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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 178
CD-OPD: Contextual Digest for On-Policy Distillation
Teacher takeover in on-policy distillation (OPD) can produce incorrect responses even when the student and teacher independently answer the same problem correctly, a failure we quantify using…Operator-Mismatch Contrastive Learning for World-Relative Trajectory Coherence
World models are increasingly judged by how well they rank plausible futures, yet plausibility alone is the wrong target.GreenNIDS: A Benchmark Workflow and Analysis Dataset for NetFlow Intrusion Detection Experiments
Comparing the predictive performance and computational cost of flow-based intrusion detection experiments requires both quantities to be available for each run.Multi-site Learning for Forecasting Day-Ahead Wind-Power Production for Onshore Wind Farms in Great Britain with Deep Neural Networks
Accurate day-ahead wind-power production forecasting is increasingly important for energy system operations, market activity and balancing Great Britain's grid as wind generation grows.Whistle and Wrong Lyrics: Synthetic Queries Are Not Enough for Real-World Query-by-Melody
People look up a song by humming it, but also by whistling it or singing it with the wrong words.Mining the Self-Rewriting System: A Vision for Data Mining on AI-Native Infrastructure
Classical data mining treats data as generated outside the learner.TILDE: Teacher-in-the-Loop Distillation from Embodied Foundation Models
Large-scale action pretraining equips Embodied Foundation Models (EFMs) with strong manipulation capabilities, with particular strengths in generalization and closed-loop robustness, but reproducing…Simulating Infant Senses with Age-Tuned Networks
What a newborn sees, hears and feels, and how this changes month by month, has been measured sense by sense, with different methods and in different units.Outcome-Anchored Credit Reallocation for Agentic Knowledge Graph Question Answering
Agentic knowledge graph question answering (KGQA) requires large language models (LLMs) to solve complex questions through multi-turn interactions with a knowledge graph (KG).Structured Relevance Determination and Channel Additive Gaussian Processes for Image Data
We introduce radial basis function (RBF) kernels for Gaussian processes that use structured hyperparameter sharing for image-valued inputs.The Hidden Look-Ahead: Point-in-Time Leakage in Electricity Market Data
Empirical studies of electricity prices routinely state that their models use only information available at the time of the trading decision.Paloma: Phase-Conditioned Residual Modulation for Time Series Forecasting
Explicitly modeling periodicity has proven to be an efficient approach for time series forecasting, where the residual, obtained after removing the periodic component from the time series, is…MOSAIC: Multi-Objective Search for Allocating Inference Cache Across LLM Decoder Layers
The Key-Value (KV) cache is the main memory bottleneck of long-context LLM inference.RARE: Reinforcement Learning with Adaptive Rubric Evolution for Open-Ended Generation
Verifiable rewards have advanced reinforcement learning in mathematics and code.Enhanced Masked Diffusion Unlearning
Masked diffusion language models (MDLMs) generate text by iteratively unmasking tokens with bidirectional context.Semi-supervised learning on singular data sets
We propose to model singular data clouds by subanalytic sets, and develop an approach to semi-supervised learning in this setting.BiDex-100: A Comprehensive Benchmark for Bimanual Dexterous Manipulation Across 100+ Simulation Tasks
Bimanual manipulation with dexterous hands has attracted growing attention in research on robotic action models.Reliability-Preserving Compression for Deployable Medical Image Segmentation
Post-training compression of pretrained medical segmentation models commonly uses quantization, pruning, or distillation to reduce model size and computational cost.On Reasoning Correctness Directions
We present experimental evidence suggesting that information about whether a model's reasoning is on the right track can be linearly decodable without defining a causally useful direction for…Near-Optimal Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
We study the last iterate of stochastic gradient descent (SGD) with a constant stepsize on smooth convex objectives in the low-noise and interpolation regimes, where the variance $σ_\star^2$ of the…