✨STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flow

📝 Summary:
STARFlow-V introduces a normalizing flow-based model for end-to-end video generation, offering robust causal prediction and high quality. It achieves strong visual fidelity and temporal consistency using a global-local latent architecture and flow-score matching, establishing NFs as a promising a...

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20462
• PDF: https://arxiv.org/pdf/2511.20462
• Project Page: https://starflow-v.github.io
• Github: https://github.com/apple/ml-starflow

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#VideoGeneration #NormalizingFlow #GenerativeAI #MachineLearning #DeepLearning

✨CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning

📝 Summary:
CLaRa improves retrieval-augmented generation by using unified embedding-based compression and joint end-to-end optimization. It introduces SCP for semantic compression and trains both reranker and generator with a single loss, achieving state-of-the-art results.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18659
• PDF: https://arxiv.org/pdf/2511.18659
• Github: https://github.com/apple/ml-clara

🔹 Models citing this paper:
• https://huggingface.co/probejie/CLaRa-Base
• https://huggingface.co/probejie/CLaRa-E2E
• https://huggingface.co/probejie/CLaRa-Instruct

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#RAG #MachineLearning #GenerativeAI #NLP #DeepLearning

✨ROOT: Robust Orthogonalized Optimizer for Neural Network Training

📝 Summary:
ROOT is a robust optimizer for LLMs addressing dimensional fragility and outlier noise. It employs adaptive Newton iterations for precise orthogonalization and proximal optimization to suppress noise, yielding improved stability, faster convergence, and better performance.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20626
• PDF: https://arxiv.org/pdf/2511.20626
• Github: https://github.com/huawei-noah/noah-research

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#Optimizer #NeuralNetworks #LLMs #DeepLearning #MachineLearning

✨NVIDIA Nemotron Parse 1.1

📝 Summary:
Nemotron-Parse-1.1 is a lightweight OCR and document parsing model with improved capabilities. It excels in general OCR, markdown, structured tables, and text extraction from images using an encoder-decoder architecture. The model achieves competitive accuracy and is publicly released.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20478
• PDF: https://arxiv.org/pdf/2511.20478

🔹 Models citing this paper:
• https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1
• https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1-TC

✨ Spaces citing this paper:
• https://huggingface.co/spaces/prithivMLmods/NVIDIA-Nemotron-Parse-OCR

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#OCR #DocumentParsing #DeepLearning #AI #NVIDIA

✨Terminal Velocity Matching

📝 Summary:
Terminal Velocity Matching TVM generalizes flow matching for high-fidelity generative modeling. It achieves state-of-the-art ImageNet performance with minimal steps, e.g., 1.99 FID in 4 NFEs, through improved diffusion transition modeling and adapted transformers.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19797
• PDF: https://arxiv.org/pdf/2511.19797

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#GenerativeAI #FlowMatching #DeepLearning #ComputerVision #DiffusionModels

✨Inferix: A Block-Diffusion based Next-Generation Inference Engine for World Simulation

📝 Summary:
Inferix is a next-gen inference engine for immersive world simulation, generating high-quality interactive videos. It uses semi-autoregressive block-diffusion with LLM-style KV Cache for efficient, stable generation, enabling real-time world dynamics.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20714
• PDF: https://arxiv.org/pdf/2511.20714
• Github: https://github.com/alibaba-damo-academy/Inferix

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#WorldSimulation #DiffusionModels #GenerativeAI #AIResearch #RealtimeAI

✨Latent Collaboration in Multi-Agent Systems

📝 Summary:
LatentMAS enables LLM agents to collaborate directly in latent space, surpassing text-based communication. This boosts reasoning quality, accuracy, and efficiency speed, tokens without extra training.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20639
• PDF: https://arxiv.org/pdf/2511.20639
• Github: https://github.com/Gen-Verse/LatentMAS

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#LLM #MultiAgentSystems #LatentSpace #AIAgents #ArtificialIntelligence

✨Monet: Reasoning in Latent Visual Space Beyond Images and Language

📝 Summary:
Monet is a new framework enabling MLLMs to reason directly in latent visual space using continuous embeddings as intermediate visual thoughts. It addresses training challenges with a three-stage distillation pipeline and introduces VLPO, outperforming on visual reasoning tasks.

🔹 Publication Date: Published on Nov 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21395
• PDF: https://arxiv.org/pdf/2511.21395
• Github: https://github.com/NOVAglow646/Monet

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#MLLM #VisualReasoning #LatentSpace #AI #DeepLearning

✨Revisiting Generalization Across Difficulty Levels: It's Not So Easy

📝 Summary:
This paper shows that large language models do not consistently generalize across different task difficulties. Training on only easy or hard data is insufficient for broad improvement. This highlights the need for diverse difficulty levels in both training and evaluation datasets for LLMs.

🔹 Publication Date: Published on Nov 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21692
• PDF: https://arxiv.org/pdf/2511.21692
• Github: https://github.com/BatsResearch/Cross-Difficulty

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#LLM #AIResearch #MachineLearning #Generalization #DatasetDesign

✨Frequency-Adaptive Sharpness Regularization for Improving 3D Gaussian Splatting Generalization

📝 Summary:
This paper introduces Frequency-Adaptive Sharpness Regularization FASR to improve 3DGS generalization in novel view synthesis. FASR adaptively adjusts regularization based on local image frequency, preventing overfitting and reconstructing fine details better than prior methods.

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17918
• PDF: https://arxiv.org/pdf/2511.17918
• Project Page: https://bbangsik13.github.io/FASR
• Github: https://bbangsik13.github.io/FASR

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#3DGS #NeuralRendering #ComputerVision #DeepLearning #AI

✨MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots

📝 Summary:
MobileVLA-R1 is a unified framework for quadruped robots that improves vision-language-action through supervised chain-of-thought alignment and GRPO reinforcement learning. This two-stage training enhances reasoning and control stability. It achieves superior performance in complex environments, ...

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17889
• PDF: https://arxiv.org/pdf/2511.17889
• Project Page: https://aigeeksgroup.github.io/MobileVLA-R1/
• Github: https://github.com/AIGeeksGroup/MobileVLA-R1

✨ Datasets citing this paper:
• https://huggingface.co/datasets/AIGeeksGroup/MobileVLA-CoT

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#Robotics #VisionLanguageModels #ReinforcementLearning #MobileRobots #AI

✨SPHINX: A Synthetic Environment for Visual Perception and Reasoning

📝 Summary:
Sphinx is a synthetic environment for visual perception and reasoning, using procedurally generated puzzles to evaluate large vision-language models. It shows that current state-of-the-art models perform poorly, but reinforcement learning with verifiable rewards substantially improves accuracy.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20814
• PDF: https://arxiv.org/pdf/2511.20814
• Github: https://github.com/xashru/sphinx

✨ Datasets citing this paper:
• https://huggingface.co/datasets/xashru/sphinx

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#AI #ComputerVision #ReinforcementLearning #VisionLanguageModels #SyntheticEnvironments

✨I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation

📝 Summary:
This paper presents I-GLIDE, a new framework for remaining useful life RUL prediction. It uses RaPP as a health indicator, enhanced by uncertainty quantification, and 'indicator groups' to model specific degradation mechanisms from multi-sensor data. This approach improves RUL prediction accuracy...

🔹 Publication Date: Published on Nov 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21208
• PDF: https://arxiv.org/pdf/2511.21208
• Project Page: https://lucasandrei.com/pages/i_glide.html
• Github: https://github.com/LucasStill/I-GLIDE

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#RULPrediction #Prognostics #MachineLearning #SensorData #UncertaintyQuantification

I want to share a tool that I genuinely believe can make a real difference for anyone building apps: PacketSDK. Many developers have strong active-user bases but still struggle to increase revenue. That’s exactly why this solution stands out—it adds extra income without disrupting users or interfering with your existing monetization methods.

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✨Harmony: Harmonizing Audio and Video Generation through Cross-Task Synergy

📝 Summary:
Harmony improves audio-visual synchronization in generative AI. It introduces a Cross-Task Synergy training paradigm, a Global-Local Decoupled Interaction Module, and Synchronization-Enhanced CFG. This significantly enhances generation fidelity and fine-grained audio-visual alignment, achieving s...

🔹 Publication Date: Published on Nov 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21579
• PDF: https://arxiv.org/pdf/2511.21579
• Project Page: https://sjtuplayer.github.io/projects/Harmony/
• Github: https://github.com/sjtuplayer/Harmony

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#GenerativeAI #AudioVisual #DeepLearning #AISynchronization #AIResearch

✨Block Cascading: Training Free Acceleration of Block-Causal Video Models

📝 Summary:
Block Cascading accelerates block-causal video generation via training-free parallelization. It starts future blocks with partially denoised predecessors, transforming sequential pipelines into parallel cascades for a 2x speedup without quality loss.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20426
• PDF: https://arxiv.org/pdf/2511.20426
• Project Page: https://hmrishavbandy.github.io/block_cascading_page/
• Github: https://hmrishavbandy.github.io/block_cascading_page/

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#VideoGeneration #AIAcceleration #ParallelProcessing #DeepLearning #ComputerVision

✨Image-Free Timestep Distillation via Continuous-Time Consistency with Trajectory-Sampled Pairs

📝 Summary:
TBCM is a self-contained method that distills diffusion models by extracting latent representations directly from the teacher model trajectory. This eliminates external data, greatly improving efficiency and quality for few-step generation with reduced resources.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20410
• PDF: https://arxiv.org/pdf/2511.20410
• Github: https://github.com/hustvl/TBCM

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#DiffusionModels #ModelDistillation #GenerativeAI #AIResearch #MachineLearning

✨RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale

📝 Summary:
RAISECity uses an agentic framework with multimodal tools for reality-aligned, high-quality, city-scale 3D world generation. It iteratively refines scenes, achieving superior precision and fidelity compared to existing methods.

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18005
• PDF: https://arxiv.org/pdf/2511.18005
• Github: https://github.com/tsinghua-fib-lab/RAISECity

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#3DGeneration #GenerativeAI #MultimodalAI #VirtualWorlds #ComputerGraphics

✨Multimodal Evaluation of Russian-language Architectures

📝 Summary:
Mera Multi is the first open multimodal evaluation framework for Russian-language AI, addressing a lack of such benchmarks. It introduces 18 new instruction-based tasks across text, image, audio, and video, created with Russian cultural specificity and a leakage prevention methodology.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15552
• PDF: https://arxiv.org/pdf/2511.15552
• Project Page: https://mera.a-ai.ru/en/multi
• Github: https://github.com/MERA-Evaluation/MERA_MULTIMODAL/tree/main

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#MultimodalAI #RussianAI #AIEvaluation #Benchmarks #AIresearch

✨WizardCoder: Empowering Code Large Language Models with Evol-Instruct

📝 Summary:
WizardCoder is a Code LLM fine-tuned using Evol-Instruct for complex instructions. It significantly outperforms open-source and major closed LLMs on code generation benchmarks.

🔹 Publication Date: Published on Jun 14, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2306.08568
• PDF: https://arxiv.org/pdf/2306.08568
• Github: https://github.com/nlpxucan/WizardLM

🔹 Models citing this paper:
• https://huggingface.co/WizardLMTeam/WizardCoder-Python-34B-V1.0
• https://huggingface.co/WizardLMTeam/WizardCoder-15B-V1.0
• https://huggingface.co/alpindale/WizardLM-2-8x22B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_V2_196k
• https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1
• https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_70k

✨ Spaces citing this paper:
• https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
• https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard
• https://huggingface.co/spaces/FallnAI/Quantize-HF-Models

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#CodeLLM #LLM #AIE #CodeGeneration #EvolInstruct