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Computer Science & AI
Much of the new work in computer science and AI appears first as a preprint on arXiv. ReadSearch reads those listings every day, with papers OpenAlex files under the same subjects, and this page shows the newest it has, from machine learning and language to systems, theory and security.
The newest 1,000 papers, from 4 November 2026 to 5 November 2026: 1,000 arXiv preprints.
Newest papers, page 38
Editing Large Language Models with Geometry-Aware Regularization
Knowledge editing has become a key technique for updating knowledge in large language models.Mobility-Aware Mobile Edge Computing for Energy-Balanced Data Collection in IoT-Enabled Underwater Sensor Networks
Underwater wireless sensor networks (UWSNs) face critical challenges in energy imbalance, connectivity, and data collection due to harsh environments.ProGram: A Grammar for Progressive Computation Pipelines
Progressive Visual Analytics (PVA) systems have demonstrated the effectiveness of time-bounded, incremental computation for exploring large-scale data.GPUMAP: GPU-Accelerated Progressive Uniform Manifold Approximation and Projection
Dimensionality reduction (DR) techniques are fundamental to the analysis of high-dimensional data, yet their substantial computational cost limits their use in interactive settings.Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data
Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced.Annotator-Free Object Detection via Foundation Model Collaboration
Object detection research remains constrained by high annotation costs and the closed-set assumptions of supervised models, limiting scalability to novel categories.Beyond Terminal Constraints: Reliable Robot Control with Learned Safe Sets
Ensuring safety in advanced robot control methods such as Model Predictive Control (MPC) and Reinforcement Learning remains an open challenge.BiCG-Net: Target-Specific Image Quality Assessment via Observation Modeling and In-Image Differencing
We estimate the quality of a specific target region (e.g., a train body) in an image, rather than the whole image.CoSafe: Cooperative Runtime Safety Assurance for Mixed-Traffic Autonomous Driving
With the rapid development of autonomous driving systems (ADSes), real-world roads are evolving into mixed-traffic environments where autonomous vehicles (AVs) and other traffic participants (e.g…Cross-Modal Semantic Successive Refinement for Generative Image Communication over Underwater Acoustic Channels
Underwater acoustic communication faces challenges such as limited available bandwidth and complex channel environments, which impose demanding requirements on efficient image transmission and…Loop Before You Leap: What Recurrent Depth Can and Cannot Buy in Data-Limited Pretraining
Multimodal models have often scored below text-only baselines in the BabyLM Challenge (Klerings et al., 2024; Takmaz et al., 2025).Performance Portability of Out-of-Kernel Optimizations on GPUs: A Molecular Docking Case Study
Achieving performance portability is challenging for irregular scientific applications built as repeated multi-kernel GPU pipelines, because throughput depends on out-of-kernel decisions as much as…Sublinear Approximations for Spanning Tree Congestion
The spanning tree congestion of a connected graph \(G\) is the minimum, over all spanning trees \(T\) of \(G\), of the maximum number of edges of \(G\) crossing a cut induced by deleting an edge of…Riemannian Optimization-based Whole-body Obstacle Avoidance for Mobile Continuum Robots in Cluttered Environments
Inspired by the coordination of global motion and local shape adaptation in snakes, elephant trunks, and octopus arms, we propose a unified planning and control framework for mobile continuum robots…A Definition of Catastrophic Performance
A central challenge in AI is specifying objectives that align with human preferences.FedXCrop: Privacy-Preserving Crop Disease Detection via Federated Learning with Post-Aggregation Explainability under Non-IID Farm Distributions
Deep learning can classify crop diseases accurately from benchmark leaf images, but centralized training requires farms to share raw data, and predictions without explanations offer limited support…Consistent Submodular Maximization with Full Foresight
Online algorithms with bounded recourse face three intertwined obstacles: uncertainty about the future, computational limits, and a limited budget for changes.Explainable Anomaly Detection in 5G Networks through Continuous Time Graphs
5G networks increasingly resemble distributed systems in their design.Understanding the Perception of Transliterated Offensive Social Media Posts Among Banglish and Hinglish Users
Effective content moderation depends on understanding not just what harmful content looks like, but how users perceive and respond to it.Separation Is a Riesz Energy: How Many Environments Does ICA Need?
Can independent components be recovered when all of them are Gaussian?
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