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
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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with diffusion reward fine-tuning, not inference efficiency or compression.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Structural engineering application with classical ML, not LLM benchmarking.
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.
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.
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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies serving scheduler policies rather than establishing LLM evaluation methodologies.
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.
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	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	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	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.
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.
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	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.
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.
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.
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.
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.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical multicalibration analysis, not practical deployment safety or guardrails.
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.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies transformers to particle track reconstruction, not LLM evaluation.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on high-energy physics track reconstruction, not model efficiency.
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.
Softmax-Free Attention in Spiking Transformers as the Zero-Temperature Limit of Two-Level Systems This paper provides a thermodynamic framework explaining energy efficiency and optimization issues in softmax-free spiking transformers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical attention thermodynamics, not AI deployment safety or guardrails.
Softmax-Free Attention in Spiking Transformers as the Zero-Temperature Limit of Two-Level Systems This paper provides a thermodynamic framework explaining energy efficiency and optimization issues in softmax-free spiking transformers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents thermodynamic theory for spiking attention, not LLM benchmark evaluation.
Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching This paper proposes a conditional flow matching model to predict the temporal evolution of wildfire acreage from weather and terrain data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies machine learning to wildfire prediction, not AI deployment safety.
Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching This paper proposes a conditional flow matching model to predict the temporal evolution of wildfire acreage from weather and terrain data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on wildfire forecasting surrogates, not efficient model architectures.
Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching This paper proposes a conditional flow matching model to predict the temporal evolution of wildfire acreage from weather and terrain data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies flow matching to wildfire size forecasting, not LLM evaluation.
Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems This paper presents a neural network method that separately learns conservative and dissipative dynamics to model passive mechanical systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on mechanical system identification, not model efficiency techniques.
Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems This paper presents a neural network method that separately learns conservative and dissipative dynamics to model passive mechanical systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on mechanical system modeling, not AI deployment safety or robustness.
Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems This paper presents a neural network method that separately learns conservative and dissipative dynamics to model passive mechanical systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents Lagrangian neural network physics modeling, not LLM evaluation.
A 4-Qubit Variational Quantum Classifier for Sequence-Specific Classification of DNMT3A and DNMT3B Flanking Methylation Preferences This paper applies a simulated four-qubit variational quantum classifier to distinguish sequence-specific DNA methylation preferences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses biological sequence classification, not AI deployment safety.
A 4-Qubit Variational Quantum Classifier for Sequence-Specific Classification of DNMT3A and DNMT3B Flanking Methylation Preferences This paper applies a simulated four-qubit variational quantum classifier to distinguish sequence-specific DNA methylation preferences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies quantum classifiers for DNA methylation, not LLM evaluation.
A 4-Qubit Variational Quantum Classifier for Sequence-Specific Classification of DNMT3A and DNMT3B Flanking Methylation Preferences This paper applies a simulated four-qubit variational quantum classifier to distinguish sequence-specific DNA methylation preferences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantum classification circuits, not efficient neural model techniques.
Certified Robustness via Sparse Second-Order Cone Relaxations This paper proposes a hierarchy of sparse second-order cone programming relaxations for certified neural network robustness. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on verification optimization techniques, not LLM benchmarks or evaluation methods.
Certified Robustness via Sparse Second-Order Cone Relaxations This paper proposes a hierarchy of sparse second-order cone programming relaxations for certified neural network robustness. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses verification relaxations, not small or efficient model serving.
Limit Theory for Online Expectile Estimation under Short and Long Range Dependence This paper develops asymptotic theory and convergence limits for online expectile estimation under dependent time series data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical risk estimation theory, not AI safety or guardrails.
Limit Theory for Online Expectile Estimation under Short and Long Range Dependence This paper develops asymptotic theory and convergence limits for online expectile estimation under dependent time series data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistics paper on expectile estimation, not LLM evaluation.
Limit Theory for Online Expectile Estimation under Short and Long Range Dependence This paper develops asymptotic theory and convergence limits for online expectile estimation under dependent time series data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies online statistical estimators, not small or efficient language models.
A Nonlinear Time-Series Theory for Constant-step SGD with Momentum This paper provides theoretical convergence and distributional limits for constant-step stochastic gradient descent with momentum viewed as a time series. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimization convergence limits, not AI safety or system reliability.
A Nonlinear Time-Series Theory for Constant-step SGD with Momentum This paper provides theoretical convergence and distributional limits for constant-step stochastic gradient descent with momentum viewed as a time series. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines theoretical dynamics of SGDM, not efficient serving or quantization.

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