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
Which Geometry on Which Layer? Measured Sign-Spectral Assignment in Mixed-Optimizer Training The authors provide a theoretical criterion to decide whether neural network layers should receive sign-based or spectral optimization updates during training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimization theory rather than deployment safety, guardrails, or hallucination.
Which Geometry on Which Layer? Measured Sign-Spectral Assignment in Mixed-Optimizer Training The authors provide a theoretical criterion to decide whether neural network layers should receive sign-based or spectral optimization updates during training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates optimization layer geometry rather than LLM benchmarks or evaluation methods.
Which Geometry on Which Layer? Measured Sign-Spectral Assignment in Mixed-Optimizer Training The authors provide a theoretical criterion to decide whether neural network layers should receive sign-based or spectral optimization updates during training. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies general neural network optimizer geometry, not model efficiency or compression.
Calibrating LLM Explanations to Human Decisions with Reinforcement Learning The authors introduce a reinforcement learning method that optimizes language model explanations specifically to improve human downstream decision accuracy. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, quantization, distillation, or serving efficiency.
Calibrating LLM Explanations to Human Decisions with Reinforcement Learning The authors introduce a reinforcement learning method that optimizes language model explanations specifically to improve human downstream decision accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL training for explanations rather than evaluation methods.
A Geometrical Approach to Constructing Neural Networks for Perfect Training-set Classification The authors develop a geometric construction method to build two-hidden-layer neural networks that achieve exact classification on given training sets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Discusses exact geometric hyperplane fitting for classification, not small LLM architectures or efficiency.
A Geometrical Approach to Constructing Neural Networks for Perfect Training-set Classification The authors develop a geometric construction method to build two-hidden-layer neural networks that achieve exact classification on given training sets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on geometric construction of toy classifiers, not LLM benchmarks or evaluation.
A Geometrical Approach to Constructing Neural Networks for Perfect Training-set Classification The authors develop a geometric construction method to build two-hidden-layer neural networks that achieve exact classification on given training sets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Presents a training-set overfitting method rather than deployment safety, robustness, or reliability.
Deeply unrolled alternating projections for phase retrieval The authors propose a deep unrolling algorithm combining alternating projections with a learned denoiser for phase retrieval under changing measurement conditions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates inverse imaging algorithms rather than AI safety, guardrails, or alignment.
Deeply unrolled alternating projections for phase retrieval The authors propose a deep unrolling algorithm combining alternating projections with a learned denoiser for phase retrieval under changing measurement conditions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on signal reconstruction techniques, not small or efficient language models.
Deeply unrolled alternating projections for phase retrieval The authors propose a deep unrolling algorithm combining alternating projections with a learned denoiser for phase retrieval under changing measurement conditions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers inverse phase retrieval algorithms, not language model benchmarking.
Near-Optimal Regret Bounds for Monotone Stochastic Optimization under Semi-Bandit Feedback The authors derive an online-to-offline reduction achieving near-optimal regret bounds for strongly monotone stochastic optimization under semi-bandit feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses theoretical multi-armed bandit optimization without relation to AI deployment safety.
Near-Optimal Regret Bounds for Monotone Stochastic Optimization under Semi-Bandit Feedback The authors derive an online-to-offline reduction achieving near-optimal regret bounds for strongly monotone stochastic optimization under semi-bandit feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of stochastic semi-bandits, not LLM evaluation or benchmarking.
Near-Optimal Regret Bounds for Monotone Stochastic Optimization under Semi-Bandit Feedback The authors derive an online-to-offline reduction achieving near-optimal regret bounds for strongly monotone stochastic optimization under semi-bandit feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on bandit regret bounds, not small models or efficient inference techniques.
GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference The authors present an autotuning framework for WebGPU kernels to improve cross-platform inference speeds for language models on edge devices. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents GPU kernel implementations rather than LLM benchmarks or evaluation methods.
GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference The authors present an autotuning framework for WebGPU kernels to improve cross-platform inference speeds for language models on edge devices. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on WebGPU execution performance rather than AI safety, red teaming, or alignment.
Accounting for model error in ensemble variational data assimilation through learned generative dynamics The authors present an ensemble variational data assimilation algorithm that incorporates learned generative forecasting models to account for complex model errors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on physical data assimilation error rather than AI safety or reliability guardrails.
Accounting for model error in ensemble variational data assimilation through learned generative dynamics The authors present an ensemble variational data assimilation algorithm that incorporates learned generative forecasting models to account for complex model errors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with weather and climate forecast models, not small language models or efficiency.
Accounting for model error in ensemble variational data assimilation through learned generative dynamics The authors present an ensemble variational data assimilation algorithm that incorporates learned generative forecasting models to account for complex model errors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers geophysical data assimilation, not language model evaluation.
Monitoring for Shifts at Unknown Times: Parameter-Free Conditional Conformal Martingales The authors introduce a parameter-free conformal martingale method modeled as a universal portfolio to quickly detect distribution shifts at unknown times. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Develops conformal martingales for distribution shift, not LLM benchmarks or evaluations.
Monitoring for Shifts at Unknown Times: Parameter-Free Conditional Conformal Martingales The authors introduce a parameter-free conformal martingale method modeled as a universal portfolio to quickly detect distribution shifts at unknown times. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on sequential shift detection algorithms, not model compression or efficient serving.
HarnessTTT: Learn to Optimize Harness at Test-Time for Scientific Discovery The authors propose a test-time learning method that trains a proposer to generate prompts, tools, and controls guiding scientific discovery agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on discovery performance optimization rather than safety guardrails, jailbreaks, or robustness.
HarnessTTT: Learn to Optimize Harness at Test-Time for Scientific Discovery The authors propose a test-time learning method that trains a proposer to generate prompts, tools, and controls guiding scientific discovery agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses prompt and tool optimization for discovery, not model quantization, distillation, or serving.
HarnessTTT: Learn to Optimize Harness at Test-Time for Scientific Discovery The authors propose a test-time learning method that trains a proposer to generate prompts, tools, and controls guiding scientific discovery agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on test-time harness optimization for scientific discovery, not benchmark evaluation design.
Robustly Reliable Learning for Regression The authors analyze robustly reliable learning for regression under data corruptions and propose an algorithm to compute optimal prediction intervals for Lipschitz predictors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical study of regression prediction bounds, not LLM evaluation benchmarks.
Robustly Reliable Learning for Regression The authors analyze robustly reliable learning for regression under data corruptions and propose an algorithm to compute optimal prediction intervals for Lipschitz predictors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on adversarial regression bounds rather than small models or efficient inference.
Variable Input Masked Autoencoders for Wrist-Worn Accelerometry The authors introduce a masked autoencoder that pretrains wearable sensor representations across varied recording durations and sampling rates using cross-resolution views. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates wearable sensor encoders, not language model evaluation or benchmarks.
Variable Input Masked Autoencoders for Wrist-Worn Accelerometry The authors introduce a masked autoencoder that pretrains wearable sensor representations across varied recording durations and sampling rates using cross-resolution views. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes masked autoencoding for sensor signals, not small LLM architectures or compression.
Variable Input Masked Autoencoders for Wrist-Worn Accelerometry The authors introduce a masked autoencoder that pretrains wearable sensor representations across varied recording durations and sampling rates using cross-resolution views. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on sensor pretraining techniques rather than deployment safety or guardrails.
Measure Neural Mappings The authors define measure neural mappings and establish quantitative error bounds for self-attention transformers acting on probability measures in unbounded spaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical operator learning without addressing AI safety, robustness, or deployment guardrails.
Measure Neural Mappings The authors define measure neural mappings and establish quantitative error bounds for self-attention transformers acting on probability measures in unbounded spaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of attention on probability measures, not LLM benchmark evaluation.
Measure Neural Mappings The authors define measure neural mappings and establish quantitative error bounds for self-attention transformers acting on probability measures in unbounded spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical bounds for attention over continuous measures, not model compression or efficiency.
A Method for Exact Enforcement of Boundary Conditions on General Curved Triangular Domains: Algorithm and Application to Scientific Machine Learning The authors present a mathematical method to enforce Dirichlet, Neumann, and Robin boundary conditions exactly on curved triangular domains for scientific machine learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns mathematical PDE solvers rather than AI deployment safety, alignment, or monitoring.
A Method for Exact Enforcement of Boundary Conditions on General Curved Triangular Domains: Algorithm and Application to Scientific Machine Learning The authors present a mathematical method to enforce Dirichlet, Neumann, and Robin boundary conditions exactly on curved triangular domains for scientific machine learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific machine learning on curved domains, not small models or efficient inference.
A Method for Exact Enforcement of Boundary Conditions on General Curved Triangular Domains: Algorithm and Application to Scientific Machine Learning The authors present a mathematical method to enforce Dirichlet, Neumann, and Robin boundary conditions exactly on curved triangular domains for scientific machine learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses boundary value problems in numerical analysis, not LLM benchmarks.
JustOPD: On-Policy Distillation from Autoregressive to Diffusion Language Models The authors introduce an on-policy distillation method to transfer knowledge from autoregressive language models into diffusion language models using autoregressive rollouts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a knowledge distillation technique rather than a benchmark or evaluation method.
JustOPD: On-Policy Distillation from Autoregressive to Diffusion Language Models The authors introduce an on-policy distillation method to transfer knowledge from autoregressive language models into diffusion language models using autoregressive rollouts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses model distillation mechanics rather than safety, hallucinations, or deployment guardrails.
Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL The authors argue that cooperative multi-agent reinforcement learning systems require test-time coordination mechanisms rather than relying solely on training-time centralized information. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses execution frameworks in multi-agent reinforcement learning, not small or efficient models.
Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL The authors argue that cooperative multi-agent reinforcement learning systems require test-time coordination mechanisms rather than relying solely on training-time centralized information. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on MARL coordination strategies rather than AI safety, red-teaming, or model alignment.
Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL The authors argue that cooperative multi-agent reinforcement learning systems require test-time coordination mechanisms rather than relying solely on training-time centralized information. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a coordination paradigm in reinforcement learning, not LLM evaluation methodologies.
When Sparsity Breaks Connectivity: Functional Reachability and Guided Topology Dynamics in Extreme Sparse Training The authors develop a reachability-guided dynamic sparse training method that preserves functional pathways during extreme network pruning and regrowth. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies sparse training dynamics rather than LLM evaluation or benchmark design.
When Sparsity Breaks Connectivity: Functional Reachability and Guided Topology Dynamics in Extreme Sparse Training The authors develop a reachability-guided dynamic sparse training method that preserves functional pathways during extreme network pruning and regrowth. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on extreme pruning and sparsity rather than deployment safety, hallucinations, or jailbreaks.
Monarch-Muon: Structure-Aware Muon for Fast and Memory-Efficient Pretraining The authors propose an optimizer for Monarch-factored weights that replaces dense matrix orthogonalization with smaller block updates to reduce memory and pretraining time. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimization efficiency during pretraining rather than safety, alignment, or guardrails.
Monarch-Muon: Structure-Aware Muon for Fast and Memory-Efficient Pretraining The authors propose an optimizer for Monarch-factored weights that replaces dense matrix orthogonalization with smaller block updates to reduce memory and pretraining time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an optimization algorithm rather than an LLM evaluation framework or benchmark.
Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure The authors present a verification workflow combining reachability analysis with Bayesian optimization to close residual test bins in chip design verification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on hardware verification coverage closure rather than language model evaluation.
Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure The authors present a verification workflow combining reachability analysis with Bayesian optimization to close residual test bins in chip design verification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with semiconductor functional verification, not AI safety guardrails or reliability in deployment.
Formal-Verification-Guided Bayesian Optimization for Non-Intrusive Last-Mile Coverage Closure The authors present a verification workflow combining reachability analysis with Bayesian optimization to close residual test bins in chip design verification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Discusses Bayesian optimization for chip verification, not small language models or efficient inference.
On Sleep-Wake Disorder Identification from Polysomnographic Representations The authors compare self-supervised and sleep-stage-supervised representations for identifying sleep-wake disorders across thousands of polysomnography recordings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines medical representation learning, not model compression or efficient serving.
On Sleep-Wake Disorder Identification from Polysomnographic Representations The authors compare self-supervised and sleep-stage-supervised representations for identifying sleep-wake disorders across thousands of polysomnography recordings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on clinical diagnostic benchmarking rather than AI safety guardrails or LLM reliability.
On Sleep-Wake Disorder Identification from Polysomnographic Representations The authors compare self-supervised and sleep-stage-supervised representations for identifying sleep-wake disorders across thousands of polysomnography recordings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates sleep polysomnography models rather than LLM benchmarks or evaluation methods.
Effect of Spherical Parameter Space on Number of Non-Forgetting Tasks and Capacity of Continual Learning The authors analyze continual learning on a spherical parameter space to determine how many tasks a model can learn without catastrophic forgetting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of continual learning parameter spaces, not LLM evaluation methods.
Effect of Spherical Parameter Space on Number of Non-Forgetting Tasks and Capacity of Continual Learning The authors analyze continual learning on a spherical parameter space to determine how many tasks a model can learn without catastrophic forgetting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical paper on task non-forgetting without focus on deployment safety or alignment.
Effect of Spherical Parameter Space on Number of Non-Forgetting Tasks and Capacity of Continual Learning The authors analyze continual learning on a spherical parameter space to determine how many tasks a model can learn without catastrophic forgetting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes spherical parameter geometry in continual learning, not small models or efficient inference.
Selective Inference for Clustering Under Unknown Covariance The authors introduce a selective hypothesis test for comparing cluster means under an unknown covariance matrix without inflating false-positive rates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on classical statistical hypothesis testing rather than AI system safety and reliability.
Selective Inference for Clustering Under Unknown Covariance The authors introduce a selective hypothesis test for comparing cluster means under an unknown covariance matrix without inflating false-positive rates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers post-clustering statistical inference, not language model evaluation or benchmarks.
Selective Inference for Clustering Under Unknown Covariance The authors introduce a selective hypothesis test for comparing cluster means under an unknown covariance matrix without inflating false-positive rates. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Concerns theoretical statistics for clustering, not efficient model inference or compression.
From Orbit Matching to Docking: A Hierarchical Reinforcement-Learning Approach to Multiphase Rendezvous Trajectory Design The paper proposes a hierarchical reinforcement learning framework to automate multi-phase spacecraft rendezvous and docking trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Does not benchmark or evaluate language models.
From Orbit Matching to Docking: A Hierarchical Reinforcement-Learning Approach to Multiphase Rendezvous Trajectory Design The paper proposes a hierarchical reinforcement learning framework to automate multi-phase spacecraft rendezvous and docking trajectories. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on spacecraft guidance trajectories, not industrial visual inspection or defect detection.
From Orbit Matching to Docking: A Hierarchical Reinforcement-Learning Approach to Multiphase Rendezvous Trajectory Design The paper proposes a hierarchical reinforcement learning framework to automate multi-phase spacecraft rendezvous and docking trajectories. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on reinforcement learning for spacecraft docking, not small or efficient language models.
From Orbit Matching to Docking: A Hierarchical Reinforcement-Learning Approach to Multiphase Rendezvous Trajectory Design The paper proposes a hierarchical reinforcement learning framework to automate multi-phase spacecraft rendezvous and docking trajectories. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with spacecraft control dynamics rather than AI deployment safety or language model alignment.

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