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 112
Quantifying Meaning with SCAPE-T: Semantic Complexity from Encoder-Only Transformer Attention
Automated semantic complexity estimation remains underexplored in NLP, despite important applications in linguistics and interpretability.BoardSafety: AI-Ready Data from Manufacturer Documentation for Development-Board Safety
The development of large language models (LLMs) are capable to assist with hardware experiments.A Comprehensive Framework for All Stages of Large-Model Training: From Data and Tokenization, through Pretraining, to Post-Training Alignment
The capabilities of large language models (LLMs) and multimodal LLMs (MLLMs) arise from the synergy of the full training pipeline, including data and tokenization, pretraining, and post-training…LegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery
Equation discovery solvers often use different data interfaces, equation representations and evaluation criteria, making their results difficult to compare.The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting
Web platforms run on multivariate time series, from service telemetry and Web traffic logs to sensor streams served through Web APIs.LLMs in Cloud: A Discussion on Architecture, Orchestration, and Inference
The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental paradigm shift from stateless deep learning toward distributed, memory-intensive…A Unified Analysis of Importance-Corrected Momentum Actor-Critic over a Markovian Trajectory
We present a generic actor-critic method that combines a ridge-regularized pathwise least-squares temporal-difference (LSTD) critic with importance-corrected momentum (ICM) actors.MarineLife-16K: An Attribute-Rich Video-Text Dataset for Motion-Aware Marine Video Understanding
Marine video understanding is crucial for a wide range of real-world applications.Energy Efficient Lung Tumor Segmentation with a modified CBAM ENet Skip Model
Lung tumor segmentation plays a vital role in early detection and treatment planning of lung cancer.Error-Subspace Calibration for Multi-Task Model Merging
Model merging combines task-specific expert models into a single model without joint training on their original datasets, but often incurs a performance gap relative to the individual experts.Novel or Not? PatEx-Gym: Agentic Prior-Art Search for Novelty Assessment in Patent Examination
Patent examination is a challenging real-world task that requires agents to discover sparse prior art, iteratively refine queries, inspect technical evidence, and produce novelty assessments…Can Agentic Code Review Catch Vulnerabilities Before They Are Merged?
LLM agents have been increasingly adopted for security-focused code review, but existing benchmarks mostly derive the reviewed changes from their later fixes instead of the real-world…Pacifo: An Efficient Fully Homomorphic Encryption-based Lightweight ResNet for Trustworthy Machine Learning as a Service
Machine Learning as a Service (MLaaS) has become a popular paradigm for deploying AI applications.SkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents
Skill learning enables web agents to reuse prior experience for long-horizon interactive tasks.FraudSociety: A Generative Agent-Based Society for the Financial-Fraud Economy
Financial fraud caused over \$15.9 billion in losses in the United States in 2025, according to the Federal Trade Commission~\citep{ftc2026databook}.CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems
Large language model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across diverse tasks.SEDATE: Do Large Language Models Share an Aggression Circuit Across Languages?
As the applications of AI continue to expand across various multimodal and multilingual domains, ensuring safe and responsible deployment of these systems has become an important concern.Learning When to Collaborate: Selective Multi-Agent Medical Reasoning via Uncertainty-Aware Routing
Multi-agent systems have emerged as a promising paradigm for multimodal medical reasoning, but existing methods rely on two assumptions that hinder practical deployment: (i) always-active dense…Hybrid-time PINNs: Discontinuity-Aware Physics-Informed Neural Networks for Learning Hybrid Dynamical Systems
Hybrid dynamical systems flow continuously but jump at discrete events.WorldFailBench: Do World Models Understand Policy Failures?
Robot policy evaluation informs deployment decisions and policy improvement, but physical testing and simulator preparation require substantial effort.