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
Tracking a Small Set of Experts in Online Convex Optimization This paper proves a computational separation between information-theoretic and polynomial-time regret when tracking small expert subsets in online convex optimization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical study of expert tracking bounds, not model compression or efficient serving.
Tracking a Small Set of Experts in Online Convex Optimization This paper proves a computational separation between information-theoretic and polynomial-time regret when tracking small expert subsets in online convex optimization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical optimization paper, unrelated to deployment safety or AI guardrails.
Tracking a Small Set of Experts in Online Convex Optimization This paper proves a computational separation between information-theoretic and polynomial-time regret when tracking small expert subsets in online convex optimization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical online convex optimization regret bounds, not LLM evaluation.
Continual Learning for Reliable In-Sequence Molecular Communication This paper introduces a continual learning replay method to incrementally evaluate and adapt molecular communication models across changing experimental settings. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Addresses molecular communication simulation, not language model evaluation.
Continual Learning for Reliable In-Sequence Molecular Communication This paper introduces a continual learning replay method to incrementally evaluate and adapt molecular communication models across changing experimental settings. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on molecular networking simulations, not AI system deployment safety.
Continual Learning for Reliable In-Sequence Molecular Communication This paper introduces a continual learning replay method to incrementally evaluate and adapt molecular communication models across changing experimental settings. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Applies continual learning to bio-nanomachines, not small or efficient models.
The Accuracy Well: Nonmonotone Accuracy Dynamics at Vanishing Initialization This paper analyzes non-monotone accuracy dynamics during early neural network training when models are initialized with vanishingly small weights. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on general neural network training phenomena, not AI safety or deployment guardrails.
The Accuracy Well: Nonmonotone Accuracy Dynamics at Vanishing Initialization This paper analyzes non-monotone accuracy dynamics during early neural network training when models are initialized with vanishingly small weights. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Explores optimization dynamics under vanishing initialization, not model efficiency or quantization.
The Accuracy Well: Nonmonotone Accuracy Dynamics at Vanishing Initialization This paper analyzes non-monotone accuracy dynamics during early neural network training when models are initialized with vanishingly small weights. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes training dynamics in standard vision architectures, not LLM benchmarking.
What Does Disagreement Mean? A Semantic Identifiability Frontier for Pairwise Preference Learning This paper establishes theoretical limits on what forms of annotator disagreement can be identified from pairwise preference feedback data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on preference learning theory, not small models or efficient inference.
Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model This paper evaluates whether a pretrained tabular foundation model can perform power grid steady-state analysis without task-specific retraining. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Evaluates TabPFN on power grid analysis, not model compression or efficient serving.
Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model This paper evaluates whether a pretrained tabular foundation model can perform power grid steady-state analysis without task-specific retraining. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies tabular models for power grids, not LLM evaluation frameworks.
Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model This paper evaluates whether a pretrained tabular foundation model can perform power grid steady-state analysis without task-specific retraining. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on power grid physical simulation accuracy, not AI deployment safety.
Decoupled Marginal Sharpening for Training-Free Inference-Time Scaling This paper proposes a decoupled marginal sharpening method to improve inference-time reasoning in language models without weight updates or reward models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Optimizes reasoning path probabilities rather than AI safety, guardrails, or red teaming.
Decoupled Marginal Sharpening for Training-Free Inference-Time Scaling This paper proposes a decoupled marginal sharpening method to improve inference-time reasoning in language models without weight updates or reward models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an inference sampling technique, not an LLM benchmark or evaluation methodology.
Average-Reward Policy Identification from a Single Continuing Trajectory This paper provides matching upper and lower bounds for learning near-optimal policies from a single continuing trajectory in average-reward reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies trajectory navigation complexity in theoretical RL, not model compression or serving.
Average-Reward Policy Identification from a Single Continuing Trajectory This paper provides matching upper and lower bounds for learning near-optimal policies from a single continuing trajectory in average-reward reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on theoretical RL sample complexity, not LLM evaluation benchmarks.
Average-Reward Policy Identification from a Single Continuing Trajectory This paper provides matching upper and lower bounds for learning near-optimal policies from a single continuing trajectory in average-reward reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses RL policy exploration bounds, not deployment safety, guardrails, or hallucination.
Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records This paper introduces a graph-enhanced framework to discover disease trajectories and forecast temporal clinical endpoints from electronic health records. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study model compression, distillation, or efficient model serving.
Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records This paper introduces a graph-enhanced framework to discover disease trajectories and forecast temporal clinical endpoints from electronic health records. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical endpoint prediction, not LLM benchmarking or evaluation.
Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records This paper introduces a graph-enhanced framework to discover disease trajectories and forecast temporal clinical endpoints from electronic health records. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on healthcare forecasting rather than AI safety or system guardrails.
A Two-Level Clinical Novelty Prioritization Framework for Disease Trajectories Discovered from Longitudinal Electronic Health Records A two-level prioritization framework evaluates semantic novelty and reliability of disease trajectory patterns extracted from electronic health records. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses clinical motif ranking rather than AI safety or LLM guardrails.
A Two-Level Clinical Novelty Prioritization Framework for Disease Trajectories Discovered from Longitudinal Electronic Health Records A two-level prioritization framework evaluates semantic novelty and reliability of disease trajectory patterns extracted from electronic health records. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient LLM inference.
A Two-Level Clinical Novelty Prioritization Framework for Disease Trajectories Discovered from Longitudinal Electronic Health Records A two-level prioritization framework evaluates semantic novelty and reliability of disease trajectory patterns extracted from electronic health records. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical EHR trajectory analysis, not LLM benchmarks or evaluation.
Bayesian Learning to Select Fair Classifiers The paper presents an online Thompson Sampling bandit policy to select classifiers that balance predictive accuracy and subgroup fairness under delayed feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on online bandit selection, not model compression or serving efficiency.
The Physics Is the Network: Fast, Training-Free Neural Emulation PolyNets embeds continuous polynomial equations into recurrent neural structures to perform fast, training-free physics simulations with known parameters. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific PDE solvers rather than LLM inference or distillation.
The Physics Is the Network: Fast, Training-Free Neural Emulation PolyNets embeds continuous polynomial equations into recurrent neural structures to perform fast, training-free physics simulations with known parameters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address deployment safety, red-teaming, or LLM guardrails.
The Physics Is the Network: Fast, Training-Free Neural Emulation PolyNets embeds continuous polynomial equations into recurrent neural structures to perform fast, training-free physics simulations with known parameters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on physics-based PDE emulation, not LLM benchmarking or evaluation.
Revisiting Simple Baselines for Relational Deep Learning The paper demonstrates that simple feature propagation and shallow classifiers yield competitive baselines compared to complex architectures in relational deep learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates relational graph architectures, not LLM compression or inference serving.
Revisiting Simple Baselines for Relational Deep Learning The paper demonstrates that simple feature propagation and shallow classifiers yield competitive baselines compared to complex architectures in relational deep learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on AI safety, alignment, hallucination, or guardrails.
Revisiting Simple Baselines for Relational Deep Learning The paper demonstrates that simple feature propagation and shallow classifiers yield competitive baselines compared to complex architectures in relational deep learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates relational graph baselines on RelBench, not LLM evaluation methods.
Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation DualGCN uses dual-branch graph knowledge distillation with topology-smoothed semantic priors to address item cold-start collapse in recommender systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Does not study LLM evaluation, benchmark contamination, or LLM-as-a-judge.
Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation DualGCN uses dual-branch graph knowledge distillation with topology-smoothed semantic priors to address item cold-start collapse in recommender systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaking, red-teaming, or guardrails.
Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation DualGCN uses dual-branch graph knowledge distillation with topology-smoothed semantic priors to address item cold-start collapse in recommender systems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on graph collaborative filtering distillation, not LLM compression or efficiency.
Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation DualGCN uses dual-branch graph knowledge distillation with topology-smoothed semantic priors to address item cold-start collapse in recommender systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on recommender systems rather than retrieval-augmented generation for LLMs.
Creating SquashFS Images to Support HPC and AI Datasets The paper benchmarks eleven workflows for packing small-file datasets into SquashFS images to ease metadata loads on high-performance computing systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on HPC file system compression, not retrieval-augmented generation.
$λ_0$: Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation HumanVerse provides an egocentric dataset and alignment pipeline to transfer human loco-manipulation behaviors directly to humanoid robots without target-task fine-tuning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses humanoid loco-manipulation robotics, not industrial visual quality control.
$λ_0$: Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation HumanVerse provides an egocentric dataset and alignment pipeline to transfer human loco-manipulation behaviors directly to humanoid robots without target-task fine-tuning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic loco-manipulation policies rather than multimodal foundation models.
Action Jets: Learning the Local Shape of the Action-conditioned Data Manifold Action Jets models the local shape of action-conditioned manifolds via Taylor approximations to enable efficient short-horizon robotic planning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Investigates predictive world models for planning, not industrial inspection.
Action Jets: Learning the Local Shape of the Action-conditioned Data Manifold Action Jets models the local shape of action-conditioned manifolds via Taylor approximations to enable efficient short-horizon robotic planning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on vision-action dynamics manifolds without multimodal language or document understanding.
How Far Can Coding Agents Go in Visual Generation? The study evaluates coding agents generating images via executable code, analyzing their strengths in geometric constraints over photorealistic drawing. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Studies coding agents generating images, not industrial defect inspection.
How Far Can Coding Agents Go in Visual Generation? The study evaluates coding agents generating images via executable code, analyzing their strengths in geometric constraints over photorealistic drawing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address safety, jailbreaking, hallucinations, or deployment guardrails.
How Far Can Coding Agents Go in Visual Generation? The study evaluates coding agents generating images via executable code, analyzing their strengths in geometric constraints over photorealistic drawing. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, distillation, or serving efficiency.
RingNav: Task-Shaped Circular Memory for Visual Heading RingNav is a compact visual heading estimator that restricts persistent memory updates to a circular manifold to improve trajectory tracking. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on general robot visual navigation, not industrial inspection.
RingNav: Task-Shaped Circular Memory for Visual Heading RingNav is a compact visual heading estimator that restricts persistent memory updates to a circular manifold to improve trajectory tracking. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on pure visual SLAM heading estimation without multimodal interaction.
Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations CardioFAD resolves temporal ambiguity in sparse cardiac dynamic synthesis by using ECG-aligned motion-salient keyframe diffusion. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on medical cardiac dynamic synthesis, not industrial defect detection.
Why Do VLMs Fail at Remote Physiological Measurement? Rethinking with Physiological Grounding and Numerical Alignment VITAL introduces visual-physiological token alignment to enable vision-language models to perform remote physiological measurement alongside general visual reasoning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on human physiological measurement, not industrial manufacturing quality control.
WorldMemBench: A Comprehensive Benchmark for Memory of Video World Models WorldMemBench introduces an evaluation benchmark assessing static entity retention and dynamic event persistence in video world models over multi-revisit paths. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on video world models, not industrial manufacturing inspection.
MonoMIP: Multi-Height Inverse Perspective Mapping for Monocular 3D Object Detection MonoMIP combines multi-height inverse perspective mapping with a dual-decoder framework to improve bird's-eye view reasoning in monocular 3D object detection. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on autonomous driving benchmarks, not industrial visual inspection.
MonoMIP: Multi-Height Inverse Perspective Mapping for Monocular 3D Object Detection MonoMIP combines multi-height inverse perspective mapping with a dual-decoder framework to improve bird's-eye view reasoning in monocular 3D object detection. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on computer vision 3D detection without multimodal integration.
The Gap Is in the Geometry: A Convergent Meta-Analysis of Multi-Facet Aerial and Remote Sensing Video Understanding A meta-analysis shows multimodal language models struggle with geometric acquisition variations in aerial video, exposing limitations in standard QA-based evaluations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on aerial and remote sensing video, which is explicitly excluded.
DoReMe: Dual optimization of Reconstruction and Motion estimation for self-navigated free-breathing abdominal MRI using Gaussian Splatting DoReMe uses Gaussian splatting primitives to jointly optimize image reconstruction and respiratory motion estimation in self-navigated free-breathing abdominal MRI. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not study multimodal models combining text, vision, audio, or speech.
DoReMe: Dual optimization of Reconstruction and Motion estimation for self-navigated free-breathing abdominal MRI using Gaussian Splatting DoReMe uses Gaussian splatting primitives to jointly optimize image reconstruction and respiratory motion estimation in self-navigated free-breathing abdominal MRI. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on medical abdominal MRI reconstruction, not industrial visual inspection.
Agronomic Characterization and Time-Series UAV Phenotyping to Support Field Pea Breeding in Iowa The study evaluates field pea breeding in Iowa by characterizing agronomic yield performance and extracting time-series traits from drone phenotyping data. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies UAV remote sensing to crop yield, not multimodal models.
Agronomic Characterization and Time-Series UAV Phenotyping to Support Field Pea Breeding in Iowa The study evaluates field pea breeding in Iowa by characterizing agronomic yield performance and extracting time-series traits from drone phenotyping data. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses agricultural crop phenotyping, not industrial visual inspection.
Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors LidarVerse provides a unified conditional generative LiDAR world model combining geometry-preserving spatial caches with amortized sequence learning across sensors and tasks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Builds a generative LiDAR sensor model, not multimodal vision-language reasoning.
Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors LidarVerse provides a unified conditional generative LiDAR world model combining geometry-preserving spatial caches with amortized sequence learning across sensors and tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on autonomous vehicle LiDAR simulation, not industrial defect detection.
Decoupled Drift-Diffusion World Models for Consistent Future Frame Prediction D3WM decouples deterministic state transitions from spatial uncertainty to guide conditional diffusion for consistent future frame prediction in world models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Studies future frame diffusion modeling, not multimodal language-vision systems.
Decoupled Drift-Diffusion World Models for Consistent Future Frame Prediction D3WM decouples deterministic state transitions from spatial uncertainty to guide conditional diffusion for consistent future frame prediction in world models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robot action-conditioned world modeling, not industrial quality control.
A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision A gradient-regularized U-Net framework transforms images to counteract spherical eye defocus aberrations and improve visual clarity for myopic individuals. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Presents image-to-image defocus correction, not multimodal vision-language models.

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