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
MSC: Multi-Stage Caster-aware Control Framework with Uncertainty-Aware NMPC and RL-based Execution Layer This paper proposes a hierarchical control framework combining model predictive control and reinforcement learning to stabilize autonomous mobile robots with swiveling casters. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on mobile robot motion control, not visual industrial inspection.
When Participant Disjoint Validation Is Not Enough to Establish Generalization in Parkinson's Gait Classification This paper examines how site-dependent data structures can produce misleadingly high performance estimates in Parkinson's gait classification models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines medical data confounding rather than AI deployment safety or jailbreaks.
When Participant Disjoint Validation Is Not Enough to Establish Generalization in Parkinson's Gait Classification This paper examines how site-dependent data structures can produce misleadingly high performance estimates in Parkinson's gait classification models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates clinical gait analysis rather than model compression or inference efficiency.
When Participant Disjoint Validation Is Not Enough to Establish Generalization in Parkinson's Gait Classification This paper examines how site-dependent data structures can produce misleadingly high performance estimates in Parkinson's gait classification models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses clinical statistical validation, not language model benchmark contamination.
NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning This paper introduces a two-stage reinforcement learning framework using language models to optimize complex network control tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies RL to computer networks rather than model compression or efficient serving.
NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning This paper introduces a two-stage reinforcement learning framework using language models to optimize complex network control tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on networking reinforcement learning rather than LLM benchmarking methods.
NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning This paper introduces a two-stage reinforcement learning framework using language models to optimize complex network control tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses network resource allocation rather than AI safety or system guardrails.
From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos This paper adapts a simulated fluid foundation model to forecast living embryo tissue dynamics under genetic and temperature perturbations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies continuum dynamics foundation models rather than model compression or distillation.
From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos This paper adapts a simulated fluid foundation model to forecast living embryo tissue dynamics under genetic and temperature perturbations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses biological video dynamics, not deployment safety or reliability risks.
From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos This paper adapts a simulated fluid foundation model to forecast living embryo tissue dynamics under genetic and temperature perturbations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on biological dynamics transfer, not general LLM benchmarking.
Spectral Considerations on Unsupervised Time-domain EEG Representation Learning with Deep Networks This paper analyzes how unsupervised deep networks under-represent high-frequency signals when learning representations from raw time-domain EEG data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with biomedical signal representations rather than model compression or efficiency.
Spectral Considerations on Unsupervised Time-domain EEG Representation Learning with Deep Networks This paper analyzes how unsupervised deep networks under-represent high-frequency signals when learning representations from raw time-domain EEG data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on EEG signal processing rather than AI safety or guardrails.
Spectral Considerations on Unsupervised Time-domain EEG Representation Learning with Deep Networks This paper analyzes how unsupervised deep networks under-represent high-frequency signals when learning representations from raw time-domain EEG data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes EEG representation learning, not LLM evaluation methods.
Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode This paper evaluates neural network models to filter background noise in multi-telescope gamma-ray event detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses gamma event classification in astrophysics rather than model efficiency.
Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode This paper evaluates neural network models to filter background noise in multi-telescope gamma-ray event detection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on astrophysics image analysis, not language model evaluation.
Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode This paper evaluates neural network models to filter background noise in multi-telescope gamma-ray event detection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies Cherenkov telescope data, not AI safety or system reliability.
Moment-Based Anomaly Detection in High-Dimensional Mixed Data This paper presents a moment-based anomaly detection framework for high-dimensional mixed data using dependence-aware aggregation across features. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses tabular anomaly detection rather than efficient model inference or compression.
Moment-Based Anomaly Detection in High-Dimensional Mixed Data This paper presents a moment-based anomaly detection framework for high-dimensional mixed data using dependence-aware aggregation across features. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers classical anomaly detection, not language model benchmarking.
Moment-Based Anomaly Detection in High-Dimensional Mixed Data This paper presents a moment-based anomaly detection framework for high-dimensional mixed data using dependence-aware aggregation across features. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical anomaly detection in mixed data, not AI deployment safety.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on compression algorithms rather than benchmark evaluation frameworks.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a quantum circuit method rather than general LLM evaluation 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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantum machine learning bottlenecks rather than model compression 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	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.
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.
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.
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.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Causal inference tree ensembles, not model compression.
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	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	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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical bandit treatment selection, not model compression.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Statistical treatment selection, 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	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	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.
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.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical least squares optimization, not AI 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	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Robot navigation reachability, not industrial visual inspection or defect detection.
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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Robot navigation dissertation, not language model evaluation.

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