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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.
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Newest papers, page 124
Scoped Semantic Invariance as a Foundation for LLM Reliability
LLMs often answer semantically equivalent queries inconsistently, even when the underlying facts are known.GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation
Engineering standards are challenging knowledge sources for large language models (LLMs) because they are hierarchical, multimodal, and densely connected through figures, tables, cross-references…Estimation of Propagated Risk in LLM-based Multi-Agent Systems
Large Language Model (LLM) based multi-agent systems introduce a distinctive reliability challenge in which actions taken by upstream agents can affect the correctness of downstream agents.The As-If Machine: Personalized counterfactuals make distant policy risks feel close
Life experiences can shape policy attitudes, but we only live one life.Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction Patterns
Powder X-ray diffraction (XRD) is the primary high-throughput method for resolving crystal structures, a cornerstone of materials science.When Does Pre-Trained Capacity Help Causal Learning? From Scalar Prediction to Counterfactual Post-Training
How does pre-trained model capacity relate to performance after adaptation for causal tasks?Responsibility Boundaries for Agentic Systems: A Comparative Evaluation of Probabilistic Agentic Programming
Language-model agents combine reasoning, tools, memory, delegation, and external effects.A Deterministic Security Runtime for LLM Agents on MPI and Batch-Scheduled HPC Systems: Consensus-Governed, Hardware-Rooted Supervision of Every Rank
Large language model agents launch simulations on high-performance computing (HPC) systems, where one sbatch or mpirun can commit thousands of node-hours and a forged result can cost lives.AI Teaches Humans to Handle Complexity in the Game of Go
Strategic decision-making in complex environments requires balancing local stability against global coordination.TempShap: When and How Much a Feature Matters in Time-Series Forecasting
Attribution for a time-series forecaster should answer two questions that a feature-level scalar cannot: when in the input window a feature acts, and how much its influence changes across the…From Hype to ROI: An Economic Model for LLM Adoption in Information Systems and Security Operations
Large Language Models (LLMs) promise significant efficiency gains across information-intensive operations by automating repetitive language-based tasks.BenchBench-Cloning: Evaluating Molecular Cloning Design and Protocol Development
Molecular cloning is one of the most routine wet-lab workflows, yet existing evaluations of language models in biology mostly test knowledge recall or isolated steps rather than end-to-end…MOSHO: A Native Discrete Shark-Inspired Metaheuristic for Hardware-Aware Neural Architecture Search
Hardware-aware neural architecture search (NAS) requires jointly optimizing predictive accuracy and device-specific latency over discrete search spaces.Counting the Bandages: On measuring the time that AI saves
I consider what it means for AI assistance to save time.Governed Self-Evolution for Autonomous Investing: A Multi-Agent LLM System with an Invariant Core
LLM agents that act on live Web data must combine heterogeneous evidence, remember what they have done and improve from outcomes without weakening their own safeguards.Lifetime-valid decisions by continually evolving AI analysts
AI agents increasingly act as analysts whose decision can often be cast as hypothesis tests.Diagnosing Personalization Failures in Mobile GUI Agents
Unlike conventional GUI agents, personalized GUI agents require user preferences to guide sequential execution over changing GUI states.Bridging the Closed-World Gap: Interpretable Anomaly Detection via a Hybrid TSK-FCM Ensemble on the BETH Dataset
A fundamental paradox plagues the deployment of Machine Learning in cybersecurity: supervised models generalize poorly to novel, Out-of-Distribution (OoD) attacks, whereas unsupervised models often…Model Organisms of Collusion: Corrective Transfer in Untrusted Monitors
AI control protocols aim to get useful work from models that may be misaligned by monitoring their behaviour.Visual Grounding First, Multimodal In-context Learning Follows
LLMs have shown strong in-context learning (ICL) capability, but extending it to Multimodal LLMs (MLLMs) remains challenging.