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: 4,998 arXiv preprints and 2 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 106
A Smart Instant Book Filter Model based on Dual-Objective Optimization
Hospitality service in online marketplaces has become increasingly popular over the past few years, not only because it helps people easily rent short-term lodging but also because it empowers…BUSTA: Teaching Small Language Models to Reason About Binary Execution
Large Language Models (LLM) are increasingly applied to Reverse Engineering (RE) workflows.Detecting and Mitigating Query-Based System Prompt Leakage Attacks
Large Language Models (LLMs) rely on proprietary system prompts to enforce task directives, safety policies, and access controls.Spiking Neural Networks Are Harder to Steal: A Benchmark Study of Model Stealing Attacks
Machine learning models deployed behind query interfaces expose more than their predictions, because an adversary can use those predictions to rebuild the model itself.When Retrieval Is Not Enough: Obsolete Facts in Agent Memory
Memory-agent benchmarks often collapse retrieval and answer generation into one score, making it hard to tell whether a failure comes from missing an update or from not using an update that was…Ontology-guided multi-agent reasoning for explainable differential diagnosis
Effective differential diagnosis depends on well structured and unambiguous patient case descriptions, since incomplete or ambiguous clinical information readily leads to diagnostic errors and…How Stable Are LLM-Generated Proofs Under Program Refactoring? A Stress Test for Verifiable Code Generation
Benchmarks for verifiable code generation usually test a proof against one fixed implementation.Plenty of Room in the Middle: Revealing Opportunities on a GPU Cluster via Fine-Grained Monitoring
GPU clusters have become essential computing resources for AI and scientific research in academia and industry.OA-RAG: Ontology-based Agentic RAG for Faithful LLM Reasoning
Large language models (LLMs) perform well across many real-world tasks but remain prone to hallucination, limiting their reliability in domains such as biomedicine and healthcare, where answers must…CrowdCC: Jointly Benchmarking Corruption Robustness and Deployment Quantization in Crowd Counting
Crowd-counting models deployed on edge devices face two challenges: inputs are degraded along the capture and transmission pipeline, while models are often quantized to satisfy resource constraints…Executable Archaeology of Programs That Model Programming: Reanimating the Heuristic Compiler (1961) and KBEmacs (1984)
Early AI programs were often also theories of how people think, and programs that wrote programs were, in part, theories of how people program.Enhancing Clinical Classifier Robustness with Deep Representation Learning Based OOD Detection on DNA Methylation Data
DNA methylation (DNAM) array-based tumor classification is a powerful clinical diagnostic tool, but models trained on fixed reference cohorts can produce faulty predictions for tumor types not…WAKMan: Workload Aware KVCache Manager
Prefix caching enables a Large Language Model (LLM) servers to reuse the Key-Value (KV) states and accelerate prefill computation stage for requests that share a prefix with earlier prompts, thus…Reliability-Aware Short-Term Glycemic Risk Prediction Using an Anchor-Based Comparative Framework of Sequence, Multimodal, and State-Updating Digital-Twin Models
Anticipating short-term hypoglycemic and hyperglycemic risk is essential for proactive diabetes management.HIDRA: Hierarchical Dual-Routing Attention for Replay-Free Lifelong Imitation Learning
Robotic agents operating in real-world environments must adapt continuously from a stream of multimodal demonstrations, often under constraints that preclude storing or revisiting past data.SOMA: A Benchmark for Fine-Grained Egocentric Behavior and Body-Motion Understanding
Perceiving human body motion is critical for multimodal systems that understand and assist people.LoRA on a Budget: Function-Aware Post-Hoc Compression for Adapter Fleets
Low-Rank Adaptation (LoRA) has become a key mechanism for efficiently specializing foundation models into many task- and user-specific variants.MAREval: A Multi-Agent Framework for Evaluating Natural Language Recommendation Explanations
Evaluating natural language explanations in recommender systems is essential for fostering user trust, transparency, and engagement.kw-bench: Breaking Frontier Harnesses on Real-World Tasks
Each new generation of frontier model pushes the edge of what is possible.Expert lithological labels do not improve learning when used to predict marine sediment properties
Scientific drilling has produced large archives of physical, chemical and magnetic measurements linked to descriptions of the rock recovered.