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
LADDER: A Long-Horizon Robot Manipulation Benchmark This paper introduces a robot manipulation benchmark to isolate failures across scaling subgoal horizons and orchestration methods. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies long-horizon robot policies, not model compression or efficient serving.
Reliable Scale-Free Bandits via Empirical Likelihood This paper develops an empirical-likelihood bandit policy that provides asymptotic regret guarantees without knowledge of reward scales. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies stochastic bandit regret bounds, not model efficiency or serving.
Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach This paper proposes a score-matching approach for causal discovery in additive noise models under hard selection bias and truncation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses causal discovery under selection bias, not model efficiency or compression.
Same Function, Different Trajectory: No Standard Optimizer Is Equivariant to Transformer Reparameterization This paper demonstrates that standard optimizers are sensitive to parameter scaling in Transformers and proposes a symmetry-aware gradient adjustment. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses training optimization geometry, not model compression or efficient inference methods.
From Spectral Saliency to Finite Deletion: Evaluating Selective Language Model Edits This paper analyzes prediction errors in spectral saliency when deleting singular components to selectively edit language model capabilities. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies targeted capability deletion rather than model compression or inference efficiency.
Distance Prediction for Quantum Codes This paper introduces an attention-based neural network to predict code distances for bivariate bicycle quantum low-density parity-check codes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Application to quantum error correction codes, not efficient AI.
Learning a Group Element from Its Action: Galois Correspondences, Optimal ERM, and an Abel-Ruffini Threshold for the Natarajan Dimension This paper analyzes the sample and mistake complexity of learning group elements from actions using Galois correspondences and multiclass dimensions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical sample complexity analysis, not efficient model deployment.
Random Probing for Structural Self-Interactions in Graph Neural Networks This paper proposes a message-passing layer using randomized probing of operator diagonals to capture node self-interactions in graph neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a graph message passing layer, not model compression.
Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields This paper introduces a reinforcement learning navigation policy using shared spatial fields for large-scale lifelong multi-agent path finding. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers multi-agent path planning, not model compression or efficiency.
Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction This paper proposes a cross-image routing attention mechanism to predict millimeter-wave beam power from dual-endpoint vehicle-to-infrastructure camera views. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers wireless beam prediction, not efficient model architectures.
Hierarchical Time Series Forecasting with Operator-Valued Kernels This paper presents an operator-valued kernel regression framework that enforces aggregation constraints directly in hierarchical time series forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers kernel methods for forecasting, not model efficiency.
Operator Surrogates This book chapter provides an overview and mathematical introduction to finite-parametric operator surrogates for approximating maps between infinite-dimensional spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers infinite-dimensional operator approximation, not efficient language modeling.
When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability This paper introduces a spatial transformer opponent model that stabilizes reinforcement learning training under partial observability in competitive foraging games. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL opponent models, not model compression.
Optimal Online Regret for Context-Lumpable Bandits This paper develops an online algorithm achieving optimal regret bounds for stochastic contextual bandits with unknown context-lumpable cluster structures. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses theoretical regret bounds, not small or efficient models.
PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting This paper introduces a physics-informed graph neural network for modeling advection-diffusion processes to forecast solar power production. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers physics-informed graph neural networks, not model compression.
Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling This paper derives finite-sample risk bounds for Kernel Mean Matching under dependent sampling using effective sample sizes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical covariate shift, not efficient language modeling.
Bayesian Optimization of CVaR with Unknown Hyperparameters This paper presents HE-CV-UCB, an algorithm for Bayesian optimization of Conditional Value-at-Risk under unknown Gaussian process hyperparameters. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents Bayesian optimization regret bounds, not model efficiency.
Over-parameterization Is Neither Robust nor Fragile in Matrix Sensing: A Geometric Theory of Solution Robustness This paper analyzes the geometric robustness of over-parameterized low-rank matrix sensing under the Burer-Monteiro factorization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers theoretical over-parameterization in matrix sensing, not model compression.
On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning This paper provides optimal learning rate bounds for random feature kernel ridge regression under misspecified models and heavy-tailed noise. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with kernel random feature theory, not language model efficiency.
Beyond Task Success: A Theory of Representation Reuse This paper develops a theoretical framework characterizing when representations learned on a source task remain recoverable and reusable for future tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Discusses downstream representation reuse, not model compression or efficiency.
Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling This paper proposes a forecast-error distribution entropy framework to detect nonlinear predictive coupling across multiple time horizons. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series forecasting metrics, not model efficiency.
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs This paper introduces LunaTP, a parallel execution framework that accelerates tensor product convolutions in equivariant machine learning interatomic potentials. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on equivariant interatomic simulations, not small/efficient LLMs.
Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification This paper benchmarks eighteen continual learning methods on a sequential radar waveform recognition task across task- and class-incremental settings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Continual learning for radar waveforms, not efficient LLM serving.
Convergence-Rate-Guided Algorithm Design of Expected Improvement for Continuous Simulation Optimization This paper develops finite-sample bounds to guide algorithm design for Expected Improvement in continuous simulation optimization with heteroscedastic noise. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Stochastic kriging optimization algorithms, not model efficiency.
Graph Metanetworks for Transformer Based Language Models This paper develops graph metanetworks that respect the weight-space symmetries of transformer language models, including attention matrices and token embeddings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Weight-space symmetry modeling, not model compression or efficiency.
ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization This paper introduces ProTok, a unified discrete tokenizer for protein sequence, structure, and functional annotations using modular multi-codebook quantization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Protein representation tokenization, not small or efficient models.
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements This paper presents Lifting, a framework combining repetition encoding and neural decoding for mid-circuit quantum state discrimination on superconducting hardware. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Quantum measurement framework, not efficient model architectures.
SoK: Rethinking Membership Inference in Tabular Machine Learning This paper provides a systematization of knowledge on membership inference attacks in tabular machine learning across small-to-medium datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Privacy evaluation of tabular models, not model efficiency.
Recalibrating Many Questions with Few Labels: Minimax Rates and Identification Limits This paper provides minimax rates and limits for recalibrating predictions across many binary questions when each question has few ground-truth labels. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical calibration analysis, not efficient serving or compression.
Steering the Fidelity in State Preparation on Noisy Bosonic Systems with Risk-Seeking Optimization This paper introduces a risk-seeking optimization method to prepare target quantum states with high fidelity on noisy bosonic quantum hardware. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on quantum computing states, not model efficiency.
Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Bandits This paper analyzes heterogeneous asynchronous multi-player bandits, proves the centralized allocation problem is NP-hard, and studies greedy approximation policies. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Multi-player bandit policies, not small or efficient LLMs.
Euclidean Distance-Based Flow Matching This paper replaces squared Euclidean distance with Euclidean distance in Flow Matching to improve distributional robustness against data contamination. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Generative flow matching method, not model efficiency techniques.
Understanding Circulant Permutation to Extend the CMinHash Estimator The paper derives explicit variance approximations to extend circulant permutation techniques to different MinHash estimators. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Algorithmic hashing theory, not small or efficient LLMs.
Last-Iterate Convergence of Clipped SGD with Linear Decay The paper provides last-iterate convergence bounds for clipped stochastic gradient descent under heavy-tailed noise and causal adversarial perturbations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Convergence bounds for optimization, not model compression or efficiency.
Linear Last-Iterate Convergence Rates for Optimistic Learning in Harmonic Games The paper establishes linear last-iterate convergence rates for optimistic learning algorithms in multiplayer harmonic games. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical game theory analysis, not efficient model deployment.
Fairness under Prediction Error in Predict-then-Optimize Pipelines The paper analyzes how prediction errors affect demographic parity fairness violations in budget-constrained allocation problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses algorithmic fairness, not model compression or efficiency.
Deterministic Prediction as Global Velocity: Flow Matching for Probabilistic Time-Series Forecast The paper proposes a flow-matching model that integrates deterministic forecasting as a velocity component for stable probabilistic time-series predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses time-series forecasting architectures, not small or efficient LLMs.
Minimax rates for pointwise CATE estimation: the role of design smoothness The paper derives minimax risk bounds showing how covariate design smoothness affects pointwise conditional average treatment effect estimation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with theoretical statistics, not model efficiency or compression.
BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling The paper introduces a training-free conditional sampling method for pretrained Schrödinger bridge generative models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses continuous transport sampling, not small or efficient language models.
A Refresh Interval Does Not Fully Specify a Refresh Schedule: Learning-Rate-Dependent Placement in Kronecker-Preconditioned Language Model Training The paper demonstrates that the placement timing of curvature updates in Kronecker-preconditioned optimizers significantly affects language model training loss. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimizer scheduling during pretraining, not model compression or inference efficiency.
On Collusion-Free Teaching in Planar Graphs The paper proves theoretical teaching complexity bounds for closed neighborhoods in planar graphs without collusion. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical teaching complexity, not model compression or efficiency.
Learning Source Structure: Bayesian Prediction with Transformers The paper compares transformer sequence predictions against an optimal Bayesian predictor across structured Markov sources. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies theoretical sequence prediction, not model compression or efficient serving.
Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains The paper proposes a reinforcement learning method to selectively transfer data for carbon-footprint modeling in new manufacturing settings. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with domain transfer policies, not small or efficient LLM architectures.
Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization The paper develops an off-policy reinforcement learning method with token-wise importance sampling and proximal KL regularization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL optimization algorithms rather than model compression or efficient serving.
Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks The paper analyzes how network depth influences the internal loop geometry and spatial reach of gauge-equivariant graph neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical physics GNN representations, not model efficiency.
Statistical Vignettes on the Grokking Phenomenon The paper analytically characterizes the mechanisms and conditions behind the grokking phenomenon in a simplified learning setup. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical learning study, not small models, pruning, or efficient inference.
Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks The paper presents a neural network architecture for simultaneous model selection and parameter posterior estimation in chemical reaction networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses Bayesian inference for chemistry, not small or efficient language models.
Exact Topological Compliance: Generating Persistence-Equivalent Graphs The paper introduces methods to generate graphs whose topological persistence diagrams match target descriptors exactly rather than approximately. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on topological graph generation, not model compression or efficient serving.
Data Free Training of Diffusion Models for Lattice Field Theories The paper presents a method for training diffusion models in lattice field theory directly from their own generated samples. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies lattice field theory sampling rather than efficient LLM inference or distillation.
Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective The paper analyzes training instability cascades in large recommender systems and proposes targeted gradient, weight, and loss interventions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on recommender training divergence rather than model compression or efficient inference.
MoSAIC: Learning DAG Scheduling Heuristics from Optimal Schedules through Motif-Aware Recovery and LLM-Guided Reasoning The paper introduces a framework that uses language models to extract task-scheduling priority functions from optimal solver solutions on directed acyclic graphs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with DAG task scheduling, not model compression or efficient serving.
Human Pose Estimation by Probabilistic Mixtures of Gaussian and Uniform Distributions The paper proposes a continuous probabilistic Gaussian mixture framework to improve human pose estimation under dense occlusions without retraining models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on probabilistic pose estimation modeling rather than model compression or efficient serving.
Token-Budgeted Candidate Retrieval for 3D Scene-Grounded VLMs: Category Names vs. Pairwise Geometry This paper evaluates category name matching versus pairwise geometry metrics for candidate retrieval in context-constrained 3D vision-language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies context retrieval for 3D VLMs, not model compression.
Agreement versus Softmax under Spatial Disambiguation This paper evaluates calibration differences between object-level softmax and agreement metrics when resolving ambiguous 3D referring queries. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines metric calibration in 3D referring, not model efficiency.
BigCell: Generating Gigapixel Whole-Slide Images This paper presents a flow-matching transformer pipeline to generate full gigapixel histopathology whole-slide images. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses large-scale histopathology generation, not small or efficient model compression.
Design Choices for Multimodal Transformer Learning with Missing and Longitudinal Healthcare Data This paper studies transformer architectural choices for handling missing modalities and longitudinal observations in multimodal healthcare datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates tokenization for missing data, not small or efficient model serving.
TartanMatch: Towards Universal Dense Correspondence Across Modalities This paper presents a unified model for dense visual correspondence across 25 pairs of multi-modal sensor inputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant A cross-sensor matching model, not a small language model or model compression technique.
Metric-Driven Reinforcement Learning for Object Hallucination Reduction in LVLM-Based Image Captioning The paper proposes a reinforcement learning framework using natural language metrics to reduce object hallucination in vision-language image captioning. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on hallucination mitigation rather than small or efficient architectures.
CIViC: Evidence Gates for Audio-Conditioned Visual Prediction on Proxy Tasks The paper presents a four-gate verification protocol to evaluate whether audio-conditioned visual prediction models genuinely rely on input audio signals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses audio-visual feature gating, not model compression or efficient serving.
Harnessing SPAD Noise: Test-Time Gated Attention for Indoor Depth Estimation The paper introduces a lightweight test-time attention framework that leverages single-photon sensor noise as an informative prior for indoor depth estimation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on sensor depth estimation, not small language model compression.

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