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
Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting The paper audits test-time adaptation methods for time-series forecasting under realistic, delay-aware conditions to evaluate actual streaming performance. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Audits test-time adaptation for time-series forecasting, not LLMs or language benchmarks.
Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting The paper audits test-time adaptation methods for time-series forecasting under realistic, delay-aware conditions to evaluate actual streaming performance. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on time-series forecasting filters, not small language models or model distillation.
Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting The paper audits test-time adaptation methods for time-series forecasting under realistic, delay-aware conditions to evaluate actual streaming performance. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies time-series label delays, not AI safety, hallucination, or alignment guardrails.
RIFT: Relative Isolation From Trees For Anomaly Detection The paper introduces RIFT, a deterministic, parameter-free anomaly detection algorithm based on minimum spanning trees inspired by Isolation Forests. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Presents an unsupervised anomaly detection baseline, not LLM safety, red-teaming, or guardrails.
RIFT: Relative Isolation From Trees For Anomaly Detection The paper introduces RIFT, a deterministic, parameter-free anomaly detection algorithm based on minimum spanning trees inspired by Isolation Forests. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on tabular anomaly detection, not small language models or efficient inference.
RIFT: Relative Isolation From Trees For Anomaly Detection The paper introduces RIFT, a deterministic, parameter-free anomaly detection algorithm based on minimum spanning trees inspired by Isolation Forests. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a tree-based anomaly detection algorithm, not LLM benchmarks or evaluation.
AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift The paper presents AdaptLSTM, an adaptive online learning method that selectively updates cloud workload forecasts upon detecting distribution drift. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on time-series online learning for cloud workloads, not LLM evaluation.
AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift The paper presents AdaptLSTM, an adaptive online learning method that selectively updates cloud workload forecasts upon detecting distribution drift. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Applies to time-series drift adaptation, not small language models or model compression.
AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift The paper presents AdaptLSTM, an adaptive online learning method that selectively updates cloud workload forecasts upon detecting distribution drift. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers cloud workload drift forecasting, not AI safety, jailbreaks, or guardrails.
Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on spatial RL dynamics, not small or efficient language models.
Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers spatial pattern formation in RL, not deployment safety or guardrails.
Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint	AI agents via arxiv · 12 Oct, 16:29	Not relevant Studies game-theoretic tabular Q-learning in public goods games, not LLM-based agents.
Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies multi-agent reinforcement learning dynamics, not LLM evaluation.
Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity The paper develops an online-to-nonconvex conversion framework for stochastic nonsmooth nonconvex composite optimization achieving optimal oracle complexity bounds. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on optimization theory, not small language models, distillation, or serving.
Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity The paper develops an online-to-nonconvex conversion framework for stochastic nonsmooth nonconvex composite optimization achieving optimal oracle complexity bounds. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies abstract optimization convergence bounds, not deployment safety or robustness.
Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity The paper develops an online-to-nonconvex conversion framework for stochastic nonsmooth nonconvex composite optimization achieving optimal oracle complexity bounds. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant A theoretical paper on nonsmooth nonconvex optimization, not LLM benchmarks.
asdex: Automatic Sparse Differentiation in JAX The paper presents asdex, an automatic sparse differentiation library in JAX that uses graph coloring to compute sparse Jacobians efficiently. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers scientific computing automatic differentiation, not safety, guardrails, or reliability.
asdex: Automatic Sparse Differentiation in JAX The paper presents asdex, an automatic sparse differentiation library in JAX that uses graph coloring to compute sparse Jacobians efficiently. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on scientific differentiation in JAX, not small language models or inference efficiency.
asdex: Automatic Sparse Differentiation in JAX The paper presents asdex, an automatic sparse differentiation library in JAX that uses graph coloring to compute sparse Jacobians efficiently. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents a sparse automatic differentiation toolkit in JAX, not LLM evaluation.
Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning The paper introduces RefineMix to train discrete diffusion models under data scarcity using out-of-distribution data at chosen diffusion timesteps. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses data scarcity in diffusion, not AI safety, alignment, or deployment reliability.
Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning The paper introduces RefineMix to train discrete diffusion models under data scarcity using out-of-distribution data at chosen diffusion timesteps. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a diffusion training framework for scientific data, not LLM evaluation benchmarks.
Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning The paper introduces RefineMix to train discrete diffusion models under data scarcity using out-of-distribution data at chosen diffusion timesteps. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on discrete diffusion data efficiency in science, not model compression or efficient serving.
SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction The paper presents SplitJEPA, learning invariant and variant latent world state representations directly without using reconstruction objectives. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with latent world models for robotics, not deployment safety, guardrails, or hallucination.
SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction The paper presents SplitJEPA, learning invariant and variant latent world state representations directly without using reconstruction objectives. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on representation learning via JEPA, not evaluating or benchmarking language models.
SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction The paper presents SplitJEPA, learning invariant and variant latent world state representations directly without using reconstruction objectives. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not address small language models, compression, quantization, or efficient serving.
Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube This paper derives sharp sample complexity thresholds for Gaussian regression over bounded-degree polynomial subspaces on the Boolean cube. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Analyzes theoretical Gaussian regression on the Boolean cube, not LLM evaluation.
Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube This paper derives sharp sample complexity thresholds for Gaussian regression over bounded-degree polynomial subspaces on the Boolean cube. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical math paper on sampling thresholds, not AI safety or deployment guardrails.
Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube This paper derives sharp sample complexity thresholds for Gaussian regression over bounded-degree polynomial subspaces on the Boolean cube. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on mathematical statistical learning bounds, not model compression or efficiency.
Closing the Horizon Gap in Policy Optimization for Adversarial MDPs This paper establishes tighter regret bounds for adversarial Markov decision processes by incorporating regularized Q-functions into policy optimization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Presents theoretical online MDP bounds rather than AI safety or reliability guardrails.
Closing the Horizon Gap in Policy Optimization for Adversarial MDPs This paper establishes tighter regret bounds for adversarial Markov decision processes by incorporating regularized Q-functions into policy optimization. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies theoretical regret bounds in MDPs rather than LLM evaluation.
Closing the Horizon Gap in Policy Optimization for Adversarial MDPs This paper establishes tighter regret bounds for adversarial Markov decision processes by incorporating regularized Q-functions into policy optimization. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses theoretical reinforcement learning regret, not model compression or efficiency.
Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders This paper uses bilevel optimization and kernel autoencoders to learn finite-dimensional Koopman operator embeddings from nonlinear dynamical data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with dynamical systems theory rather than AI deployment safety or reliability.
Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders This paper uses bilevel optimization and kernel autoencoders to learn finite-dimensional Koopman operator embeddings from nonlinear dynamical data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies dynamical systems and Koopman embeddings, not language model evaluation.
Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders This paper uses bilevel optimization and kernel autoencoders to learn finite-dimensional Koopman operator embeddings from nonlinear dynamical data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on nonlinear dynamical system modeling, not model compression or efficiency.
Marformer: A Transformer for Predicting Missing Data Distributions This paper presents a transformer architecture that predicts conditional marginal distributions for missing data in a single forward pass. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a tabular data model for missing values rather than LLM evaluation.
Marformer: A Transformer for Predicting Missing Data Distributions This paper presents a transformer architecture that predicts conditional marginal distributions for missing data in a single forward pass. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on missing data estimation rather than safety guardrails or alignment.
Marformer: A Transformer for Predicting Missing Data Distributions This paper presents a transformer architecture that predicts conditional marginal distributions for missing data in a single forward pass. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on predicting missing marginal distributions, not model compression or efficiency.
Prospective Prediction of OOD Degradation from Source-Side Training Dynamics This paper analyzes source-side training dynamics to prospectively detect out-of-distribution performance degradation before evaluation. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not address small models, pruning, quantization, or efficient inference.
Prospective Prediction of OOD Degradation from Source-Side Training Dynamics This paper analyzes source-side training dynamics to prospectively detect out-of-distribution performance degradation before evaluation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on predicting OOD failure from training dynamics rather than LLM benchmark evaluation.
Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment This paper introduces a regional weather forecasting framework that aligns pretrained global model guidance with high-resolution regional representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with meteorological forecasting rather than AI deployment safety or guardrails.
Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment This paper introduces a regional weather forecasting framework that aligns pretrained global model guidance with high-resolution regional representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Applies models to weather prediction rather than LLM evaluation or benchmarks.
Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment This paper introduces a regional weather forecasting framework that aligns pretrained global model guidance with high-resolution regional representations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on regional weather forecasting architecture, not model compression or efficient serving.
A Unified Bellman Operator for Safety-Critical Reinforcement Learning This paper develops a unified Bellman operator to optimize task performance and enforce constraints within safety-critical reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents a safe RL theoretical operator rather than LLM benchmarking.
A Unified Bellman Operator for Safety-Critical Reinforcement Learning This paper develops a unified Bellman operator to optimize task performance and enforce constraints within safety-critical reinforcement learning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on reinforcement learning algorithms rather than small models or efficient serving.
Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure This paper provides statistical sample complexity guarantees for transformer-based masked diffusion models learning distributions with latent tree structures. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on theoretical statistical guarantees, not safety, guardrails, or alignment.
Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure This paper provides statistical sample complexity guarantees for transformer-based masked diffusion models learning distributions with latent tree structures. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents theoretical sample complexity bounds for masked diffusion, not LLM evaluation.
Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure This paper provides statistical sample complexity guarantees for transformer-based masked diffusion models learning distributions with latent tree structures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant A theoretical statistical analysis of masked diffusion, not practical model efficiency or compression.
Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers This paper investigates the relationship between circuit depth, training dataset size, and generalization bounds in adaptively grown quantum classifiers. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies quantum circuit generalization on MNIST rather than LLM evaluation.
Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers This paper investigates the relationship between circuit depth, training dataset size, and generalization bounds in adaptively grown quantum classifiers. Peer-reviewed	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates quantum machine learning circuits rather than efficient classical neural networks.
Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers This paper investigates the relationship between circuit depth, training dataset size, and generalization bounds in adaptively grown quantum classifiers. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on quantum classifier scaling laws, not AI safety or deployment reliability.
FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Focuses on RL safety filtering for physical systems rather than industrial computer vision.
FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a safe RL framework rather than LLM benchmarking or evaluation.
FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not study small language models or efficient deployment techniques.
VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on learning generalist robot control policies rather than deployment safety mechanisms.
VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not address model compression, pruning, or efficient inference serving.
VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Develops a humanoid control policy without focusing on LLM evaluation or benchmarks.
VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Focuses on humanoid locomotion and motor control, not industrial computer vision.
Density Ratio Estimation with Stein Displacement Fields This paper estimates density ratios by parameterizing distribution shift as a displacement field using the Stein operator. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Presents theoretical density ratio algorithms rather than model compression or efficient inference.
Density Ratio Estimation with Stein Displacement Fields This paper estimates density ratios by parameterizing distribution shift as a displacement field using the Stein operator. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Investigates mathematical density ratio estimation, not language model evaluation.
Density Ratio Estimation with Stein Displacement Fields This paper estimates density ratios by parameterizing distribution shift as a displacement field using the Stein operator. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers distribution shift estimation in statistics, not deployment guardrails or AI safety.

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