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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 148
A Transformer-based Approach for Real-time Multi-agent Path Planning with Varying Initial/Final Conditions
We address the problem of real-time multi-agent path planning under prescribed initial and final configurations, formulated as a high-dimensional optimal control problem subject to inter-agent…Soft Ontology Priors over Latent Concept Posteriors for Weakly Supervised Neuro-Symbolic Learning
Neuro-symbolic (NeSy) predictors that supervise only the task label suffer from reasoning shortcuts: distinct concept mappings achieve the same maximum likelihood, leaving the recovered concepts…Escaping the Phylogenetic Trap: Persistent Homology Enables Cross-Species Ion Channel Classification via Multimodal Graph Neural Networks
Protein classifiers are highly sensitive to evaluation protocol: random train/test splits induce phylogenetic leakage, allowing models to exploit taxon-specific correlations and substantially…AcoustiPrint: Physics-Guided Neural Design of Manufacturable Acoustic Structures
Recent work in additive manufacturing and neural inverse design enables the creation of complex three-dimensional structures to manipulate physical fields, yet these designs must also adhere to…High-Value Training Data from Noisy, Diverse Sources
Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision.Memory-Augmented Online Test-Time Adaptation for EEG Motor Imagery via Perturbation Consistency and Confidence Calibration
Recently, electroencephalography (EEG)-based brain--computer interfaces (BCIs) have gained traction as portable neural measurement systems.Towards Faithful Data Selection: Statistical Limits and Scalable Algorithms
What does it mean to have an optimal data selection algorithm?Stability Analysis of Signed Graphs under Structural Perturbations
Signed graph learning has emerged as a powerful framework for modeling networks with both positive and negative interactions.Same Beliefs, Different Policies: Learning Reusable Representations for Early Stopping in LLM Reasoning
Reasoning LLMs often continue reasoning after reaching a correct answer, wasting computation and sometimes replacing it with an incorrect one.aggmetrics: Aggregating Classification Performance Measures for Machine Learning
aggmetrics is a Python package that extends any binary performance measure expressed in the four confusion-matrix counts to multiclass, multilabel, ordinal, cost-sensitive, soft-label and…Short-to-Long: Detached Features for Long-Horizon Value Learning
Learning useful representations from continually changing value targets remains a challenge in deep reinforcement learning.Flow-based Spectral Kernel Learning for Nonstationary Attention
Kernelized attention provides a useful perspective for understanding self-attention as a similarity-based aggregation mechanism, but existing approaches often rely on fixed or stationary kernels…Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility
Traffic management has traditionally focused on toll-based and road pricing solutions.DiffLTM: Continuous Diffusions Large Tabular Models for Conditional Generation
Conditional generation is a key capability to be supported by Large Tabular Models, enabling a number of applications.Rethinking SAE Evaluation: An Atomic Interpretable Unit Framework Reveals Hidden Polysemanticity and Redundancy
Sparse autoencoders (SAEs) were originally motivated by discovering monosemantic features, a property that characterizes individual features but does not specify what the learned feature set should…FIGFLEX: A Mixed-Precision FP-INT GEMM Accelerator Co-Designed with Low-Bit LLM Quantization
Weight-only quantization keeps LLM activations in floating point and stores weights as low-bit integers, turning the dominant workload into floating-point-integer (FP-INT) general matrix…Optimal Multitask Representations Under a Rate Constraint: An Information Bottleneck Analysis
Which pretraining objective yields the best representation for downstream tasks under an information budget?NEST: Nested Embedding Representation for Multiscale Clustering
Real-world data often exhibits cluster structure at multiple resolutions, yet existing methods typically learn a single partition or construct an explicit hierarchy.LLM2Cert: LLM Synthesis of Certificates with SMT/Lean Verification
Lyapunov functions and barrier certificates give state-space guarantees of stability and safety, but synthesizing and verifying them---especially for nonlinear, high-dimensional systems---remains an…Decision Making and Ride Comfort Evaluation Based on Lane-Change Prediction for Autonomous Vehicles
Predicting surrounding vehicles' lane-change intentions enables autonomous vehicles to anticipate potential hazards and respond proactively rather than relying solely on reactive maneuvers.