A field on ReadSearch
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 16
StitchEdit: Stitching Depth Priors into Image Editors via Per-Layer Gradient Probing
Text-guided image editing with diffusion models struggles with spatial edits (e.g. manipulating objects, changing viewpoints, manipulating occlusions) that depend on the 3D structure understanding…AraEngage: Interpretable Linguistic Correlates of Engagement in Arabic Facebook News
Engagement features developed for English are routinely applied to Arabic, but unvocalized Arabic changes what a lexical feature measures.ZeroRAG: Adaptive Semantic Peer Routing for Decentralized Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a standard approach to enhance the reliability of large language model (LLM) outputs by retrieving documents relevant to a query and supplying them in the LLM…Flux as a Codec: Zero-Shot Lossy Compression with Flux
We propose FluxC, a zero-shot codec that uses Flux as a compression prior.How Generative AI Redefines Personalization and Diversity in the Newsroom
The tension between news personalization and information diversity has traditionally been understood as a matter of different content reaching different people, with diversity evaluated by the range…AttnSR: Attention Step Reward for Long-Horizon Agentic RL
Long-horizon LLM agents make multi-step decisions, yet outcome-reward-only reinforcement learning provides little direct supervision for individual steps.The Blindfolded Controller: Decision Model-Guided Training for Reinforcement Learning
Reinforcement learning (RL) provides a framework for continuous improvement through interaction with an environment.Towards Scalable Diagnostic-Driven Whole-Slide Image Retrieval for Pathology Archives
The growing adoption of digital pathology has rapidly expanded collections of hematoxylin-and-eosin whole-slide images, yet efficient navigation through these archives remains challenging, as…Joint Modeling of Conditional Mean and Variability in Multilevel Functional Data: Application to Postprandial Glucose Trajectories in the AEGIS Study
Functional data analysis provides a framework for studying time-varying associations between covariates and high-frequency biological signals.WAM-Sim0.1: World Anchored Memory Meets Online RL for Consistent and Realistic Driving Simulation
Autoregressive driving world models systematically suffer from structural drift, identity drift, and quality decay over long rollout horizons due to transient conditioning signals that lack…CardioLM: A multilingual suite of small language models for the cardiology domain
Cardiovascular care generates large volumes of clinical free-text documentation in local languages.IDPrior: Decoupling Identity Generation from Facial Image Synthesis
Large-scale identity-labeled face datasets are essential for training modern face recognition models, yet collecting and annotating such data is costly and raises privacy and ethical concerns.tok: a textual concrete syntax of BPMN and DMN for humans and AI agents
tok is a plain-text notation for Business Process Model and Notation (BPMN) 2.0.2 process models and Decision Model and Notation (DMN) 1.5 decisions.Operational Indirect Rate-Distortion Functions for Classification Tasks
In goal-oriented communications, indirect source coding involves two sources: a semantic source $X$ and an observation $Z$, which is the only source available at the encoder.ChemE-as-Code: An Open-Source Platform for Agentic AI in Chemical Engineering
ChemE-as-Code provides an open-source environment in which AI agents built on large language models (LLMs) can carry out chemical engineering work and engineers can check it.Circe: Training-Free LLM Self-Evaluation via Component Interaction Consistency
As large language models (LLMs) are increasingly deployed in real-world applications, evaluating the correctness of their responses is crucial for reliable use.Beyond the Keyword: A Human-Annotated Benchmark for Arabic Anxiety-Related Discourse
Keyword retrieval returns expressions of personal anxiety together with many other uses of anxiety-related language.Leakage- and Label-Aware Benchmarking of Class-Imbalance Strategies for Multimodal Chest X-Ray and EHR Classification
Benchmarks of class-imbalance strategies for multimodal chest X-ray (CXR) and electronic health record (EHR) prediction are often evaluated on record-level splits of cohorts in which one patient…Requirements Nobody Wrote Down: Emergence in a Public-Sector ML Pipeline
Governments increasingly use machine learning (ML) to handle data volumes beyond their analytical capacity, yet little is known about how requirements arise and evolve as an ML experiment moves…Invariance before structure: two modes of emergent compression in representation learning
Learned representations show striking invariance across biological and artificial neural networks, with different substrates and variations driven by drift, heterogeneity and initialisation.
Get the few papers worth your time in Computer Science & AI every morning.
Choose your topics and ReadSearch reads the new papers in them each day, then brings you the few worth reading, each with its key finding in a sentence.