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
HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning The paper proposes a hyperbolic aggregation framework for self-supervised brain wave representation learning across heterogeneous electrode montages. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with brain-region EEG models, not efficient model serving.
DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning The paper proposes an implicit integration approach for flow-based reinforcement learning policies to enable stable off-policy optimization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Implicit flow policy optimization in RL, not model compression.
DR-APO: Decoupled-Representation Advantage-Guided Policy Optimization from Continuous Control to Embodied Navigation The paper introduces an advantage-guided policy optimization framework that decouples policy representation from reinforcement learning policy updates. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Reinforcement learning optimization method, not model compression or serving.
ACED: Reference-Anchored Aggregation of Natural-Language and Executable Evidence for Mathematical Reasoning The paper combines natural language reasoning with executable program evidence using reference anchoring to improve mathematical problem solving. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies aggregation for math reasoning, not model compression or efficiency.
Learning Deployable Causal Action Geometry under Temporal Non-Stationarity The paper presents a causal action geometry framework to estimate stable decision-response shapes from non-stationary time series. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Causal inference under non-stationarity, not efficient model architectures.
IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models The paper introduces an industrial time-series forecasting benchmark to evaluate foundation models on multi-rate and multivariate data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series benchmarking and simulation, not efficient model serving.
Counting with Attention and LayerNorm: Norm Scaling and Representation Geometry The paper studies sequence-length parameter bounds and representation geometry required for attention layers to perform counting tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical representation analysis of single-layer attention, not efficient model architectures.
Guidance Without Labels: Flow Matching from Coupled Gaussian Mixtures The paper applies optimal transport with Gaussian mixtures to improve flow matching sample efficiency without requiring training labels. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Generative flow matching method, not model quantization, distillation, or serving.
RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations The paper introduces a model that predicts wireless propagation across varying physical scenes, transceiver positions, and antenna configurations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies deep learning to wireless propagation, not small efficient models.
Compression Through Time: An Interplay of Separability, Stationarity and Capacity The paper investigates information-theoretic and geometric compression mechanisms over time within recurrent neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Information-theoretic analysis of LSTM dynamics, not model compression or deployment.
Amortized Variational Inference of Latent Intensities from Replicated Point Patterns The paper develops an amortized variational inference method to estimate latent point process intensities from replicated event patterns. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian point process inference, not LLM efficiency.
Spectral Costs of Multinomial and Residual Resampling in High-Dimensional Particle Ensembles: A Free-Probability Analysis The paper analyzes the spectral properties and limits of multinomial and residual resampling in high-dimensional particle filtering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of particle ensembles, not efficient model inference.
Universal Distributed Feature Extraction Complementary to Side Information via H-Score The paper introduces a nested H-score method for distributed feature extraction that avoids redundancy with receiver-side auxiliary information. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Information-theoretic distributed feature extraction, not small or efficient models.
Amortised adaptive importance sampling The paper proposes an amortized adaptive importance sampling method using a lightweight neural network to iteratively update proposal distributions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers statistical adaptive importance sampling, not model compression or serving.
CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model The paper transfers quadrupedal locomotion skills from single side-view videos to robots by pinning unobserved lateral motion to stance width. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant About robot kinematics without depth models, not LLM efficiency.
NFMap: Topographic 2D Manifold Learning with Invertible Normalizing Flows The paper presents an invertible normalizing flow method for two-dimensional topographic manifold learning and data visualization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 2D data visualization methods, not efficient model deployment.
Efficient Preconditioned Optimization Methods with Frequent Directions The paper integrates matrix sketching via frequent directions into adaptive optimization methods to reduce preconditioning computation overhead. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant General mathematical optimization paper rather than small models, distillation, or serving.
One-Layer Transformers Provably Learn In-Context K-Nearest Neighbor Prediction with Chain-of-Thought The paper theoretically analyzes how one-layer transformers learn in-context nearest-neighbor prediction when using chain-of-thought reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of one-layer transformers rather than practical model efficiency or compression.
When Repeated Measurements Hurt Tabular Foundation Models The paper studies how repeated input measurements can degrade out-of-sample prediction quality in tabular foundation models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies tabular foundation model behavior, not model compression.
Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects The paper evaluates machine learning models on metadata and acoustic features to predict audio popularity on an online sharing platform. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies standard ML to audio data, not model compression.
DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression The paper proposes a dynamic auxiliary search strategy to guide symbolic regression solvers by constructing a high-potential functional subspace. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses symbolic regression search, not model efficiency.
On the Calibration of Crowdsourcing Aggregators The paper provides theoretical and empirical analyses of calibration in crowdsourced label aggregation across human and language model datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on label aggregation calibration, not model compression.
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa The paper evaluates neural network architectures to downscale ERA5 atmospheric data to high-resolution satellite precipitation estimates across West Africa. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies U-Nets to meteorology, not efficient LLM serving.
Calibrated $α$-Barycenters for Multiobjective $f$-Divergence Optimization The paper develops a calibrated barycenter framework to accelerate multiobjective optimization over divergence matching problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with abstract divergence optimization, not model compression.
Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling The paper introduces a center-estimated neural posterior estimator to improve sample-size extrapolation in simulation-based inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies statistical posterior estimation, not model efficiency.
Mean Accuracy Is the Wrong Estimand for a Release Decision The paper shows mean benchmark accuracy misses tail risks in fine-tuned models, proposing recipe certification cards for deployment decisions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses evaluation methodology rather than model compression or inference.
Feature Normalization under Finite Training Budgets The paper analyzes when feature standardization improves risk bounds for rare subgroups under finite stochastic gradient training budgets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistics on feature normalization, not efficient model serving.
Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models The paper proposes a post-training tail correction method for diffusion and flow models using radial-angular extreme value decomposition. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on distribution tail modeling, not model compression.
GpFlash: Maximizing Speed and Minimizing Memory for Gaussian Process Training and Prediction The paper introduces GpFlash, an open library designed to speed up Gaussian process training and prediction while reducing memory footprint. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Gaussian processes rather than small language models.
Better together? A performance theory of human-machine skill policies under generalization difficulty The paper develops a formal performance theory analyzing skill allocation and workflow design in joint human-machine systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers task allocation theory, not model compression or efficiency.
From Parallel to Sequential: Small-Batch Noise Restores Simplicity Bias of Muon The paper demonstrates that small-batch stochastic gradient noise restores sequential feature learning in the Muon optimizer. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimizer theory rather than inference efficiency.
Sequence Composition in EHR Foundation Model Pretraining The paper analyzes sequence composition strategies during electronic health record foundation model pretraining to improve downstream clinical prediction. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies pretraining data composition rather than model compression methods.
Learning Operators for Symmetry Discovery: From Lie Groups to Local Symmetry Geometry The paper formulates continuous symmetry discovery across function families as an inverse symmetry operator problem using differential characteristics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with Lie groups and operators, not model efficiency.
AI-Assisted Tumor-Growth Law Discovery from Sparse Data: Refitting and LLM-Based Ranking The paper evaluates symbolic regression, refitting techniques, and language model rankings for inferring tumor-growth laws from sparse observation data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on tumor growth discovery rather than model compression.
ML Engineering Agents - A Data Management Perspective The tutorial examines machine learning engineering agents from a data management view, highlighting challenges in pipeline execution, data discovery, and debugging. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Discusses pipeline automation agents, not model compression or serving.
Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning The paper presents a temporal reinforcement learning framework that selects physiologically aligned time windows for motor imagery brain-computer interfaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on brain-computer interfaces, not model compression or efficiency.
Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization The paper provides theoretical convergence and tracking error bounds for Matrix Shampoo optimization in nonstationary stochastic settings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Mathematical optimization theory, not small or efficient models.
Making Tensor Decoupling More Robust and Less Parametric The paper provides two modifications to make tensor decoupling methods more robust to magnitude mismatches and eliminate manual spline tuning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses spline-based tensor decoupling, not model compression.
Learning to Design Convex Relaxations for Nonconvex Optimization The paper presents AutoConvexRelax, which learns a reinforcement learning policy to automatically construct valid convex relaxations for nonconvex optimization problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical optimization surrogates, not small models.
DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers The paper proposes a multi-agent diffusion transformer framework to generate realistic air traffic scenes and trajectory patterns from recorded data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Diffusion transformer for trajectory data, not model efficiency.
Learning to Design SR Solvers: Harness Optimization for Symbolic Regression The paper introduces HarnessSR to jointly optimize agent workflows and prompts for discovering symbolic regression equations from data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses symbolic regression workflows, not efficient or small models.
Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation The paper presents GenTuRBO, using pretrained generative models as adaptive proposal trust regions for discrete Bayesian optimization tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Optimization framework for discrete designs, not model efficiency.
Constant-Depth Rational-Weight GNNs and their Connection to Schanuel's Conjecture The paper proves constant-depth rational-weight graph neural networks match the 1-WL expressiveness bound under Schanuel's conjecture from number theory. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical GNN expressiveness, not model compression.
Intrinsic Geometry Matching: learning observation-equivariant process geometry The paper proposes a method to learn the intrinsic geometry and diffusion operators of underlying processes from distorted, indirect observations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical manifold learning method, not model efficiency.
A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration The paper introduces a physics-coupled graph neural operator to predict mid-plane stress tensor fields in concrete during projectile penetration. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Neural operator for materials physics, not efficient models.
Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates This paper develops a deferred-decision trajectory optimization controller with conformal prediction guarantees for multi-agent target interception. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers multi-agent target pursuit, not model compression.
Mixture of GFlowNets This paper introduces an asynchronous population of generative flow networks coupled through soft clustering to improve mode coverage. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates GFlowNet mode coverage, not efficient LLM serving.
LLM Evaluation is a Forecasting Problem This paper frames language model evaluation as a forecasting problem, using time-series techniques on historical data to predict post-deployment performance. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines performance forecasting over time, not model compression.
Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer This paper uses neural network variational Monte Carlo simulations to demonstrate the formation of honeycomb electron-hole crystals in bilayers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Physics domain application, not efficient or small models.
Measuring Variable Importance Using Proportional Inclusive Marginal Effects This paper introduces a model-free variable importance measure that uses active variable selection to prevent nonactive variables from receiving spurious credit. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a statistical sensitivity metric, not model compression.
In-context classification: Calibration and noise in linear attention with softmax readout This paper analyzes in-context classification in linear attention models, establishing theoretical asymptotic bounds separating calibration from noise effects. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies linear attention theory, not model efficiency or distillation.
TILE: Bridging LLMs and Table Foundation Models for Tabular Learning with Strings This paper introduces an interface for extracting context-rich language model embeddings to improve table foundation models on mixed tabular data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Develops tabular embedding representations, not model compression or efficiency.
Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts This paper proposes a nearest-neighbor functional curve completion method for intraday probabilistic revisions of renewable energy forecasts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies power curve completion, not model efficiency techniques.
Masked Diffusion for Travel Diaries Synthesis This paper presents a masked diffusion model that synthesizes complete household travel survey diaries from tokenized sequences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses domain data synthesis rather than small or efficient models.
Masked Attention Pooling Is Invariant to Padding and Permutation: A Formal Proof in Lean This paper provides a formal Lean proof establishing that guarded masked attention pooling is invariant to padding and row permutation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with formal verification of pooling, not model compression.
OpenDGO: Benchmarking Multi-Agent Organizations Across Diverse Application Domains This paper introduces a benchmark evaluating multi-agent organizational structures across diverse domains and collaborative interaction patterns. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-agent collaboration architectures rather than model compression or serving efficiency.
SCOPE Before You Sample: Conformal Scores Guide Posterior Exploration for Fast and Calibrated Bayesian Predictive Inference This paper proposes a method that uses conformal score gradients to guide Markov chain Monte Carlo exploration for fast, calibrated Bayesian inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian MCMC sampling acceleration rather than small language models.
Implicit and explicit reparameterizations of the Folded Normal and Rice distributions This paper develops and evaluates implicit and explicit reparameterized gradient estimators for Folded Normal and Rice distributions in variational inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical reparameterization tricks rather than small or efficient models.
Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells This paper presents an end-to-end spiking neural network that decodes and reconstructs dynamic visual stimuli directly from biological retinal spike trains. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines biological spiking networks for prostheses, not small language models or serving.
A Generalized McDiarmid Inequality: Sample Reuse and Non-Uniform Unbounded Differences This paper develops a generalized McDiarmid concentration inequality capturing sample reuse and unbounded differences for stochastic optimization and generative modeling. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on probability concentration bounds rather than efficient model serving.

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