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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 222
When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence
This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly can reality change before an intelligent system…Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech
Imagined speech decoding from EEG offers a promising route toward naturalistic brain-computer interfaces.Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth
Calibrating a conformal prediction set at each early exit does not preserve coverage once a data-dependent exit is selected.Verdict: Encode Once, Decide Many
Automating organizational work means answering many small typed questions about one piece of state (does this email need a reply, how urgent is it, who owns it) with probabilities good enough to act…Sliced Wasserstein Sketches with Error Certificates
Compact representations that support geometrically meaningful comparisons are valuable for learning from probability distributions.EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis
Understanding why listeners enjoy music is difficult because the experience is subjective and changes as a piece unfolds.Privacy-Preserving Text Inference for Health-Misinformation Classification and Semantic Retrieval
Health-misinformation analysis presents a dual challenge: misleading claims can affect public well-being, while systems designed to detect them may expose sensitive user-submitted text.Optimal Explicit and Implicit Regularization Strengths in High-Dimensional Continual Linear Regression
We study continual linear regression in a high-dimensional regime, where a learner observes a sequence of tasks and updates its parameters sequentially.Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards
Reinforcement learning with verifiable rewards often trains language models on responses generated by a different, possibly stale behavior policy.O-RAN-Native Jamming Detection Under Network Heterogeneity
Radio-frequency jamming threatens cellular network operations in contested environments.Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception
A mesh of interceptors defending a fixed site must spread over the predicted arrival point of an approaching object.Linear Independence of Polynomial Compositions with Bounded Input Degree
We prove the weak composition-independence conjectures of Kohn, Marchetti, Massarenti and Mella.Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors
We investigate whether per-layer, per-task-phase (prompt/reasoning/response) expert-routing statistics in a Mixture-of-Experts (MoE) language model can guide layer-wise mixed-precision quantization…CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation
Reasoning distillation requires chain-of-thought (CoT) data with correct answers, but obtaining such data efficiently remains challenging.Amortizing Sequential Experimental Design in Reasoning LLMs
Despite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs) struggle to adaptively gather information in experimental design tasks.Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning
Evaluator-guided reasoning presents a local control problem at every step: trust the current answer, return a fallback, or spend more inference.A Phenomenology of Neural Geometry in Vision-Language-Action Models
Robotic manipulation is inherently geometric, but what shape does the world take inside the policy that performs it?Analog RF Computing for Integrated Data and AI Flows Over Wireless Networks
Many resource-constrained edge devices require artificial intelligence (AI) services but are not powerful enough to store or execute neural networks (NNs).Denoising Score Matching in Diffusion Models: The Effect of Rank Constraints on Generalization
Architectural biases restrict the class of functions that a diffusion score network can represent, but their effect on generalization remains poorly understood.Complete or Sparse: A Tale of Two Identifiabilities
Understanding when neural networks learn interpretable representations is central to mechanistic interpretability.