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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 214
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification
Population health stratification frameworks, such as the Italian DM77, organize patients by clinical need and care pathways but offer limited guidance on which care changes to prioritize under…Reinforcement Learning via Advantage Guidance
Reinforcement learning (RL) holds the promise of learning a policy that can outperform what is shown in the data.More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
Self-distillation in agentic reinforcement learning has produced a growing number of methods.StateCraft: Augmenting Video World Models with Abstract-State Reasoning
Video world models aim to simulate complex environments with realistic appearance and dynamics.Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning
Negative guidance of diffusion models generates samples that avoid an undesired condition, specified either by reference samples or by a conditional score alongside an unconditional model.Efficient contextual partial monitoring with general function approximation
We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback.ReproBAIT: Can coding agents build their own biological AI tools?
Large language models are increasingly competent at leveraging scientific knowledge and existing computational tools to execute complex workflows.Calibration and Risk-Budgeted Decisions with System One Models
LLM workflows and agents rely on a set of binary decisions like allow/block an input, or discard/retain a retrieved document.VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting
Spatio-temporal forecasting is fundamental to many real-world systems, yet deployment environments are inherently varying: temporal distributions may evolve, spatial domains may change, and…When Does Depth Help? Understanding Layerwise Inference in Tabular Foundation Models
Transformer-based tabular foundation models (TFMs) achieve state-of-the-art performance on small to medium-sized tabular prediction tasks while processing diverse inputs through a shared stack of…JointServe: Serving Multi-Model Agents on Memory-Constrained GPUs
LLM agents are evolving from a single model calling tools to planners that orchestrate multiple expert models whose capabilities live in distinct weights.MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation
LLMs absorb vast knowledge from training data, yet their inherently probabilistic nature offers no guarantee that this knowledge will surface in their outputs.CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
Although multivariate time series forecasting has broad application prospects, effectively incorporating exogenous variables remains challenging.Rent-Seeking Language Models: Reward Models Set the Prices, Frequent Audits Curb the Rent
Reward model overoptimization in RLHF is usually described as reward hacking.Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon
Memory-efficient subspace optimization has emerged as a practical approach to reducing the memory cost of LLM training while preserving optimization performance.Energy Consumption Analysis of Discrete Diffusion and Autoregressive Language Models
As Large Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern.Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance
In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by poor-quality training data, which obscures…On the Optimisation Landscape of Overparameterised Weighted MMD
We study the optimisation landscape of the maximum mean discrepancy (MMD) over weighted atomic measures.Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards
Training LLM agents on long-horizon interactive tasks with reinforcement learning typically relies on human-annotated tasks and reliable verification, both expensive to construct for stateful…SPARS: A Stability-Plasticity-Aware Region Selection Framework for Continual Learning
Continual learning aims to acquire new knowledge while retaining previously learned knowledge.