuning and policy optimization, enhan...
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04753
• PDF: https://arxiv.org/pdf/2512.04753
• Github: https://github.com/RlinL/EtCon
==================================
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✨Beyond Unified Models: A Service-Oriented Approach to Low Latency, Context Aware Phonemization for Real Time TTS
📝 Summary:
A framework is proposed to improve phonemization quality in TTS systems without sacrificing real-time performance through lightweight context-aware phonemization and a service-oriented architecture. A...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08006
• PDF: https://arxiv.org/pdf/2512.08006
• Github: https://github.com/MahtaFetrat/Piper-with-LCA-Phonemizer
==================================
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📝 Summary:
A framework is proposed to improve phonemization quality in TTS systems without sacrificing real-time performance through lightweight context-aware phonemization and a service-oriented architecture. A...
🔹 Publication Date: Published on Dec 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08006
• PDF: https://arxiv.org/pdf/2512.08006
• Github: https://github.com/MahtaFetrat/Piper-with-LCA-Phonemizer
==================================
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❤1
✨Composing Concepts from Images and Videos via Concept-prompt Binding
📝 Summary:
Bind & Compose introduces a one-shot method for composing visual concepts from images and videos. It binds concepts to prompt tokens using hierarchical binders and novel strategies, achieving superior consistency, fidelity, and motion quality.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09824
• PDF: https://arxiv.org/pdf/2512.09824
• Project Page: https://refkxh.github.io/BiCo_Webpage/
• Github: https://github.com/refkxh/bico
==================================
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📝 Summary:
Bind & Compose introduces a one-shot method for composing visual concepts from images and videos. It binds concepts to prompt tokens using hierarchical binders and novel strategies, achieving superior consistency, fidelity, and motion quality.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09824
• PDF: https://arxiv.org/pdf/2512.09824
• Project Page: https://refkxh.github.io/BiCo_Webpage/
• Github: https://github.com/refkxh/bico
==================================
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✨TED-4DGS: Temporally Activated and Embedding-based Deformation for 4DGS Compression
📝 Summary:
TED-4DGS efficiently compresses dynamic 3D scenes using sparse anchor-based 3D Gaussian Splatting with novel temporal activation and embedding-based deformation. It optimizes rate-distortion with an implicit neural representation hyperprior and autoregressive model, achieving state-of-the-art com...
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05446
• PDF: https://arxiv.org/pdf/2512.05446
==================================
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#4DGS #3DCompression #NeuralRendering #ComputerVision #DynamicScenes
📝 Summary:
TED-4DGS efficiently compresses dynamic 3D scenes using sparse anchor-based 3D Gaussian Splatting with novel temporal activation and embedding-based deformation. It optimizes rate-distortion with an implicit neural representation hyperprior and autoregressive model, achieving state-of-the-art com...
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05446
• PDF: https://arxiv.org/pdf/2512.05446
==================================
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✨VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory
📝 Summary:
VideoSSM proposes a hybrid state-space memory model for long video generation. It unifies autoregressive diffusion with global state-space memory and local context to achieve state-of-the-art temporal consistency and motion stability. This enables scalable, interactive minute-scale video synthesis.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04519
• PDF: https://arxiv.org/pdf/2512.04519
==================================
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#VideoGeneration #GenerativeAI #DiffusionModels #StateSpaceModels #DeepLearning
📝 Summary:
VideoSSM proposes a hybrid state-space memory model for long video generation. It unifies autoregressive diffusion with global state-space memory and local context to achieve state-of-the-art temporal consistency and motion stability. This enables scalable, interactive minute-scale video synthesis.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04519
• PDF: https://arxiv.org/pdf/2512.04519
==================================
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✨BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain
📝 Summary:
An automated framework identifies and explains visual representations in human brain fMRI data using unsupervised decomposition and natural language denoscriptions. This large-scale method reveals thousands of interpretable visual concepts, including previously unknown fine-grained representations ...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08560
• PDF: https://arxiv.org/pdf/2512.08560
• Project Page: https://navvewas.github.io/BrainExplore/
✨ Spaces citing this paper:
• https://huggingface.co/spaces/mcosarinsky/BrainExplore-demo
==================================
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#Neuroscience #BrainMapping #fMRI #AIResearch #DataScience
📝 Summary:
An automated framework identifies and explains visual representations in human brain fMRI data using unsupervised decomposition and natural language denoscriptions. This large-scale method reveals thousands of interpretable visual concepts, including previously unknown fine-grained representations ...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08560
• PDF: https://arxiv.org/pdf/2512.08560
• Project Page: https://navvewas.github.io/BrainExplore/
✨ Spaces citing this paper:
• https://huggingface.co/spaces/mcosarinsky/BrainExplore-demo
==================================
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❤1
✨IF-Bench: Benchmarking and Enhancing MLLMs for Infrared Images with Generative Visual Prompting
📝 Summary:
IF-Bench is introduced as the first benchmark to evaluate multimodal large language models on infrared images using diverse assessment strategies. It includes varied infrared images and question-answer pairs for systematic evaluation of over 40 models. The paper also proposes GenViP, a training-f...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09663
• PDF: https://arxiv.org/pdf/2512.09663
🔹 Models citing this paper:
• https://huggingface.co/casiatao/Qwen-Edit-2509-FT
✨ Datasets citing this paper:
• https://huggingface.co/datasets/casiatao/IF-Bench
==================================
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#MLLMs #InfraredImaging #Benchmarking #GenerativeAI #AIResearch
📝 Summary:
IF-Bench is introduced as the first benchmark to evaluate multimodal large language models on infrared images using diverse assessment strategies. It includes varied infrared images and question-answer pairs for systematic evaluation of over 40 models. The paper also proposes GenViP, a training-f...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09663
• PDF: https://arxiv.org/pdf/2512.09663
🔹 Models citing this paper:
• https://huggingface.co/casiatao/Qwen-Edit-2509-FT
✨ Datasets citing this paper:
• https://huggingface.co/datasets/casiatao/IF-Bench
==================================
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✨Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation
📝 Summary:
DualVLN is a dual-system model for vision-language navigation. It integrates a VLM global planner with a fast local policy for smooth actions, enabling robust real-time control and long-horizon planning in dynamic environments.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08186
• PDF: https://arxiv.org/pdf/2512.08186
• Project Page: https://internrobotics.github.io/internvla-n1-dualvln.github.io/
• Github: https://github.com/InternRobotics/InternNav
🔹 Models citing this paper:
• https://huggingface.co/InternRobotics/InternVLA-N1-System2
• https://huggingface.co/InternRobotics/InternVLA-N1-w-NavDP
• https://huggingface.co/InternRobotics/InternVLA-N1-DualVLN
==================================
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📝 Summary:
DualVLN is a dual-system model for vision-language navigation. It integrates a VLM global planner with a fast local policy for smooth actions, enabling robust real-time control and long-horizon planning in dynamic environments.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08186
• PDF: https://arxiv.org/pdf/2512.08186
• Project Page: https://internrobotics.github.io/internvla-n1-dualvln.github.io/
• Github: https://github.com/InternRobotics/InternNav
🔹 Models citing this paper:
• https://huggingface.co/InternRobotics/InternVLA-N1-System2
• https://huggingface.co/InternRobotics/InternVLA-N1-w-NavDP
• https://huggingface.co/InternRobotics/InternVLA-N1-DualVLN
==================================
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✨TrackingWorld: World-centric Monocular 3D Tracking of Almost All Pixels
📝 Summary:
TrackingWorld provides dense 3D tracking of pixels in a world-centric coordinate system by upsampling sparse 2D tracks and optimizing camera poses and 3D coordinates. AI-generated summary Monocular 3D...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08358
• PDF: https://arxiv.org/pdf/2512.08358
• Project Page: https://igl-hkust.github.io/TrackingWorld.github.io/
==================================
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📝 Summary:
TrackingWorld provides dense 3D tracking of pixels in a world-centric coordinate system by upsampling sparse 2D tracks and optimizing camera poses and 3D coordinates. AI-generated summary Monocular 3D...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08358
• PDF: https://arxiv.org/pdf/2512.08358
• Project Page: https://igl-hkust.github.io/TrackingWorld.github.io/
==================================
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✨Rethinking Chain-of-Thought Reasoning for Videos
📝 Summary:
This paper demonstrates that concise chains of thought and reduced visual tokens efficiently enable video reasoning in MLLMs. Their framework improves inference speed and performance, proving long, human-like reasoning is not necessary for effective video understanding.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09616
• PDF: https://arxiv.org/pdf/2512.09616
• Github: https://github.com/LaVi-Lab/Rethink_CoT_Video
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper demonstrates that concise chains of thought and reduced visual tokens efficiently enable video reasoning in MLLMs. Their framework improves inference speed and performance, proving long, human-like reasoning is not necessary for effective video understanding.
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09616
• PDF: https://arxiv.org/pdf/2512.09616
• Github: https://github.com/LaVi-Lab/Rethink_CoT_Video
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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✨Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction
📝 Summary:
MineROI-Net is a Transformer model predicting Bitcoin ASIC hardware profitability within one year, addressing acquisition timing. It achieves 83.7% accuracy, outperforming baselines, and precisely identifies profitable or unprofitable periods to reduce financial risk.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05402
• PDF: https://arxiv.org/pdf/2512.05402
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#DeepLearning #Bitcoin #CryptoMining #FinancialModeling #AIResearch
📝 Summary:
MineROI-Net is a Transformer model predicting Bitcoin ASIC hardware profitability within one year, addressing acquisition timing. It achieves 83.7% accuracy, outperforming baselines, and precisely identifies profitable or unprofitable periods to reduce financial risk.
🔹 Publication Date: Published on Dec 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05402
• PDF: https://arxiv.org/pdf/2512.05402
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#DeepLearning #Bitcoin #CryptoMining #FinancialModeling #AIResearch
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