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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 98
Pro-Active Inference
Estimating a population mean is fundamental across scientific applications, but observing every outcome can be prohibitively expensive.Beyond Multiplicity Correction: Sharp Retrieval Thresholds for Attention with Correlated Distractors
Redundant context affects attention in two distinct ways: multiplicity adds normalization mass, while score variation within correlated groups can produce extreme individual scores.Good Rankings, Poor Choices? Rethinking Language-Only Vision-Language Model Selection
Language-Only Vision-Language Model Selection (LOVM) aims to identify the Vision-Language Model (VLM) with the highest zero-shot accuracy on a target task using only textual information.Machine Learning Applications in the Solar Interior and Atmosphere
The solar interior and atmosphere constitute a strongly coupled, multiscale dynamical system defined by continuous temporal evolution.IsoAct: Geometry-Aware Post-hoc Debiasing via Isometric Actions
Learning representations that are robust to nuisance factors is essential for reliable deployment, but remains challenging when the representations lie on non-Euclidean manifolds.Intrinsic Information Rates of Summaries in Simulation-Based Inference
Simulation-based inference often encodes observations $Y$ into a fixed-dimensional summary $S$, with the posterior conditioning only on $S$.Stable Continuous-Time Consistency Distillation: An Empirical Study with a Multistep Extension
Continuous-time consistency models are an attractive route to few-step generation.Sparse Reconstruction by Direct Measurement-to-Field Generation in Function Space
Understanding physical systems often requires recovering their full spatial state from measurements available only at sparse locations.SearchHorizonBench: A Benchmark for Search Value in AI Agents
AI agents must decide when another search is worth its cost.Discovering Topics from Linear Autoencoders
Linear recommenders and topic models are similar in that they implicitly learn from co-occurrences of items.Mechanistic Insights into LoRA: Layer Sparsity for Adaptive Fine-Tuning via Path Patching
Low-Rank Adaptation (LoRA) has emerged as the dominant paradigm for parameter-efficient fine-tuning of large language models.TimeWorld: Towards Action-Conditioned Multimodal World Models for Time Series Forecasting
Time series forecasting supports decision-making in web applications such as online retail and network management, where planned actions can reshape future dynamics.Centralized Critics for N-Agent Ad Hoc Teamwork
Centralized training with decentralized execution (CTDE) has become the dominant paradigm in multi-agent reinforcement learning (MARL), motivating extensive study of what privileged information…When Does Data Value Reduce to Class Balance? A Coverage View of Per-Point Data Valuation
Per-point data-valuation scores -- Data Shapley, influence, leverage -- are routinely used to select training data: keep the points with the top $k$ scores.Precedence-Aware Resource Allocation: Extending the AUTOC Framework to Multi-Level Treatments
Many real-world decision problems require allocating limited resources across individuals to maximize total benefit often through interventions that vary in intensity rather than binary treat-or-not…Can System-1-Style Decision Models Replace Dedicated Rerankers in RAG? A Systems Evaluation
Retrieval-augmented generation (RAG) systems often use a dedicated reranker to choose which retrieved candidates reach the language model.Interpretable Brain Age Prediction Using White Matter Fiber Orientation Distributions with Rotation-Equivariant Deep Learning
Diffusion MRI (dMRI) provides information about white matter microstructure that can support brain age prediction.Transporting Quantiles to Codebook: Scalable Vector Quantization without Codebook Collapse
Effective discrete representation learning with VQ-VAE serves as a fundamental component of modern autoregressive generative models.Streaming Recurrent Memory
Natural intelligence learns from a continuous stream of sensorimotor experience: data can arrive at high bandwidth, and behavior adapts to feedback as soon as it arrives, even when it is delayed.The Noise-Transmission Burden of Diffusion Network: Why Image Prediction Can Outperform Noise Prediction
Clean-image prediction ($x$-prediction) and noise prediction yield equivalent population-optimal denoisers, yet can perform very differently in finite diffusion networks.