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
Harnessing SPAD Noise: Test-Time Gated Attention for Indoor Depth Estimation The paper introduces a lightweight test-time attention framework that leverages single-photon sensor noise as an informative prior for indoor depth estimation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies sensor-based depth estimation, not deployment AI safety or guardrails.
Harnessing SPAD Noise: Test-Time Gated Attention for Indoor Depth Estimation The paper introduces a lightweight test-time attention framework that leverages single-photon sensor noise as an informative prior for indoor depth estimation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a depth estimation method, not LLM evaluation benchmarks.
Harnessing SPAD Noise: Test-Time Gated Attention for Indoor Depth Estimation The paper introduces a lightweight test-time attention framework that leverages single-photon sensor noise as an informative prior for indoor depth estimation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on sensor depth estimation, not small language model compression.
Harnessing SPAD Noise: Test-Time Gated Attention for Indoor Depth Estimation The paper introduces a lightweight test-time attention framework that leverages single-photon sensor noise as an informative prior for indoor depth estimation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on sensor noise for depth prediction, not vision-language multimodal models.
Rethinking Supervision in Document Unwarping: A Self-consistent Flow-free Approach The paper introduces a flow-free deep network for smartphone document image unwarping using geometric consistency and undistorted document supervision. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on smartphone document unwarping, not factory quality control.
EmoTrace-Bench: Diagnosing Emotional Expression in Text-to-Video Generation through Controlled Cues and Intensity The paper introduces a benchmark to evaluate text-to-video models across controlled emotional expression levels and specific audiovisual affective cues. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Benchmark for text-to-video emotion generation, not industrial visual inspection.
Semantic Saliency from Multi-Modal Large Language Model Scene Understanding Maps The paper develops an automated method that measures semantic saliency by evaluating how removing individual objects alters multimodal model scene descriptions. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies semantic saliency modeling, not factory defect inspection.
Structured Stateful Relational Scripts with a High-Quality Dataset for Multi-Shot Audio-Visual Video Generation The paper introduces structured stateful relational scripts and a paired dataset to coordinate persistent identities and audio events in multi-shot video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on video generation, not manufacturing or inspection.
Semantically Steerable Image Tokenization The paper proposes an adaptive image tokenizer that balances pixel reconstruction and semantic features to prevent discarding critical semantic content during compression. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Deals with image tokenization compression, not industrial defect detection.
GNFormer: Gaussian Neural Field Transformer for Efficient Camera-based Occupancy Prediction The paper introduces a 3D Gaussian neural field transformer for efficient, camera-based occupancy prediction in robotic perception systems. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Occupancy prediction for autonomous vehicles and UAVs, not manufacturing lines.
GNFormer: Gaussian Neural Field Transformer for Efficient Camera-based Occupancy Prediction The paper introduces a 3D Gaussian neural field transformer for efficient, camera-based occupancy prediction in robotic perception systems. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D occupancy prediction without cross-modal language or multimodal reasoning.
UMAS3D: Unified Mask-Free 3D Editing with Region-Adaptive Source Preservation The paper proposes a training-free 3D editing framework that estimates edit regions without masks to preserve unedited source geometry and appearance. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D asset editing, not industrial visual inspection.
A Plausible Future Is Not a Causal Future: Can Video Models Generate How the World Unfolds? The paper presents a simulator-grounded benchmark to evaluate whether image-to-video generation models capture causal physical cascades and counterfactual dynamics faithfully. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates video world models, not industrial defect detection or manufacturing.
Genesis: Visual Augmentation Primitives for Multimodal Chain-of-Thought Reasoning The paper introduces an image augmentation pipeline that generates diverse visual variants to anchor multimodal chain-of-thought reasoning in visual evidence. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on multimodal visual reasoning benchmarks, not industrial visual inspection.
Truncation Samplers Are Nested: Comparing Decoding Rules Is a Measurement Problem The paper presents a theoretical framework using nesting properties to calculate distance bounds and compare language model truncation sampling algorithms efficiently. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies language model sampling theory, not industrial computer vision.
Truncation Samplers Are Nested: Comparing Decoding Rules Is a Measurement Problem The paper presents a theoretical framework using nesting properties to calculate distance bounds and compare language model truncation sampling algorithms efficiently. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on sampling divergence measurement rather than AI safety or guardrails.
Truncation Samplers Are Nested: Comparing Decoding Rules Is a Measurement Problem The paper presents a theoretical framework using nesting properties to calculate distance bounds and compare language model truncation sampling algorithms efficiently. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Analyzes text generation decoding rules, not multimodal models.
Ellipsoids from Tangents The paper introduces an algorithm that fits multiple ellipsoids in multi-dimensional data by leveraging tangent equations rather than raw points alone. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical ellipsoid fitting from tangents, not multimodal AI.
Ellipsoids from Tangents The paper introduces an algorithm that fits multiple ellipsoids in multi-dimensional data by leveraging tangent equations rather than raw points alone. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Presents a geometric algorithm for fitting ellipsoids, not industrial visual inspection.
FireNav: Decision-Oriented Reconstruction Evidence Routing for Vision-Language Navigation in Indoor Fire Environments The paper presents a framework aligning visual image restoration with navigation decisions for vision-language navigation in degraded indoor fire environments. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses indoor fire navigation for robots, not industrial defect inspection.
Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection This paper introduces a lightweight detection framework combining early channel recalibration and multiscale fusion for real-time traffic sign identification. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on traffic sign detection for autonomous driving, explicitly excluded.
Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection This paper introduces a lightweight detection framework combining early channel recalibration and multiscale fusion for real-time traffic sign identification. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on standard object detection architectures (YOLO) for vision alone.
Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision This paper introduces a dual-branch framework combining mask-level and image-level supervision for open-vocabulary mammography lesion segmentation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses medical image segmentation under mixed supervision, not multimodal cross-modal modeling.
Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision This paper introduces a dual-branch framework combining mask-level and image-level supervision for open-vocabulary mammography lesion segmentation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical mammography segmentation, not industrial quality control.
1Step-WAM: Your World Action Model but Faster using 1-Step Action Generation This paper accelerates world action models by compressing multi-step action denoising into a single forward step parallelized with visual prefilling. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic action generation, not industrial visual inspection.
1Step-WAM: Your World Action Model but Faster using 1-Step Action Generation This paper accelerates world action models by compressing multi-step action denoising into a single forward step parallelized with visual prefilling. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on single-step action denoising in world action models for robotics.
SurgCast: Action-Conditioned Future Skeletons for Controllable Surgical World Models This paper proposes predicting future instrument skeletons from planned actions to guide visually and structurally consistent surgical video generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on robot action-conditioned surgical video generation rather than multimodal reasoning.
SurgCast: Action-Conditioned Future Skeletons for Controllable Surgical World Models This paper proposes predicting future instrument skeletons from planned actions to guide visually and structurally consistent surgical video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Applies to surgical video generation and robotics, not manufacturing inspection.
Sparse All-Layer Connector for Domain Generalised Semantic Segmentation This paper introduces a sparse connector allowing cross-layer feature sharing across different foundation models for domain-generalized semantic segmentation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies domain generalized semantic segmentation, not industrial defect inspection.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on multiple instance learning in pathology images, not multimodal models.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on digital pathology whole-slide images, not industrial inspection.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a pathology pseudo-labeling method, not LLM evaluation or benchmarks.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies weakly supervised pathology learning, not AI safety or deployment guardrails.
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology This paper introduces prototype-purity pseudo-labeling to extract accurate instance-level annotations from weakly supervised whole-slide pathology images. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address small models, pruning, quantization, or efficient serving.
Recovering Low-Bit Failures in Vision-Language-Action Policies via Quantization-Conditioned Reinforcement Learning This paper proposes offline reinforcement learning conditioned on quantization levels to recover task completion performance in quantized vision-language-action policies. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic vision-language-action policies, not industrial defect detection.
PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation This paper introduces a benchmark evaluating whether vision-language models can infer underlying physical laws from motion videos and generate runnable simulation code. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics video-to-code generation, not manufacturing defect detection.
Visually Legible but Adversarial: Watermark-Constrained Attacks on End-to-End VLMs for Document Understanding This paper introduces a question-agnostic adversarial text-watermarking method that severely degrades document understanding in vision-language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates document understanding attacks, not industrial manufacturing inspection.
ScenePlacer: Reformulating Scene-Level Object Pose Estimation as Visibility Field Learning This paper proposes estimating 3D object poses within partial scenes by predicting visibility fields in canonical object space. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D object pose estimation for generative graphics scenes.
ScenePlacer: Reformulating Scene-Level Object Pose Estimation as Visibility Field Learning This paper proposes estimating 3D object poses within partial scenes by predicting visibility fields in canonical object space. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses 3D shape alignment and point clouds, not cross-modal vision-language reasoning.
RSAJEV: Training-Free Abstaining Prototype Memory for Eliminating Silent Hallucination in JEV-Based Remote Sensing Classification This paper introduces a training-free prototype memory readout to prevent out-of-vocabulary overconfidence and silent hallucinations in remote sensing classification. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on remote sensing scene classification, not industrial quality inspection.
SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts This paper presents a spiking mixture-of-experts architecture to improve the resilience of multimodal 3D occupancy perception against missing sensor inputs. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses autonomous driving 3D occupancy perception, explicitly out of scope.
Frozen History: When Temporal Losses Stop Accumulation in Learned Real-Time Supersampling This paper demonstrates that accurate registration can cause temporal consistency losses to freeze history accumulation and degrade real-time supersampling accuracy. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Investigates real-time supersampling losses, not industrial visual inspection.
Frozen History: When Temporal Losses Stop Accumulation in Learned Real-Time Supersampling This paper demonstrates that accurate registration can cause temporal consistency losses to freeze history accumulation and degrade real-time supersampling accuracy. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on video supersampling and temporal losses, not multimodal reasoning.
SlotAVS: Object-Centric Audio-Visual Segmentation via Bi-Modal Slot Attention This paper introduces a competitive bi-modal slot attention method to cleanly segment distinct sounding objects in multi-source audio-visual scenes. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on audio-visual segmentation, not industrial inspection or defect detection.
Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics This paper presents a framework to reconstruct 4D hand-object interactions from human demonstration videos to supervise robot manipulation policies. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies 4D hand reconstruction for robotics, not industrial manufacturing inspection.
Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics This paper presents a framework to reconstruct 4D hand-object interactions from human demonstration videos to supervise robot manipulation policies. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 3D/4D pose reconstruction and robotics retargeting rather than multimodal foundation models.
Data-Centric Recursive Self-Improvement for Navigation via Policy-Synchronized Language-Action Dual Bootstrapping This paper introduces a recursive self-improvement method that unifies instruction-following and instruction generation within a shared model for vision-language navigation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on vision-language navigation, not industrial inspection or defect detection.
Reference-Efficient Multimodal Bitstream-Corrupted Video Recovery via Depth-Conditioned Adaptation This paper presents a depth-conditioned video recovery framework that selects a single reliable RGB reference to restore damaged video bitstreams efficiently. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies corrupted video bitstream restoration, not manufacturing visual inspection.
Totem: Object-Centric 3D Memory for World-Consistent Video Generation This paper proposes an object-centric 3D memory framework to maintain persistent object representations and world consistency during video generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Proposes 3D object-centric memory for video synthesis, not multimodal models.
Totem: Object-Centric 3D Memory for World-Consistent Video Generation This paper proposes an object-centric 3D memory framework to maintain persistent object representations and world consistency during video generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on video generation under camera trajectories, not industrial inspection.
ChangeT$^3$: Evaluating Vision Test-Time Training for Remote Sensing Change Detection This paper evaluates visual test-time training for remote sensing change detection using a Siamese encoder-decoder architecture with adaptive fast weights. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with bitemporal satellite image change detection without multimodal models.
ChangeT$^3$: Evaluating Vision Test-Time Training for Remote Sensing Change Detection This paper evaluates visual test-time training for remote sensing change detection using a Siamese encoder-decoder architecture with adaptive fast weights. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Addresses satellite remote sensing change detection, not industrial production line vision.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Targets multilabel ECG image classification without multimodal reasoning.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses training sampling under class imbalance, not model compression or efficiency.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Evaluates ECG signal and image classification, not industrial defect inspection.
ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an imbalanced training sampler rather than an evaluation benchmark.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces an unsupervised test-time adaptation technique, not an evaluation framework.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on test-time adaptation accuracy rather than model compression or efficiency.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Targets mobile robotics and autonomous vehicles, not manufacturing inspection.
Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement This paper presents an unsupervised test-time adaptation method that uses movement-induced prediction variance to adapt object recognition models in mobile agents. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses purely on vision test-time adaptation without multimodal elements.

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