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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 166
Dynamical Batching: How Class-Cycling Training Shapes the Internal Structure of Neural Networks
We investigate whether cyclically varying the class composition of training batches can modify both the performance and the internal organization of neural networks.TaQFormer: A Treatment-as-Query Transformer with Data-Fusion for Multidimensional Treatment Modeling in Coupon Allocation
We study coupon allocation on an online marketplace offering multiple service categories.OceanTensorCube and ST-MMT: A Multimodal Data and Transformer System for Seven-Day Forecasting of Pyropia Bleaching
Bleaching of cultured Pyropia (laver) causes losses in Korean seaweed farming.RECAP: Reciprocal Sparse Attention and Hierarchical Caching for Efficient Video Diffusion
Diffusion transformers generate high-quality videos but remain computationally expensive.SEAM: Spectral Embedding Alignment and Merging for Low-Resource TTS
Expanding the vocabulary of conditional text-to-speech (TTS) models typically requires extensive fine-tuning with paired audio, creating a major bottleneck for low-resource and cross-lingual…A Pitfall in the Evaluation of Gene Perturbation Models
Gene perturbation models aim to predict transcriptional responses to unseen genetic interventions, but comparisons rely on heterogeneous evaluation metrics and differentially-expressed (DE) gene…Trust Your Targets: Is Target Propagation Block-Diagonal Gauss-Newton?
Target propagation (TP) is a backpropagation alternative in which local targets carry the global learning signal.Learning SOCP Solver Dynamics with Graph Neural Networks
Second-order cone programs (SOCPs) are a fundamental class of convex optimization problems widely applied across engineering domains.Toward Online Robust Zero-Sum Markov Games with Function Approximation
Online robust zero-sum Markov games pose a coupled operator-learning and game-solving problem: from nominal interaction data, the learner must recover a worst-case minimax Bellman operator while…CAST: Cast-Admissible Generative Design and Shape Optimization with Paired Depth Images
Deep generative design can produce complex geometries, but unconstrained representations may create undercuts that prevent release from a two-part mold.TriScore: Post-Hoc Out-of-Distribution Detection with Energy, Boundary Probes, and Transform Consistency
Post-hoc out-of-distribution detection usually relies on a single signal extracted from a frozen classifier, such as confidence, energy, features, or gradients.The Control Layer Behind Rule-Following Agents Trading Across Market Conditions
Trading discipline often fails people.Typed Decision Models under Prevalence Shift: Decision Training, Calibration, and Implicit Priors
Typed decision models (TDMs) return a probability over caller-defined options, and Jev is described by its vendor as calibrated.CIS-Transolver: A Cross-Layer Incremental-State Neural Operator for Fine-Scale Automotive Aerodynamic Flow Prediction
Neural operators offer an efficient data-driven approach to rapid prediction of complex automotive aerodynamic flows.Logit Averaging During Training Sets a New State of the Art for Non-Invasive Speech BCI
Non-invasive brain-computer interfaces are in practice limited by signal-to-noise ratio, and recent state-of-the-art methods address this limitation by aggregating over repeated trials of the same…PhyPro-FM: A Physics and Prompt-Guided Graph Foundation Model for Contingency Analysis
The continuous evolution of next-generation power systems, characterized by the high penetration of renewable energy resources and dynamic topology variations, poses formidable challenges to…An Empirical Study of Parameter-Efficient Fine-Tuning for Mixture-of-Experts LLMs
Parameter-efficient fine-tuning (PEFT) methods are critical for adapting large models under resource-constrained settings.CTLite: Lightweight Irregular Multivariate Time Series Forecasting with Reusable Continuous-Time Summaries
Irregular multivariate time series forecasting (IMTSF) requires capturing temporal dynamics and cross-variable dependencies from sparse, asynchronous observations.Selecting the Best Few with Human Evaluators: Adaptively Combining Ratings and Pairwise Comparisons
This paper considers a decision-maker tasked with identifying the $k$ best items from a large pool of candidates, such as job applicants, LLMs, award nominees, or new product ideas.ForagerSim: Evaluating Embodied Map-Based Reasoning in Large-Scale Dynamic Worlds
Animals build cognitive maps of vast environments, use them to perform map-based navigation, and might even be reusing the same circuitry used in spatial reasoning for creating and using maps of…