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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 13
Learning Pseudo-Riemannian Manifolds for Heterophilic Graphs via Graph Signature
Several widely used families of Graph Neural Networks carry a positive-definite geometric prior.Consistency Learning and Distillation for One-Step MIMO-OFDM Channel Estimation
Iterative neural channel estimators can improve recovery under pilot contamination, but repeated network evaluations increase inference cost.The Split Protocol Decides the Result: Re-Evaluating Encrypted Traffic Classification
Encrypted traffic classifiers are routinely reported at 95-99% accuracy.When Does Risk-Controlled Throughput Forecasting Actually Control Risk? A Reproduction Study on LEO Satellite Link
Risk-budgeted throughput forecasting converts a point forecast of LEO satellite capacity into a conservative bound whose overestimation rate is held at a user-specified budget, so that admission…Conformal Prediction for Distribution-to-Distribution Regression
Conformal Prediction (CP) provides a model-agnostic framework for constructing finite-sample prediction sets with coverage guarantees and has become a tool for reliable decision-making in high-risk…Mass Producing Training Data with Surprising Side Effects
Training data can have unexpected side effects on language model behavior outside the training domain: insecure code data can induce broad misalignment, and outdated bird names can induce a…Learning to Tune with Biased Gradients: Iterative Feedback Tuning of Model-Free Control on Throttle Valves
Iterative feedback tuning (IFT) learns the parameters of a fixed-structure controller by stochastic gradient descent.CoRTeC: A Differentially Private Release for Synthetic Tabular Data, Decodable by Any Generator
The records that could train the most useful models, such as a hospital's patient files, are often the ones an institution cannot share.Flexible Intensities Matter: A Comprehensive Re-evaluation of Hawkes Processes and Neural Temporal Point Processes
Temporal point processes (TPPs) are the canonical framework for event sequence data, with applications spanning seismology, social media, and electronic health records.EEGDash Corpus: A Training-Ready Public Corpus for EEG Foundation Models
Foundation models for electroencephalography (EEG) are pretrained on tens of thousands of hours of recordings, and their accuracy grows with that amount.Give Up to Get More: Priors, Not Policies, Govern Test-Time Compute Allocation
When many inputs share a budget of repeated attempts by a large language model (LLM), a common rule gives harder inputs more.From hydrological laws to catchment models: A process-based model matches deep-learning accuracy on three continents
Dooge asked whether hydrology has laws at the catchment scale.KolTa: A Physics-Informed Active Learning Framework for Formulation Discovery with Limited Data
Finding high-performing formulations is difficult when few have been measured and experimental budgets are limited.GIST: Gibbs-Targeted Hierarchical Data Selection for Offline RLVR
Offline reinforcement learning with verifiable rewards (RLVR) trains on a fixed pool of sampled responses and typically supervises every token of every response, although responses and tokens are…Zero-Shot Coordination among LLM Agents
We study zero-shot coordination (ZSC), where independently developed agents must coordinate at test time.A Bag of Tricks for Defending Deep Learning-based Face Recognition Pipelines against Backdoor Attacks
The protection of mature, multi-stage Face Recognition Systems is crucial to system providers.D3D: Decoupled Diffusion Drafting for Asynchronous Speculative Decoding
Target-conditioned diffusion drafters predict candidate tokens in parallel, yet each new block must wait for updated target-model hidden states.Partially Interpretable Decision Trees
Partially interpretable models extend simple interpretable models with the option to defer complicated inputs to an accurate but uninterpretable black-box model.PARGAR: Pilot-Aware Graph Attention for Sparse-Pilot Neural OFDM Receivers
Reliable channel estimation and symbol detection in 5G New Radio (NR), as well as emerging 6G radio systems, rely on demodulation reference signals (DMRS).Hardwired Distributed Processing Units for Inference: Neural Decisions at the Sensor that Free the Host
We show that a hardwired distributed processing unit (HDPU) can take a neural decision at the sensor and so remove the inference from the host.