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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 201
Efficient Pairwise Difference Learning: A Comparative Study of Encoding Strategies and Training Pair Selection
Pairwise Difference Learning for Classification (PDC) enhances supervised learning in low-data regimes by training a binary similarity classifier on pairs of instances rather than individual…The Self-Improvement Problem
With the rise of large language models, agentic systems are becoming increasingly autonomous, as they write code, call tools, revise prompts, and propose modifications to their own components.Conditional Mean Flows: Few-Step Densities for Simulation-Based Inference
Flow matching has emerged as a powerful generative family for simulation-based inference (SBI).When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks
Corporate financial disclosures often unfold over time, with announcements, prepared remarks, and discussion spanning multiple high-frequency return intervals.Approaching Backpropagation Accuracy with Weight Perturbation on MNIST
Weight perturbation (WP) estimates learning directions from random parameter perturbations and a scalar change in loss, without backpropagating derivatives.Diffusion Language Models as Open Quantum Systems
Diffusion language models generate text by reversing a classical diffusion process over token sequences.Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model
In this paper, we present a lightweight algorithm for modelling tabular data containing both continuous and discrete variables.Router Drift Control: Mitigating Off-Policy Degradation in Mixture-of-Experts Reinforcement Learning
Reinforcement learning (RL) can become off-policy due to training-inference mismatch, mini-batch updates, and asynchronous training.MARS: A Momentum-Aware Adaptive Rating System for Personalized Assessment
Adaptive assessment systems personalize learning by estimating learner proficiency and recommending suitable questions.ShiftQAT: Quantization-Aware Training for the Numerical Contract of a Fixed-Function Edge CNN Accelerator
Fixed-function CNN accelerators make edge inference fast and energy-efficient, but they execute only networks that obey a precise numerical contract.Human Interventions as Labels: Localizing, Steering, and (Sometimes) Editing the Computations Behind LLM Agent Failures
When an LLM agent misbehaves, a human steps in, and that intervention is usually discarded or replayed as training data.Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm
Robust covariance estimation under entry-wise contamination and missing data is challenging in various signal processing applications.How language model agents reshape price competition
Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets, standard learning procedures typically select the stage-game Nash outcome with prices…Encoded but Exposed: Language Embeddings can Reveal More Than They Should in Recommender Systems
Language embeddings are increasingly used in recommenders to represent item content, but their privacy risks remain poorly understood.International Multi-Centre Validation of Unsupervised Domain Adaptation for Precise Discrimination between Normal and Abnormal Retinal Development
Arrested retinal development can cause childhood visual impairment, but grading optical coherence tomography scans is difficult to standardise across devices and centres.FIRM: Fisher-Informed Retention Merging for Federated LLM Unlearning
Federated LLM unlearning addresses targeted knowledge removal when forget and retain data remain distributed across private clients and are inaccessible to the server.Reliable Knowledge Acquisition for Continual Learning Agents
As agents powered by foundation models move into open-ended environments, continual learning from experience becomes essential.Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization
Echo State Networks (ESNs), as lightweight implementations of reservoir computing, provide efficient solutions for temporal data processing in resource-constrained environments.Pairwise AUC Optimization Needs Corrective Power: A Unified View
Direct pairwise AUC-surrogate training is attractive because it trains toward the ranking metric used at test time, especially under class imbalance.The Delegation Dilemma: Economies of Scale, Accountability, and Polycentric Governance
Many collective-action systems are neither fully decentralized nor fully centralized: agents voluntarily organize into institutions that share organizational costs and delegate decisions to…