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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 109
A Nonconvex Framework for Joint Sparse Sample Weighting and Parameter Estimation
We propose a joint sparse sample weighting and parameter estimation (SWAPE) framework that jointly optimizes model parameters and sample weights to train a model while simultaneously assigning an…How Do Small Coding Agents Use Code Graphs and Embeddings?
Coding agents are given embedding search and code-graph tools on the premise that a repository's structure is something the model lacks and the tool supplies.QECGBERT: A Scalable ECG Foundation Model Based on Self-Supervised Language Learning via VQ-VAE Waveform Tokenization
AI foundation models play a pivotal role in advancing heart disease diagnosis from electrocardiogram (ECG) data, particularly for rare diseases where the lack of sufficient labeled data hinders…Performance of Neural Operator Baselines on 'The Well' Dataset
The Well, a 15-terabyte dataset spanning 16 physical simulation domains, provides an extensive benchmark for developing and evaluating machine learning surrogate models for complex physical systems.Hierarchical Cooperative GFlowNets
Generative Flow Networks (GFlowNets) are trained to sample objects from compositional spaces with probability proportional to a reward function, but ineffective exploration can cause learned…EmoNet: A Lightweight Attention-Based CNN for Speech Emotion Recognition
Speech Emotion Recognition (SER) is a fundamental task in affective computing that enables applications such as mental health assessment, human-computer interaction, and automated customer support…Constrained Gradient Equilibrium: Approachability under Locally Constrained Adversaries
Gradient equilibrium (GEQ) is a recent framework in online learning that requires the time-averaged gradients along the learner's trajectory to vanish.Resolving the Mobility-Equity-Emissions Trilemma: A Causal Machine Learning Framework for Dynamic Tolling Policy
We propose a causal machine learning framework to integrate dynamic tolling for mobility, equity, and climate objectives, resolving a long-standing policy trilemma.Steering the Teacher: Improving On-Policy Self-Distillation for Thinking Models
On-policy self-distillation (OPSD) trains a student on its own reasoning trajectories using token-level supervision from a reference-conditioned teacher initialized from the same model.Partial Modal Tensor Regression
With the rapid development of modern scientific research, tensor data have become increasingly prevalent across a wide range of fields.Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems
Conventional federated learners implicitly optimize for global means, creating a "one-size-fits-all" paradigm that inevitably suppresses the minority under cross-site heterogeneity.Cross-User Adaptation for sEMG Gesture Recognition via Continual Learning
Prosthetic and assistive interfaces increasingly depend on decoding hand gestures from surface electromyography (sEMG).The Information Geometry of Representation Formation
Most analyses of neural representations focus on trained endpoints, leaving the process by which internal structure forms comparatively unmeasured.BriLLM-MetaPred: A Representation-Prediction Unity Language Model with Adaptive Self-Measurement
We present BriLLM-MetaPred, the first engineering implementation of the MetaPred machine-learning framework established by a mathematical theory of AGI, instantiated for language modeling.Deep Scientific Reasoning under Physical Constraints: Structure-Aware Spectrum Prediction
Structured scientific spectra encode rich physical information while obeying hard constraints, such as conservation laws and characteristic spectral geometry.The Symmetry of Differential Neural Tangent Kernel under the Orthogonal Group
The differential neural tangent kernel (DNTK) is a tool for analyzing the training dynamics of overparameterized physics informed neural networks.TCSP3.0: Template-Based Crystal Structure Prediction with Coefficient-Preserving Element Substitution and Structural Diversity
Template-based crystal structure prediction (CSP) transfers a known crystal structure to a new composition by element substitution, so its accuracy depends on retrieving suitable templates, mapping…Prior-Data Fitted Networks for In-Context Off-Policy Evaluation
Off-policy evaluation (OPE) is essential for assessing decision policies before deployment when online experimentation is costly or unsafe.Holding a Steady Course: Knowledge Evolution from Expertise and Lessons for Continual Agent Learning
Autonomous agents are increasingly deployed across sequences of related tasks, yet they often fail to retain and improve their knowledge over time.A Sign Law for Entropy Drift under Sampler Mismatch in RLVR
In reinforcement learning with verifiable rewards (RLVR), whether entropy rises or falls can depend on choices that matter little under exact sampling, such as whether over-length responses are…