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
Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data Evaluates Transformer models trained with quantile loss on historical vessel position data to produce probabilistic ship trajectory forecasts. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on trajectory prediction models, not efficient model compression or serving.
Rationally Linearized MLPs Linearizes multilayer perceptrons using low-degree rational approximations of activation functions to reduce inference costs across 3D models and language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with architectural inference efficiency, not AI safety, jailbreaks, or guardrails.
Rationally Linearized MLPs Linearizes multilayer perceptrons using low-degree rational approximations of activation functions to reduce inference costs across 3D models and language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on MLP linearization techniques, not benchmark creation or LLM evaluation.
Simultaneous Competitive and Regret Guarantees for TCP Acknowledgment Presents an online randomized TCP acknowledgment algorithm that provides simultaneous competitive ratio bounds and sublinear regret against the best expert. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Online algorithms paper for networking TCP ACK, unrelated to LLM benchmarks.
Simultaneous Competitive and Regret Guarantees for TCP Acknowledgment Presents an online randomized TCP acknowledgment algorithm that provides simultaneous competitive ratio bounds and sublinear regret against the best expert. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses theoretical online algorithms for TCP, not small or efficient models.
Simultaneous Competitive and Regret Guarantees for TCP Acknowledgment Presents an online randomized TCP acknowledgment algorithm that provides simultaneous competitive ratio bounds and sublinear regret against the best expert. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical competitive analysis for network packets, unrelated to AI safety.
$L_0$ Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression Applies Monte Carlo Tree Search with generalized rollout policy adaptation to optimize out-of-sample feature selection for high-dimensional financial regression. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on sparse regression feature selection, not efficient model compression or serving.
$L_0$ Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression Applies Monte Carlo Tree Search with generalized rollout policy adaptation to optimize out-of-sample feature selection for high-dimensional financial regression. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Deals with financial feature selection via search, not LLM benchmarks or evaluation.
$L_0$ Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression Applies Monte Carlo Tree Search with generalized rollout policy adaptation to optimize out-of-sample feature selection for high-dimensional financial regression. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Applies tree search to tabular regression, unrelated to AI deployment safety.
Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure Combines hierarchical forecasting with conformal prediction under missing data to forecast electric vehicle charging demand with valid prediction intervals across evolving networks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers infrastructure forecasting uncertainty, not AI deployment safety or guardrails.
Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure Combines hierarchical forecasting with conformal prediction under missing data to forecast electric vehicle charging demand with valid prediction intervals across evolving networks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on time-series conformal forecasting, not small models or efficient inference.
Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure Combines hierarchical forecasting with conformal prediction under missing data to forecast electric vehicle charging demand with valid prediction intervals across evolving networks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies conformal prediction for EV demand forecasting, not LLM evaluation or benchmarks.
Lower Bounds for Randomized Linear-Oracle Optimization over Strongly Convex Sets Investigates theoretical lower bounds for randomized linear minimization oracle optimization algorithms over strongly convex sets. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical optimization lower bounds paper, unrelated to LLM evaluation.
Lower Bounds for Randomized Linear-Oracle Optimization over Strongly Convex Sets Investigates theoretical lower bounds for randomized linear minimization oracle optimization algorithms over strongly convex sets. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Pure mathematical optimization theory, unrelated to AI safety or reliability.
Lower Bounds for Randomized Linear-Oracle Optimization over Strongly Convex Sets Investigates theoretical lower bounds for randomized linear minimization oracle optimization algorithms over strongly convex sets. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on mathematical optimization lower bounds, not small models or efficient inference.
EchoTab: Learning Measurement-Grounded Representations for Echocardiographic Diagnosis through Tabular Reconstruction Proposes a measurement-grounded representation framework using tabular tokens to link multi-view echocardiograms to clinical diagnostic tasks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on echocardiographic representation learning, not efficient model inference or compression.
EchoTab: Learning Measurement-Grounded Representations for Echocardiographic Diagnosis through Tabular Reconstruction Proposes a measurement-grounded representation framework using tabular tokens to link multi-view echocardiograms to clinical diagnostic tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with medical imaging diagnostic representations, not AI safety or deployment guardrails.
EchoTab: Learning Measurement-Grounded Representations for Echocardiographic Diagnosis through Tabular Reconstruction Proposes a measurement-grounded representation framework using tabular tokens to link multi-view echocardiograms to clinical diagnostic tasks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes medical imaging representation learning, not LLM benchmarks or evaluation.
A small cognitive core explains human behavior at scale Presents a cognitive architecture based on episodic memory hierarchies and context-dependent error updates to predict human choice behavior. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Cognitive psychology computational model of human choices, not an LLM evaluation benchmark.
A small cognitive core explains human behavior at scale Presents a cognitive architecture based on episodic memory hierarchies and context-dependent error updates to predict human choice behavior. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Investigates human choice psychology, not AI deployment reliability or guardrails.
A small cognitive core explains human behavior at scale Presents a cognitive architecture based on episodic memory hierarchies and context-dependent error updates to predict human choice behavior. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Presents a cognitive architecture, not methods for compressing or serving efficient models.
CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope Proposes an unconstrained parameterization and equivariant flow matching model for generating valid correlation matrices. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Deals with geometry of correlation matrix generation, not small or efficient models.
CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope Proposes an unconstrained parameterization and equivariant flow matching model for generating valid correlation matrices. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on financial matrix generation, not AI deployment safety or guardrails.
CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope Proposes an unconstrained parameterization and equivariant flow matching model for generating valid correlation matrices. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a flow matching method for correlation matrices, not an LLM benchmark.
Vocal Intensity Conversion with Continuous SPL Control via Adversarial Training Presents a cycle-consistent adversarial model enabling continuous sound pressure level adjustments for speech vocal intensity conversion. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Covers speech synthesis and vocal intensity conversion, not LLM evaluation or benchmarks.
Vocal Intensity Conversion with Continuous SPL Control via Adversarial Training Presents a cycle-consistent adversarial model enabling continuous sound pressure level adjustments for speech vocal intensity conversion. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on audio GAN conversion techniques, not model compression or efficient inference.
Vocal Intensity Conversion with Continuous SPL Control via Adversarial Training Presents a cycle-consistent adversarial model enabling continuous sound pressure level adjustments for speech vocal intensity conversion. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with audio signal intensity conversion, not AI deployment safety or reliability.
Some remarks on the connection between generative diffusion and optimal transport Analyzes theoretical connections between generative diffusion models, flow matching, and entropic optimal transport formulations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of diffusion and optimal transport, not LLM evaluation or benchmarks.
Some remarks on the connection between generative diffusion and optimal transport Analyzes theoretical connections between generative diffusion models, flow matching, and entropic optimal transport formulations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Mathematical study of generative diffusion dynamics, not efficient inference or small models.
Some remarks on the connection between generative diffusion and optimal transport Analyzes theoretical connections between generative diffusion models, flow matching, and entropic optimal transport formulations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on optimal transport theory, not practical AI safety or reliability in deployment.
Kernel-based External Validity of Experimental Studies Develops a kernel-based maximum mean discrepancy framework to formalize and estimate the external validity and replicability of experimental studies. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical statistics framework for experimental validity, unrelated to efficient model inference.
Kernel-based External Validity of Experimental Studies Develops a kernel-based maximum mean discrepancy framework to formalize and estimate the external validity and replicability of experimental studies. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on statistical replication theory rather than AI deployment safety, alignment, or guardrails.
Kernel-based External Validity of Experimental Studies Develops a kernel-based maximum mean discrepancy framework to formalize and estimate the external validity and replicability of experimental studies. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses theoretical statistical external validity using kernels, not LLM evaluation benchmarks.
JitterVLA: A Replay Benchmark for Motion Quality in VLA-Driven Human-Robot Manipulation Introduces a replay benchmark to evaluate robot motion smoothness and controller latency in vision-language-action manipulation pipelines. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies physical robot trajectory controller delays rather than software AI safety or guardrails.
JitterVLA: A Replay Benchmark for Motion Quality in VLA-Driven Human-Robot Manipulation Introduces a replay benchmark to evaluate robot motion smoothness and controller latency in vision-language-action manipulation pipelines. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Benchmark focuses on robotic motion quality in controllers, not LLM evaluation methods.
JitterVLA: A Replay Benchmark for Motion Quality in VLA-Driven Human-Robot Manipulation Introduces a replay benchmark to evaluate robot motion smoothness and controller latency in vision-language-action manipulation pipelines. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on motion smoothing in robot controllers, not model compression or efficient inference.
JitterVLA: A Replay Benchmark for Motion Quality in VLA-Driven Human-Robot Manipulation Introduces a replay benchmark to evaluate robot motion smoothness and controller latency in vision-language-action manipulation pipelines. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Studies trajectory jitter and controller smoothing in robotics, not industrial vision or inspection.
Private and Distributionally Robust Federated Multiclass Classification Develops a differentially private and distributionally robust ADMM framework for multiclass classification across distributed client data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical federated learning privacy framework, not deployment safety, guardrails, or LLM hallucination.
Private and Distributionally Robust Federated Multiclass Classification Develops a differentially private and distributionally robust ADMM framework for multiclass classification across distributed client data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on federated classification algorithms, not LLM evaluation or benchmarks.
Private and Distributionally Robust Federated Multiclass Classification Develops a differentially private and distributionally robust ADMM framework for multiclass classification across distributed client data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses federated optimization with differential privacy, not small language models or efficient inference.
PANDA: Prior-guided Attentional Dual-path Architecture Presents a dual-path architecture combining normalized attention and incidence priors for hypergraph message-passing neural networks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with graph representation learning, not deployment safety, guardrails, or alignment.
PANDA: Prior-guided Attentional Dual-path Architecture Presents a dual-path architecture combining normalized attention and incidence priors for hypergraph message-passing neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents hypergraph neural network architecture, unrelated to LLM evaluation benchmarks.
PANDA: Prior-guided Attentional Dual-path Architecture Presents a dual-path architecture combining normalized attention and incidence priors for hypergraph message-passing neural networks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on hypergraph attention mechanisms, not efficient language model compression or inference.
Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals Applies a surrogate functional framework incorporating ground-state electron density to improve molecular property prediction models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on surrogate functionals for chemistry, not model compression or efficient serving.
Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals Applies a surrogate functional framework incorporating ground-state electron density to improve molecular property prediction models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses chemical molecular property prediction, not LLM evaluation or benchmarks.
Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals Applies a surrogate functional framework incorporating ground-state electron density to improve molecular property prediction models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Concerned with quantum chemistry calculations, not AI deployment safety or robustness.
A Critical Look at Power Sampling when Test Time Sharpening LLMs Examines sampling methods for test-time sharpening in language models and shows simple best-of-N approaches yield comparable gains. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies test-time sampling distributions for reasoning rather than developing evaluation benchmarks.
A Critical Look at Power Sampling when Test Time Sharpening LLMs Examines sampling methods for test-time sharpening in language models and shows simple best-of-N approaches yield comparable gains. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on reasoning generation sampling rather than AI safety, red-teaming, or guardrails.
A Critical Look at Power Sampling when Test Time Sharpening LLMs Examines sampling methods for test-time sharpening in language models and shows simple best-of-N approaches yield comparable gains. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates power sampling versus Best-of-N, not model compression or efficient on-device serving.
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy Introduces a benchmark evaluating tabular foundation models across point prediction, distributional calibration, and prediction interval efficiency. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Benchmark is specifically for tabular foundation models, not LLM evaluation or language benchmarks.
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy Introduces a benchmark evaluating tabular foundation models across point prediction, distributional calibration, and prediction interval efficiency. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Studies tabular prediction calibration, not small or efficient language model methods.
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy Introduces a benchmark evaluating tabular foundation models across point prediction, distributional calibration, and prediction interval efficiency. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Evaluates calibration in tabular models, not LLM deployment safety, jailbreaks, or hallucinations.
Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields Proposes a scalable variational inference method using sparse autoregressive structures and normalizing flows for non-Gaussian spatial field models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on variational inference for spatial statistics, not LLM benchmarks or evaluation.
Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields Proposes a scalable variational inference method using sparse autoregressive structures and normalizing flows for non-Gaussian spatial field models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses spatial statistics inference algorithms, not small or efficient language models.
Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields Proposes a scalable variational inference method using sparse autoregressive structures and normalizing flows for non-Gaussian spatial field models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical statistical inference paper with no focus on AI safety, guardrails, or reliability.
On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models Compares seven calendar feature encoding techniques across four time series foundation models to evaluate impact on forecast accuracy. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with time series forecasting accuracy rather than deployment safety, guardrails, or hallucination.
On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models Compares seven calendar feature encoding techniques across four time series foundation models to evaluate impact on forecast accuracy. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on feature encoding techniques, not small or efficient model compression or serving.
On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models Compares seven calendar feature encoding techniques across four time series foundation models to evaluate impact on forecast accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates calendar feature encodings in time series models, not language model evaluation benchmarks.
VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs Introduces a parameterized quantum-walk model optimized via backpropagation to rank network node responses to seed perturbations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses graph perturbation response ranking rather than AI safety or deployment robustness.
VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs Introduces a parameterized quantum-walk model optimized via backpropagation to rank network node responses to seed perturbations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on graph perturbation ranking via quantum walk algorithms, not efficient model architectures.

11,641–11,700 of 13,754← Previous Next →