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
Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS This paper introduces a transformer-based track reconstruction algorithm using line segment tracking objects for high-pileup particle collisions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on high-energy physics track reconstruction, not model efficiency.
Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS This paper introduces a transformer-based track reconstruction algorithm using line segment tracking objects for high-pileup particle collisions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies transformers to particle track reconstruction, not LLM evaluation.
Softmax-Free Attention in Spiking Transformers as the Zero-Temperature Limit of Two-Level Systems This paper provides a thermodynamic framework explaining energy efficiency and optimization issues in softmax-free spiking transformers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical attention thermodynamics, not AI deployment safety or guardrails.
Softmax-Free Attention in Spiking Transformers as the Zero-Temperature Limit of Two-Level Systems This paper provides a thermodynamic framework explaining energy efficiency and optimization issues in softmax-free spiking transformers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents thermodynamic theory for spiking attention, not LLM benchmark evaluation.
Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching This paper proposes a conditional flow matching model to predict the temporal evolution of wildfire acreage from weather and terrain data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Applies machine learning to wildfire prediction, not AI deployment safety.
Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching This paper proposes a conditional flow matching model to predict the temporal evolution of wildfire acreage from weather and terrain data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on wildfire forecasting surrogates, not efficient model architectures.
Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching This paper proposes a conditional flow matching model to predict the temporal evolution of wildfire acreage from weather and terrain data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies flow matching to wildfire size forecasting, not LLM evaluation.
Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems This paper presents a neural network method that separately learns conservative and dissipative dynamics to model passive mechanical systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on mechanical system identification, not model efficiency techniques.
Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems This paper presents a neural network method that separately learns conservative and dissipative dynamics to model passive mechanical systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents Lagrangian neural network physics modeling, not LLM evaluation.
Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems This paper presents a neural network method that separately learns conservative and dissipative dynamics to model passive mechanical systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on mechanical system modeling, not AI deployment safety or robustness.
A 4-Qubit Variational Quantum Classifier for Sequence-Specific Classification of DNMT3A and DNMT3B Flanking Methylation Preferences This paper applies a simulated four-qubit variational quantum classifier to distinguish sequence-specific DNA methylation preferences. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies quantum classifiers for DNA methylation, not LLM evaluation.
A 4-Qubit Variational Quantum Classifier for Sequence-Specific Classification of DNMT3A and DNMT3B Flanking Methylation Preferences This paper applies a simulated four-qubit variational quantum classifier to distinguish sequence-specific DNA methylation preferences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantum classification circuits, not efficient neural model techniques.
A 4-Qubit Variational Quantum Classifier for Sequence-Specific Classification of DNMT3A and DNMT3B Flanking Methylation Preferences This paper applies a simulated four-qubit variational quantum classifier to distinguish sequence-specific DNA methylation preferences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses biological sequence classification, not AI deployment safety.
Certified Robustness via Sparse Second-Order Cone Relaxations This paper proposes a hierarchy of sparse second-order cone programming relaxations for certified neural network robustness. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses verification relaxations, not small or efficient model serving.
Certified Robustness via Sparse Second-Order Cone Relaxations This paper proposes a hierarchy of sparse second-order cone programming relaxations for certified neural network robustness. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on verification optimization techniques, not LLM benchmarks or evaluation methods.
Limit Theory for Online Expectile Estimation under Short and Long Range Dependence This paper develops asymptotic theory and convergence limits for online expectile estimation under dependent time series data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies online statistical estimators, not small or efficient language models.
Limit Theory for Online Expectile Estimation under Short and Long Range Dependence This paper develops asymptotic theory and convergence limits for online expectile estimation under dependent time series data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical risk estimation theory, not AI safety or guardrails.
Limit Theory for Online Expectile Estimation under Short and Long Range Dependence This paper develops asymptotic theory and convergence limits for online expectile estimation under dependent time series data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistics paper on expectile estimation, not LLM evaluation.
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.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines theoretical dynamics of SGDM, not efficient serving or quantization.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimization convergence limits, not AI safety or system reliability.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on fluid dynamics neural operators, not language model evaluation.
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.
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.
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.
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.
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	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.
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.
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.
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.
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	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	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	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.
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.
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.
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.
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	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.
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.
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.
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.
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.
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.

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