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
Adaptive Robust Confidence Sets in High Dimensions This paper analyzes the statistical costs and computational lower bounds of constructing adaptive confidence sets under unknown Huber contamination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant High-dimensional mathematical statistics paper, not LLM evaluation.
Adaptive Robust Confidence Sets in High Dimensions This paper analyzes the statistical costs and computational lower bounds of constructing adaptive confidence sets under unknown Huber contamination. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical query lower bounds, not model efficiency.
Adaptive Robust Confidence Sets in High Dimensions This paper analyzes the statistical costs and computational lower bounds of constructing adaptive confidence sets under unknown Huber contamination. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical theory on Huber contamination, not AI deployment safety.
A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks This paper presents a multi-GPU training framework for temporal graph neural networks that relaxes event dependencies to reduce synchronization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Multi-GPU systems paper for graph networks, not LLM evaluation.
A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks This paper presents a multi-GPU training framework for temporal graph neural networks that relaxes event dependencies to reduce synchronization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on GPU training throughput, not AI safety or reliability.
Inference on the Value of Policy Improvement in Adaptive Experiments This paper develops an asymptotically normal estimator to measure the value of policy improvement in adaptive experimental settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical theory for adaptive experiments, not AI safety or guardrails.
Inference on the Value of Policy Improvement in Adaptive Experiments This paper develops an asymptotically normal estimator to measure the value of policy improvement in adaptive experimental settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical econometrics and statistics paper, not LLM evaluation benchmarks.
Inference on the Value of Policy Improvement in Adaptive Experiments This paper develops an asymptotically normal estimator to measure the value of policy improvement in adaptive experimental settings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical inference on policy improvement, not model efficiency or compression.
Grounding Deep Learning Framework Testing in Data Flow This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Tests deep learning execution frameworks, not code generation or assistants.
Grounding Deep Learning Framework Testing in Data Flow This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant About bug detection in frameworks, not small or efficient models.
Grounding Deep Learning Framework Testing in Data Flow This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on software testing of DL frameworks, not LLM benchmarks.
Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry This paper benchmarks deep learning architectures for anomaly detection on real-world cloud telemetry using timing-aware evaluation metrics. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Evaluates existing time-series models, not techniques for model efficiency.
Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry This paper benchmarks deep learning architectures for anomaly detection on real-world cloud telemetry using timing-aware evaluation metrics. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on IT cloud telemetry monitoring, not AI model safety.
Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry This paper benchmarks deep learning architectures for anomaly detection on real-world cloud telemetry using timing-aware evaluation metrics. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Benchmarks time-series models for cloud telemetry, not LLM evaluation.
Guarded Block Transfers for Polytope Distance: Linear Convergence with Explicit Constants and Gap-Certified Screening for SVM Training This paper presents guarded block transfers for polytope distance computation to accelerate support vector machine training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Optimization theory paper for SVMs, not LLM evaluation benchmarks.
Guarded Block Transfers for Polytope Distance: Linear Convergence with Explicit Constants and Gap-Certified Screening for SVM Training This paper presents guarded block transfers for polytope distance computation to accelerate support vector machine training. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with classical SVM optimization algorithms, not LLM efficiency.
Guarded Block Transfers for Polytope Distance: Linear Convergence with Explicit Constants and Gap-Certified Screening for SVM Training This paper presents guarded block transfers for polytope distance computation to accelerate support vector machine training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on convex optimization for SVMs, not AI safety.
How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility This paper analyzes how shared representations specialize among multiple targets and how optimizer state influences this specialization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Study of representation specialization, not practical model compression or efficiency.
How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility This paper analyzes how shared representations specialize among multiple targets and how optimizer state influences this specialization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes representation formation in toy neural networks, not LLM benchmarks.
How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility This paper analyzes how shared representations specialize among multiple targets and how optimizer state influences this specialization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Basic science study of representation drift, not deployment AI safety.
GuidanceNFT: NFT-Based Reward Fine-Tuning of Guided Diffusion Models This paper introduces an online flow matching method to fine-tune guided diffusion models toward specific rewards without losing prompt alignment. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with diffusion reward fine-tuning, not inference efficiency or compression.
GuidanceNFT: NFT-Based Reward Fine-Tuning of Guided Diffusion Models This paper introduces an online flow matching method to fine-tune guided diffusion models toward specific rewards without losing prompt alignment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on fine-tuning diffusion models, not LLM benchmark methodologies.
GuidanceNFT: NFT-Based Reward Fine-Tuning of Guided Diffusion Models This paper introduces an online flow matching method to fine-tune guided diffusion models toward specific rewards without losing prompt alignment. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant About diffusion alignment with prompts, not system deployment safety guardrails.
A Hybrid Gaussian Process-Extreme Gradient Boosting Machine Learning Framework for Robust Knockdown Factor Prediction and Reliability-Based Design of Thin Spherical Shell Caps This paper combines Gaussian process regression and gradient boosting to predict buckling knockdown factors for thin spherical shell caps. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Structural engineering application with classical ML, not LLM benchmarking.
A Hybrid Gaussian Process-Extreme Gradient Boosting Machine Learning Framework for Robust Knockdown Factor Prediction and Reliability-Based Design of Thin Spherical Shell Caps This paper combines Gaussian process regression and gradient boosting to predict buckling knockdown factors for thin spherical shell caps. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mechanical buckling prediction, not AI safety or deployment reliability.
A Hybrid Gaussian Process-Extreme Gradient Boosting Machine Learning Framework for Robust Knockdown Factor Prediction and Reliability-Based Design of Thin Spherical Shell Caps This paper combines Gaussian process regression and gradient boosting to predict buckling knockdown factors for thin spherical shell caps. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Uses standard GPR and XGBoost for regression, not model efficiency.
Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning This paper establishes contraction properties and finite-sample bounds for model-free learning in average-reward entropy-regularized mean-field games. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical game theory paper, not AI safety or guardrails.
Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning This paper establishes contraction properties and finite-sample bounds for model-free learning in average-reward entropy-regularized mean-field games. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes reinforcement learning convergence in games, not model efficiency.
Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning This paper establishes contraction properties and finite-sample bounds for model-free learning in average-reward entropy-regularized mean-field games. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical paper on mean-field games, not LLM evaluation benchmarks.
Minimax Rates and Improved Spectral Convergence for Multi-Manifold Clustering This paper establishes minimax rates and improved spectral convergence bounds for multi-manifold clustering using Laplace-Beltrami operator estimators. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistics paper on manifold clustering, not LLM evaluation.
Minimax Rates and Improved Spectral Convergence for Multi-Manifold Clustering This paper establishes minimax rates and improved spectral convergence bounds for multi-manifold clustering using Laplace-Beltrami operator estimators. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical clustering analysis, not AI safety or deployment robustness.
Minimax Rates and Improved Spectral Convergence for Multi-Manifold Clustering This paper establishes minimax rates and improved spectral convergence bounds for multi-manifold clustering using Laplace-Beltrami operator estimators. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of spectral convergence, not model compression or serving.
Lightweight Scheduling Policies for Continuously Batched LLM Serving Across GPU Architectures This paper evaluates lightweight scheduling policies for continuous batching in large language model serving across different GPU architectures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on GPU serving throughput and latency, not safety risks.
Lightweight Scheduling Policies for Continuously Batched LLM Serving Across GPU Architectures This paper evaluates lightweight scheduling policies for continuous batching in large language model serving across different GPU architectures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies serving scheduler policies rather than establishing LLM evaluation methodologies.
Two Linear Programs are Better than One This paper proves that virtual extension complexity for polytopes can be exponentially smaller than standard extension complexity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Pure theory on polytope extension complexity, not practical model efficiency.
Two Linear Programs are Better than One This paper proves that virtual extension complexity for polytopes can be exponentially smaller than standard extension complexity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical math and polyhedral combinatorics paper, not LLM evaluation.
Two Linear Programs are Better than One This paper proves that virtual extension complexity for polytopes can be exponentially smaller than standard extension complexity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Abstract complexity theory paper, not AI safety or reliability.
Sample-Optimal Multi-Group Learning with an ERM Oracle This paper introduces an empirical risk minimization oracle algorithm that achieves optimal sample complexity for multi-group learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses theoretical sample complexity, not efficient model serving or quantization.
Sample-Optimal Multi-Group Learning with an ERM Oracle This paper introduces an empirical risk minimization oracle algorithm that achieves optimal sample complexity for multi-group learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical multi-group learning paper, not deployment safety or robustness guardrails.
Sample-Optimal Multi-Group Learning with an ERM Oracle This paper introduces an empirical risk minimization oracle algorithm that achieves optimal sample complexity for multi-group learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical learning paper on ERM oracles, not LLM benchmarks.
Fast and Accurate: Improving Personalized LLM Performance by Semantic Concentration Inspired Workload Split between RAG and Fine-Tuning This paper splits personalized language model workloads between parameter-efficient fine-tuning and retrieval based on historical data semantic concentration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a system architecture, not a benchmark or evaluation method.
Fast and Accurate: Improving Personalized LLM Performance by Semantic Concentration Inspired Workload Split between RAG and Fine-Tuning This paper splits personalized language model workloads between parameter-efficient fine-tuning and retrieval based on historical data semantic concentration. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses latency and accuracy via workload partitioning, not safety or guardrails.
Mesh-Free Convolution: Learned Spectral Attenuation and Transport This paper proposes continuous-space mesh-free convolutions to improve the stability of learned operators for partial differential equations across resolutions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on PDE operator learning architectures, not LLM evaluation benchmarks.
Mesh-Free Convolution: Learned Spectral Attenuation and Transport This paper proposes continuous-space mesh-free convolutions to improve the stability of learned operators for partial differential equations across resolutions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant About mesh-free convolutions for continuous PDEs, not model efficiency or compression.
Mesh-Free Convolution: Learned Spectral Attenuation and Transport This paper proposes continuous-space mesh-free convolutions to improve the stability of learned operators for partial differential equations across resolutions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with numerical PDE stability, not AI safety or deployment guardrails.
Error-Based Bidirectional Predictive Coding Enables Deep, 4-Bit Predictive Coding Networks This paper combines bidirectional and error-based predictive coding to enable deep, 4-bit quantized predictive coding networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on predictive coding architectures, not LLM evaluation benchmarks.
Error-Based Bidirectional Predictive Coding Enables Deep, 4-Bit Predictive Coding Networks This paper combines bidirectional and error-based predictive coding to enable deep, 4-bit quantized predictive coding networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses low-precision neural architectures, not AI deployment safety or guardrails.
Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching This paper develops a physics-guided machine learning surrogate model to predict and control hydraulic saturation profiles in heap leaching. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies machine learning to hydraulic control, not AI deployment safety.
Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching This paper develops a physics-guided machine learning surrogate model to predict and control hydraulic saturation profiles in heap leaching. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents surrogate modeling for mining hydraulics, not LLM evaluation.
Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching This paper develops a physics-guided machine learning surrogate model to predict and control hydraulic saturation profiles in heap leaching. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics-guided surrogates for control, not model efficiency.
Small area learning This paper develops a semi-supervised learning framework for small area estimation that accommodates covariate and conditional distribution shifts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with survey statistics and area estimation, not AI deployment safety.
Small area learning This paper develops a semi-supervised learning framework for small area estimation that accommodates covariate and conditional distribution shifts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers survey statistics and estimation, not small or efficient models.
Small area learning This paper develops a semi-supervised learning framework for small area estimation that accommodates covariate and conditional distribution shifts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents small area estimation statistics framework, not LLM evaluation.
Instance-Adaptive Online Multiclass Multicalibration This paper provides an online multiclass multicalibration algorithm with error bounds that adapt to the geometric dimension of outcome means. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies online prediction theory, not efficient model techniques.
Instance-Adaptive Online Multiclass Multicalibration This paper provides an online multiclass multicalibration algorithm with error bounds that adapt to the geometric dimension of outcome means. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical analysis of multicalibration algorithms, not LLM evaluation.
Instance-Adaptive Online Multiclass Multicalibration This paper provides an online multiclass multicalibration algorithm with error bounds that adapt to the geometric dimension of outcome means. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical multicalibration analysis, not practical deployment safety or guardrails.
Harmonic Representations in Symbolic Music Transformers are Localized and Controllable This paper shows that harmonic representations in symbolic music transformers are localized across middle layers and can guide music generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies harmonic steering in music models, not AI deployment safety or jailbreaks.
Harmonic Representations in Symbolic Music Transformers are Localized and Controllable This paper shows that harmonic representations in symbolic music transformers are localized across middle layers and can guide music generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on representation analysis and controllability, not model compression or efficiency.
Harmonic Representations in Symbolic Music Transformers are Localized and Controllable This paper shows that harmonic representations in symbolic music transformers are localized across middle layers and can guide music generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates internal representations in music transformers, not LLM evaluation benchmarks.
Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS This paper introduces a transformer-based track reconstruction algorithm using line segment tracking objects for high-pileup particle collisions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies machine learning to physics detectors, not AI deployment safety.

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