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
Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration Learns dexterous manipulation policies from a single human video by abstracting motions into sequential scene graphs for reinforcement learning. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on reinforcement learning policy acquisition from demonstration rather than multimodal modeling.
Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback Presents an embodied coding agent framework using active physical and visual probing to refine aerial manipulation policies. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on embodied robotic control programs rather than multimodal foundational model architectures.
Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback Presents an embodied coding agent framework using active physical and visual probing to refine aerial manipulation policies. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an aerial agent framework rather than general LLM evaluation benchmarks.
Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback Presents an embodied coding agent framework using active physical and visual probing to refine aerial manipulation policies. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression, quantization, or efficient model serving.
Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback Presents an embodied coding agent framework using active physical and visual probing to refine aerial manipulation policies. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not focus on LLM safety, guardrails, or alignment risks.
Scaling Multi-Teacher Distillation for Digital Pathology Introduces an architecture with teacher-specific tokens and mixture-of-experts to scale multi-teacher distillation in digital pathology. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on unimodal vision encoder distillation for pathology slides rather than multimodal models.
MemReg: Streaming Outdoor LiDAR Point Cloud Registration with Hybrid Memory Buffers Proposes a multi-frame outdoor LiDAR registration network using hybrid feature and pose memory buffers to leverage temporal sequences. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals strictly with 3D LiDAR point clouds without cross-modal interaction.
ForcingBooster: Adaptive Patch-wise Denoising for Fast Autoregressive Video Diffusion Introduces an adaptive patch-wise denoising framework that stops easy patches early to speed up autoregressive video diffusion. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on video diffusion generation speedup rather than industrial computer vision tasks.
Feature Competition and Its Representational Origins in Vision Language Models: Evidence from the Stroop Paradigm Benchmarks vision-language models on the Stroop paradigm to evaluate feature competition and text-versus-color bias. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Studies psychological Stroop effect in general VLMs, not industrial computer vision.
WoodBR-Cross: A public wood-image dataset and individual-tree-disjoint benchmark for species identification Presents an image dataset and an individual-tree-disjoint evaluation benchmark for automated wood species identification. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses purely on single-modality wood image classification, not multimodal models.
Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation Evaluates explainability methods across CNN, ViT, and Mamba models using eye-tracking gaze data as ground truth. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Studies attention explainability via eye-tracking, not industrial inspection tasks.
Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation Evaluates explainability methods across CNN, ViT, and Mamba models using eye-tracking gaze data as ground truth. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on interpretability and attention maps, not model efficiency or compression.
Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation Evaluates explainability methods across CNN, ViT, and Mamba models using eye-tracking gaze data as ground truth. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates explainability methods using gaze alignment, not LLM evaluation benchmarks.
Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation Evaluates explainability methods across CNN, ViT, and Mamba models using eye-tracking gaze data as ground truth. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Analyzes vision architectures on image datasets without multimodal cross-modal modeling.
Zebra3D: Efficient Hybrid Foundation Models For Scalable 3D Reconstruction Proposes a hybrid linear and sparse attention framework to accelerate feed-forward 3D foundation models across many views. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on computational efficiency and reconstruction accuracy, not safety or guardrails.
Zebra3D: Efficient Hybrid Foundation Models For Scalable 3D Reconstruction Proposes a hybrid linear and sparse attention framework to accelerate feed-forward 3D foundation models across many views. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general 3D reconstruction foundation models, not industrial inspection.
Zebra3D: Efficient Hybrid Foundation Models For Scalable 3D Reconstruction Proposes a hybrid linear and sparse attention framework to accelerate feed-forward 3D foundation models across many views. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses 3D vision reconstruction, not multimodal vision-language reasoning.
Zebra3D: Efficient Hybrid Foundation Models For Scalable 3D Reconstruction Proposes a hybrid linear and sparse attention framework to accelerate feed-forward 3D foundation models across many views. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an efficient model architecture rather than benchmark evaluation frameworks.
StereoEngine: Scaling Stereo Bimanual Robot Data without Stereo Cameras Synthesizes stereo manipulation data from monocular robot demonstrations using estimated depth and video diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on robotic training data generation, not safety guardrails or reliability monitoring.
StereoEngine: Scaling Stereo Bimanual Robot Data without Stereo Cameras Synthesizes stereo manipulation data from monocular robot demonstrations using estimated depth and video diffusion models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on synthetic data generation for policy training, not model compression or efficient inference.
StereoEngine: Scaling Stereo Bimanual Robot Data without Stereo Cameras Synthesizes stereo manipulation data from monocular robot demonstrations using estimated depth and video diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents data generation techniques for robotics rather than LLM/AI evaluation benchmarks.
Reference-Free Food Volume Estimation from Egocentric Stereo Video Presents an automatic reference-free method to reconstruct and estimate food portion volumes using egocentric stereo video. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Uses standard 3D reconstruction and segmentation methods without multimodal cross-modality learning.
Reference-Free Food Volume Estimation from Egocentric Stereo Video Presents an automatic reference-free method to reconstruct and estimate food portion volumes using egocentric stereo video. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Applies to dietary intake monitoring from wearable cameras, not industrial vision.
Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability Develops a kernelized parameter sensitivity atlas to amortize input-specific gradient attribution across vision, forecasting, and biology models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with parameter sensitivity atlases, not multimodal fusion or vision-language models.
Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability Develops a kernelized parameter sensitivity atlas to amortize input-specific gradient attribution across vision, forecasting, and biology models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an interpretability attribution method rather than a general LLM evaluation benchmark.
Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability Develops a kernelized parameter sensitivity atlas to amortize input-specific gradient attribution across vision, forecasting, and biology models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on parameter sensitivity attribution, not model compression or efficient serving.
Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability Develops a kernelized parameter sensitivity atlas to amortize input-specific gradient attribution across vision, forecasting, and biology models. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Develops general interpretability theory across architectures, not industrial vision systems.
Shift-Aware Identity-Guided Latent Refinement for Referring Audio--Visual Segmentation Introduces a shift-aware identity-guided latent refinement framework using dual tokens to resolve target drift in referring audio-visual segmentation. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on audio-visual object segmentation, not industrial manufacturing or visual inspection.
Domain Adaptation of SAM 2.1 for Building-Component Segmentation in Image-Based 3D Reconstruction Fine-tunes SAM 2.1 on multi-class building images to improve 2D segmentation for image-based 3D reconstruction. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies 2D vision models to 3D radiance fields without multimodal text or audio features.
Spend Where It Matters: Particle Search with MH Refinement for Budget-Constrained Audio-Visual Alignment Presents a training-free framework combining variance-adaptive particle search with Metropolis-Hastings refinement for budget-constrained audio-visual alignment. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Investigates audio-visual generative alignment, not industrial defect detection or robotics perception.
Structure-Aware Matching for Robust UAV Thermal-to-Satellite Geo-Localization Proposes a structure-aware matching framework exploiting directional and low-frequency patterns for UAV thermal-to-satellite geo-localization. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on UAV and satellite geo-localization, not industrial manufacturing or plant inspection.
Structure-Aware Matching for Robust UAV Thermal-to-Satellite Geo-Localization Proposes a structure-aware matching framework exploiting directional and low-frequency patterns for UAV thermal-to-satellite geo-localization. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with cross-view thermal-to-optical matching, not multimodal text-image or cross-domain LLMs.
APEX: Asynchronous Prefix Denoising for Extreme Compression Introduces an asynchronous prefix denoising framework for extreme latent compression in diffusion models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies to image autoencoding and generation, not multimodal vision-language models.
APEX: Asynchronous Prefix Denoising for Extreme Compression Introduces an asynchronous prefix denoising framework for extreme latent compression in diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with compression efficiency, not AI safety, alignment, or guardrails.
APEX: Asynchronous Prefix Denoising for Extreme Compression Introduces an asynchronous prefix denoising framework for extreme latent compression in diffusion models. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general diffusion image generation and latent compression, not industrial vision.
APEX: Asynchronous Prefix Denoising for Extreme Compression Introduces an asynchronous prefix denoising framework for extreme latent compression in diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a generative architecture rather than evaluation methodologies or benchmarks.
CaptchaAgent: Verifiable Visual Reasoning for CAPTCHA Solving Presents a structured visual reasoning agent that uses intermediate symbolic representations to solve complex visual CAPTCHAs. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on CAPTCHA solving using web agents, not industrial visual inspection.
Selective Spatio-Temporal Fruit Inspector (S$^2$FI): A Mamba-based Framework for Rotational Fruit Quality Assessment Proposes a lightweight fruit sorting system combining 3D convolutions and Mamba sequence modeling for rotational visual quality inspection. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses purely on video and 3D vision modeling without language or other modal integration.
ROSETTA: Rotation Subspace Estimation for Training-free Transmodal Alignment Presents a training-free calibration layer that aligns mismatched cross-modal embedding spaces via local rotation estimation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses sensor coordinate cross-modal alignment without language or multimodal LLM components.
ROSETTA: Rotation Subspace Estimation for Training-free Transmodal Alignment Presents a training-free calibration layer that aligns mismatched cross-modal embedding spaces via local rotation estimation. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates autonomous driving sensor alignment, not industrial manufacturing or defect inspection.
Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic Collaboration Develops a multi-modal image fusion framework that combines structural guidance and interactive gated attention to improve perception. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on low-level visual image fusion (sensor fusion), not vision-language foundation models.
Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic Collaboration Develops a multi-modal image fusion framework that combines structural guidance and interactive gated attention to improve perception. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Deals with multi-modal image fusion algorithms, not industrial manufacturing or quality control.
MeshBind: Point-to-Mesh Binding for Non-Rigid Scene Flow Automatic Annotation Presents an automatic scene flow annotation pipeline using articulated mesh binding for non-rigid objects in autonomous driving datasets. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Involves 3D vision and LiDAR sensors, not multimodal language or cross-modality reasoning.
MeshBind: Point-to-Mesh Binding for Non-Rigid Scene Flow Automatic Annotation Presents an automatic scene flow annotation pipeline using articulated mesh binding for non-rigid objects in autonomous driving datasets. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Targeted at autonomous driving pedestrian scene flow annotation, not industrial vision inspection.
AD-SRF: Attack-Defense Semantic Reinforcement for Multi-Modal Image Fusion Proposes an attack-defense game framework to reinforce semantic attributes in multi-modal image fusion while preserving visual quality. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on multi-modal image fusion (e.g. visible/infrared), not industrial inspection applications.
AD-SRF: Attack-Defense Semantic Reinforcement for Multi-Modal Image Fusion Proposes an attack-defense game framework to reinforce semantic attributes in multi-modal image fusion while preserving visual quality. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with low-level sensor image fusion, not vision-language or multimodal LLMs.
A Lightweight Velocity Correction Framework for Improving Instruction Following in Few-Step Text-to-Image Generation Introduces a lightweight velocity corrector to improve prompt following in few-step text-to-image generation models without full fine-tuning. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on text-to-image generative models, not industrial vision or inspection.
JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning Proposes a domain adaptive object detection framework using segmentation-guided consistency learning to handle distribution shifts. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general autonomous driving/weather domain adaptation, not industrial manufacturing or inspection.
JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning Proposes a domain adaptive object detection framework using segmentation-guided consistency learning to handle distribution shifts. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses vision-only object detection and segmentation, not multimodal cross-modal modeling.
BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models Introduces a federated vision-language model tuning framework combining global shared adapters with client-specific personalized projection adapters. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on a federated fine-tuning framework rather than new LLM/VLM evaluation or benchmarks.
BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models Introduces a federated vision-language model tuning framework combining global shared adapters with client-specific personalized projection adapters. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses general federated vision-language learning, not industrial inspection or manufacturing tasks.
BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models Introduces a federated vision-language model tuning framework combining global shared adapters with client-specific personalized projection adapters. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on federated generalization and personalization, not AI safety, guardrails, or hallucination mitigation.
BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models Introduces a federated vision-language model tuning framework combining global shared adapters with client-specific personalized projection adapters. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Proposes parameter-efficient adapters for federated learning, not core model compression, quantization, or efficient serving.
What Egocentric Video Embeddings Encode and How to Combine Them with Inertial Sensors for Human Activity Recognition Analyzes what egocentric video embeddings encode and demonstrates complementary sensor fusion with wearable inertial measurement units. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Studies egocentric video and inertial sensors, not vision-language or standard multimodal foundation models.
What Egocentric Video Embeddings Encode and How to Combine Them with Inertial Sensors for Human Activity Recognition Analyzes what egocentric video embeddings encode and demonstrates complementary sensor fusion with wearable inertial measurement units. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on human activity recognition and wearables, not industrial vision or inspection.
UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning Develops a unified motion deblurring framework using Fourier convolution learning to handle diverse real-world blur kernels. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with unimodal image deblurring and restoration without multimodal components.
UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning Develops a unified motion deblurring framework using Fourier convolution learning to handle diverse real-world blur kernels. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Presents a general photographic motion deblurring method, not an industrial vision application.
In-Context Learning Can Help Vision-Language Models Overcome Training Priors Demonstrates that visual in-context demonstrations help vision-language models overcome conflicting pretraining priors on counterfactual tasks. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Investigates visual in-context learning in VLMs, not industrial visual inspection.
Video Codec-Guided Training-Free Token Compression for Video Large Language Models Proposes a training-free token compression method for video language models using video codec motion vectors and residuals as priors. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on token compression in video LLMs, not industrial inspection or robotics perception.
Learning Representations to Guide Pixel Diffusion Transformers Investigates internal representation utility in pixel diffusion transformers and proposes Representation Guidance to improve generation performance. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses visual representation learning for pixel diffusion transformers, not industrial vision tasks.

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