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
SWORD: Scene-Grounded Wasserstein Object-Relation Distance for 3D Scene Graph Prediction Introduces a scene-grounded Wasserstein distance objective to optimize graph-level semantic proximity in 3D scene graph prediction tasks. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with 3D computer vision scene graph prediction rather than multimodal vision-language models.
A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry Proposes a differentiable graph-level loss function to measure semantic and structural discrepancies between predicted 3D scene graphs and ground truth. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Proposes a geometric training loss rather than AI deployment safety, hallucination, or alignment.
A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry Proposes a differentiable graph-level loss function to measure semantic and structural discrepancies between predicted 3D scene graphs and ground truth. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D scene graph supervision for generative graphics, not industrial inspection.
A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry Proposes a differentiable graph-level loss function to measure semantic and structural discrepancies between predicted 3D scene graphs and ground truth. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scene graph loss formulation, not model compression or efficient inference.
A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry Proposes a differentiable graph-level loss function to measure semantic and structural discrepancies between predicted 3D scene graphs and ground truth. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a training loss for 3D scene graphs, not LLM benchmark evaluation methods.
A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry Proposes a differentiable graph-level loss function to measure semantic and structural discrepancies between predicted 3D scene graphs and ground truth. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies 3D scene graph prediction in computer vision, not multimodal language models.
DT-LoRA: Reaching Beyond the Tangent Space of Low-Rank Adaptation Introduces a low-rank adaptation method that splits rank budgets into stable and dynamic branches to capture residual gradients outside the tangent space. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies parameter-efficient fine-tuning optimization rather than AI safety, guardrails, or hallucination prevention.
DT-LoRA: Reaching Beyond the Tangent Space of Low-Rank Adaptation Introduces a low-rank adaptation method that splits rank budgets into stable and dynamic branches to capture residual gradients outside the tangent space. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a parameter-efficient fine-tuning algorithm rather than an LLM evaluation benchmark or framework.
DT-LoRA: Reaching Beyond the Tangent Space of Low-Rank Adaptation Introduces a low-rank adaptation method that splits rank budgets into stable and dynamic branches to capture residual gradients outside the tangent space. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on a general parameter-efficient fine-tuning technique rather than multimodal vision-language models.
DT-LoRA: Reaching Beyond the Tangent Space of Low-Rank Adaptation Introduces a low-rank adaptation method that splits rank budgets into stable and dynamic branches to capture residual gradients outside the tangent space. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Proposes a general fine-tuning parameter-efficient technique, not industrial defect or visual quality inspection.
AttenIR: Aligning Dense Visual Attention with Mesh Warping for Unsupervised Image Retargeting Proposes an unsupervised image retargeting framework combining dense visual attention with mesh warping to preserve content without manual annotations. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with traditional vision mesh deformation for retargeting, not multimodal language models.
AttenIR: Aligning Dense Visual Attention with Mesh Warping for Unsupervised Image Retargeting Proposes an unsupervised image retargeting framework combining dense visual attention with mesh warping to preserve content without manual annotations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on image resizing and retargeting, not industrial inspection or quality control.
DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting Presents a framework that segments long-horizon human manipulation videos into skills and translates them into executable robotic actions. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic dexterous manipulation from human video demonstrations, not industrial visual inspection.
DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting Presents a framework that segments long-horizon human manipulation videos into skills and translates them into executable robotic actions. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies robotic imitation learning and video inpainting, not multimodal vision-language reasoning.
When, Where, and Why: Hierarchical Causal Explainability for Video Face Understanding with MLLMs Introduces an explainability framework and weakly supervised video-text corpus to evaluate causal and temporal facial expression understanding in multimodal models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on human facial expression understanding in videos, not industrial inspection or defect detection.
MHVF: Training-Free GUI Grounding via Verification-Guided Multi-Hypothesis Search This paper introduces a training-free framework combining multi-hypothesis search and local visual verification to ground interface instructions in graphical user interfaces. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on GUI element localization for web agents, not manufacturing defect detection.
Multimodal Fusion of RGB and Complementary Modalities for Semantic Segmentation This paper proposes a bidirectional polarity-aware cross-modality fusion module to improve multimodal semantic segmentation across diverse complementary sensor streams. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates multi-sensor semantic segmentation on autonomous driving and general datasets, not industrial inspection.
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations This paper proposes a hierarchical framework for learning whole-arm robot contact manipulation tasks from human demonstrations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general robotic whole-arm contact manipulation rather than visual defect inspection.
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations This paper proposes a hierarchical framework for learning whole-arm robot contact manipulation tasks from human demonstrations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses physical manipulator safety constraints rather than AI deployment safety, jailbreaks, or hallucinations.
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations This paper proposes a hierarchical framework for learning whole-arm robot contact manipulation tasks from human demonstrations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on contact trajectory optimization, not model compression or efficient serving.
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations This paper proposes a hierarchical framework for learning whole-arm robot contact manipulation tasks from human demonstrations. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Uses a vision-language model tangentially for constraint generation in a robotics control pipeline.
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations This paper proposes a hierarchical framework for learning whole-arm robot contact manipulation tasks from human demonstrations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies robotic arm contact trajectory optimization, not LLM evaluation or benchmark trustworthiness.
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis This paper introduces an active learning framework balancing uncertainty with submodular spatial coverage for chest X-ray lesion segmentation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on active learning annotation budgets rather than model compression or efficient serving.
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis This paper introduces an active learning framework balancing uncertainty with submodular spatial coverage for chest X-ray lesion segmentation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical chest X-ray segmentation rather than manufacturing or industrial inspection.
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis This paper introduces an active learning framework balancing uncertainty with submodular spatial coverage for chest X-ray lesion segmentation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines active learning coverage in medical imaging rather than LLM safety, guardrails, or alignment.
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis This paper introduces an active learning framework balancing uncertainty with submodular spatial coverage for chest X-ray lesion segmentation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an active learning acquisition method and medical consistency metrics, not an LLM benchmark.
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis This paper introduces an active learning framework balancing uncertainty with submodular spatial coverage for chest X-ray lesion segmentation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals solely with unimodal medical X-ray imaging, not multimodal language or vision-language models.
What Makes a Good Visual Representation for Spatial Understanding in MLLMs? This paper analyzes how different visual representation types affect the 3D spatial reasoning capabilities of multimodal large language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies visual representations for 3D spatial reasoning in multimodal LLMs, not industrial inspection.
DVTR-KD: Distilling Vision-Language Models via Intermediate Visual Token Reconstruction This paper presents a knowledge distillation framework that reconstructs dense teacher visual tokens to train efficient compressed student vision-language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general vision-language model compression, not industrial defect or visual quality inspection.
Frequency Routing and Identity Conditioning for Thermal-to-Visible Face Translation This paper proposes a parameter-efficient framework using frequency-routed adaptation to translate thermal face images into visible-spectrum photographs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on thermal-to-visible face translation for biometric recognition, not industrial inspection.
Frequency Routing and Identity Conditioning for Thermal-to-Visible Face Translation This paper proposes a parameter-efficient framework using frequency-routed adaptation to translate thermal face images into visible-spectrum photographs. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with cross-spectral thermal-visible image translation rather than multimodal language or foundation models.
MicroCLIP: Connecting Text and Microscopy Images via Contrastive Language--Image Pretraining This paper introduces a vision-language model trained on paired microscopy image-text data for open-set biological image classification and retrieval. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on biological microscopy, not industrial visual quality control.
LoopREG: Representation Supervision Enables Test-Time Recurrent Depth in Diffusion Transformers This paper introduces representation supervision to enable effective test-time recurrent depth scaling in diffusion Transformers. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on diffusion transformer computation, not industrial defect detection.
LoopREG: Representation Supervision Enables Test-Time Recurrent Depth in Diffusion Transformers This paper introduces representation supervision to enable effective test-time recurrent depth scaling in diffusion Transformers. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on recurrent depth in vision transformers without multimodal text-image integration.
Diagnose-to-Generate: Eliminating Structural Distortions in Flow Models via Patch-Level Grounded Alignment This paper introduces a grounded alignment framework using anomaly detection datasets and patch-level policy optimization to fix distortions in flow generators. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on image generation artifacts, not industrial production line inspection.
HierarchiRetina: A Diabetic Retinopathy Screening and Grading Cascade with Heterogeneous Mixture-of-Experts Lesion Segmentation This paper proposes a multi-stage cascade with heterogeneous mixture-of-experts lesion segmentation for diabetic retinopathy screening and grading. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on retinal disease screening, not industrial manufacturing quality control.
HierarchiRetina: A Diabetic Retinopathy Screening and Grading Cascade with Heterogeneous Mixture-of-Experts Lesion Segmentation This paper proposes a multi-stage cascade with heterogeneous mixture-of-experts lesion segmentation for diabetic retinopathy screening and grading. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on clinical eye fundus segmentation without multimodal language-vision capabilities.
PUGS: Physics-Guided Uncertainty Modeling for Underwater Dynamic 3D Gaussian Splatting This paper introduces a physics-guided uncertainty model to improve dynamic 3D Gaussian splatting in turbid underwater environments. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on underwater dynamic scene reconstruction, not factory inspection.
PUGS: Physics-Guided Uncertainty Modeling for Underwater Dynamic 3D Gaussian Splatting This paper introduces a physics-guided uncertainty model to improve dynamic 3D Gaussian splatting in turbid underwater environments. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on dynamic 3D Gaussian splatting without multimodal cross-data interaction.
Sea-Net: A Small and Inherently Interpretable Neural Network for Time Series Classification This paper presents a lightweight, interpretable neural network architecture for resource-constrained time-series classification on microcontrollers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on architectural interpretability, not AI deployment safety or jailbreak prevention.
Sea-Net: A Small and Inherently Interpretable Neural Network for Time Series Classification This paper presents a lightweight, interpretable neural network architecture for resource-constrained time-series classification on microcontrollers. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 1D time-series data rather than multimodal vision-language models.
Sea-Net: A Small and Inherently Interpretable Neural Network for Time Series Classification This paper presents a lightweight, interpretable neural network architecture for resource-constrained time-series classification on microcontrollers. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series classification, not industrial computer vision inspection.
Sea-Net: A Small and Inherently Interpretable Neural Network for Time Series Classification This paper presents a lightweight, interpretable neural network architecture for resource-constrained time-series classification on microcontrollers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series classifiers, not LLM evaluation and benchmarking methods.
dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions This paper presents a diffusion MRI foundation model pretrained on masked reconstruction to enable diverse downstream neuroimaging tasks from sparse scans. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on diffusion MRI data reconstruction without multimodal language integration.
dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions This paper presents a diffusion MRI foundation model pretrained on masked reconstruction to enable diverse downstream neuroimaging tasks from sparse scans. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on brain MRI analysis, not industrial visual inspection.
Web Scale on a Budget: Tail-Aware Retrieval Evaluation This paper introduces a tail-aware evaluation method that estimates web-scale image retrieval performance using small random distractor samples. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Studies visual instance retrieval scaling laws rather than multimodal language models.
Web Scale on a Budget: Tail-Aware Retrieval Evaluation This paper introduces a tail-aware evaluation method that estimates web-scale image retrieval performance using small random distractor samples. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on web-scale instance retrieval, not industrial manufacturing inspection.
Web Scale on a Budget: Tail-Aware Retrieval Evaluation This paper introduces a tail-aware evaluation method that estimates web-scale image retrieval performance using small random distractor samples. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on instance image retrieval benchmarks, not RAG for language models.
PolUR: Polarization Aware Multimodal Degradation Perception for Underwater Image Restoration This paper introduces a multimodal language-driven framework combining polarization and RGB cues to identify degradations and restore underwater images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on underwater exploration imaging, not industrial manufacturing inspection.
Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection This paper proposes an uncertainty-aware semi-supervised network with supervised contrastive learning to detect sugarcane leaf diseases from limited labeled data. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Applies unimodal computer vision methods without multimodal interaction.
Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection This paper proposes an uncertainty-aware semi-supervised network with supervised contrastive learning to detect sugarcane leaf diseases from limited labeled data. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on agricultural leaf disease, not manufacturing or factory inspection.
Surface Reconstruction from Range Scans Under Visibility Constraints This paper presents a neural surface reconstruction formulation from range scans using sensor visibility constraints and robust ray intersection regression. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on pure 3D range scan reconstruction without multimodal integration.
Surface Reconstruction from Range Scans Under Visibility Constraints This paper presents a neural surface reconstruction formulation from range scans using sensor visibility constraints and robust ray intersection regression. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D surface reconstruction, not industrial visual quality control.
Dynamic Interpolation for Training-Time Prediction Reshaping in Low-Light Image Enhancement This paper proposes a parameter-free training module that dynamically reshapes prediction errors to improve low-light image enhancement models. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on unimodal computer vision training dynamics for image enhancement.
Dynamic Interpolation for Training-Time Prediction Reshaping in Low-Light Image Enhancement This paper proposes a parameter-free training module that dynamically reshapes prediction errors to improve low-light image enhancement models. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general low-light image enhancement, not industrial visual inspection.
FlowSim: Rendering-Free Closed-Loop Simulation for Evaluating End-to-End Driving Systems This paper introduces a rendering-free closed-loop simulation method that predicts internal latent states to evaluate autonomous driving systems without image generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on autonomous driving latent rollouts rather than multimodal language-vision models.
FlowSim: Rendering-Free Closed-Loop Simulation for Evaluating End-to-End Driving Systems This paper introduces a rendering-free closed-loop simulation method that predicts internal latent states to evaluate autonomous driving systems without image generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Autonomous driving simulation is explicitly excluded from this industrial vision topic.
COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection This paper introduces a data-aware subspace adaptation method using semantic proxy sets to improve generalizable AI-generated image detection. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses solely on image deepfake detection using vision models without multimodal reasoning.
COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection This paper introduces a data-aware subspace adaptation method using semantic proxy sets to improve generalizable AI-generated image detection. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on deepfake detection, not industrial manufacturing quality control.
Data-Free Continual Learning for World Action Model with Rectified Generative Replay This paper proposes a rectified generative replay framework enabling robotic world action models to continually learn new tasks without storing past data. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotics action generation, not industrial visual inspection.

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