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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 91
MLE-World: Predicting Machine Learning Experiment Outcomes with a Learned World Model
AI research agents can propose, execute, and improve programs that solve machine learning tasks.Action-Value Calibration via Continuation Sharing for Long-Horizon Agentic Reinforcement Learning
Reinforcement learning for long-horizon agents relies on feedback from sequences of interactions, making credit assignment to individual actions difficult.How Much Can Best-of-N Help a World Action Model? Measuring Selectable Headroom and Realizing It in Closed Loop
World action models (WAMs) predict the visual consequences of the actions they generate, which makes test-time selection a natural way to improve a fixed policy: imagine the futures of candidate…SimplexLoRA: Dynamic Rank Updating via Alternating Minimization with Simplex Projection
The success of large language models in real-world applications relies on parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA), which inserts compact, trainable adapters into…Retrospective macromolecular pool fraction mapping from conventional clinical MRI: a physics-guided self-supervised deep learning framework
Macromolecular pool fraction (MPF) is a quantitative imaging biomarker sensitive to myelin integrity and other changes in tissue macromolecular content, but specialized acquisitions and…Sharpness-Aware Muon: Matrix-Aware Optimization with Momentum-Guided Perturbations
Muon is a matrix-aware optimizer that combines momentum with orthogonalization to produce structured update directions with strong empirical behavior.Doubly Robust Deep Kernel Tests for Structured Outcomes
We propose a test of whether two interventions induce the same outcome distribution when outcomes are structured and high-dimensional, such as images, text and graphs.When Is a Relation Worth Trusting? Manipulation Costs, Exposure, and the Trust Trap in Multi-Relational Graph Learning
Multi-relational graph detectors learn from clean data how much to trust each relation, but an adversary pays a different price to manipulate each relation and each action; taking such costs as…Your Policy Is Secretly a Harness: The Decision Geometry of Agent Interfaces
Language model agents are post-trained and deployed through a harness, the prompts, tools, and control logic that present a task to the model and execute its outputs.Watermark Basins: Black-Box Detection of Distilled LLMs
Proprietary language models and their reasoning traces are vulnerable to API-based distillation, where an adversary trains a student model on the teacher model's outputs.Low-degree polynomials lower bounds for clustering in small dimension
We study the computational limits of clustering $n$ observations drawn from a mixture of $K$ isotropic distributions in $\mathbb R^d$, in the regime where the number $K$ of groups is much larger…Where Should the Evaluation Budget Go in LLM-Driven Agent Scaffold Evolution?
Large language model (LLM)-driven agent scaffold evolution improves agent workflows through iterative candidate generation and evaluation.Bayesian Inference of Expensive Simulators: Survey and Comparison of Design Choices
We present a survey of approaches to Bayesian inference with simulator forward models, focusing on settings with prohibitively expensive simulators.Structured Representation Probing for Risk Anticipation in End-to-End Autonomous Driving
End-to-end driving policies form structured internal representations of traffic, ego behavior, and road constraints.AnalogScout: Simulation-Efficient PVT-Aware Analog Sizing with LLM Agents
Analog device sizing must satisfy all specifications across process, voltage, and temperature (PVT) corners to ensure tape-out success, but simulating every candidate at all corners dominates the…Scale-aware and spatially-localised optimisation of kilometre-scale MLWP models
Preserving fine-scale structure without degrading spatial accuracy is a central challenge for kilometre-scale machine-learning weather prediction (MLWP).WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization
Cooperative Coevolution (CC) and Non-Decomposition Algorithms (NDAs) are two primary approaches for large-scale global optimization (LSGO).CoS-VQ: Coordinated Shared-Scale Vector Quantization for Large Language Models
Large language models (LLMs) require substantial memory, motivating low-bit weight quantization for efficient deployment.Normative Deviation Modeling for EEG-based Depression Detection
Web-enabled digital health services are expanding access to physiological monitoring and mental health screening.Online Interval Selection with Binary Predictions: Optimal Consistency and a Continuous Robustness Tradeoff
We study online interval selection with binary predictions of membership in a final optimal solution.