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
Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming This paper evaluates classical and deep learning direction-of-arrival estimators across accuracy and latency metrics for real-time integrated sensing and communication. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies radar/communication signal processing rather than efficient language models.
Training Dynamics of Transformers on Unbounded Inputs: Attention Learning Can Lag Output Layer Learning This paper proves that training shallow transformers on unbounded inputs causes attention weight learning to lag behind output layer learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of attention training dynamics, not model compression or efficiency.
When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch This paper presents a task-conditioned imputation framework and evaluates model robustness under train-test missingness mechanism mismatches. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Imputation framework for tabular data, not small or efficient models.
NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection This paper proposes a discrete spatiotemporal tokenization framework for asynchronous event detection and boundary estimation in continuous EEG recordings. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Spatiotemporal model for brain-computer interfaces, not LLM efficiency.
Fractional is Better: Learnable Derivative Orders in Neural Operator Learning This paper shows that providing learnable fractional derivative features improves theoretical bounds and empirical accuracy in neural operator learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on neural operator design for PDE surrogates, not model compression.
ReAl: Refine neural operators via aligning dynamical flow This paper introduces a flow-matching plug-in module to recover high-frequency physical structures in pretrained neural partial differential equation solvers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant PDE surrogate modeling technique, not small models or efficient inference.
Hypothesis Selection via Weighted Medians This paper provides a fast hypothesis selection algorithm using weighted medians that improves runtime dependence on failure probability. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical distribution learning algorithms, not model compression or efficient serving.
From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort This paper presents a mathematical review analyzing when human corrections translate into reusable learning versus interactive refinements in image segmentation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Reviews human feedback in image segmentation, not model efficiency or distillation.
HRTF Upsampling and Personalization via Neural Operators This paper develops a neural operator framework to upsample and personalize continuous head-related transfer function fields from sparse spatial measurements. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Neural operator for head-related transfer functions, not LLM compression or inference efficiency.
Training Language Models to Fail Gracefully This paper introduces a reinforcement learning approach that trains language models to execute safe fallbacks instead of unreliable guesses. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents an RL training method for fallbacks, not model compression or serving.
Posterior Frequencies and the Cost of Not Fitting: A Correction to a Memorization Lower Bound This paper provides counterexamples to an established memorization lower bound and derives exact replacements using full-sample posterior probabilities. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical bound correction for statistical learning, not efficient model architectures.
Hyperspherical Contrastive Representations Unlock Planning and Exploration This paper presents a self-supervised reinforcement learning method that uses hyperspherical representations and spherical interpolation to infer planning waypoints. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Proposes contrastive representations for robot planning, not small model optimization.
Tracking a Small Set of Experts in Online Convex Optimization This paper proves a computational separation between information-theoretic and polynomial-time regret when tracking small expert subsets in online convex optimization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical study of expert tracking bounds, not model compression or efficient serving.
Continual Learning for Reliable In-Sequence Molecular Communication This paper introduces a continual learning replay method to incrementally evaluate and adapt molecular communication models across changing experimental settings. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Applies continual learning to bio-nanomachines, not small or efficient models.
The Accuracy Well: Nonmonotone Accuracy Dynamics at Vanishing Initialization This paper analyzes non-monotone accuracy dynamics during early neural network training when models are initialized with vanishingly small weights. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Explores optimization dynamics under vanishing initialization, not model efficiency or quantization.
What Does Disagreement Mean? A Semantic Identifiability Frontier for Pairwise Preference Learning This paper establishes theoretical limits on what forms of annotator disagreement can be identified from pairwise preference feedback data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on preference learning theory, not small models or efficient inference.
Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model This paper evaluates whether a pretrained tabular foundation model can perform power grid steady-state analysis without task-specific retraining. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Evaluates TabPFN on power grid analysis, not model compression or efficient serving.
Average-Reward Policy Identification from a Single Continuing Trajectory This paper provides matching upper and lower bounds for learning near-optimal policies from a single continuing trajectory in average-reward reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies trajectory navigation complexity in theoretical RL, not model compression or serving.
Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records This paper introduces a graph-enhanced framework to discover disease trajectories and forecast temporal clinical endpoints from electronic health records. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study model compression, distillation, or efficient model serving.
A Two-Level Clinical Novelty Prioritization Framework for Disease Trajectories Discovered from Longitudinal Electronic Health Records A two-level prioritization framework evaluates semantic novelty and reliability of disease trajectory patterns extracted from electronic health records. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient LLM inference.
Bayesian Learning to Select Fair Classifiers The paper presents an online Thompson Sampling bandit policy to select classifiers that balance predictive accuracy and subgroup fairness under delayed feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on online bandit selection, not model compression or serving efficiency.
The Physics Is the Network: Fast, Training-Free Neural Emulation PolyNets embeds continuous polynomial equations into recurrent neural structures to perform fast, training-free physics simulations with known parameters. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific PDE solvers rather than LLM inference or distillation.
Revisiting Simple Baselines for Relational Deep Learning The paper demonstrates that simple feature propagation and shallow classifiers yield competitive baselines compared to complex architectures in relational deep learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates relational graph architectures, not LLM compression or inference serving.
Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation DualGCN uses dual-branch graph knowledge distillation with topology-smoothed semantic priors to address item cold-start collapse in recommender systems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on graph collaborative filtering distillation, not LLM compression or efficiency.
How Far Can Coding Agents Go in Visual Generation? The study evaluates coding agents generating images via executable code, analyzing their strengths in geometric constraints over photorealistic drawing. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, distillation, or serving efficiency.
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies recurrent neural mechanisms, not model quantization or efficient inference.
Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Not related to model compression, quantization, or small LLMs.
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Surveys existing detector architectures rather than proposing efficient serving techniques.
Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates evidence acquisition loops, not model compression or small models.
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Proposes representation regularization, not model distillation, quantization, or pruning.
Learning to Select Features with Manifold Deep Unfolding PCA The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on dimensionality reduction, not model compression or efficient serving.
Self-Conditioning Enables Deep Sequential Reasoning in Flow Matching Proves theoretically that self-conditioning enables genuine multi-step reasoning in flow matching where standard samplers fail. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses circuit complexity and reasoning depth rather than model efficiency or compression.
Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors Extracts generative image priors directly from the loss-gradient geometry of pretrained discriminative models without retraining. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Mentions calibration secondary use, but main focus is generative score functions.
On the Limits of Representation-Based Test-Time Adaptation for Multimodal LLMs Analyzes why feature-based test-time adaptation fails to recover accuracy on corrupted inputs in generative multimodal language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Investigates representation adaptation failure, not compression or efficient inference.
SAM3RL: Memory Control via Reinforcement Learning for Visual Object Tracking Trains a reinforcement learning controller to manage frame memory dynamically for visual object tracking in foundation models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies visual memory controllers in tracking models, not language model compression or inference efficiency.
Continual pretraining Prostate MRI ViT Encoder for task alignment Applies low-rank continual pretraining to vision transformers to improve task alignment in prostate magnetic resonance imaging segmentation. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on representation alignment in medical ViTs rather than model compression or efficient serving.
Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR Uses contrastive uncertainty learning during training to improve anatomical boundary segmentation in pelvic magnetic resonance imaging. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies segmentation uncertainty representations rather than efficient model inference or compression.
Resolution Allocation with Learnable Resizing and Spectral Filter Parameterization Investigates the interaction between learnable image resizing and spectral-domain convolutional filter parameterization in neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Analyzes image classification filters rather than model compression, quantization, or efficient serving.
Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes This paper proposes a mixture-of-experts transformer architecture using discrete Hodge-Laplacian operators to solve steady partial differential equations on unstructured meshes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on mesh PDE solving, not language model efficiency.
An Initial Study of Physiological Markers for Deepfake Speech Detection This paper investigates 36 handcrafted physiological markers from speech production to detect deepfake speech using standard classifiers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Explores speech feature engineering, not model compression or efficient inference.
Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model This paper proposes an adaptive Gaussian mixture model framework to detect physical-layer channel state information spoofing attacks in wireless localization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Applies standard GMMs to wireless signals, not efficient neural model serving.
Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries This paper analyzes the mathematical limits of statistical safety certification for embodied and agentic AI facing adaptive adversaries. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses theoretical adversary testing bounds, not efficient serving or model compression.
Did the LLM Answer the Question? A Semantic Reconstruction Game for Jailbreak Evaluation This paper proposes a semantic reconstruction game to accurately evaluate jailbreak success by filtering out harmless repetitions and refusals. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on jailbreak evaluation accuracy, not model compression or efficient serving.
A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference This paper constructs an empirical graph of 62 private transformer inference systems to evaluate comparability, cryptographic progress, and scaling behavior. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on cryptographic secure computation, not model compression, distillation, or quantization.
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection This paper analyzes latent backdoor poisoning in flow-based intrusion detection and introduces a perturbation-based diagnostic and mitigation methodology. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Uses pruning solely for backdoor defense in intrusion detection, not efficient inference.
Can LLMs Access their World Knowledge for Event Prediction? The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not explore small models, pruning, distillation, or efficient inference.
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Uses RoBERTa for masking without proposing model efficiency methods.
Tracing Morphology, Semantics, and Divergence in Language Acquisition The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies checkpoints across scales without proposing efficiency or compression techniques.
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Mentions open-weight models in passing without focusing on efficiency techniques.
Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not investigate model compression, distillation, or efficient serving.
DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision The paper introduces a benchmark evaluating the robustness of typed decision models against text, image, and prompt injection attacks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model efficiency, distillation, or quantization techniques.
DivAlgo: A systematic evaluation of algorithmic diversity for code generation models The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient serving.
From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not explore small models, pruning, distillation, or efficient inference.
Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient inference.
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation The paper proposes a method combining offline execution-verified prompt updates and test-time refinement to improve program generation in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or small models.
How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Analyzes benchmarking setups rather than model compression techniques.
NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model pruning, distillation, or efficient serving.
Weighted Ensemble Sampling for Rare-Event Estimation in Language Models The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on rare-event sampling efficiency, not model compression.
From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring The paper presents a framework to formalize compute, labor, and workflow costs as core measurement design criteria for automated essay scoring. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines deployment economics rather than model compression techniques.
Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance The paper evaluates the ability of language model agents to diagnose appliance faults using a simulated appliance benchmark based on sensor data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or efficient inference.

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