✨Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length
📝 Summary:
Live Avatar enables real-time, high-fidelity, infinite-length avatar generation using a 14B-parameter diffusion model. It employs Timestep-forcing Pipeline Parallelism and the Rolling Sink Frame Mechanism to overcome limitations, achieving 20 FPS on 5 GPUs. This is the first practical system at t...
🔹 Publication Date: Published on Dec 4, 2025
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/live-avatar-streaming-real-time-audio-driven-avatar-generation-with-infinite-length
• PDF: https://arxiv.org/pdf/2512.04677
• Project Page: https://liveavatar.github.io/
• Github: https://github.com/Alibaba-Quark/LiveAvatar
🔹 Models citing this paper:
• https://huggingface.co/Quark-Vision/Live-Avatar
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ahm98alex/liveavatar-test
• https://huggingface.co/spaces/sdavignon/liveavatar
==================================
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#LiveAvatar #AvatarGeneration #RealtimeAI #DiffusionModels #GenerativeAI
📝 Summary:
Live Avatar enables real-time, high-fidelity, infinite-length avatar generation using a 14B-parameter diffusion model. It employs Timestep-forcing Pipeline Parallelism and the Rolling Sink Frame Mechanism to overcome limitations, achieving 20 FPS on 5 GPUs. This is the first practical system at t...
🔹 Publication Date: Published on Dec 4, 2025
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/live-avatar-streaming-real-time-audio-driven-avatar-generation-with-infinite-length
• PDF: https://arxiv.org/pdf/2512.04677
• Project Page: https://liveavatar.github.io/
• Github: https://github.com/Alibaba-Quark/LiveAvatar
🔹 Models citing this paper:
• https://huggingface.co/Quark-Vision/Live-Avatar
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ahm98alex/liveavatar-test
• https://huggingface.co/spaces/sdavignon/liveavatar
==================================
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#LiveAvatar #AvatarGeneration #RealtimeAI #DiffusionModels #GenerativeAI
Arxivexplained
Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length - Explained Simply
By Yubo Huang, Hailong Guo, Fangtai Wu et al.. # Live Avatar: Real-Time AI Avatars That Never Stop
**The Problem We've All Been Waiting to Solve**...
**The Problem We've All Been Waiting to Solve**...
👍1
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❤2
✨The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
📝 Summary:
The Well is a new 15TB collection of 16 diverse physics simulation datasets. It provides comprehensive data from various domains for benchmarking machine learning models in physical systems, addressing gaps in current standard datasets. A unified PyTorch interface aids usage.
🔹 Publication Date: Published on Nov 30, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.00568
• PDF: https://arxiv.org/pdf/2412.00568
• Github: https://github.com/PolymathicAI/the_well
✨ Datasets citing this paper:
• https://huggingface.co/datasets/polymathic-ai/rayleigh_benard
• https://huggingface.co/datasets/polymathic-ai/gray_scott_reaction_diffusion
• https://huggingface.co/datasets/polymathic-ai/turbulence_gravity_cooling
✨ Spaces citing this paper:
• https://huggingface.co/spaces/polymathic-ai/TheWell
==================================
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✓ https://t.iss.one/DataScienceT
#MachineLearning #PhysicsSimulations #AIforScience #Datasets #PyTorch
📝 Summary:
The Well is a new 15TB collection of 16 diverse physics simulation datasets. It provides comprehensive data from various domains for benchmarking machine learning models in physical systems, addressing gaps in current standard datasets. A unified PyTorch interface aids usage.
🔹 Publication Date: Published on Nov 30, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.00568
• PDF: https://arxiv.org/pdf/2412.00568
• Github: https://github.com/PolymathicAI/the_well
✨ Datasets citing this paper:
• https://huggingface.co/datasets/polymathic-ai/rayleigh_benard
• https://huggingface.co/datasets/polymathic-ai/gray_scott_reaction_diffusion
• https://huggingface.co/datasets/polymathic-ai/turbulence_gravity_cooling
✨ Spaces citing this paper:
• https://huggingface.co/spaces/polymathic-ai/TheWell
==================================
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✓ https://t.iss.one/DataScienceT
#MachineLearning #PhysicsSimulations #AIforScience #Datasets #PyTorch
arXiv.org
The Well: a Large-Scale Collection of Diverse Physics Simulations...
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of...
❤1
✨ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas
📝 Summary:
ASTRA automates training tool-augmented language models for multi-step decision-making. It uses synthetic data and verifiable reinforcement learning, integrating SFT and online RL. This achieves state-of-the-art performance.
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21558
• PDF: https://arxiv.org/pdf/2601.21558
• Github: https://lianjiatech.github.io/astra.blog/
🔹 Models citing this paper:
• https://huggingface.co/Emperorizzis/ASTRA-14B-Thinking-v1
• https://huggingface.co/Emperorizzis/ASTRA-32B-Thinking-v1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Emperorizzis/ASTRA-SFT-1k
• https://huggingface.co/datasets/Emperorizzis/ASTRA-RL-1k
==================================
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#AI #ReinforcementLearning #LanguageModels #MultiStepDecisionMaking #MachineLearning
📝 Summary:
ASTRA automates training tool-augmented language models for multi-step decision-making. It uses synthetic data and verifiable reinforcement learning, integrating SFT and online RL. This achieves state-of-the-art performance.
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21558
• PDF: https://arxiv.org/pdf/2601.21558
• Github: https://lianjiatech.github.io/astra.blog/
🔹 Models citing this paper:
• https://huggingface.co/Emperorizzis/ASTRA-14B-Thinking-v1
• https://huggingface.co/Emperorizzis/ASTRA-32B-Thinking-v1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Emperorizzis/ASTRA-SFT-1k
• https://huggingface.co/datasets/Emperorizzis/ASTRA-RL-1k
==================================
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#AI #ReinforcementLearning #LanguageModels #MultiStepDecisionMaking #MachineLearning
✨DreamActor-M2: Universal Character Image Animation via Spatiotemporal In-Context Learning
📝 Summary:
DreamActor-M2 is a universal character animation framework. It uses in-context learning to fuse appearance and motion cues, along with self-bootstrapped data synthesis for RGB-driven animation. This approach overcomes motion injection tradeoffs and pose prior limitations, achieving superior fidel...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21716
• PDF: https://arxiv.org/pdf/2601.21716
• Project Page: https://grisoon.github.io/DreamActor-M2/
• Github: https://grisoon.github.io/DreamActor-M2/
==================================
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#CharacterAnimation #AI #ComputerVision #DeepLearning #GenerativeAI
📝 Summary:
DreamActor-M2 is a universal character animation framework. It uses in-context learning to fuse appearance and motion cues, along with self-bootstrapped data synthesis for RGB-driven animation. This approach overcomes motion injection tradeoffs and pose prior limitations, achieving superior fidel...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21716
• PDF: https://arxiv.org/pdf/2601.21716
• Project Page: https://grisoon.github.io/DreamActor-M2/
• Github: https://grisoon.github.io/DreamActor-M2/
==================================
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#CharacterAnimation #AI #ComputerVision #DeepLearning #GenerativeAI
✨DenseGRPO: From Sparse to Dense Reward for Flow Matching Model Alignment
📝 Summary:
DenseGRPO addresses sparse rewards in flow matching models by providing dense, step-wise rewards for intermediate denoising steps. It uses these rewards to adaptively calibrate exploration, improving alignment with human preferences in text-to-image generation.
🔹 Publication Date: Published on Jan 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20218
• PDF: https://arxiv.org/pdf/2601.20218
==================================
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#AI #MachineLearning #ReinforcementLearning #TextToImage #GenerativeAI
📝 Summary:
DenseGRPO addresses sparse rewards in flow matching models by providing dense, step-wise rewards for intermediate denoising steps. It uses these rewards to adaptively calibrate exploration, improving alignment with human preferences in text-to-image generation.
🔹 Publication Date: Published on Jan 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20218
• PDF: https://arxiv.org/pdf/2601.20218
==================================
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#AI #MachineLearning #ReinforcementLearning #TextToImage #GenerativeAI
✨TTCS: Test-Time Curriculum Synthesis for Self-Evolving
📝 Summary:
TTCS is a co-evolving test-time training framework for LLMs. It improves reasoning by using a question synthesizer to create challenging variants and a reasoning solver updated via self-consistency, with policies mutually guiding each other. This strengthens reasoning on various tasks.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22628
• PDF: https://arxiv.org/pdf/2601.22628
• Github: https://github.com/XMUDeepLIT/TTCS
==================================
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#LLM #AI #MachineLearning #CurriculumLearning #SelfEvolving
📝 Summary:
TTCS is a co-evolving test-time training framework for LLMs. It improves reasoning by using a question synthesizer to create challenging variants and a reasoning solver updated via self-consistency, with policies mutually guiding each other. This strengthens reasoning on various tasks.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22628
• PDF: https://arxiv.org/pdf/2601.22628
• Github: https://github.com/XMUDeepLIT/TTCS
==================================
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#LLM #AI #MachineLearning #CurriculumLearning #SelfEvolving
✨Real-Time Aligned Reward Model beyond Semantics
📝 Summary:
RLHF faces reward overoptimization from reward model misalignment. R2M introduces a new framework that uses real-time policy feedback to dynamically adapt the reward model. This improves alignment by responding to continuous policy distribution shifts beyond just semantics.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22664
• PDF: https://arxiv.org/pdf/2601.22664
==================================
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#ReinforcementLearning #AI #MachineLearning #RewardModels #AIAlignment
📝 Summary:
RLHF faces reward overoptimization from reward model misalignment. R2M introduces a new framework that uses real-time policy feedback to dynamically adapt the reward model. This improves alignment by responding to continuous policy distribution shifts beyond just semantics.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22664
• PDF: https://arxiv.org/pdf/2601.22664
==================================
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#ReinforcementLearning #AI #MachineLearning #RewardModels #AIAlignment
✨SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization
📝 Summary:
Sweet Spot Learning SSL is a novel RL framework employing tiered rewards for differentiated guidance. This directs agents to optimal solution regions, significantly boosting sample efficiency and cross-task transferability.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22491
• PDF: https://arxiv.org/pdf/2601.22491
==================================
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#ReinforcementLearning #AgenticAI #MachineLearning #Optimization #SampleEfficiency
📝 Summary:
Sweet Spot Learning SSL is a novel RL framework employing tiered rewards for differentiated guidance. This directs agents to optimal solution regions, significantly boosting sample efficiency and cross-task transferability.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22491
• PDF: https://arxiv.org/pdf/2601.22491
==================================
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#ReinforcementLearning #AgenticAI #MachineLearning #Optimization #SampleEfficiency
✨Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data
📝 Summary:
Routing the Lottery framework discovers multiple specialized subnetworks tailored to different data conditions, outperforming traditional pruning methods while using fewer parameters and identifying s...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22141
• PDF: https://arxiv.org/pdf/2601.22141
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Routing the Lottery framework discovers multiple specialized subnetworks tailored to different data conditions, outperforming traditional pruning methods while using fewer parameters and identifying s...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22141
• PDF: https://arxiv.org/pdf/2601.22141
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization
📝 Summary:
PLaT introduces a latent reasoning framework that decouples reasoning from verbalization, enabling dynamic termination and improved scalability over traditional approaches. AI-generated summary Chain-...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21358
• PDF: https://arxiv.org/pdf/2601.21358
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
PLaT introduces a latent reasoning framework that decouples reasoning from verbalization, enabling dynamic termination and improved scalability over traditional approaches. AI-generated summary Chain-...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21358
• PDF: https://arxiv.org/pdf/2601.21358
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨THINKSAFE: Self-Generated Safety Alignment for Reasoning Models
📝 Summary:
ThinkSafe is a self-aligned framework that enhances safety in large reasoning models. It uses lightweight refusal steering and fine-tuning on self-generated responses to preserve reasoning performance and reduce computational costs. ThinkSafe significantly improves safety without degrading native...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23143
• PDF: https://arxiv.org/pdf/2601.23143
==================================
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#AISafety #LLMs #AIAlignment #MachineLearning #DeepLearning
📝 Summary:
ThinkSafe is a self-aligned framework that enhances safety in large reasoning models. It uses lightweight refusal steering and fine-tuning on self-generated responses to preserve reasoning performance and reduce computational costs. ThinkSafe significantly improves safety without degrading native...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23143
• PDF: https://arxiv.org/pdf/2601.23143
==================================
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#AISafety #LLMs #AIAlignment #MachineLearning #DeepLearning
✨MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning
📝 Summary:
MemOCR is a multimodal memory agent for long-horizon reasoning that compresses interaction histories into visual layouts. It adaptively allocates memory space, visually prioritizing crucial evidence while compressing details, outperforming text-based baselines under tight budgets.
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21468
• PDF: https://arxiv.org/pdf/2601.21468
==================================
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#AI #MultimodalAI #LongHorizonReasoning #MemoryNetworks #ComputerVision
📝 Summary:
MemOCR is a multimodal memory agent for long-horizon reasoning that compresses interaction histories into visual layouts. It adaptively allocates memory space, visually prioritizing crucial evidence while compressing details, outperforming text-based baselines under tight budgets.
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21468
• PDF: https://arxiv.org/pdf/2601.21468
==================================
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#AI #MultimodalAI #LongHorizonReasoning #MemoryNetworks #ComputerVision
✨Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification
📝 Summary:
This paper presents a framework that interleaves formal logic verification with natural language generation to improve LLM reasoning. It actively detects and corrects errors during the reasoning process. This method significantly outperforms state-of-the-art models on various reasoning benchmarks.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22642
• PDF: https://arxiv.org/pdf/2601.22642
==================================
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✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper presents a framework that interleaves formal logic verification with natural language generation to improve LLM reasoning. It actively detects and corrects errors during the reasoning process. This method significantly outperforms state-of-the-art models on various reasoning benchmarks.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22642
• PDF: https://arxiv.org/pdf/2601.22642
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨PaperBanana: Automating Academic Illustration for AI Scientists
📝 Summary:
_paperbanana is an agentic framework that automates the creation of publication-ready academic illustrations using advanced vision-language models and image generation techniques. AI-generated summary...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23265
• PDF: https://arxiv.org/pdf/2601.23265
• Project Page: https://dwzhu-pku.github.io/PaperBanana/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
_paperbanana is an agentic framework that automates the creation of publication-ready academic illustrations using advanced vision-language models and image generation techniques. AI-generated summary...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23265
• PDF: https://arxiv.org/pdf/2601.23265
• Project Page: https://dwzhu-pku.github.io/PaperBanana/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought
📝 Summary:
ReGuLaR introduces a variational auto-encoding framework that compresses reasoning processes into latent space while maintaining performance through image-rendered explicit reasoning chains for guidan...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23184
• PDF: https://arxiv.org/pdf/2601.23184
• Github: https://github.com/FanmengWang/ReGuLaR
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
ReGuLaR introduces a variational auto-encoding framework that compresses reasoning processes into latent space while maintaining performance through image-rendered explicit reasoning chains for guidan...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23184
• PDF: https://arxiv.org/pdf/2601.23184
• Github: https://github.com/FanmengWang/ReGuLaR
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨LMK > CLS: Landmark Pooling for Dense Embeddings
📝 Summary:
Landmark pooling improves long-context representation learning by partitioning sequences into chunks and using landmark tokens to preserve both global and local information more effectively than tradi...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21525
• PDF: https://arxiv.org/pdf/2601.21525
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Landmark pooling improves long-context representation learning by partitioning sequences into chunks and using landmark tokens to preserve both global and local information more effectively than tradi...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21525
• PDF: https://arxiv.org/pdf/2601.21525
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨DINO-SAE: DINO Spherical Autoencoder for High-Fidelity Image Reconstruction and Generation
📝 Summary:
A novel vision autoencoder framework combines semantic representation with pixel-level reconstruction using spherical latent space and Riemannian flow matching for improved fidelity and efficiency. AI...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22904
• PDF: https://arxiv.org/pdf/2601.22904
==================================
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📝 Summary:
A novel vision autoencoder framework combines semantic representation with pixel-level reconstruction using spherical latent space and Riemannian flow matching for improved fidelity and efficiency. AI...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22904
• PDF: https://arxiv.org/pdf/2601.22904
==================================
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✨NativeTok: Native Visual Tokenization for Improved Image Generation
📝 Summary:
NativeTok introduces a novel visual tokenization approach that enforces causal dependencies during image encoding, using a Meta Image Transformer and Mixture of Causal Expert Transformer for efficient...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22837
• PDF: https://arxiv.org/pdf/2601.22837
• Github: https://github.com/wangbei1/Nativetok
==================================
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📝 Summary:
NativeTok introduces a novel visual tokenization approach that enforces causal dependencies during image encoding, using a Meta Image Transformer and Mixture of Causal Expert Transformer for efficient...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22837
• PDF: https://arxiv.org/pdf/2601.22837
• Github: https://github.com/wangbei1/Nativetok
==================================
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✨DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding
📝 Summary:
DIFFA-2, a diffusion-based large audio language model, achieves competitive audio understanding performance with improved efficiency over autoregressive counterparts through enhanced encoding, dual ad...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23161
• PDF: https://arxiv.org/pdf/2601.23161
• Github: https://github.com/NKU-HLT/DIFFA
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DIFFA-2, a diffusion-based large audio language model, achieves competitive audio understanding performance with improved efficiency over autoregressive counterparts through enhanced encoding, dual ad...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23161
• PDF: https://arxiv.org/pdf/2601.23161
• Github: https://github.com/NKU-HLT/DIFFA
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research