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
A Nonlinear Time-Series Theory for Constant-step SGD with Momentum This paper provides theoretical convergence and distributional limits for constant-step stochastic gradient descent with momentum viewed as a time series. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of optimization algorithms, not LLM benchmark evaluation.
CLNO: Conservative Local Neural Operator for Compressible Flow Prediction This paper proposes a local neural operator for compressible flow prediction that enforces discrete physical conservation across subdomains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on fluid dynamics neural operators, not language model evaluation.
CLNO: Conservative Local Neural Operator for Compressible Flow Prediction This paper proposes a local neural operator for compressible flow prediction that enforces discrete physical conservation across subdomains. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses physics simulation PDE operators, not LLM efficiency or quantization.
CLNO: Conservative Local Neural Operator for Compressible Flow Prediction This paper proposes a local neural operator for compressible flow prediction that enforces discrete physical conservation across subdomains. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies neural operators to compressible flows, not deployment safety guardrails.
Backward Conformal Prediction under Covariate Shift This paper extends backward conformal prediction to covariate shift settings using importance weighting derived from sorted score vectors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies statistical conformal prediction bounds, not LLM evaluation benchmarks.
Backward Conformal Prediction under Covariate Shift This paper extends backward conformal prediction to covariate shift settings using importance weighting derived from sorted score vectors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on uncertainty estimation theory, not small or efficient model techniques.
How the Classical Baseline Shapes Machine-Learning Speed Claims in Topology Optimization This paper audits speedup claims of machine learning models in structural topology optimization against modern classical solvers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Audits evaluation claims in topology optimization, not methods for efficient models.
How the Classical Baseline Shapes Machine-Learning Speed Claims in Topology Optimization This paper audits speedup claims of machine learning models in structural topology optimization against modern classical solvers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates speed comparison claims in physics engineering, not deployment safety or jailbreaks.
Interpretable Graph Partitioning for Spatial Prediction This paper develops an interpretable graph partitioning approach to create simplified surrogate models for spatial point predictions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses spatial prediction regionalization, not language model evaluation or benchmarks.
Interpretable Graph Partitioning for Spatial Prediction This paper develops an interpretable graph partitioning approach to create simplified surrogate models for spatial point predictions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Discusses spatial model interpretability, not AI safety or deployment reliability guardrails.
Interpretable Graph Partitioning for Spatial Prediction This paper develops an interpretable graph partitioning approach to create simplified surrogate models for spatial point predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents graph partitioning for spatial surrogates, not model efficiency or distillation.
Memorization in image classification: test set behavior and scaling laws This paper studies the behavior, scaling laws, and predictive variance of image classification networks trained to memorize random labels. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies memorization capacity versus generalization, not model efficiency or compression methods.
Memorization in image classification: test set behavior and scaling laws This paper studies the behavior, scaling laws, and predictive variance of image classification networks trained to memorize random labels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on CNN memorization theory, not deployment safety or hallucination guardrails.
Memorization in image classification: test set behavior and scaling laws This paper studies the behavior, scaling laws, and predictive variance of image classification networks trained to memorize random labels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies memorization dynamics in image CNNs, not LLM evaluation or benchmarks.
CloneOT: predicting clone-specific perturbation responses with lineage-supervised neural inverse optimal transport This paper introduces a neural inverse optimal transport framework that uses cell lineage tracking to learn perturbation response costs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on biology modeling, not model compression or efficient serving.
CloneOT: predicting clone-specific perturbation responses with lineage-supervised neural inverse optimal transport This paper introduces a neural inverse optimal transport framework that uses cell lineage tracking to learn perturbation response costs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses computational biology perturbation responses, not AI safety or deployment robustness.
CloneOT: predicting clone-specific perturbation responses with lineage-supervised neural inverse optimal transport This paper introduces a neural inverse optimal transport framework that uses cell lineage tracking to learn perturbation response costs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies optimal transport to single-cell perturbation prediction, not LLM benchmark evaluation.
SelectTTRL: Representation-Guided Data Selection for Efficient Test-Time Reinforcement Learning This paper presents a method using internal representation similarity to identify which test problems benefit from test-time reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on test-time training data selection, not evaluation benchmarks.
SelectTTRL: Representation-Guided Data Selection for Efficient Test-Time Reinforcement Learning This paper presents a method using internal representation similarity to identify which test problems benefit from test-time reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with sample efficiency during test-time training, not safety or guardrails.
Beyond Policy Rollouts: Embedding-Space Directional Exploration with Anchored Noise for LLM RLVR This paper introduces an embedding perturbation technique to improve exploration diversity during reinforcement learning with verifiable rewards for language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL training exploration rather than model compression or efficiency.
Beyond Policy Rollouts: Embedding-Space Directional Exploration with Anchored Noise for LLM RLVR This paper introduces an embedding perturbation technique to improve exploration diversity during reinforcement learning with verifiable rewards for language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an RL exploration technique rather than benchmark evaluation methodology.
Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment This paper identifies modality imbalance failure modes in multimodal knowledge graph alignment and proposes methods to address them. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers multimodal knowledge graphs rather than AI deployment safety or jailbreaks.
Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment This paper identifies modality imbalance failure modes in multimodal knowledge graph alignment and proposes methods to address them. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an entity alignment framework rather than evaluation benchmarks.
Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment This paper identifies modality imbalance failure modes in multimodal knowledge graph alignment and proposes methods to address them. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on knowledge graph alignment rather than model compression or efficiency.
XForecast: The KDD 2026 Finance Day Challenge on News-Grounded Financial Forecasting This paper introduces a financial forecasting benchmark and competition that evaluates four-week stock movement predictions using both prices and news. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents a financial benchmark rather than methods for model compression or efficiency.
XForecast: The KDD 2026 Finance Day Challenge on News-Grounded Financial Forecasting This paper introduces a financial forecasting benchmark and competition that evaluates four-week stock movement predictions using both prices and news. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial stock forecasting rather than AI safety, red-teaming, or guardrails.
Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks This paper proposes a distributional regression framework using ReLU neural networks to model conditional event probabilities on arbitrary response spaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical statistical theory, not LLM evaluation benchmarks.
Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks This paper proposes a distributional regression framework using ReLU neural networks to model conditional event probabilities on arbitrary response spaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses mathematical regression theory rather than deployment safety or robustness.
Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks This paper proposes a distributional regression framework using ReLU neural networks to model conditional event probabilities on arbitrary response spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers statistical regression theory rather than efficient model inference.
BARTER: Compact Dynamic Sparse Training with Standard Dense Kernels This paper presents a dynamic sparse training method that reduces compute overhead by maintaining compact tensors with standard dense kernels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on sparse training speedups rather than evaluation benchmark design.
BARTER: Compact Dynamic Sparse Training with Standard Dense Kernels This paper presents a dynamic sparse training method that reduces compute overhead by maintaining compact tensors with standard dense kernels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses training efficiency rather than deployment safety, hallucinations, or guardrails.
Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions This paper studies equilibrium learning in unbounded stochastic games using continuous trajectories to fit Gaussian process dynamics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on stochastic game theory rather than model compression or efficiency.
Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions This paper studies equilibrium learning in unbounded stochastic games using continuous trajectories to fit Gaussian process dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates theoretical game theory, not language model evaluation.
Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions This paper studies equilibrium learning in unbounded stochastic games using continuous trajectories to fit Gaussian process dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies theoretical equilibrium learning rather than AI safety or reliability.
Exploration strategies for Gaussian process bandit optimization with inference regret guarantees This paper introduces exploration queries for Gaussian process Bayesian optimization to provide theoretical guarantees on the recommended final solution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies theoretical Bayesian optimization regret bounds, not LLM evaluation.
Exploration strategies for Gaussian process bandit optimization with inference regret guarantees This paper introduces exploration queries for Gaussian process Bayesian optimization to provide theoretical guarantees on the recommended final solution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers theoretical bandit regret rather than deployment safety or jailbreak prevention.
Exploration strategies for Gaussian process bandit optimization with inference regret guarantees This paper introduces exploration queries for Gaussian process Bayesian optimization to provide theoretical guarantees on the recommended final solution. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses Gaussian process optimization rather than model compression or efficiency.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on mechanical caster control rather than AI safety or alignment.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers robotic trajectory control rather than small or quantized models.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses robot motion execution rather than LLM benchmark evaluation.
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	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.
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.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies continuum dynamics foundation models rather than model compression or distillation.
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	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	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	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	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.
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.
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.

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