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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 88
HeteGCD: Statistics-Aware Dual-Prototype Learning for Generalized Category Discovery on Heterophilous Graphs
Heterophilous graphs are prevalent in real-world applications, where connected nodes often exhibit dissimilar attributes or belong to different classes, posing significant challenges to conventional…RankFormer: Rethinking Time Series Transformers through Representation Capacity Analysis and Progressive Decoding
Multivariate time series forecasting plays a critical role in Web and cyber-physical systems, where large-scale services and IoT infrastructures generate complex temporal data.Conservative Dueling Bandits: Preference Learning under Baseline Constraints
Contextual dueling bandits learn from pairwise comparisons, the same feedback used to fine-tune language models from human preferences.SMEBENCH: Benchmarking Neural Music Editing with Synthetized MIDI Edits
Neural music editing requires a system to perform a requested change while preserving the musical content that should remain unchanged.Nonlinear Direct Feedback Alignment for Scalable Backpropagation-Free Training
Backpropagation (BP) remains the predominant algorithm for training deep neural networks, but it is biologically implausible and suffers from the backward locking problem, constraining…Neuronal Identity as an Organizational Basis for Analyzing Neural Population Dynamics
Individual neurons exhibit diverse molecular, morphological, and electrophysiological properties that shape their distinct roles in neural circuits.Generative Refinement Learning for Temporal Interpolation in Scientific Machine Learning
Computationally expensive simulations and real-world observations often provide data at a lower temporal resolution than downstream tasks require.Robust LLM Data Selection via Content-Invariant Gradient Attribution
Gradient-based data attribution has become an important tool for high-quality data selection in the instruction fine-tuning of large language models (LLMs), as it estimates the contribution of…Frequency-Uniform Log-Regret Reinforcement Learning for Diffusions
High-frequency control problems are often studied through time discretization, but refining the timestep $δ$ increases the discrete horizon $H=T/δ$ and can make standard finite-horizon regret…Batching the Reflective Optimization Loop: Parallel Proposals Make GEPA Faster and Better
Reflective text optimization makes agentic systems better, but the optimization loop is itself a resource-hungry workload whose sequential structure leaves parallel evaluation capacity idle.Does LLM Pre-Training Typically Occur at the Edge of Stability?
Quadratic approximations provide a common lens for studying neural network optimization, but recent evidence challenges their predictive validity.Select-TTT: Query-Conditioned Selective Fast Weight Updates for In-Place Test-Time Training
Test-time training (TTT) enables a deployed language model to internalize incoming context on-the-fly into a small set of fast-weight parameters, rather than merely treating it as transient context…API-TAIL: Detecting Model Substitution in LLM APIs through Temperature-Aligned Likelihood Tails
Commercial language model APIs return only text, allowing a provider to cut costs by quietly routing a fraction of user requests to a cheaper substitute model while billing for the advertised one.LLM knowledge for improved Out-of-Distribution Tabular Prediction - A Cross-Validated Regularisation Approach
Prediction models degrade when the deployment population differs from the one used to train them, and training data cannot say which feature-response relationships will transfer.SetGRPO: Calibrating Grader-Induced Policy Optimization
Rubric-based reinforcement learning uses fine-grained grader feedback to train large language models on open-ended tasks, but grader randomness can alter GRPO updates in ways that score…Variable-Length Feedback Coding With Universal Decoding and Bandit Channel Selection
We study variable-length feedback (VLF) coding over $K$ additive white Gaussian noise (AWGN) channels whose noise variances are unknown to both terminals but lie in a known interval.TAPESTRY: A Single Backbone for Video Generation and Robot Control
Pretrained video generation models encode rich priors about physical interactions, but leveraging them for robot control typically requires either resource-intensive video generation at test time or…Joint Distributional Copula Regression for Insurance Claim Frequency and Severity via Boosting
In this paper, we introduce a model-based boosting approach for fitting structured additive distributional copula regression models for bivariate responses, with particular emphasis on jointly…When All Rollouts Fail: Policy-Compatible Joint Decoding for Hard-Problem Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) derives its learning signal from binary rewards via on-policy exploration and becomes a powerful paradigm for improving the reasoning of large…Boundary Geometry Determines Optimal Shallow ReLU Approximation under Weighted Coefficient Constraints
We study how boundary geometry affects $L^q$ approximation by shallow ReLU neural networks on convex bodies $K\subset\mathbb R^d$.