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
CAPABLE: Capability-Aware Policy Adaptation via Behavioral Latent Encoding Introduces an adaptation framework for frozen vision-language-action policies using residual reinforcement learning and online capability inference during joint faults. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a policy adaptation framework, not LLM evaluation.
CAPABLE: Capability-Aware Policy Adaptation via Behavioral Latent Encoding Introduces an adaptation framework for frozen vision-language-action policies using residual reinforcement learning and online capability inference during joint faults. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on robotic policy adaptation rather than model compression or efficient serving.
CAPABLE: Capability-Aware Policy Adaptation via Behavioral Latent Encoding Introduces an adaptation framework for frozen vision-language-action policies using residual reinforcement learning and online capability inference during joint faults. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Focuses on robotic joint fault adaptation, not industrial visual inspection or defect detection.
Efficient quadratic entropy with distance sketches Presents scalable methods using distance sketches and random feature embeddings to efficiently approximate quadratic entropy across large distributions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on scalable entropy calculation, not AI deployment safety.
Efficient quadratic entropy with distance sketches Presents scalable methods using distance sketches and random feature embeddings to efficiently approximate quadratic entropy across large distributions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Approximates quadratic entropy for distributions, not LLM benchmarking.
Efficient quadratic entropy with distance sketches Presents scalable methods using distance sketches and random feature embeddings to efficiently approximate quadratic entropy across large distributions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant A general statistical metric approximation, not small or efficient neural models.
Example-driven Parametrisations for Bayesian Shape Optimisation Learns linear shape parameterisations from past designs using PCA to improve sample efficiency in Bayesian shape optimisation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses engineering shape optimization, not LLM evaluation.
Example-driven Parametrisations for Bayesian Shape Optimisation Learns linear shape parameterisations from past designs using PCA to improve sample efficiency in Bayesian shape optimisation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Not related to AI safety, hallucinations, or deployment reliability.
Example-driven Parametrisations for Bayesian Shape Optimisation Learns linear shape parameterisations from past designs using PCA to improve sample efficiency in Bayesian shape optimisation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Bayesian optimization for physical designs, not model compression or efficient LLMs.
Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits Analyzes test-time compute strategies, including parameter adaptation and prediction aggregation, to improve predictions in tabular foundation models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies test-time compute on tabular foundation models, not evaluating LLMs.
Test-Time Compute for Tabular Foundation Models: Mechanisms, Gains, and Limits Analyzes test-time compute strategies, including parameter adaptation and prediction aggregation, to improve predictions in tabular foundation models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on tabular model prediction performance, not safety or guardrails.
Ghost tasking for parametrized Gaussian Processes solving linear differential equations The paper proposes ghost tasking, adding auxiliary tasks to enable parametrized Gaussian processes for non-parametrizable linear differential equations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Physics-informed Gaussian processes, not efficient or small language models.
Ghost tasking for parametrized Gaussian Processes solving linear differential equations The paper proposes ghost tasking, adding auxiliary tasks to enable parametrized Gaussian processes for non-parametrizable linear differential equations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses linear differential equations, not AI safety or deployment robustness.
Ghost tasking for parametrized Gaussian Processes solving linear differential equations The paper proposes ghost tasking, adding auxiliary tasks to enable parametrized Gaussian processes for non-parametrizable linear differential equations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on Gaussian processes for differential equations, not LLM evaluation.
CausalDreamer: Learning Predictive World Models with Latent Disentanglement The paper introduces CausalDreamer, disentangling world model latent representations along controllability and reward relevance dimensions for control. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on world models for RL control, not small language model efficiency.
CausalDreamer: Learning Predictive World Models with Latent Disentanglement The paper introduces CausalDreamer, disentangling world model latent representations along controllability and reward relevance dimensions for control. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Investigates latent disentanglement in RL world models, not deployment safety or reliability guardrails.
CausalDreamer: Learning Predictive World Models with Latent Disentanglement The paper introduces CausalDreamer, disentangling world model latent representations along controllability and reward relevance dimensions for control. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a world model architecture for control, not LLM evaluation frameworks.
Efficient and Generalizable Archetypal Analysis for Discrete Data The paper develops an active-set likelihood framework for archetypal analysis on discrete binary, count, and categorical data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant A general unsupervised statistical learning method, not small or efficient language models.
Efficient and Generalizable Archetypal Analysis for Discrete Data The paper develops an active-set likelihood framework for archetypal analysis on discrete binary, count, and categorical data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses discrete data representation, not AI safety, alignment, or guardrails.
Efficient and Generalizable Archetypal Analysis for Discrete Data The paper develops an active-set likelihood framework for archetypal analysis on discrete binary, count, and categorical data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on statistical archetypal analysis, not benchmarking or evaluating LLMs.
Diffusion Removes Langevin's Conditioning Dependence: A Sharp Gaussian Analysis The paper analyzes Gaussian diffusion sampling to show how time-dependent trajectories eliminate condition number dependence seen in Langevin dynamics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical sampling analysis for diffusion models, not efficient small model inference or distillation.
Diffusion Removes Langevin's Conditioning Dependence: A Sharp Gaussian Analysis The paper analyzes Gaussian diffusion sampling to show how time-dependent trajectories eliminate condition number dependence seen in Langevin dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of diffusion sampling convergence, not LLM evaluation or benchmarks.
Diffusion Removes Langevin's Conditioning Dependence: A Sharp Gaussian Analysis The paper analyzes Gaussian diffusion sampling to show how time-dependent trajectories eliminate condition number dependence seen in Langevin dynamics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Examines mathematical convergence rates, not deployed AI safety or reliability.
Enriched Holomorphic Neural Networks for 2D and 3D Laplace Problems The paper combines holomorphic neural networks with singular basis functions to solve 2D and 3D Laplace boundary-value problems. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Solves PDE boundary value problems, not evaluating or benchmarking language models.
Enriched Holomorphic Neural Networks for 2D and 3D Laplace Problems The paper combines holomorphic neural networks with singular basis functions to solve 2D and 3D Laplace boundary-value problems. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on scientific computing PDE solvers, not small language models or efficient LLM serving.
Enriched Holomorphic Neural Networks for 2D and 3D Laplace Problems The paper combines holomorphic neural networks with singular basis functions to solve 2D and 3D Laplace boundary-value problems. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Not related to AI safety, hallucinations, prompt injection, or deployment guardrails.
Exploiting Gradients in Bayesian Inference of Expensive Simulators The paper proposes using simulator output gradients within Bayesian active learning to infer input parameters of expensive differential equation simulators. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with differential equation simulators, not AI safety or deployment reliability.
Exploiting Gradients in Bayesian Inference of Expensive Simulators The paper proposes using simulator output gradients within Bayesian active learning to infer input parameters of expensive differential equation simulators. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on gradient-assisted Bayesian inference for simulators, not LLM evaluation.
Exploiting Gradients in Bayesian Inference of Expensive Simulators The paper proposes using simulator output gradients within Bayesian active learning to infer input parameters of expensive differential equation simulators. Peer-reviewed	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Covers Bayesian active learning for simulators, not small language models or serving.
Differentiable Systematic Resampling for Variational Sequential Monte Carlo The paper introduces Differentiable Systematic Resampling, a smooth relaxation enabling gradient-based optimization in variational sequential Monte Carlo. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents a differentiable resampling algorithm for particle filters, not LLM evaluation.
Differentiable Systematic Resampling for Variational Sequential Monte Carlo The paper introduces Differentiable Systematic Resampling, a smooth relaxation enabling gradient-based optimization in variational sequential Monte Carlo. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Covers state estimation and sequential Monte Carlo, not small language models.
Differentiable Systematic Resampling for Variational Sequential Monte Carlo The paper introduces Differentiable Systematic Resampling, a smooth relaxation enabling gradient-based optimization in variational sequential Monte Carlo. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies particle filter resampling methods, not AI safety or deployment guardrails.
SCORE: Spectral Correlation Estimation for Multivariate Gaussians The paper introduces SCORE, an efficient spectral framework for approximating and estimating high-dimensional structured Gaussian covariance matrices. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents covariance matrix approximation methods for multivariate Gaussians, not LLM benchmarks.
SCORE: Spectral Correlation Estimation for Multivariate Gaussians The paper introduces SCORE, an efficient spectral framework for approximating and estimating high-dimensional structured Gaussian covariance matrices. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on Gaussian covariance estimation, not small language models or model distillation.
SCORE: Spectral Correlation Estimation for Multivariate Gaussians The paper introduces SCORE, an efficient spectral framework for approximating and estimating high-dimensional structured Gaussian covariance matrices. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with multivariate Gaussian statistics, not AI deployment safety or reliability.
Credal Machine Learning for Risk-Averse Decision Making The paper proposes a credal machine learning framework that models epistemic uncertainty with probability distribution sets for risk-averse decision-making. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes credal sets for risk-averse ML decision making, not LLM evaluation.
Credal Machine Learning for Risk-Averse Decision Making The paper proposes a credal machine learning framework that models epistemic uncertainty with probability distribution sets for risk-averse decision-making. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on decision-theoretic risk bounds, not small language models or compression.
A structure-preserving neural density functional for the ions of a polymer electrolyte The paper develops a neural density functional preserving physical symmetries to model ion correlations in inhomogeneous polymer electrolytes. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes neural density functionals for physical polymer electrolytes, not LLM evaluation.
A structure-preserving neural density functional for the ions of a polymer electrolyte The paper develops a neural density functional preserving physical symmetries to model ion correlations in inhomogeneous polymer electrolytes. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers molecular dynamics and electrolyte physics, not AI safety or guardrails.
A structure-preserving neural density functional for the ions of a polymer electrolyte The paper develops a neural density functional preserving physical symmetries to model ion correlations in inhomogeneous polymer electrolytes. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on computational physics modeling, not small language models or inference efficiency.
Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting The paper evaluates 1,368 quantum neural network configurations on financial time series forecasting using spot exchange rate data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on quantum neural network circuits, not small language models or efficient serving.
Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting The paper evaluates 1,368 quantum neural network configurations on financial time series forecasting using spot exchange rate data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates quantum neural networks on financial forecasting, not LLMs or language benchmarks.
Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting The paper evaluates 1,368 quantum neural network configurations on financial time series forecasting using spot exchange rate data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Evaluates financial time series forecasting, not AI safety, hallucination, or guardrails.
Bayesian Optimisation under State-Preservation Constraints The paper proposes a Bayesian optimization approach that uses linear response approximations to maintain state-preservation constraints in PDE systems. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with physical design optimization, not AI safety or deployment reliability.
Bayesian Optimisation under State-Preservation Constraints The paper proposes a Bayesian optimization approach that uses linear response approximations to maintain state-preservation constraints in PDE systems. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on engineering optimization, not small language models or efficient inference.
Bayesian Optimisation under State-Preservation Constraints The paper proposes a Bayesian optimization approach that uses linear response approximations to maintain state-preservation constraints in PDE systems. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Covers PDE-constrained Bayesian optimization for engineering, not LLM evaluation.
Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints The paper introduces MA-OPS to achieve optimal regret in adversarial Markov decision processes subject to stochastic hard constraints. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of constrained Markov decision processes, not LLM evaluation.
Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints The paper introduces MA-OPS to achieve optimal regret in adversarial Markov decision processes subject to stochastic hard constraints. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers abstract constrained MDP theory, not AI safety or deployment guardrails.
Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints The paper introduces MA-OPS to achieve optimal regret in adversarial Markov decision processes subject to stochastic hard constraints. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical reinforcement learning regret bounds, not small language models or compression.
Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection? The paper investigates synthetic graph generation and introduces TS-GGAD to train generalist graph anomaly detection models without real-world training graphs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses synthetic data for graph models, not small language models or efficient serving.
Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection? The paper investigates synthetic graph generation and introduces TS-GGAD to train generalist graph anomaly detection models without real-world training graphs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on graph neural networks and graph anomaly detection, not LLM evaluation.
Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection? The paper investigates synthetic graph generation and introduces TS-GGAD to train generalist graph anomaly detection models without real-world training graphs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers graph anomaly detection algorithms, not LLM safety, jailbreaks, or red-teaming.
DataSense-Bench: The First Step Toward an AI Scientist The paper introduces DataSense-Bench to evaluate whether AI agents can select and rank effective training data subsets for fine-tuning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on evaluating data selection capabilities, not safety, guardrails, or alignment risks.
DataSense-Bench: The First Step Toward an AI Scientist The paper introduces DataSense-Bench to evaluate whether AI agents can select and rank effective training data subsets for fine-tuning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on agent data-selection benchmarking, not model compression or efficient architectures.
Quickest Change Detection with Diffusion-Integrated Scores The paper proposes DI-SCUSUM, a training-free quickest change detection method using diffusion-smoothed empirical score differences. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on classical distribution shift detection, not small language models or efficient serving.
Quickest Change Detection with Diffusion-Integrated Scores The paper proposes DI-SCUSUM, a training-free quickest change detection method using diffusion-smoothed empirical score differences. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers statistical change-point detection, not AI safety, jailbreaks, or model reliability.
Quickest Change Detection with Diffusion-Integrated Scores The paper proposes DI-SCUSUM, a training-free quickest change detection method using diffusion-smoothed empirical score differences. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a statistical quickest change detection method, not LLM evaluation.
ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach The paper proposes ISBO, combining log Gaussian Cox processes with the INLA-SPDE approach for scalable spatio-temporal Bayesian optimization. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents Bayesian optimization for spatio-temporal data, not LLM evaluation.
ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach The paper proposes ISBO, combining log Gaussian Cox processes with the INLA-SPDE approach for scalable spatio-temporal Bayesian optimization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers spatio-temporal Bayesian optimization, not deployment safety or LLM guardrails.
ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach The paper proposes ISBO, combining log Gaussian Cox processes with the INLA-SPDE approach for scalable spatio-temporal Bayesian optimization. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on Gaussian processes and spatial modeling, not small language models.

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