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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 229
AlignPred: Error-Controlled KV Cache Compression via Alignment and Predictive Coding
Long-running agents retain KV caches across turns and sessions for hours or days.Rarity Determines Minimax Rates of Semantic Entropy From Weak Pairwise Judgments With a Known Channel
The semantic entropy of a language model is the entropy of the meanings of its sampled answers.All-electron machine-learned orbital-free density functional for periodic CHNO systems
We introduce STRUCTURES-P, a machine-learned all-electron orbital-free density functional for periodic CHNO systems.Exact power indices for plurality-voting ensembles
Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted class, with ties typically broken at random.Counterfactual Certification for Sequential Unlearning with Long-Run Performance Guarantees
Certified machine unlearning ensures the proximity between the unlearned model and a model retrained without the data to be forgotten.Improving Neural Symbolic Regression with a Diverse Prior and Post-training Alignment
Symbolic regression is the problem of finding an expression that fits a dataset.Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentially.Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models
Diffusion language models (DLMs) enable flexible parallel generation by resolving tokens according to the model's evolving predictability rather than a fixed left-to-right order.Correcting or Continuing? What On-Policy Distillation Learns from Student Errors
On-policy distillation (OPD) trains a student on its own rollouts under a frozen teacher, mitigating the distribution mismatch of off-policy supervision.FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation
Graph fraud explanation generation emerges as an important task, which requires jointly leveraging graph structure and content information to produce human-readable explanations for a given risk…SHELDON: Reasoning through Dual Search over Hypothesis and Observation Spaces
Test-time scaling has enabled LLMs to solve increasingly complex reasoning, discovery, and optimization tasks.ChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression
Some observation-window-based KV cache compressors use prompt attention to identify important tokens but allocate uniform or predefined budgets across heads, overlooking input-dependent differences…Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be limited by their training methods, with…Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees
Tabular learning tasks often suffer from class imbalance, which is often addressed via weighting-based strategies.High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics
High-fidelity three-dimensional turbulent flow fields are essential for scientific and engineering analyses, yet obtaining them through direct numerical simulation or high-resolution measurements…inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs
This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine whether specific samples were used during…LADDER: A Long-Horizon Robot Manipulation Benchmark
General-purpose robot manipulation policies learn short-horizon skills effectively, but degrade rapidly on long-horizon tasks.Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption
Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions.Reliable Scale-Free Bandits via Empirical Likelihood
We study stochastic bandits without knowledge of reward scales or tail parameters.From Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning
Transformers can implement reinforcement-learning algorithms in context, yet how off-policy control emerges within Transformers remains poorly understood.