✨Test-time scaling of diffusions with flow maps

📝 Summary:
The Flow Map Trajectory Tilting FMTT algorithm enhances test-time diffusion models by using flow maps to align better with user rewards. This approach solves the ill-posed problem of reward gradients, achieving superior reward ascent for improved sampling and novel image editing.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22688
• PDF: https://arxiv.org/pdf/2511.22688
• Project Page: https://flow-map-trajectory-tilting.github.io/

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#DiffusionModels #GenerativeAI #ImageEditing #MachineLearning #FlowMaps

✨REASONEDIT: Towards Reasoning-Enhanced Image Editing Models

📝 Summary:
REASONEDIT integrates MLLM reasoning thinking and reflection into image editing models. This enables a thinking-editing-reflection loop, improving instruction understanding and editing accuracy by interpreting abstract instructions and correcting results. The approach achieves significant perform...

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22625
• PDF: https://arxiv.org/pdf/2511.22625

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#ImageEditing #AIReasoning #MLLM #ComputerVision #AI

✨The Collapse of Patches

📝 Summary:
Patch collapse is a novel image modeling perspective where observing certain patches reduces uncertainty in others. An autoencoder learns patch dependencies to determine an optimal realization order. This improves masked image modeling and promotes vision efficiency, achieving high accuracy with ...

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22281
• PDF: https://arxiv.org/pdf/2511.22281
• Github: https://github.com/wguo-ai/CoP

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#ImageModeling #ComputerVision #Autoencoders #DeepLearning #MaskedImageModeling

✨Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information

📝 Summary:
Focused Chain-of-Thought F-CoT is an input-centric method that improves LLM reasoning efficiency. It structures query information into a concise context, guiding models to focus reasoning. This approach reduces token usage by 2-3x while maintaining accuracy on arithmetic problems.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22176
• PDF: https://arxiv.org/pdf/2511.22176

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#LLM #ChainOfThought #AI #NLP #Efficiency

✨SO-Bench: A Structural Output Evaluation of Multimodal LLMs

📝 Summary:
SO-Bench is a new benchmark evaluating MLLMs ability to generate schema-compliant structured outputs from visual inputs. It reveals significant gaps in current models performance, highlighting the need for better multimodal structured reasoning.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21750
• PDF: https://arxiv.org/pdf/2511.21750

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#MultimodalLLMs #StructuredOutput #LLMEvaluation #AIResearch #ComputerVision

✨Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield

📝 Summary:
This study challenges the understanding of Distribution Matching Distillation DMD for text-to-image generation. It reveals that CFG Augmentation is the primary driver of few-step distillation, while distribution matching acts as a regularizer. This new insight enables improved distillation method...

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22677
• PDF: https://arxiv.org/pdf/2511.22677
• Project Page: https://tongyi-mai.github.io/Z-Image-blog/
• Github: https://github.com/Tongyi-MAI/Z-Image/tree/main

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#TextToImage #GenerativeAI #DiffusionModels #ModelDistillation #AIResearch

✨FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning

📝 Summary:
FedRE is a federated learning framework for model-heterogeneous environments. Clients create and upload entangled representations and entangled-label encodings to train a global classifier. This method enhances performance, protects privacy, and reduces communication overhead.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22265
• PDF: https://arxiv.org/pdf/2511.22265

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#FederatedLearning #MachineLearning #AI #PrivacyPreservingAI #RepresentationLearning

✨Vision Bridge Transformer at Scale

📝 Summary:
Vision Bridge Transformer ViBT is a large-scale model for conditional generation. It efficiently translates data by directly modeling input-to-output trajectories, unlike diffusion models. ViBT scales to billions of parameters, achieving robust performance in image and video editing tasks.

🔹 Publication Date: Published on Nov 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23199
• PDF: https://arxiv.org/pdf/2511.23199
• Project Page: https://yuanshi9815.github.io/ViBT_homepage/
• Github: https://github.com/Yuanshi9815/ViBT

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Yuanshi/ViBT

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#VisionTransformer #GenerativeAI #ComputerVision #DeepLearning #AI

✨OralGPT-Omni: A Versatile Dental Multimodal Large Language Model

📝 Summary:
OralGPT-Omni is the first dental MLLM for comprehensive image analysis, using TRACE-CoT reasoning. It introduces the MMOral-Uni benchmark and dramatically outperforms GPT-5, advancing intelligent dentistry.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22055
• PDF: https://arxiv.org/pdf/2511.22055
• Github: https://github.com/isbrycee/OralGPT

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#DentalAI #MLLM #GenerativeAI #HealthcareTech #MedicalImaging

✨World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models

📝 Summary:
LVLMs struggle to preserve cultural identities in mixed visual scenes. Researchers created CultureMix, a VQA benchmark, finding consistent failures and background reliance. Supervised fine-tuning with diverse culture mixing data significantly improves model consistency and reduces background sens...

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22787
• PDF: https://arxiv.org/pdf/2511.22787

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#VisionLanguageModels #CulturalAI #ComputerVision #AIML #AIResearch

✨RefineBench: Evaluating Refinement Capability of Language Models via Checklists

📝 Summary:
RefineBench evaluates language models' self-refinement and guided refinement capabilities using 1,000 problems and a checklist. It finds that LMs perform poorly at self-refinement, often failing to improve without guidance, but excel at guided refinement with targeted feedback.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22173
• PDF: https://arxiv.org/pdf/2511.22173

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

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#LLM #AI #NLP #ModelEvaluation #Refinement

✨From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images

📝 Summary:
MLLMs struggle with human cognitive perception of images like memorability or aesthetics. CogIP-Bench evaluates this gap, showing post-training significantly improves alignment. This enhances human-like perception and improves creative AI tasks.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22805
• PDF: https://arxiv.org/pdf/2511.22805
• Project Page: https://follen-cry.github.io/MLLM-Cognition-project-page/

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#MLLM #CognitiveAI #ImagePerception #AIAlignment #AIResearch

✨Adversarial Flow Models

📝 Summary:
Adversarial flow models unify adversarial and flow-based generative models for stable training and efficient one-step generation. They learn a deterministic noise-to-data mapping, achieving record FIDs of 1.94 on ImageNet-256px with a single pass, outperforming consistency models.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22475
• PDF: https://arxiv.org/pdf/2511.22475

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#GenerativeAI #DeepLearning #AdversarialModels #FlowModels #ImageSynthesis

✨Find the Leak, Fix the Split: Cluster-Based Method to Prevent Leakage in Video-Derived Datasets

📝 Summary:
This paper introduces a cluster-based frame selection strategy for video datasets. It groups similar frames to prevent information leakage and create more balanced and reliable dataset partitions for training, validation, and testing.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13944
• PDF: https://arxiv.org/pdf/2511.13944

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#VideoDatasets #DataLeakage #MachineLearning #Clustering #DatasetSplitting

✨YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection

📝 Summary:
A new Mixture-of-Experts framework uses adaptive routing among multiple YOLOv9-T experts. This improves object detection performance, achieving higher mAP and AR.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13344
• PDF: https://arxiv.org/pdf/2511.13344

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#ObjectDetection #YOLO #MixtureOfExperts #DeepLearning #ComputerVision

✨Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models

📝 Summary:
This paper introduces a dual-backbone framework combining convolutional and transformer representations with top-k pooling to detect abnormal events in surveillance videos. The weakly supervised model achieved 90.7% AUC on the UCF-Crime dataset.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13276
• PDF: https://arxiv.org/pdf/2511.13276

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#ComputerVision #DeepLearning #Surveillance #AnomalyDetection #WeaklySupervisedLearning

✨CaptionQA: Is Your Caption as Useful as the Image Itself?

📝 Summary:
CaptionQA assesses if AI captions adequately substitute images for downstream tasks. This benchmark uses over 33000 visual questions across 4 domains. It shows large utility gaps as MLLMs perform up to 32% worse with captions than with images.

🔹 Publication Date: Published on Nov 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.21025
• PDF: https://arxiv.org/pdf/2511.21025
• Github: https://github.com/bronyayang/CaptionQA

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Borise/CaptionQA

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#AICaptions #MultimodalAI #ComputerVision #AIevaluation #NLP

✨Fast3Dcache: Training-free 3D Geometry Synthesis Acceleration

📝 Summary:
Fast3Dcache accelerates 3D diffusion model inference using a training-free geometry-aware caching framework. It uses dynamic cache quotas and spatiotemporal stability criteria to reuse computations, achieving significant speed-up and FLOPs reduction with minimal geometric quality degradation.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22533
• PDF: https://arxiv.org/pdf/2511.22533
• Project Page: https://fast3dcache-agi.github.io/
• Github: https://fast3dcache-agi.github.io/

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#3DGeometry #DiffusionModels #ComputerVision #DeepLearning #ComputationalEfficiency

✨OmniRefiner: Reinforcement-Guided Local Diffusion Refinement

📝 Summary:
OmniRefiner enhances reference-guided image generation by overcoming fine detail loss. It uses a two-stage framework: a fine-tuned diffusion editor for global coherence, then reinforcement learning for localized detail accuracy. This significantly improves detail preservation and consistency.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19990
• PDF: https://arxiv.org/pdf/2511.19990
• Github: https://github.com/yaoliliu/OmniRefiner

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#DiffusionModels #ImageGeneration #ReinforcementLearning #GenerativeAI #ComputerVision

✨DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning

📝 Summary:
DeepSeekMath-V2 trains a self-verifying LLM for theorem proving. It uses a verifier as a reward model to incentivize rigorous, step-by-step derivations and issue resolution in proofs. This approach achieves gold-level scores in major math competitions.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22570
• PDF: https://arxiv.org/pdf/2511.22570

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#DeepSeekMath #LLM #AI #MathematicalReasoning #TheoremProving

✨Layer-Aware Video Composition via Split-then-Merge

📝 Summary:
Split-then-Merge is a novel framework improving generative video composition. It learns dynamic foreground-background interactions by unsupervisedly splitting unlabeled videos into layers and then self-composing them. This approach achieves state-of-the-art performance and addresses data scarcity.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20809
• PDF: https://arxiv.org/pdf/2511.20809
• Project Page: https://split-then-merge.github.io/

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#VideoComposition #GenerativeAI #ComputerVision #DeepLearning #AIResearch