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.
All2,430In brief3More (on-topic)455Not relevant1,957Not assessed6Older27Could not confirm6Blogs with no paper16All blogs19
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| Automated Assembly Instruction Generation from CAD Models Using Grounded Large Language Models: A Human-in-the-Loop Framework Builds a human-in-the-loop framework using grounded language models to automatically generate natural-language assembly instructions from CAD models. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Applies standard LLMs to CAD models, not model efficiency or distillation. | |
| RobustLDS: Learning linear dynamical systems under adversarial corruptions Develops robust estimators and optimization algorithms to learn linear dynamical systems from trajectories corrupted by adversarial outliers. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Theoretical estimation of dynamical systems, not small model efficiency. | |
| Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations Presents a probabilistic framework to disaggregate coarse socioeconomic observations into fine spatial scales using satellite embeddings. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Socioeconomic spatial modeling, not efficient language models or pruning. | |
| Market Impact Estimation via Causal Foundation Models Introduces a continuous-time causal prior-data fitted network simulator to estimate market impact without confounding biases. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on market impact estimation, not small or efficient language models. | |
| Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring Provides theoretical guarantees for efficiently learning Cox survival analysis models using first-order optimization under outcome-dependent truncation and censoring. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on survival regression, not model compression or efficient serving. | |
| TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning Develops a low-cost piezoresistive glove system that aligns human touch demonstrations with robot sensors via contact events. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Discusses low-cost tactile hardware, not small language models or inference efficiency. | |
| Score-Based Learning of Cluster DAGs from Interventions Presents a score-based method to learn cluster causal directed acyclic graphs from interventional data under linear Gaussian assumptions. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on causal DAG learning, not small or efficient neural models. | |
| Interval-valued SHAP in Tree-Based Models Introduces an imprecise Dirichlet model method to calculate interval-valued Shapley values and analyze attribution robustness in tree-based models. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Addresses explainability in tree-based models, not model efficiency or distillation. | |
| Reliability-Aware Future Conditioning for Temporally Robust Robot Manipulation Proposes a reliability-aware conditioning method that mitigates harmful temporal misalignments when using generated video futures for robot manipulation. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Not focused on small models or computational efficiency. | |
| CAPABLE: Capability-Aware Policy Adaptation via Behavioral Latent Encoding Introduces an adaptation framework for frozen vision-language-action policies using residual reinforcement learning and online capability inference during joint faults. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on robotic policy adaptation rather than model compression or efficient serving. | |
| Efficient quadratic entropy with distance sketches Presents scalable methods using distance sketches and random feature embeddings to efficiently approximate quadratic entropy across large distributions. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant A general statistical metric approximation, not small or efficient neural models. | |
| Example-driven Parametrisations for Bayesian Shape Optimisation Learns linear shape parameterisations from past designs using PCA to improve sample efficiency in Bayesian shape optimisation. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Bayesian optimization for physical designs, not model compression or efficient LLMs. | |
| Ghost tasking for parametrized Gaussian Processes solving linear differential equations The paper proposes ghost tasking, adding auxiliary tasks to enable parametrized Gaussian processes for non-parametrizable linear differential equations. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Physics-informed Gaussian processes, not efficient or small language models. | |
| CausalDreamer: Learning Predictive World Models with Latent Disentanglement The paper introduces CausalDreamer, disentangling world model latent representations along controllability and reward relevance dimensions for control. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on world models for RL control, not small language model efficiency. | |
| Efficient and Generalizable Archetypal Analysis for Discrete Data The paper develops an active-set likelihood framework for archetypal analysis on discrete binary, count, and categorical data. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant A general unsupervised statistical learning method, not small or efficient language models. | |
| Diffusion Removes Langevin's Conditioning Dependence: A Sharp Gaussian Analysis The paper analyzes Gaussian diffusion sampling to show how time-dependent trajectories eliminate condition number dependence seen in Langevin dynamics. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Theoretical sampling analysis for diffusion models, not efficient small model inference or distillation. | |
| Enriched Holomorphic Neural Networks for 2D and 3D Laplace Problems The paper combines holomorphic neural networks with singular basis functions to solve 2D and 3D Laplace boundary-value problems. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on scientific computing PDE solvers, not small language models or efficient LLM serving. | |
| Exploiting Gradients in Bayesian Inference of Expensive Simulators The paper proposes using simulator output gradients within Bayesian active learning to infer input parameters of expensive differential equation simulators. Peer-reviewed | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Covers Bayesian active learning for simulators, not small language models or serving. | |
| Differentiable Systematic Resampling for Variational Sequential Monte Carlo The paper introduces Differentiable Systematic Resampling, a smooth relaxation enabling gradient-based optimization in variational sequential Monte Carlo. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Covers state estimation and sequential Monte Carlo, not small language models. | |
| SCORE: Spectral Correlation Estimation for Multivariate Gaussians The paper introduces SCORE, an efficient spectral framework for approximating and estimating high-dimensional structured Gaussian covariance matrices. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on Gaussian covariance estimation, not small language models or model distillation. | |
| Credal Machine Learning for Risk-Averse Decision Making The paper proposes a credal machine learning framework that models epistemic uncertainty with probability distribution sets for risk-averse decision-making. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on decision-theoretic risk bounds, not small language models or compression. | |
| A structure-preserving neural density functional for the ions of a polymer electrolyte The paper develops a neural density functional preserving physical symmetries to model ion correlations in inhomogeneous polymer electrolytes. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on computational physics modeling, not small language models or inference efficiency. | |
| Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting The paper evaluates 1,368 quantum neural network configurations on financial time series forecasting using spot exchange rate data. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on quantum neural network circuits, not small language models or efficient serving. | |
| Bayesian Optimisation under State-Preservation Constraints The paper proposes a Bayesian optimization approach that uses linear response approximations to maintain state-preservation constraints in PDE systems. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on engineering optimization, not small language models or efficient inference. | |
| Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints The paper introduces MA-OPS to achieve optimal regret in adversarial Markov decision processes subject to stochastic hard constraints. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Theoretical reinforcement learning regret bounds, not small language models or compression. | |
| Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection? The paper investigates synthetic graph generation and introduces TS-GGAD to train generalist graph anomaly detection models without real-world training graphs. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Addresses synthetic data for graph models, not small language models or efficient serving. | |
| DataSense-Bench: The First Step Toward an AI Scientist The paper introduces DataSense-Bench to evaluate whether AI agents can select and rank effective training data subsets for fine-tuning. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on agent data-selection benchmarking, not model compression or efficient architectures. | |
| Quickest Change Detection with Diffusion-Integrated Scores The paper proposes DI-SCUSUM, a training-free quickest change detection method using diffusion-smoothed empirical score differences. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on classical distribution shift detection, not small language models or efficient serving. | |
| ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach The paper proposes ISBO, combining log Gaussian Cox processes with the INLA-SPDE approach for scalable spatio-temporal Bayesian optimization. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on Gaussian processes and spatial modeling, not small language models. | |
| Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting The paper audits test-time adaptation methods for time-series forecasting under realistic, delay-aware conditions to evaluate actual streaming performance. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on time-series forecasting filters, not small language models or model distillation. | |
| RIFT: Relative Isolation From Trees For Anomaly Detection The paper introduces RIFT, a deterministic, parameter-free anomaly detection algorithm based on minimum spanning trees inspired by Isolation Forests. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on tabular anomaly detection, not small language models or efficient inference. | |
| AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift The paper presents AdaptLSTM, an adaptive online learning method that selectively updates cloud workload forecasts upon detecting distribution drift. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Applies to time-series drift adaptation, not small language models or model compression. | |
| Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on spatial RL dynamics, not small or efficient language models. | |
| Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity The paper develops an online-to-nonconvex conversion framework for stochastic nonsmooth nonconvex composite optimization achieving optimal oracle complexity bounds. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on optimization theory, not small language models, distillation, or serving. | |
| asdex: Automatic Sparse Differentiation in JAX The paper presents asdex, an automatic sparse differentiation library in JAX that uses graph coloring to compute sparse Jacobians efficiently. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on scientific differentiation in JAX, not small language models or inference efficiency. | |
| Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning The paper introduces RefineMix to train discrete diffusion models under data scarcity using out-of-distribution data at chosen diffusion timesteps. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on discrete diffusion data efficiency in science, not model compression or efficient serving. | |
| SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction The paper presents SplitJEPA, learning invariant and variant latent world state representations directly without using reconstruction objectives. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Does not address small language models, compression, quantization, or efficient serving. | |
| Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube This paper derives sharp sample complexity thresholds for Gaussian regression over bounded-degree polynomial subspaces on the Boolean cube. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on mathematical statistical learning bounds, not model compression or efficiency. | |
| Closing the Horizon Gap in Policy Optimization for Adversarial MDPs This paper establishes tighter regret bounds for adversarial Markov decision processes by incorporating regularized Q-functions into policy optimization. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Addresses theoretical reinforcement learning regret, not model compression or efficiency. | |
| Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders This paper uses bilevel optimization and kernel autoencoders to learn finite-dimensional Koopman operator embeddings from nonlinear dynamical data. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on nonlinear dynamical system modeling, not model compression or efficiency. | |
| Marformer: A Transformer for Predicting Missing Data Distributions This paper presents a transformer architecture that predicts conditional marginal distributions for missing data in a single forward pass. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on predicting missing marginal distributions, not model compression or efficiency. | |
| Prospective Prediction of OOD Degradation from Source-Side Training Dynamics This paper analyzes source-side training dynamics to prospectively detect out-of-distribution performance degradation before evaluation. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Does not address small models, pruning, quantization, or efficient inference. | |
| Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment This paper introduces a regional weather forecasting framework that aligns pretrained global model guidance with high-resolution regional representations. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on regional weather forecasting architecture, not model compression or efficient serving. | |
| A Unified Bellman Operator for Safety-Critical Reinforcement Learning This paper develops a unified Bellman operator to optimize task performance and enforce constraints within safety-critical reinforcement learning. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on reinforcement learning algorithms rather than small models or efficient serving. | |
| Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure This paper provides statistical sample complexity guarantees for transformer-based masked diffusion models learning distributions with latent tree structures. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant A theoretical statistical analysis of masked diffusion, not practical model efficiency or compression. | |
| Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers This paper investigates the relationship between circuit depth, training dataset size, and generalization bounds in adaptively grown quantum classifiers. Peer-reviewed | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Investigates quantum machine learning circuits rather than efficient classical neural networks. | |
| FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Does not study small language models or efficient deployment techniques. | |
| VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Does not address model compression, pruning, or efficient inference serving. | |
| Density Ratio Estimation with Stein Displacement Fields This paper estimates density ratios by parameterizing distribution shift as a displacement field using the Stein operator. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Presents theoretical density ratio algorithms rather than model compression or efficient inference. | |
| A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control This paper analyzes data sampling distributions across parallel simulations to improve exploration efficiency in reinforcement learning for robot control. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on RL training sample selection, not small or efficient model architectures. | |
| CSF: Contextual Safety Filtering for Motion Generators This paper introduces a training-free safety filter that evaluates scene context to constrain generated humanoid motion trajectories. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Does not address model compression, quantization, or efficient model serving. | |
| DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models This paper proposes a reward aggregation method to stabilize and scale reinforcement learning fine-tuning for diffusion models. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Targets RL fine-tuning stability for diffusion generation, not model efficiency or compression. | |
| Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective This paper introduces a benchmark and a diffusion aggregation algorithm to evaluate and balance accuracy and realism in event sequence forecasting. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on event sequence generative modeling, not model compression or efficient serving. | |
| SynAct: Synergistic Active Feedback Learning for LLM Agents This paper proposes an active learning framework that selectively requests expert feedback at high-value decision steps during LLM agent workflows. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on active feedback mechanisms for agents, not model compression or efficiency. | |
| Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK This paper applies a convolutional Gaussian neural process and genetic optimization to select optimal aerosol monitoring sensor locations across the UK. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on environmental sensor optimization, not small or efficient language models. | |
| Amortised Inference through One-Step Implicit Sampling This paper introduces a one-step implicit sampler using short-time MCMC dynamics to enable efficient amortized inference for Bayesian posteriors. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on high-dimensional Bayesian sampling algorithms, not small or efficient LLMs. | |
| Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy This paper uses a ConvNeXt architecture to detect low-voltage EEG intervals as biomarkers for neonatal hypoxic-ischaemic encephalopathy. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on clinical EEG classification, not small or efficient language models. | |
| Critical Mass: Structure-Disjoint Splits Drift With the Libraries They Are Built On This paper optimizes the Maximum Common Edge Subgraph distance calculation to evaluate structure-disjoint data splits on large molecular libraries. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Addresses molecular graph partitioning, not small language models or model efficiency. | |
| Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion Combines structural graph backbone construction with reinforcement learning completion to design network topologies that maximize algebraic connectivity. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on graph topology optimization, not small or efficient language models. | |
| Primal-Dual Flow Matching for Sample-Wise Constrained Generation Introduces primal-dual flow matching to enforce sample-wise constraints in generative modeling using binary membership feedback. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Deals with constrained continuous generative flow models, not small or efficient LLMs. |
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