Every paper checked — nothing hidden

All papers

Every paper checked in the week of 12–18 Oct 2026 · Small and efficient models — nothing is hidden. Brief papers first.

All2,430 In brief3 More (on-topic)455 Not relevant1,957 Not assessed6 Older27 Could not confirm6 Blogs with no paper16 All blogs19

Paper	Topic	Relevance
A Hybrid Gaussian Process-Extreme Gradient Boosting Machine Learning Framework for Robust Knockdown Factor Prediction and Reliability-Based Design of Thin Spherical Shell Caps This paper combines Gaussian process regression and gradient boosting to predict buckling knockdown factors for thin spherical shell caps. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Uses standard GPR and XGBoost for regression, not model efficiency.
Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning This paper establishes contraction properties and finite-sample bounds for model-free learning in average-reward entropy-regularized mean-field games. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Analyzes reinforcement learning convergence in games, not model efficiency.
Minimax Rates and Improved Spectral Convergence for Multi-Manifold Clustering This paper establishes minimax rates and improved spectral convergence bounds for multi-manifold clustering using Laplace-Beltrami operator estimators. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of spectral convergence, not model compression or serving.
Two Linear Programs are Better than One This paper proves that virtual extension complexity for polytopes can be exponentially smaller than standard extension complexity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Pure theory on polytope extension complexity, not practical model efficiency.
Sample-Optimal Multi-Group Learning with an ERM Oracle This paper introduces an empirical risk minimization oracle algorithm that achieves optimal sample complexity for multi-group learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses theoretical sample complexity, not efficient model serving or quantization.
Mesh-Free Convolution: Learned Spectral Attenuation and Transport This paper proposes continuous-space mesh-free convolutions to improve the stability of learned operators for partial differential equations across resolutions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant About mesh-free convolutions for continuous PDEs, not model efficiency or compression.
Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching This paper develops a physics-guided machine learning surrogate model to predict and control hydraulic saturation profiles in heap leaching. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics-guided surrogates for control, not model efficiency.
Small area learning This paper develops a semi-supervised learning framework for small area estimation that accommodates covariate and conditional distribution shifts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers survey statistics and estimation, not small or efficient models.
Instance-Adaptive Online Multiclass Multicalibration This paper provides an online multiclass multicalibration algorithm with error bounds that adapt to the geometric dimension of outcome means. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies online prediction theory, not efficient model techniques.
Harmonic Representations in Symbolic Music Transformers are Localized and Controllable This paper shows that harmonic representations in symbolic music transformers are localized across middle layers and can guide music generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on representation analysis and controllability, not model compression or 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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on high-energy physics track reconstruction, not model efficiency.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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	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.
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.
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.
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.
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.
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.
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.
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.
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.
Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics The paper presents a generative flow matching surrogate to simulate bubbly flow interface deformation and trajectories rapidly. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific simulation surrogates rather than small or quantized language models.
naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation The paper introduces an R package implementing nature-inspired optimization algorithms for spatial fuzzy clustering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers spatial clustering optimisation, not small or efficient models.
QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing The paper evaluates an eight-qubit variational circuit bottleneck to improve confidence calibration in synthetic speech detection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantum machine learning bottlenecks rather than model compression techniques.
Sampling from Neural Operator Priors to Solve Partial Differential Equations The paper combines neural operator priors with sequential Monte Carlo to improve sampling efficiency when solving partial differential equations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific ML for PDEs, not model compression or efficient serving.
Taming Nonconvexity with Epigraph Lifting The paper presents an epigraph lifting and difference-of-convex scheme to solve inverse problems regularized by neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Nonconvex optimization lifting methods, not small language models.
ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors The paper proposes a black-box framework correcting time series foundation model forecasts using retrieved historical analogues and error residuals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Time-series forecast correction method, not efficient model compression.
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition The paper introduces an interpretable causal forest extension using functional decomposition to explain covariate contributions to treatment effect heterogeneity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Causal inference tree ensembles, not model compression.
A temporal graph dissimilarity measure capturing epidemic spreading behavior The paper develops a dissimilarity metric for temporal networks based on how epidemic spreading processes unfold across them. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Temporal network dissimilarity metric, not efficient language models.
Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality The paper proposes an adaptive treatment selection algorithm controlling false discovery rates under partial feedback with minimax optimality. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical bandit treatment selection, not model compression.
Trust-Calibrated VLA Policy Post-Training with Evolving Imagination The paper introduces a trust-calibrated reinforcement learning framework coupling vision-language-action policy optimization with an evolving simulator. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Vision-language-action policy refinement, not model compression.
Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features The paper derives asymptotic risk bounds for constant and warmup-stable-decay learning rate schedules in streaming least-squares regression. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical SGD learning-rate schedules, not model compression.
Learning Reachability-Based Safety Representations for Real-Time Robot Navigation in Unknown and Dynamic Environments The dissertation presents offline-trained neural implicit representations of reachability corrections for real-time safe robot navigation in dynamic environments. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Dynamic robot navigation controllers, not efficient language models.
Reinforcement Learning with Hawkes-Structured Networks for Market Making The paper proposes a neural architecture and reinforcement learning method to optimize market making under non-Markovian Hawkes order-flow dynamics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant RL architecture for order-flow dynamics, not model compression.
Learning, Compression, and Meta-Complexity The paper surveys theoretical relationships linking machine learning algorithms, data compression methods, and computational meta-complexity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical data compression and complexity theory, not efficient LLM deployment.
Learning Graded Sparse Mechanisms: A Representation Theorem and Certified Generalization The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theory of graded sparse mechanisms, not model quantization/distillation.
Neural Network Preconditioning: A Case Study for the Solution of the Parametric Helmholtz Equation The paper uses a self-supervised convolutional neural network as a preconditioner to accelerate iterative solvers for parametric Helmholtz equations. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Preconditioning Helmholtz equations, not small language models.
DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields The paper establishes two provably convergent differentiable algorithms for pairwise conditional random field regularization in image and signal classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Probabilistic graphical models convergence, not efficient LLM serving.
Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements The paper introduces a computationally efficient method combining prediction error methods and moving horizon estimation to identify nonlinear dynamic models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on dynamical system identification, not model compression.
Optimal Transport PAC-Bayes Bounds The paper derives PAC-Bayesian generalization bounds that substitute standard relative entropy with general optimal transport metrics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical learning theory bounds, not small or efficient models.
The Language of Looking: Motion Tokens Reveal Domain-Dependent Local Structure in Gaze Sequences The paper analyzes human gaze sequences across datasets using motion tokens to demonstrate domain-dependent local transition patterns. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies human gaze sequence motion tokens, not model compression.
$\texttt{NFDiag}$: High-dimensional MCMC Convergence Diagnosis with Normalizing Flows The paper proposes using normalizing flows to evaluate high-dimensional Markov Chain Monte Carlo convergence from multivariate joint posteriors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Normalizing flows for MCMC, not small or efficient models.
LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR The paper presents a language-aware framework using language models and mixture-of-experts adaptors to improve speech recognition during code-switching. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses code-switching ASR biasing, not small or efficient models.
Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models The paper introduces a data-anchored penalty to prevent distributional bias and leakage in physics-informed diffusion models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses residual regularized diffusion models, not model efficiency or compression.
OctMole: Multiscale 3D Molecular Generation with Hierarchical Spatial Flows The paper introduces a multiscale octree flow framework for generating three-dimensional molecular structures and atom placements. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a 3D molecular flow model, not model compression or SLMs.

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