AI & Machine Learning · Computer Science & AI
Artificial Intelligence
The newest papers in Artificial Intelligence that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,993 arXiv preprints and 7 OpenAlex records.
Get the few papers worth your time in Artificial Intelligence every morning.
Follow Artificial Intelligence and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 26
Personas Are Not People: Identity as State, Not Attribute
Synthetic populations, large sets of simulated people, are increasingly used in place of real people to pretest surveys, simulate users and communities, and evaluate AI systems.LoopVQ: Sub 1-bit KV Cache Quantization for Looped Transformers
Looped transformers are a parameter-efficient alternative to standard transformers for language modeling.You don't need long context
Later tokens are costlier to generate than earlier tokens.HARP: Training-Free Dual-Profile Agentic Communication for LLM-Based Recommendation
In LLM-based recommendation, memory acts as a user-level agent in a memory-augmented backbone.PyRUA-DataFarm: LLM Agent as a Cost-Efficient Teleoperator for Real-Ready Robot Data
Teleoperation remains the default way to collect robot demonstrations, but every episode costs robot and operator time, and every new task starts from scratch.A simple adaptive accelerated optimistic dual averaging method for unconstrained convex optimization in $p$-norm geometry
Efficient preconditioning plays an important role in large-scale first-order optimization.MedPhysBench: A Benchmark for Evaluating Language Models on Medical Physics Calculations and Error Detection
Medical physicists turn a prescription into a delivered dose and check one another's calculations, because one error in a beam calibration or a monitor-unit (MU) calculation can affect every patient…Weight-space programming for auditing clinical decision networks
Machine learning models in radiotherapy are commissioned by testing them on cases with known answers, which may not reveal a shortcut that the test cases share with the training data.Adversarial Speculative Reasoning Improves Long Horizon Agents
Speculative decoding has been treated purely as a systems optimization that speeds up large language model decoding without changing the model behavior.Suppressing Misaligned Behaviors in Language Models, Durably
Undesired model behaviors can emerge or re-emerge at any stage of training.Aligning Language Models with General Preferences: An Empirical Study of Direct Nash Optimization
Direct Nash Optimization (DNO) aligns a language model to a general preference function by approximating the Nash equilibrium of a two-player preference game.From Autonomous Agents to Cognitive Symbionts: Toward AI-Augmented Human Intelligence
For a decade, progress in artificial intelligence has been measured by how much a machine can do on its own.DreamX: Video Diffusion Model is a Universal Latent Action Learner
Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet invariant to changes in embodiment and…Constraint-Preserving Flow Matching for Spectral Graph Generation
Spectral graph generation models graphs through the eigenvalues and eigenvectors of their Laplacians, providing a compact and continuous alternative to direct graph construction.SurgTransfer: Multi-Step Latent Action Modeling for Action Transfer in Surgical World Model
Surgical world models have the potential to provide intra-operative guidance, accelerate risk-free training and facilitate transfer of expert surgical maneuvers.LIBRA: Programmatic Induction of Executable Tool Libraries
Large language model agents increasingly retrieve tools from large catalogs, yet most retrievers rank each tool independently by request similarity.Jev with Foresight: Safe Language-Guided Autonomous Driving via a Language World Model
Large language models understand passengers' driving commands, but as driving agents they reason in generated text for seconds and must infer each action's consequences implicitly from the scene.Towards Robust Long-Lived Agents: Benchmarking Experience Lifecycles in Evolving Environments
As agents become more capable, they are moving beyond one-off tasks toward sustained assistance across successive user goals.Hybrid LLM-Deterministic Pipelines for Enterprise-Scale Code Migration at Bloomberg
Large organizations carry a long tail of infrastructure migrations that are worth doing but rarely urgent enough to compete with feature work, because each one costs significant engineering time per…Can Large Language Models Review the Scientific Literature? A Test Against a 102-Article Benchmark
Large language models (LLMs) are increasingly used to support scientific literature reviews, yet evaluations often examine individual tasks rather than the review process as a whole.