Every paper checked — nothing hidden

All papers

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
How Far Can Coding Agents Go in Visual Generation? The study evaluates coding agents generating images via executable code, analyzing their strengths in geometric constraints over photorealistic drawing. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Studies coding agents generating images, not industrial defect inspection.
How Far Can Coding Agents Go in Visual Generation? The study evaluates coding agents generating images via executable code, analyzing their strengths in geometric constraints over photorealistic drawing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address safety, jailbreaking, hallucinations, or deployment guardrails.
RingNav: Task-Shaped Circular Memory for Visual Heading RingNav is a compact visual heading estimator that restricts persistent memory updates to a circular manifold to improve trajectory tracking. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on general robot visual navigation, not industrial inspection.
RingNav: Task-Shaped Circular Memory for Visual Heading RingNav is a compact visual heading estimator that restricts persistent memory updates to a circular manifold to improve trajectory tracking. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on pure visual SLAM heading estimation without multimodal interaction.
Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations CardioFAD resolves temporal ambiguity in sparse cardiac dynamic synthesis by using ECG-aligned motion-salient keyframe diffusion. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on medical cardiac dynamic synthesis, not industrial defect detection.
Why Do VLMs Fail at Remote Physiological Measurement? Rethinking with Physiological Grounding and Numerical Alignment VITAL introduces visual-physiological token alignment to enable vision-language models to perform remote physiological measurement alongside general visual reasoning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on human physiological measurement, not industrial manufacturing quality control.
WorldMemBench: A Comprehensive Benchmark for Memory of Video World Models WorldMemBench introduces an evaluation benchmark assessing static entity retention and dynamic event persistence in video world models over multi-revisit paths. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on video world models, not industrial manufacturing inspection.
MonoMIP: Multi-Height Inverse Perspective Mapping for Monocular 3D Object Detection MonoMIP combines multi-height inverse perspective mapping with a dual-decoder framework to improve bird's-eye view reasoning in monocular 3D object detection. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on autonomous driving benchmarks, not industrial visual inspection.
MonoMIP: Multi-Height Inverse Perspective Mapping for Monocular 3D Object Detection MonoMIP combines multi-height inverse perspective mapping with a dual-decoder framework to improve bird's-eye view reasoning in monocular 3D object detection. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on computer vision 3D detection without multimodal integration.
The Gap Is in the Geometry: A Convergent Meta-Analysis of Multi-Facet Aerial and Remote Sensing Video Understanding A meta-analysis shows multimodal language models struggle with geometric acquisition variations in aerial video, exposing limitations in standard QA-based evaluations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on aerial and remote sensing video, which is explicitly excluded.
DoReMe: Dual optimization of Reconstruction and Motion estimation for self-navigated free-breathing abdominal MRI using Gaussian Splatting DoReMe uses Gaussian splatting primitives to jointly optimize image reconstruction and respiratory motion estimation in self-navigated free-breathing abdominal MRI. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on medical abdominal MRI reconstruction, not industrial visual inspection.
DoReMe: Dual optimization of Reconstruction and Motion estimation for self-navigated free-breathing abdominal MRI using Gaussian Splatting DoReMe uses Gaussian splatting primitives to jointly optimize image reconstruction and respiratory motion estimation in self-navigated free-breathing abdominal MRI. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not study multimodal models combining text, vision, audio, or speech.
Agronomic Characterization and Time-Series UAV Phenotyping to Support Field Pea Breeding in Iowa The study evaluates field pea breeding in Iowa by characterizing agronomic yield performance and extracting time-series traits from drone phenotyping data. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies UAV remote sensing to crop yield, not multimodal models.
Agronomic Characterization and Time-Series UAV Phenotyping to Support Field Pea Breeding in Iowa The study evaluates field pea breeding in Iowa by characterizing agronomic yield performance and extracting time-series traits from drone phenotyping data. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses agricultural crop phenotyping, not industrial visual inspection.
Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors LidarVerse provides a unified conditional generative LiDAR world model combining geometry-preserving spatial caches with amortized sequence learning across sensors and tasks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Builds a generative LiDAR sensor model, not multimodal vision-language reasoning.
Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors LidarVerse provides a unified conditional generative LiDAR world model combining geometry-preserving spatial caches with amortized sequence learning across sensors and tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on autonomous vehicle LiDAR simulation, not industrial defect detection.
Decoupled Drift-Diffusion World Models for Consistent Future Frame Prediction D3WM decouples deterministic state transitions from spatial uncertainty to guide conditional diffusion for consistent future frame prediction in world models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robot action-conditioned world modeling, not industrial quality control.
Decoupled Drift-Diffusion World Models for Consistent Future Frame Prediction D3WM decouples deterministic state transitions from spatial uncertainty to guide conditional diffusion for consistent future frame prediction in world models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Studies future frame diffusion modeling, not multimodal language-vision systems.
A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision A gradient-regularized U-Net framework transforms images to counteract spherical eye defocus aberrations and improve visual clarity for myopic individuals. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Presents image-to-image defocus correction, not multimodal vision-language models.
A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision A gradient-regularized U-Net framework transforms images to counteract spherical eye defocus aberrations and improve visual clarity for myopic individuals. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses human optical myopia correction, not industrial manufacturing inspection.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical neural analysis without direct focus on safety or guardrails.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Explores unimodal image classification dynamics, not multimodal models.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Analyzes theoretical recurrent neural dynamics, not industrial vision inspection.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies recurrent neural mechanisms, not model quantization or efficient inference.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates neural dynamical properties, not LLM benchmark evaluation.
Long-horizon Video Generation with Slot Memory Addressed By Region Tokens SMART introduces a slot-memory mechanism addressed by region tokens to achieve bounded memory and spatially consistent recall in long-horizon video generation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Covers long-horizon video generation mechanisms, not multimodal cross-modality understanding.
Long-horizon Video Generation with Slot Memory Addressed By Region Tokens SMART introduces a slot-memory mechanism addressed by region tokens to achieve bounded memory and spatially consistent recall in long-horizon video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on video generation architectures, not industrial defect detection.
LAMP: 3D Source-Aware Lighting Estimation at Multiple Points The paper proposes a framework using vision-language reasoning to estimate point-conditioned 3D directions and physical light source contributions. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Studies 3D lighting estimation in scenes, not industrial defect inspection.
Deep Learning for Atmospheric Turbulence Mitigation: A Comparison Between GANs and Transformers The paper compares GAN and Transformer architectures for mitigating atmospheric turbulence distortions in multi-frame imaging. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on atmospheric image turbulence reduction, not factory defect detection.
Deep Learning for Atmospheric Turbulence Mitigation: A Comparison Between GANs and Transformers The paper compares GAN and Transformer architectures for mitigating atmospheric turbulence distortions in multi-frame imaging. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Compares image restoration methods on single imagery, not multimodal models.
Kandinsky S2V 1.0: Audio-Driven Avatar Video Generation The paper presents open speech-to-video models that generate long-form avatar video with expressive motion driven by an audio waveform. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Presents speech-to-video avatar generation, not industrial manufacturing vision.
Data-free Multistep Diffusion Distillation The paper proposes a data-free multistep distillation method for text-to-image diffusion models to improve generation fidelity across sampling budgets. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion model distillation, not multimodal language-vision architectures.
Data-free Multistep Diffusion Distillation The paper proposes a data-free multistep distillation method for text-to-image diffusion models to improve generation fidelity across sampling budgets. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Proposes image diffusion distillation, not industrial visual quality control.
SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models The paper proposes an interactive ultrasound world model that learns video dynamics and maps them to six-degree-of-freedom probe control. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic ultrasound medical imaging, not industrial manufacturing inspection.
SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models The paper proposes an interactive ultrasound world model that learns video dynamics and maps them to six-degree-of-freedom probe control. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Develops a diffusion-based ultrasound world model, not multimodal vision-language reasoning.
NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation The paper introduces a dataset and synthetic generation pipeline focused on recognizing accessibility-critical objects to assist blind navigation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on assistive navigation for visually impaired users, not manufacturing.
NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation The paper introduces a dataset and synthetic generation pipeline focused on recognizing accessibility-critical objects to assist blind navigation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Introduces a navigation dataset and synthetic rendering pipeline, not multimodal models.
MMKG-R1: Constructing Multimodal Knowledge Graphs via Reinforcement Learning The paper proposes a reinforcement learning framework using quality-aware rewards to guide multimodal knowledge graph construction for retrieval-augmented generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Covers multimodal knowledge graphs and RAG, not industrial defect inspection.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses sensor anomaly detection, not multimodal models.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on weather sensor networks and general vision, not manufacturing.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Examines sensor novelty detection, not language model benchmark methodologies.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Not related to model compression, quantization, or small LLMs.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Surveys existing detector architectures rather than proposing efficient serving techniques.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates vision object detectors, not LLM benchmarks or evaluation.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Addresses unimodal video object detection without multimodal integration.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmarking pedestrian trackers, not AI safety, hallucination, or guardrails.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on pedestrian tracking benchmarks, not manufacturing quality control.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Not focused on multimodal vision-language architectures or cross-modal tasks.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant General learning framework, not industrial visual inspection.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates evidence acquisition loops, not model compression or small models.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on two-loop inference optimization, not LLM benchmark evaluation.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses general autoencoder latent alignment, not industrial defect detection.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Proposes representation regularization, not model distillation, quantization, or pruning.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Covers general neural representations, not multimodal vision-language models.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on representation stitching, not LLM evaluation or benchmarks.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical latent space alignment, not AI safety or deployment guardrails.
WinARDet: Compressed Window Auto-Regression for Long-Horizon Vision-Language Grounding The paper proposes compressed window autoregression to prevent repetitive bounding box errors in vision-language object grounding tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on vision-language grounding in dense scenes, not manufacturing inspection.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Addresses classical feature selection, not LLM evaluation or benchmarks.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Not related to multimodal models or vision-language processing.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Theoretical unsupervised feature selection algorithm, not industrial vision defect detection.

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