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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 72
Diagnosing Optimizer-Landscape Interaction via Representation in Subset Selection Multitasking: Evidence from Piano Fingering
Continuous relaxations are commonly used as an encoding scheme for evolutionary multitasking optimization (EMTO), allowing heterogeneous tasks to be optimized within a unified search space.AI at a Turning Point: From ChatGPT through Neural Networks to Neuromorphic AI
The tension between the established technologies of connectionist AI and their neuromorphic challengers is attracting growing attention, above all because of the enormous energy demand of the former…Optimal Contextual Pricing in Non-Stationary Environments
We study contextual dynamic pricing in a non-stationary environment.GROME: GROunding Factorized MEmory into Video World Models
Long-horizon consistency is essential for video world models, yet it becomes difficult to maintain once observed content falls outside the context window.Possibility-based Inference: A Deterministic Framework for Robust and Reliable Uncertainty Quantification in Decision-Making
Decision-making under uncertainty remains a key issue in Machine Learning (ML), particularly in the deployment of safety-critical systems, where reliable quantification of system behavior is…SHPRR: An Algorithm for Pure Exploration under Constrained Regret
Best Arm Identification (BAI) conventionally measures the cost of experimentation by the number of samples collected.Exploring the Potential of Symbolic Regression in Precision Livestock Farming
The ability to predict farrowing time in pigs is crucial for optimal farm management and ensuring piglet survival.Surprise Alone Learns Lies: Evidence-Gated Continual Learning for LLMs from Streaming Claims
Continual learning methods for large language models increasingly decide what to learn by surprise, keeping the inputs the model finds least predictable.DWISE: Denoising Workload-Informed Scheduling Engine
Unlike autoregressive large language models (AR-LLMs), diffusion LLMs (dLLMs) generate text by iteratively denoising many tokens in parallel, offering a promising path to efficient inference.Attention, Dynamic Wave and Hybrid Sequence Mixers in Language Models
Large Language Models (LLMs) rely on Multi-Head Attention (MHA) for sequence modeling, but scaling to long contexts is constrained by the quadratic cost of attention, motivating sub-quadratic…Probabilistic Meshless Methods on Manifolds
Probabilistic meshless methods solve partial differential equations (PDEs) by placing a Gaussian process prior on the solution and conditioning it on the PDE at a finite set of collocation nodes.Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution
We study post-training for dynamically routed mixture-of-experts (MoE) models, focusing on integrating independently optimized specialists while keeping consolidation efficient.sDMD: Sinkhorn Distribution Matching Distillation
Distribution Matching Distillation (DMD) distills multi-step diffusion models into few-step generators by minimizing reverse KL divergence.TACFormer: Transformer-Based Generative Model for Target-Conditioned De Novo PROTAC Design
Targeted protein degradation (TPD) represents a fundamental shift in drug discovery, moving beyond traditional target inhibition toward the event-driven removal of disease-causing proteins.Data-driven Random-feature Approximation of Koopman Generators of Nonlinear Continuous-time Systems
Learning continuous-time dynamical systems from data is an active area of research.CovFedAvg: Local Low-Loss Curves and Federated Model Selection
Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support.Learning Calibrated Confidence for Multi-Agent Collaboration
Complex information seeking on the Web often requires collecting evidence from multiple documents and reasoning across them, as in multi-hop question answering.Low-Bit GDN Recurrent-State Quantization: Global Gradient-aware Allocation and Pure-Write Sliding Windows
Linear attention stores historical information in a recurrent state of fixed size, providing a compact memory representation for long-context inference.H$^2$R: Hierarchical Hint-Based Reward for Mathematical Reasoning via Reinforcement Learning
Reinforcement Learning with Verifiable Rewards has shown great promise in enhancing mathematical reasoning in large language models.When to spend privacy: optimal allocation in private learning
Privacy accounting bounds the cumulative privacy cost of Differentially Private Stochastic Gradient Descent but does not determine how to allocate a fixed budget across iterations.