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
Partially Informed Stochastic Optimization via Zeroth-Order Residual Tracking Introduces PISO, an optimization framework that combines partially available gradients with zeroth-order residual tracking to reduce variance from random probing. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant General stochastic optimization algorithms, not AI safety or deployment robustness.
Partially Informed Stochastic Optimization via Zeroth-Order Residual Tracking Introduces PISO, an optimization framework that combines partially available gradients with zeroth-order residual tracking to reduce variance from random probing. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents theoretical stochastic optimization algorithms, not LLM benchmarking.
Partially Informed Stochastic Optimization via Zeroth-Order Residual Tracking Introduces PISO, an optimization framework that combines partially available gradients with zeroth-order residual tracking to reduce variance from random probing. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Optimization theory paper, not focused on efficient language model architectures or compression.
Prediction Is Not a Sufficient Memory State Analyzes the dimensional requirements of exact recurrent memory states for cumulative learning under convex residual loss functions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on theoretical memory state dimension in online learning, not efficient model deployment.
Prediction Is Not a Sufficient Memory State Analyzes the dimensional requirements of exact recurrent memory states for cumulative learning under convex residual loss functions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical learning theory, not safety, guardrails, or red-teaming.
Prediction Is Not a Sufficient Memory State Analyzes the dimensional requirements of exact recurrent memory states for cumulative learning under convex residual loss functions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical characterization of cumulative learning memory states, not LLM evaluation.
Hitting-Time Bounds for Asynchronous Q-Learning Establishes finite-time error bounds for single-trajectory asynchronous Q-learning that incorporate the hitting time of the underlying Markov decision process. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical convergence bounds for asynchronous Q-learning, not LLM benchmarks.
Hitting-Time Bounds for Asynchronous Q-Learning Establishes finite-time error bounds for single-trajectory asynchronous Q-learning that incorporate the hitting time of the underlying Markov decision process. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Reinforcement learning theory, not model compression or efficient serving.
Hitting-Time Bounds for Asynchronous Q-Learning Establishes finite-time error bounds for single-trajectory asynchronous Q-learning that incorporate the hitting time of the underlying Markov decision process. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Pure theoretical analysis of Q-learning, not operational AI safety or reliability.
Designing Protein Structures from Natural Language Introduces Prompteina, an all-atom generative model and dataset for generating de novo protein structures conditioned on natural language prompts. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses protein structure design, not AI safety, guardrails, or alignment.
Designing Protein Structures from Natural Language Introduces Prompteina, an all-atom generative model and dataset for generating de novo protein structures conditioned on natural language prompts. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Applies text conditioning to protein design models, not small language model efficiency.
Designing Protein Structures from Natural Language Introduces Prompteina, an all-atom generative model and dataset for generating de novo protein structures conditioned on natural language prompts. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on protein structure generation from text prompts, not LLM evaluation methods.
DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness Develops a multi-scale biophysical spiking neural network model to simulate sleep-wake regulation and discrepancies between objective sleep and subjective restedness. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Computational neuroscience research, not AI deployment safety or jailbreak defense.
DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness Develops a multi-scale biophysical spiking neural network model to simulate sleep-wake regulation and discrepancies between objective sleep and subjective restedness. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Deals with computational neuroscience and sleep modeling, not LLM evaluation.
DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness Develops a multi-scale biophysical spiking neural network model to simulate sleep-wake regulation and discrepancies between objective sleep and subjective restedness. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Biophysical spiking neural network model for sleep disorders, not efficient LLM serving.
Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning Applies reinforcement learning to manage bundle scheduling and flow control at relay nodes in deep-space communication networks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses network bundle protocol scheduling, not small or efficient language models.
Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning Applies reinforcement learning to manage bundle scheduling and flow control at relay nodes in deep-space communication networks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on deep-space communication networking via RL, not LLM evaluation.
Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning Applies reinforcement learning to manage bundle scheduling and flow control at relay nodes in deep-space communication networks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses space communication protocols, not AI safety or deployment guardrails.
KL Before and After Mixing: A Round-Complexity Separation in Parallel Sampling Analyzes round complexity in parallel coordinate samplers, proving a separation between schedule-averaged and output-law Kullback-Leibler divergences. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical probability bounds on sampling, not deployment safety or reliability.
KL Before and After Mixing: A Round-Complexity Separation in Parallel Sampling Analyzes round complexity in parallel coordinate samplers, proving a separation between schedule-averaged and output-law Kullback-Leibler divergences. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of parallel sampling divergences, not LLM benchmarks or evaluation.
KL Before and After Mixing: A Round-Complexity Separation in Parallel Sampling Analyzes round complexity in parallel coordinate samplers, proving a separation between schedule-averaged and output-law Kullback-Leibler divergences. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on theoretical sampling complexity, not small language models or efficient deployment.
Constructive Bounds for the Topological Expressivity of Neural Networks Proves upper bounds on the topological expressivity of neural networks measured by the Betti numbers of their output spaces. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical topology paper, not AI safety, robustness, or deployment guardrails.
Constructive Bounds for the Topological Expressivity of Neural Networks Proves upper bounds on the topological expressivity of neural networks measured by the Betti numbers of their output spaces. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Pure theory of ReLU network expressivity; not about small model efficiency or distillation.
Constructive Bounds for the Topological Expressivity of Neural Networks Proves upper bounds on the topological expressivity of neural networks measured by the Betti numbers of their output spaces. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical math paper on topological expressivity, not LLM evaluation or benchmarks.
Towards Unified Evaluation of Prompt Enhancers for Video Generation Introduces PEBench, a benchmark and evidence-grounded framework to directly evaluate prompt enhancers for video generation without rendering full videos. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates prompt enhancers for video generation, not industrial vision applications.
Streaming-Aware Diffusion for Real-Time Video Super-Resolution via Cross-Step Attention The paper proposes a streaming video super-resolution framework reusing intermediate latent diffusion features across frames via cross-step attention. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on real-time video super-resolution, not industrial inspection or defect detection.
Streaming-Aware Diffusion for Real-Time Video Super-Resolution via Cross-Step Attention The paper proposes a streaming video super-resolution framework reusing intermediate latent diffusion features across frames via cross-step attention. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses video super-resolution via diffusion models, not multimodal models integrating text or other modalities.
Dino Forcing Flow Models: Do not denoise what you can predict The paper proposes predicting pretrained DINO representations directly to guide flow matching models, avoiding a secondary ODE denoising process. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Covers unimodal image generation and flow matching rather than multimodal cross-modality models.
Dino Forcing Flow Models: Do not denoise what you can predict The paper proposes predicting pretrained DINO representations directly to guide flow matching models, avoiding a secondary ODE denoising process. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general generative image flow models, not industrial visual inspection.
Memory Forcing: Attendable Mid-Horizon History for Streaming Video Generation The paper introduces Memory Forcing, partitioning cache into archive and working banks to prevent mid-horizon history loss in streaming video generation. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Proposes streaming video diffusion architectures, not industrial vision or inspection.
From Video Clips to Creation Trajectory: Sora100K for AI-Native Video Creation The paper introduces Sora100K, a dataset structuring AI-native video creation workflows into multi-turn creation trajectories and editing operations. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Applies to generative video creation workflows, not industrial computer vision tasks.
Skill-V: Verifiable Self-Evolving Skill Library for Interactive Agents The paper introduces Skill-V, a verifiable agent skill library that treats skills as falsifiable contracts tested against environment execution outcomes. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on interactive agent skill libraries, not industrial vision or defect detection.
Skill-V: Verifiable Self-Evolving Skill Library for Interactive Agents The paper introduces Skill-V, a verifiable agent skill library that treats skills as falsifiable contracts tested against environment execution outcomes. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not address multimodal reasoning across text, images, video, or audio.
Phase-aware video generation for physics-grounded dynamics and interactions The paper proposes PAVG, a dual-branch video generator modeling distinct solid-gas dynamics and cross-attention interactions for physically grounded motion. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on generative video modeling of solid-gas dynamics, not multimodal reasoning.
Phase-aware video generation for physics-grounded dynamics and interactions The paper proposes PAVG, a dual-branch video generator modeling distinct solid-gas dynamics and cross-attention interactions for physically grounded motion. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses physics-grounded video generation, not industrial visual inspection or robotics.
Seek-and-View Reasoning for Multi-View Spatial Understanding The paper proposes Vantage, a training-free framework pairing VLMs with 3D models to plan and synthesize relevant viewpoints for spatial reasoning. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general multi-view spatial reasoning benchmarks, not industrial vision.
Fast Pose Tracking of Rigid Objects with Compact Pose Graph Optimization The paper proposes a lightweight pose-graph tracking module using relative pose constraints and geometric uncertainty for real-time 6D object tracking. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on geometric pose tracking and optimization, not multimodal models.
Open-Vocabulary Audio-Visual Event Localization via Complex-Valued Fusion The paper proposes a complex-valued neural network to fuse audio and visual similarity streams for open-vocabulary audio-visual event localization. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression, quantization, or inference acceleration.
Open-Vocabulary Audio-Visual Event Localization via Complex-Valued Fusion The paper proposes a complex-valued neural network to fuse audio and visual similarity streams for open-vocabulary audio-visual event localization. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on audio-visual event localization benchmarks, not industrial operations.
Open-Vocabulary Audio-Visual Event Localization via Complex-Valued Fusion The paper proposes a complex-valued neural network to fuse audio and visual similarity streams for open-vocabulary audio-visual event localization. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a new fusion architecture, not an evaluation framework or benchmark study.
Open-Vocabulary Audio-Visual Event Localization via Complex-Valued Fusion The paper proposes a complex-valued neural network to fuse audio and visual similarity streams for open-vocabulary audio-visual event localization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address deployment safety, robustness attacks, or guardrails.
VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval The paper proposes VEDJE, compressing video features into reusable caches with feature-change prediction for efficient and scalable video-text retrieval. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on video retrieval benchmarks (MSR-VTT), not industrial inspection vision.
Pose-Free Feed-Forward 3D Inpainting via Learnable Mask Attention and Support Token Refinement The paper introduces FreeInpaint, a feed-forward framework performing 3D scene inpainting directly from unposed multi-view images using masked attention propagation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses entirely on 3D geometric view synthesis and reconstruction, not multimodal systems.
Pose-Free Feed-Forward 3D Inpainting via Learnable Mask Attention and Support Token Refinement The paper introduces FreeInpaint, a feed-forward framework performing 3D scene inpainting directly from unposed multi-view images using masked attention propagation. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses general 3D scene inpainting from unposed views, not industrial vision systems.
Relative Patch Response Learning for Generalizable AI-Generated Image Detection The paper proposes Relative Patch Response Learning to improve AI-generated image detection by mitigating self-attention-induced contextual shifts across mixed patches. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on detecting AI-generated synthetic images, not industrial inspection.
Relative Patch Response Learning for Generalizable AI-Generated Image Detection The paper proposes Relative Patch Response Learning to improve AI-generated image detection by mitigating self-attention-induced contextual shifts across mixed patches. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Proposes a computer vision detector for generated images, not a multimodal reasoning model.
Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study safety, guardrails, jailbreaking, or deployment reliability.
Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on media-bridged time series forecasting, not industrial vision applications.
Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a model architecture rather than a benchmarking or evaluation study.
Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows The paper proposes MIDAPN, a unified spatiotemporal network using identity-aware graphs and temporal learning across numerical and narrative-flow time series. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, or efficient serving.
Does Target Alignment Mean Target Recovery? An Evidence-Ladder Study of Adversarial Claims on Contrastive Encoders The paper evaluates whether contrastive encoder adversarial alignment metrics transfer to target recovery by independent models across different perturbation budgets. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Studies contrastive adversarial attacks in general vision-language encoders, not industrial vision.
Right Screen, Wrong Transition: World Models as Verifiers for GUI Agents The paper introduces LGWM, an action-conditioned world model predicting next-screen latent representations to verify GUI agent state transitions. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Deals with GUI agents for software screens, not industrial computer vision.
Learning Which Correspondences to Trust: Confidence-Weighted Event-Camera Localization in LiDAR Maps The paper presents CELL, learning pose-informative correspondence confidence end-to-end via differentiable probabilistic PnP to localize event cameras in LiDAR maps. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on geometric vision and LiDAR localization, not multimodal text-image reasoning.
FearCaut-Qwen: Affective Steering in a Vision-Language Model Shifts the Decision Criterion for Hazard Assessment The paper uses activation steering of affective circuits in vision-language models to increase hazard-detection recall for post-disaster damage assessment. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Applies VLM hazard steering to post-disaster building damage, not industrial manufacturing or inspection.
Do Not Train Away Uncertainty: Early Uncertainty Anchored Calibration The paper proposes EUA-Cal, regularizing late training against early-stage predictive uncertainty to prevent models from developing overconfidence. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses general model calibration rather than multimodal model architectures or capabilities.
Do Not Train Away Uncertainty: Early Uncertainty Anchored Calibration The paper proposes EUA-Cal, regularizing late training against early-stage predictive uncertainty to prevent models from developing overconfidence. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general calibration theory across vision and NLP, not industrial inspection.
Look Back, Think Ahead: Visual Memory on Demand for Efficient Multimodal Reasoning The paper introduces ViMoD, maintaining a compact visual context while dynamically routing back to fine-grained tokens during multi-step reasoning. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general MLLM visual token reasoning, not industrial vision applications.
LIVIN: Benchmarking Spatial and Embodied Intelligence in Digital Twins of Lived-In Homes The paper presents LIVIN, a spatial and embodied intelligence benchmark featuring digital twins of 30 lived-in homes with human-reviewed object arrangements. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates embodied AI in household digital twins, not industrial or manufacturing settings.
A Minimal Optical-Flow Representation for Vision-Based Tactile Rotation Classification in Robotic Manipulation Across Gravity Domains The paper proposes a compact optical-flow representation to classify tactile object rotation across Earth, Mars, Moon, and orbital gravity domains. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on compact optical flow tactile features, not multimodal vision-language architectures.
Perception Test 2026: Challenge Summary and Extension to City-scale Audio-Visual Reasoning The paper introduces benchmarks and summarizes winning solutions for city-scale audio-visual reasoning and spatial intelligence at the Perception Test 2026. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate model efficiency, compression, or lightweight serving.

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