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 Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Small and efficient models via acm · 17 Oct, 06:19	Not relevant Covers materials science foundation models without focusing on model efficiency or compression.
Global-in-time Spectral Duhamel-Picard Method for Post-Exposure Bake Simulation and Physics-Informed Neural Operators for EUV Lithography This paper presents a spectral Duhamel-Picard method and physics-informed neural operator for simulating post-exposure bake processes in semiconductor manufacturing. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Physics-informed neural operator for semiconductor manufacturing simulation, not LLM efficiency.
A polylogarithmic bound for list replicability This paper presents a short, simplified proof establishing a polylogarithmic upper bound for list replicability across binary concept classes. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical combinatorics on list replicability, not model compression or efficiency.
Latent Annealed Posterior Sampling with a Variance-Preserving Bridge for Sparsely Observed Random Fields This paper introduces a latent annealed posterior sampling framework with a variance-preserving bridge for inferring random fields from sparse observations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Statistical method for spatial random fields, not efficient model deployment.
Graph Additive Models disentangling feature, topology, and interactions This paper proposes an interpretable graph neural network architecture that separates node features, topological motifs, and feature-topology interactions into additive terms. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates graph model interpretability, not small or efficient LLM inference.
On the Stability and Convergence of Flow Map Training This paper provides theoretical analysis explaining why stop gradients and adaptive weighting are necessary for stable training of generative flow maps. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical convergence study for generative flow maps, not model compression or efficiency.
Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks This paper theoretically analyzes what spectral measurements and learning curves reveal about internal representations in quadratic neural networks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Not related to model efficiency, quantization, distillation, or serving.
Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility This paper examines how training data size affects the functional performance and feature reproducibility of sparse autoencoders on language models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies SAE data requirements, not efficient serving, compression, or small models.
Causal Syntax Circuits in CodeLMs: Budget and Probe Decide What Looks Localised This paper investigates internal model circuits and prompt properties that drive code language models to default to Python code output. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Does not study model compression, efficient inference, or distillation techniques.
Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting This paper proposes a quantile-guided residual control framework to improve uncertainty representation and accuracy in diffusion-based time-series forecasting models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on time-series diffusion forecasting architectures, not model efficiency or compression.
Vertical Sliced Wasserstein Distances for Meta-Measures This paper introduces a scalable vertical sliced Wasserstein distance to measure distances between probability distributions whose individual elements are themselves distributions. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Develops theoretical metrics for meta-measures, not neural network efficiency or compression.
Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows This paper uses stochastic normalizing flows with open boundary conditions to mitigate topological freezing in lattice gauge theory simulations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on physics simulation methods, not efficient model inference or compression.
Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech This paper aligns electroencephalography signals with text representations in a shared embedding space to improve the decoding of imagined speech intent. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on EEG-text contrastive learning, not efficient model architectures or compression.
Sliced Wasserstein Sketches with Error Certificates This paper develops sliced Wasserstein sketches with computable error certificates for compact representation and fast comparison of probability measures. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on optimal transport distance sketches, not model compression or serving.
EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis This paper evaluates deep learning models and temporal interpretability techniques to predict continuous music enjoyment ratings from electroencephalography signals. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Applies standard deep learning models to EEG data, not model efficiency.
Optimal Explicit and Implicit Regularization Strengths in High-Dimensional Continual Linear Regression This paper derives closed-form generalization loss expressions for high-dimensional continual linear regression, characterizing optimal step sizes and equivalent regularization strengths. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on high-dimensional regression regularization theory, not model efficiency or compression.
O-RAN-Native Jamming Detection Under Network Heterogeneity This paper develops a statistical ensemble method to detect radio-frequency jamming in heterogeneous open radio access networks using standard telemetry. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on cellular network jamming detection, not model compression or efficiency.
Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Concerns geometric coverage bounds for drones, not efficient model inference.
Linear Independence of Polynomial Compositions with Bounded Input Degree This paper resolves mathematical conjectures on polynomial compositions, proving the identifiability of neural networks using general polynomial activation functions of sufficient degree. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Mathematical proof on polynomial activations, not model compression or efficient inference.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates representation geometry in VLA policies, not model compression or efficiency.
Denoising Score Matching in Diffusion Models: The Effect of Rank Constraints on Generalization This paper analyzes the effect of rank constraints on generalization and test error in diffusion score matching networks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates theoretical score matching rank constraints, not efficient model compression or serving.
Complete or Sparse: A Tale of Two Identifiabilities This paper proves conditions under which supervised neural networks can uniquely identify and recover true underlying data latents in generative and discriminative settings. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies representation identifiability theory rather than model compression or efficiency.
Time-partitioning Gaussian Process Mixture Model This paper introduces a Bayesian nonparametric Gaussian process mixture model using an adaptive Pitman-Yor prior to detect persistent regimes in time series. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Concerns Gaussian process regime modeling, not efficient neural architectures or inference.
SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References This paper proposes a self-training speech denoising framework that corrects teacher predictions via noise filtering and observation mixing without clean references. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on unsupervised speech denoising rather than model efficiency or compression.
Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES This paper uses CatBoost and an automated SHAP analysis pipeline on clinical data to estimate biological age with interpretable biomarker insights. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Clinical tabular modeling study, not small or efficient neural models.
Machine-learning-based neutrino-event generation constrained by experimental measurements This paper introduces a generative surrogate model for neutrino-nucleus scattering calibrated directly to measured experimental cross sections without event-level data. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on neutrino event simulation, not efficient model architectures.
Do Emergent Misalignment Models Share a Circuit? This paper shows that fine-tuned language models exhibiting emergent misalignment across different harmful domains share specific attention heads responsible for the behavior. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on circuit analysis of misalignment, not model compression.
Learning Warm Starts for the Simplex Method from Measured Solver Cost This paper analyzes why machine-learning-predicted warm starts for the simplex method reduce iterations much more than actual solve time. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on simplex algorithm optimization, not neural network efficiency.
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes This paper presents a flexible doubly robust proximal learning framework to estimate causal effects on right-censored time-to-event outcomes under unmeasured confounding. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on survival causal inference, not model compression or efficiency.
Stochastic Gradient Descent: A Nonlinear Time Series Perspective The paper analyzes the convergence and asymptotic properties of stochastic gradient descent with constant step size using nonlinear time series theory. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Mathematical analysis of SGD, not small models or inference efficiency.
Sign Betting for Misspecified Regression: Variance Adaptivity and Optimal Approximation The paper analyzes the theoretical statistical properties and oracle inequalities of sign-betting regression under model misspecification. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies statistical regression theory, not small or efficient models.
Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning The paper presents a GPU-accelerated robust adaptive control framework that accounts for guaranteed future parameter learning during real-time robotic trajectory planning. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on GPU robotic control solvers, not model compression.
Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes The paper demonstrates that ensemble spread in streamflow forecasting models becomes significantly under-dispersed and underestimates error during extreme flood events. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies streamflow forecasting, not efficient model inference or compression.
Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora The paper evaluates cross-subject transfer and accuracy limits of decoding imagined speech from scalp electroencephalography signals across three public datasets. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Neuroscience EEG decoding study, not small or efficient neural models.
MIDI-REPA: Improving Text-to-Music Generation with Symbolic Representation Alignment The paper aligns intermediate representations of text-to-music models with symbolic music transcriptions to improve generation quality efficiently. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Music generation alignment technique, not model compression or serving efficiency.
Two-Stage Score Estimation for Group-Specific Diffusion Models The paper establishes theoretical convergence rates for group-specific diffusion models that learn a shared score alongside a target-specific offset. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical score estimation framework, not efficient model serving or compression.
The Blume-Capel Model as a $φ^6$ Field Theory: A Monte Carlo and Flow Model Study The paper evaluates continuous spin Blume-Capel models using Monte Carlo simulations and normalizing flows to accurately map phase diagrams. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on physics simulation methods, not efficient model inference.
Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence The paper presents an adaptive bandit sampling algorithm designed to verify that an option exceeds a performance threshold within a fixed budget. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Bandit sampling optimization theory, not small or efficient models.
Imperfect Is Perfect: Learned Column Prefilling for Branch-Price-and-Cut The paper uses neural networks to predict initial columns and dual centers, speeding up branch-price-and-cut solvers for vehicle routing problems. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on vehicle routing optimization, not efficient neural models.
CausalEvo: Causal Skill Self-Evolution for Health LLM Agents The paper introduces a causal evaluation method for healthcare language model agents to iteratively select and retain useful task skills. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on agent skill optimization, not model compression or serving.
Acceleration and its Limits in Gaussian Variational Inference The paper studies query complexity bounds and limits of momentum-based acceleration in Gaussian variational inference for strongly log-concave distributions. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Addresses optimization theory in statistics, not efficient model architectures.
On the Last-Iterate Convergence of Clipped Gradient Methods The paper provides theoretical last-iterate convergence guarantees for clipped stochastic gradient methods under heavy-tailed noise. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical analysis of optimization bounds, not model efficiency or serving.
SALTO: Solver-Anchored Lifted Transfer Operators for Nonlinear Event Inference The paper introduces an operator-learning framework using linear transfer operators to infer localized finite-amplitude source events in nonlinear dynamical systems. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Dynamical transfer operator method, not model quantization, distillation, or efficient serving.
Score Mismatch and Set-Constrained Sampling in Zero-Shot Conditional Discrete Diffusion The paper provides theoretical bounds on the approximation error caused by score mismatch during zero-shot conditional sampling in discrete diffusion models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical discrete diffusion analysis, not model compression, distillation, or serving efficiency.
Adaptive Randomized Neural Networks for PDEs: Structure-Aware Feature Redistribution and Residual-Driven Block Enrichment The paper introduces a two-stage adaptive feature construction method to improve randomized neural networks for solving partial differential equations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Randomized neural network for PDEs, not efficient language model serving or compression.
StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction The paper proposes a heavy-tailed score-matching framework for limit order book trend prediction without needing reverse diffusion sampling at inference. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Diffusion modeling for finance, not small language models or efficient inference.
Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble The paper presents an attention-based neural network and logistic regression ensemble to predict live birth outcomes before fresh embryo transfer. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Clinical tabular ensemble model, not small language models or inference serving.
Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process The paper analyzes theoretical properties of the multiplicative half-Cauchy process and provides a calibration rule linking prior beliefs to its scale parameter. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Pure Bayesian statistical theory, not model compression or inference efficiency.
Sensor Diagnosis for Fuel and Emissions Monitoring in Heavy-Duty Diesel Vehicles The paper evaluates two machine learning procedures to diagnose faulty sensors and correct fuel and emission estimates in heavy-duty diesel engines. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Automotive sensor fault diagnosis, not small language models or efficient inference.
Resisting Harmful Instructions through Training-Free Weight Editing The paper introduces a training-free weight-editing method to reduce large language model compliance with harmful instructions such as lying and sexism. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on alignment editing, not quantization, pruning, or efficient inference serving.
Poisoning the corpora of protein design systems during pre-training and retrieval The paper investigates data poisoning attacks targeting pre-training and retrieval corpora of protein language models and generative design systems. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Deals with protein generator security, not small model compression or inference efficiency.
Hard Way, The Only Way: Provable Merits of Training on Hardest Examples The paper provides a high-dimensional theoretical analysis of data pruning strategies when training models on the hardest examples. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies sample pruning theory in high dimensions, not model compression or serving.
Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs The authors introduce a graph neural ordinary differential equation framework to forecast multiscale spatiotemporal epidemic spreading dynamics. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Epidemiological neural ODE architecture, not small or efficient language model serving.
Not All Change Is Damage: Update-Matched Unlearning for Large Language Models The paper introduces an update-matched evaluation reference to balance unlearning effectiveness with downstream learning ability in language models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates knowledge unlearning rather than model quantization, distillation, or serving speed.
Rethinking Learning from Label Proportions via Moment Matching The paper presents a moment-matching framework for weakly supervised learning when labels are grouped into bags non-randomly. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Weakly supervised classification theory, not small model distillation or efficient inference.
Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means The authors establish regret lower bounds for online quantum state learning under logarithmic loss with applications to tomography. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Mathematical quantum learning paper, not model compression or efficient LLM inference.
Personalized Projected Ascent for Federated Stackelberg Optimization The paper analyzes personalized projected ascent algorithms for federated minimax problems with client-specific maximization variables. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on federated optimization theory, not small model efficiency or distillation.
Spectrum Is Not Enough: A Random-Matrix Account of Data-Free Preconditioning for Differentially Private Learning The authors use random matrix theory to establish theoretical limits on synthetic data-free preconditioning in differentially private learning. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Deals with differential privacy preconditioning, not model compression or efficient serving.
Dimension-Free PAC-Bayes Generalization for Deep Neural Networks via Dirichlet Processes The paper develops dimension-free PAC-Bayesian generalization bounds for deep neural networks using Dirichlet processes over data distributions. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates mathematical generalization bounds rather than efficient model compression.
Online Quantile Omniprediction for Proper Losses The authors introduce online algorithms to generate quantile forecasts that remain accurate across multiple proper loss functions simultaneously. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on loss calibration theory, not efficient model serving.

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