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
Fast Quantized Johnson$-$Lindenstrauss Transforms The paper introduces structured random transform constructions to accelerate 1-bit quantized Johnson-Lindenstrauss compression for key-value caches. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses KV cache quantization theory rather than safety or reliability.
Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics The paper presents a generative flow matching surrogate to simulate bubbly flow interface deformation and trajectories rapidly. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies two-phase fluid simulation, not AI deployment safety or robustness.
Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics The paper presents a generative flow matching surrogate to simulate bubbly flow interface deformation and trajectories rapidly. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific simulation surrogates rather than small or quantized language models.
Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics The paper presents a generative flow matching surrogate to simulate bubbly flow interface deformation and trajectories rapidly. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses fluid dynamics simulation surrogates, not LLM benchmark evaluation.
naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation The paper introduces an R package implementing nature-inspired optimization algorithms for spatial fuzzy clustering. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an R package for spatial clustering, not LLM evaluation.
naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation The paper introduces an R package implementing nature-inspired optimization algorithms for spatial fuzzy clustering. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses clustering algorithms rather than safety, red-teaming, or guardrails.
naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation The paper introduces an R package implementing nature-inspired optimization algorithms for spatial fuzzy clustering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers spatial clustering optimisation, not small or efficient models.
QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing The paper evaluates an eight-qubit variational circuit bottleneck to improve confidence calibration in synthetic speech detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantum machine learning bottlenecks rather than model compression techniques.
QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing The paper evaluates an eight-qubit variational circuit bottleneck to improve confidence calibration in synthetic speech detection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a quantum circuit method rather than general LLM evaluation techniques.
Sampling from Neural Operator Priors to Solve Partial Differential Equations The paper combines neural operator priors with sequential Monte Carlo to improve sampling efficiency when solving partial differential equations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Paper is about numerical PDE solving, not language model evaluation.
Sampling from Neural Operator Priors to Solve Partial Differential Equations The paper combines neural operator priors with sequential Monte Carlo to improve sampling efficiency when solving partial differential equations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates scientific sampling methods, not AI safety or deployment robustness.
Sampling from Neural Operator Priors to Solve Partial Differential Equations The paper combines neural operator priors with sequential Monte Carlo to improve sampling efficiency when solving partial differential equations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific ML for PDEs, not model compression or efficient serving.
Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning The paper shows that resampling the decoded answer state improves recursive reasoning recovery more efficiently than perturbing latent states. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with problem-solving search rather than safety, alignment, or guardrails.
Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning The paper shows that resampling the decoded answer state improves recursive reasoning recovery more efficiently than perturbing latent states. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on reasoning search methods, not benchmark design or evaluation methodology.
Taming Nonconvexity with Epigraph Lifting The paper presents an epigraph lifting and difference-of-convex scheme to solve inverse problems regularized by neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Optimization for regularized inverse problems, not LLM benchmarking.
Taming Nonconvexity with Epigraph Lifting The paper presents an epigraph lifting and difference-of-convex scheme to solve inverse problems regularized by neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical optimization method, not AI deployment safety.
Taming Nonconvexity with Epigraph Lifting The paper presents an epigraph lifting and difference-of-convex scheme to solve inverse problems regularized by neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Nonconvex optimization lifting methods, not small language models.
ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors The paper proposes a black-box framework correcting time series foundation model forecasts using retrieved historical analogues and error residuals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Time-series forecasting correction, not language model evaluation.
ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors The paper proposes a black-box framework correcting time series foundation model forecasts using retrieved historical analogues and error residuals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Time-series forecast correction method, not efficient model compression.
ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors The paper proposes a black-box framework correcting time series foundation model forecasts using retrieved historical analogues and error residuals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Time series residual forecasting, not enterprise AI safety.
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition The paper introduces an interpretable causal forest extension using functional decomposition to explain covariate contributions to treatment effect heterogeneity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Causal inference tree ensembles, not model compression.
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition The paper introduces an interpretable causal forest extension using functional decomposition to explain covariate contributions to treatment effect heterogeneity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical treatment effect decomposition, not deployment AI safety.
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition The paper introduces an interpretable causal forest extension using functional decomposition to explain covariate contributions to treatment effect heterogeneity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Causal forest interpretability, not language model evaluation.
A temporal graph dissimilarity measure capturing epidemic spreading behavior The paper develops a dissimilarity metric for temporal networks based on how epidemic spreading processes unfold across them. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Network epidemic dynamics comparison, not LLM benchmarking.
A temporal graph dissimilarity measure capturing epidemic spreading behavior The paper develops a dissimilarity metric for temporal networks based on how epidemic spreading processes unfold across them. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Temporal network dissimilarity metric, not efficient language models.
A temporal graph dissimilarity measure capturing epidemic spreading behavior The paper develops a dissimilarity metric for temporal networks based on how epidemic spreading processes unfold across them. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Epidemic graph spreading analysis, not AI deployment safety.
Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality The paper proposes an adaptive treatment selection algorithm controlling false discovery rates under partial feedback with minimax optimality. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Statistical treatment selection, not LLM benchmarking.
Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality The paper proposes an adaptive treatment selection algorithm controlling false discovery rates under partial feedback with minimax optimality. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical causal inference and regret, not AI deployment safety.
Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality The paper proposes an adaptive treatment selection algorithm controlling false discovery rates under partial feedback with minimax optimality. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical bandit treatment selection, not model compression.
Trust-Calibrated VLA Policy Post-Training with Evolving Imagination The paper introduces a trust-calibrated reinforcement learning framework coupling vision-language-action policy optimization with an evolving simulator. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Vision-language-action policy refinement, not model compression.
Trust-Calibrated VLA Policy Post-Training with Evolving Imagination The paper introduces a trust-calibrated reinforcement learning framework coupling vision-language-action policy optimization with an evolving simulator. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Robotics policy post-training, not enterprise LLM safety or guardrails.
Trust-Calibrated VLA Policy Post-Training with Evolving Imagination The paper introduces a trust-calibrated reinforcement learning framework coupling vision-language-action policy optimization with an evolving simulator. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Robot policy reinforcement learning, not LLM benchmarking.
Trust-Calibrated VLA Policy Post-Training with Evolving Imagination The paper introduces a trust-calibrated reinforcement learning framework coupling vision-language-action policy optimization with an evolving simulator. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Robot manipulation policy learning, not industrial visual quality control.
Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features The paper derives asymptotic risk bounds for constant and warmup-stable-decay learning rate schedules in streaming least-squares regression. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical least squares optimization, not AI deployment safety.
Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features The paper derives asymptotic risk bounds for constant and warmup-stable-decay learning rate schedules in streaming least-squares regression. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical SGD learning-rate schedules, not model compression.
Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features The paper derives asymptotic risk bounds for constant and warmup-stable-decay learning rate schedules in streaming least-squares regression. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical optimization risk analysis, not LLM evaluation.
Learning Reachability-Based Safety Representations for Real-Time Robot Navigation in Unknown and Dynamic Environments The dissertation presents offline-trained neural implicit representations of reachability corrections for real-time safe robot navigation in dynamic environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with robotic collision avoidance rather than LLM deployment safety.
Learning Reachability-Based Safety Representations for Real-Time Robot Navigation in Unknown and Dynamic Environments The dissertation presents offline-trained neural implicit representations of reachability corrections for real-time safe robot navigation in dynamic environments. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Dynamic robot navigation controllers, not efficient language models.
Learning Reachability-Based Safety Representations for Real-Time Robot Navigation in Unknown and Dynamic Environments The dissertation presents offline-trained neural implicit representations of reachability corrections for real-time safe robot navigation in dynamic environments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Robot navigation dissertation, not language model evaluation.
Learning Reachability-Based Safety Representations for Real-Time Robot Navigation in Unknown and Dynamic Environments The dissertation presents offline-trained neural implicit representations of reachability corrections for real-time safe robot navigation in dynamic environments. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Robot navigation reachability, not industrial visual inspection or defect detection.
Reinforcement Learning with Hawkes-Structured Networks for Market Making The paper proposes a neural architecture and reinforcement learning method to optimize market making under non-Markovian Hawkes order-flow dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial market making RL, not LLM benchmarks.
Reinforcement Learning with Hawkes-Structured Networks for Market Making The paper proposes a neural architecture and reinforcement learning method to optimize market making under non-Markovian Hawkes order-flow dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Algorithmic market making, not AI safety and guardrails.
Reinforcement Learning with Hawkes-Structured Networks for Market Making The paper proposes a neural architecture and reinforcement learning method to optimize market making under non-Markovian Hawkes order-flow dynamics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant RL architecture for order-flow dynamics, not model compression.
Learning, Compression, and Meta-Complexity The paper surveys theoretical relationships linking machine learning algorithms, data compression methods, and computational meta-complexity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Surveys theoretical computational complexity, not LLM benchmark evaluation.
Learning, Compression, and Meta-Complexity The paper surveys theoretical relationships linking machine learning algorithms, data compression methods, and computational meta-complexity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical data compression and complexity theory, not efficient LLM deployment.
Learning, Compression, and Meta-Complexity The paper surveys theoretical relationships linking machine learning algorithms, data compression methods, and computational meta-complexity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical complexity survey, not AI safety and reliability.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Statistical learning theory, not LLM evaluation or benchmarks.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theory of graded sparse mechanisms, not model quantization/distillation.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Theoretical representation theorem on multisets, not planning or tool-using agents.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Generalization bounds in learning theory, not deployment AI safety.
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation The paper uses a self-supervised convolutional neural network as a preconditioner to accelerate iterative solvers for parametric Helmholtz equations. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Numerical mathematics for PDEs, not AI deployment safety.
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation The paper uses a self-supervised convolutional neural network as a preconditioner to accelerate iterative solvers for parametric Helmholtz equations. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Numerical PDE solving via neural preconditioning, not LLM evaluation.
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation The paper uses a self-supervised convolutional neural network as a preconditioner to accelerate iterative solvers for parametric Helmholtz equations. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Preconditioning Helmholtz equations, not small language models.
DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields The paper establishes two provably convergent differentiable algorithms for pairwise conditional random field regularization in image and signal classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Probabilistic graphical models convergence, not efficient LLM serving.
DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields The paper establishes two provably convergent differentiable algorithms for pairwise conditional random field regularization in image and signal classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Convergence of random fields, not AI deployment safety.
DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields The paper establishes two provably convergent differentiable algorithms for pairwise conditional random field regularization in image and signal classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with conditional random fields, not LLM evaluation.
Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements The paper introduces a computationally efficient method combining prediction error methods and moving horizon estimation to identify nonlinear dynamic models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses system identification in dynamics, not deployment safety guardrails.
Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements The paper introduces a computationally efficient method combining prediction error methods and moving horizon estimation to identify nonlinear dynamic models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on dynamical systems identification, not LLM evaluation.
Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements The paper introduces a computationally efficient method combining prediction error methods and moving horizon estimation to identify nonlinear dynamic models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on dynamical system identification, not model compression.
Optimal Transport PAC-Bayes Bounds The paper derives PAC-Bayesian generalization bounds that substitute standard relative entropy with general optimal transport metrics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical PAC-Bayes theory, not applied AI deployment safety.

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