✨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

✨UniGame: Turning a Unified Multimodal Model Into Its Own Adversary

📝 Summary:
UniGame is a self-adversarial post-training framework that improves unified multimodal models. It resolves inconsistencies between understanding and generation by using a lightweight perturber to make the model its own adversary. This boosts consistency, understanding, generation, and robustness.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19413
• PDF: https://arxiv.org/pdf/2511.19413
• Github: https://github.com/AIFrontierLab/UniGame

==================================

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#MultimodalAI #AdversarialLearning #AIResearch #MachineLearning #ModelRobustness

✨Reinforcing Action Policies by Prophesying

📝 Summary:
ProphRL improves Vision-Language-Action policies by overcoming imitation learning limits. It uses Prophet, a learned world model simulator, with tailored reinforcement learning FA-GRPO and FlowScale for data-efficient and stable post-training. This yields significant success gains on benchmarks a...

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20633
• PDF: https://arxiv.org/pdf/2511.20633
• Project Page: https://logosroboticsgroup.github.io/ProphRL/
• Github: https://github.com/LogosRoboticsGroup/ProphRL

==================================

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#ReinforcementLearning #ProphRL #WorldModels #Robotics #DeepLearning

✨Position: The Complexity of Perfect AI Alignment -- Formalizing the RLHF Trilemma

📝 Summary:
RLHF faces an Alignment Trilemma: representativeness, tractability, and robustness are proven intractable to achieve simultaneously. Current RLHF sacrifices representativeness globally, causing biases and pathologies.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19504
• PDF: https://arxiv.org/pdf/2511.19504

==================================

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#AIAlienment #RLHF #AISafety #MachineLearning #AIResearch

✨Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild

📝 Summary:
Gradio is an open-source Python package that creates visual interfaces for ML models, making them accessible to non-specialized users via a URL. This improves collaboration by allowing easy interaction, feedback, and trust-building in interdisciplinary settings.

🔹 Publication Date: Published on Jun 6, 2019

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/1906.02569
• PDF: https://arxiv.org/pdf/1906.02569
• Github: https://github.com/gradio-app/gradio

🔹 Models citing this paper:
• https://huggingface.co/CxECHO/CE

✨ Datasets citing this paper:
• https://huggingface.co/datasets/society-ethics/papers

✨ Spaces citing this paper:
• https://huggingface.co/spaces/orYx-models/Nudge_Generator
• https://huggingface.co/spaces/society-ethics/about
• https://huggingface.co/spaces/mindmime/gradio

==================================

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#Gradio #MachineLearning #MLOps #Python #DataScience

✨NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering

📝 Summary:
NAF upsamples Vision Foundation Model features zero-shot by learning adaptive spatial-and-content weights. It outperforms VFM-specific upsamplers without retraining, achieving state-of-the-art performance across various tasks efficiently.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18452
• PDF: https://arxiv.org/pdf/2511.18452
• Github: https://github.com/valeoai/NAF?tab=readme-ov-file

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#ZeroShotLearning #ComputerVision #FeatureUpsampling #DeepLearning #AIResearch

✨G^2VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning

📝 Summary:
G^2VLM integrates 3D geometry learning into vision-language models to overcome their spatial intelligence deficits. It unifies 3D reconstruction and spatial reasoning, leveraging learned 3D features to achieve strong performance in both tasks.

🔹 Publication Date: Published on Nov 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21688
• PDF: https://arxiv.org/pdf/2511.21688
• Project Page: https://gordonhu608.github.io/g2vlm.github.io/
• Github: https://github.com/InternRobotics/G2VLM

🔹 Models citing this paper:
• https://huggingface.co/InternRobotics/G2VLM-2B-MoT

==================================

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#VisionLanguageModels #3DReconstruction #SpatialReasoning #ComputerVision #ArtificialIntelligence

✨MIRA: Multimodal Iterative Reasoning Agent for Image Editing

📝 Summary:
MIRA is a multimodal iterative reasoning agent that enhances diffusion-based image editing. It tackles complex instructions by breaking them into atomic edits via a perception-reasoning-action loop with visual feedback. This improves semantic consistency and perceptual quality, outperforming othe...

🔹 Publication Date: Published on Nov 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21087
• PDF: https://arxiv.org/pdf/2511.21087

==================================

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#AI #ImageEditing #MultimodalAI #DiffusionModels #ComputerVision

✨Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following

📝 Summary:
Multi-Crit evaluates multimodal models as judges on following diverse criteria using novel metrics. Findings reveal current models struggle with consistent adherence and flexibility to pluralistic criteria. This highlights gaps in capabilities and lays a foundation for building reliable AI evalua...

🔹 Publication Date: Published on Nov 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21662
• PDF: https://arxiv.org/pdf/2511.21662
• Project Page: https://multi-crit.github.io/
• Github: https://multi-crit.github.io/

==================================

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#MultimodalAI #AIEvaluation #BenchmarkingAI #AIJudges #MachineLearning