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Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
LensErase: A Foundation Model for Erase Anything This paper presents a foundation model and a hundred-million-image dataset for mask-based object, text, and watermark erasure in images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general-purpose image erasing, not industrial quality inspection.
SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction This paper proposes a multimodal variational autoencoder framework that separates task-relevant information to improve clinical predictions even with missing modalities. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Does not address AI safety, jailbreaks, hallucinations, or deployment guardrails.
SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction This paper proposes a multimodal variational autoencoder framework that separates task-relevant information to improve clinical predictions even with missing modalities. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not focus on model compression, quantization, or efficient serving.
SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction This paper proposes a multimodal variational autoencoder framework that separates task-relevant information to improve clinical predictions even with missing modalities. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on clinical medical predictions, not industrial defect detection.
SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction This paper proposes a multimodal variational autoencoder framework that separates task-relevant information to improve clinical predictions even with missing modalities. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates medical VAEs, not language model benchmark methodologies.
Human-Inspired Asymmetric Collaboration for Dual-Student Semi-Supervised Medical Image Segmentation This paper presents an asymmetric dual-student framework for semi-supervised medical image segmentation using adaptive reinforcement-guided augmentation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on computer vision segmentation without multimodal text-vision interaction.
Human-Inspired Asymmetric Collaboration for Dual-Student Semi-Supervised Medical Image Segmentation This paper presents an asymmetric dual-student framework for semi-supervised medical image segmentation using adaptive reinforcement-guided augmentation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical image segmentation, not industrial manufacturing inspection.
FlowCred: Hierarchical Credit Assignment and Functional Coordination for Joint Text-to-Video Post-Training Proposes FlowCred, a hierarchical credit assignment framework to coordinate prompt optimization and video generator post-training. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on text-to-video generation, not industrial visual inspection tasks.
Benchmarking Multimodal Large Language Models for Zero-Shot Human-Object Interaction Benchmarks multimodal large language models paired with object detectors for zero-shot human-object interaction recognition. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on human-object interaction reasoning, not industrial vision or defect detection.
VARI: Robust Text-to-Video Generation via Group-Structured Intent Alignment Introduces VARI, a group-structured intent alignment framework to improve text-to-video generation robustness across equivalent prompts. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses text-to-video generation, not industrial visual inspection.
Closing the Estimation Gap: Training-Free Transductive Inference for Cross-Domain Few-Shot Action Recognition Proposes TRACE, a training-free transductive inference framework to reduce the prototype estimation gap in few-shot action recognition. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with video action recognition without multimodal text or audio modeling.
Closing the Estimation Gap: Training-Free Transductive Inference for Cross-Domain Few-Shot Action Recognition Proposes TRACE, a training-free transductive inference framework to reduce the prototype estimation gap in few-shot action recognition. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on action recognition in video, not industrial quality control.
Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1) Surveys traditional model-based, signal processing, and machine learning methods for infrared small target detection. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on infrared target detection rather than multimodal models.
Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1) Surveys traditional model-based, signal processing, and machine learning methods for infrared small target detection. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Surveys infrared surveillance methods, not industrial manufacturing inspection.
Efficient Scene-Text Refinement via Pixel-Space Grouped Image Generation Introduces PixText, a pixel-space grouped generation model for efficient localized text correction in generated images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Refines text in generated images, not industrial defect or quality inspection.
LongForcing: Scaling Temporal Manifold Matching for Long-Horizon World Rollout Proposes LongForcing, a temporal manifold matching distillation approach that scales long-horizon video rollout stability. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses autoregressive video diffusion models, not industrial inspection.
Unsupervised and Supervised Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 2) Surveys deep learning approaches, models, and critical scenarios for infrared small target detection. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Surveys infrared small target detection for surveillance, not industrial manufacturing.
Unsupervised and Supervised Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 2) Surveys deep learning approaches, models, and critical scenarios for infrared small target detection. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses solely on infrared image target detection without multimodal text or audio.
Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies Examines industrial logical anomaly detection under capture shifts and proposes a reference-free classifier trained on synthetic anomalies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses computer vision robustness to image capture shifts, not LLM deployment safety.
Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies Examines industrial logical anomaly detection under capture shifts and proposes a reference-free classifier trained on synthetic anomalies. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on anomaly detection robustness, not model compression or efficient serving.
Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies Examines industrial logical anomaly detection under capture shifts and proposes a reference-free classifier trained on synthetic anomalies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates visual anomaly detection methods rather than language model benchmarks or judges.
Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies Examines industrial logical anomaly detection under capture shifts and proposes a reference-free classifier trained on synthetic anomalies. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Main focus is industrial anomaly detection rather than multimodal foundation model architectures.
Omni-RS: Unifying Multi-Task Understanding and Generation in Remote Sensing Presents Omni-RS, a unified framework and dataset supporting multiple remote sensing image understanding and generation tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Deals with remote sensing imagery, not industrial or manufacturing defect detection.
CityCode: Building Agent-Operable 3D Cities from Sparse Aerial Observations Introduces CityCode, an agent-operable representation generating parameterized 3D urban environments from sparse low-altitude aerial images. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D urban scene reconstruction from imagery without multimodal language interaction.
CityCode: Building Agent-Operable 3D Cities from Sparse Aerial Observations Introduces CityCode, an agent-operable representation generating parameterized 3D urban environments from sparse low-altitude aerial images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Covers aerial 3D urban reconstruction, not industrial visual quality control.
Visual Sevoing from Lines Presents a visual servoing approach using binormalized Plucker line coordinates to decouple rotation and translation control. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic visual servoing control, not industrial defect detection.
Visual Sevoing from Lines Presents a visual servoing approach using binormalized Plucker line coordinates to decouple rotation and translation control. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with robot motion control from geometric lines, not multimodal models.
Hallucination-aware Faithful Reasoning in Remote Sensing Presents FaithRS, a self-reflection framework with a fine-grained dataset to detect and correct reasoning hallucinations in remote sensing models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on remote sensing reasoning, not industrial defect or quality inspection.
SchemaPose: RGB-Based Category-Level Pose Estimation with Parametric Category Schema Introduces SchemaPose, a schema-guided framework for RGB-based category-level 9D object pose estimation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general RGB 3D pose estimation, not industrial anomaly detection.
SchemaPose: RGB-Based Category-Level Pose Estimation with Parametric Category Schema Introduces SchemaPose, a schema-guided framework for RGB-based category-level 9D object pose estimation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals purely with single-modality RGB 3D pose estimation without multimodal aspects.
Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching Proposes Noise2Match, a self-supervised low-dose CT denoising method that aligns adjacent slices using adaptive non-rigid matching. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses medical CT slice denoising without multimodal vision-language integration.
Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching Proposes Noise2Match, a self-supervised low-dose CT denoising method that aligns adjacent slices using adaptive non-rigid matching. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical CT denoising, not industrial inspection or manufacturing.
ReDiS: Reasoning-Diagnosis Self-Evolution for Multimodal Reasoning in Remote Sensing Presents ReDiS, a self-evolution framework separating exploration and candidate diagnosis to improve multimodal reasoning in remote sensing. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on remote sensing multimodal reasoning, not manufacturing or industrial vision.
Bifocal: Training-Free Mixed Granularity Visual Token Pruning for Multimodal LLMs Introduces Bifocal, a training-free mixed-granularity visual token pruning method for multimodal models to protect fine-grained task accuracy. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses visual token pruning in MLLMs, not industrial inspection systems.
CoTL: Compact Token Learning for 3D Scene Representation Proposes CoTL, a method to learn compact latent tokens from unposed multi-view images to reconstruct 3D Gaussian scene representations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D scene representation, not industrial defect detection.
CoTL: Compact Token Learning for 3D Scene Representation Proposes CoTL, a method to learn compact latent tokens from unposed multi-view images to reconstruct 3D Gaussian scene representations. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Covers 3D Gaussian reconstruction without multimodal text, language, or cross-modal components.
EmoGenie: Benchmarking and Evaluating Expressed and Evoked Emotion in T2I Generation with MLLMs Introduces EmoGenie, a benchmark evaluating expressed and evoked emotions in text-to-image generation and multimodal language model understanding. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies emotional evaluation of generated art, not industrial visual inspection.
$\mathrm{S}^3$-PnP: Plug-and-Play Denoising for Scientific Imaging with Self-Supervised Score Priors Proposes a self-supervised plug-and-play restoration framework that learns score priors directly from intermediate states for scientific imaging. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with scientific image denoising rather than multimodal vision-language models.
$\mathrm{S}^3$-PnP: Plug-and-Play Denoising for Scientific Imaging with Self-Supervised Score Priors Proposes a self-supervised plug-and-play restoration framework that learns score priors directly from intermediate states for scientific imaging. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific imaging like MRI, not factory or manufacturing inspection.
MFVSR: A Multi-Style and Fine-Grained Benchmark for Video Subtitle Removal Introduces MFVSR, a multi-style benchmark evaluating localized errors, temporal stability, and reliability in video subtitle removal. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Covers video restoration and inpainting rather than multimodal models or reasoning.
MFVSR: A Multi-Style and Fine-Grained Benchmark for Video Subtitle Removal Introduces MFVSR, a multi-style benchmark evaluating localized errors, temporal stability, and reliability in video subtitle removal. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses video subtitle removal, not industrial inspection or defect detection.
Simplifying and Improving On-Policy Distillation for Few-Step Diffusion Proposes Simplified Matching Distillation, a regression-based on-policy method that enables four-step generation for diffusion models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on generative diffusion models, not manufacturing defect detection or inspection.
Simplifying and Improving On-Policy Distillation for Few-Step Diffusion Proposes Simplified Matching Distillation, a regression-based on-policy method that enables four-step generation for diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on generation efficiency, not AI safety, jailbreaks, or deployment guardrails.
Simplifying and Improving On-Policy Distillation for Few-Step Diffusion Proposes Simplified Matching Distillation, a regression-based on-policy method that enables four-step generation for diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates generative diffusion models, not LLM benchmarks or evaluation methods.
Physics-Aware Deepfake Detection via Distance-Speech Consistency Detects talking-head deepfakes in dynamic videos by matching speaker distance estimates with distance-dependent acoustic energy variations. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on facial deepfake detection, not industrial manufacturing inspection.
Dual-Domain Sparse-View 4D CBCT Reconstruction with Spatial-Temporal Consistency Learning Presents STCNet, a dual-domain framework using spatial-temporal consistency learning to reconstruct sparse-view 4D CBCT images with fewer projections. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 4D CT reconstruction, not multimodal vision-language models.
Dual-Domain Sparse-View 4D CBCT Reconstruction with Spatial-Temporal Consistency Learning Presents STCNet, a dual-domain framework using spatial-temporal consistency learning to reconstruct sparse-view 4D CBCT images with fewer projections. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical radiotherapy CT reconstruction, not industrial inspection.
Meta-Video: Efficient Standardized Evaluation for Video Understanding Evaluates video benchmarks, showing that unexamined setup choices shift accuracy significantly and proposing a standardized evaluation framework. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates video-language benchmark variance, not industrial inspection.
Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models This paper evaluates whether object pointers from the Segment Anything Model can transfer to downstream clinical tumor prediction tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates representation transfer from SAM2, not model efficiency.
Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models This paper evaluates whether object pointers from the Segment Anything Model can transfer to downstream clinical tumor prediction tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies medical downstream transfer, not safety guardrails or reliability.
Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models This paper evaluates whether object pointers from the Segment Anything Model can transfer to downstream clinical tumor prediction tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes feature transferability in medical imaging, not LLM evaluation.
Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models This paper evaluates whether object pointers from the Segment Anything Model can transfer to downstream clinical tumor prediction tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical image segmentation pointers, not multimodal vision-language models.
Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models This paper evaluates whether object pointers from the Segment Anything Model can transfer to downstream clinical tumor prediction tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on clinical PET/CT tumour prediction, not industrial inspection.
Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations The paper proposes an unbalanced optimal transport method to reduce minibatch fluctuations in teacher-free one-step generative models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers generative transport theory, not LLM evaluation or benchmarks.
Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations The paper proposes an unbalanced optimal transport method to reduce minibatch fluctuations in teacher-free one-step generative models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses optimal transport theory, not small model compression.
Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations The paper proposes an unbalanced optimal transport method to reduce minibatch fluctuations in teacher-free one-step generative models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical divergence optimization, not AI safety guardrails.
Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations The paper proposes an unbalanced optimal transport method to reduce minibatch fluctuations in teacher-free one-step generative models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical generative modeling, not industrial inspection.
Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations The paper proposes an unbalanced optimal transport method to reduce minibatch fluctuations in teacher-free one-step generative models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Discusses mathematical generative dynamics, not multimodal models.
Denoise X: Self-Supervised U-Net for Quantum Mottle Suppression in Chest Radiography This paper proposes a lightweight self-supervised neural network trained without clean reference images to reduce noise in chest radiographs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical radiography denoising, not industrial inspection.
Denoise X: Self-Supervised U-Net for Quantum Mottle Suppression in Chest Radiography This paper proposes a lightweight self-supervised neural network trained without clean reference images to reduce noise in chest radiographs. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Investigates medical X-ray restoration, not multimodal language-vision models.

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