large language models

Detecção de Ameaças em Logs de Firewall utilizando Agentes Autônomos baseados em LLM

Este trabalho apresenta o AEGIS - Adaptive Enhanced Guardian Intelligence System, uma arquitetura baseada em agentes autônomos com Large Language Models — LLM (Modelos de Linguagem de Grande Escala) para detecção de ameaças em logs de firewall. A proposta analisa tráfego previamente permitido para identificar comportam...

Test-Time Computing for Referring Multimodal Large Language Models

Content-Hiding Attacks and Defenses for LLM-Based Agent-Skill Scanners

AI agent “skills” (a natural-language SKILL.md plus bundled scripts) are distributed through marketplaces and public Git repositories, and a growing set of scanners classify them as safe or malicious, increasingly with a large language model (LLM) as the judge. We ask where these LLM-based scanners actually fail. Using...

A Study on an Open-Ended Benchmark Methodology for Evaluating the Faith and Theological Soundness of Large Language Models (LLMs) — Focusing on a Pilot Evaluation in the Korean Protestant Context

The Missing Layer of Artificial Intelligence and the "We" Consciousness: Subconscious Archives and Quantum Resonance Architectures

Modern artificial intelligence (AI) systems, particularly deep learning models and large language models (LLMs), operate primarily on surface-level, conscious data layers curated by human civilization. While highly efficient in probabilistic prediction and pattern matching, these systems remain two-dimensional, devoid...

Legal Services and Artificial Intelligence: Real Productivity Gains, Fabricated Citations, and a Profession Learning to Verify

Purpose. Law is a profession built on text, which makes it an obvious target for language models, and one in which a confident error can end up in a court filing. This article asks where artificial intelligence stands in legal services in 2026 and where the profession may be by about 2035. Design and methods. A review...

Education and Artificial Intelligence: Universal Student Use, Mixed Evidence on Learning, and the Design Choices That Decide the Outcome

Purpose. No industry has seen generative AI reach its users as quickly as education, because students were among the first to adopt it. This article asks where AI stands in education in 2026, what the evidence says about its effect on learning, and where education may be by about 2035. Design and methods. A review comb...

The Software Industry and Artificial Intelligence: Nearly Universal Use, Evidence That Ranges from Large Gains to a Slowdown, and the Question of Who Gets Hired

Purpose. The software industry both builds artificial intelligence and was among the first to use it on its own work, through coding assistants. This article asks where AI stands in software in 2026, what controlled studies say about developer productivity, and where the industry may be by about 2035. Design and method...

Retail and Artificial Intelligence: Forecasting That Works, Pricing That Pays, and Shoppers Who Now Ask Chatbots

Purpose. Retail was among the first industries to use machine learning on a large scale, in forecasting and pricing, and is now watching shoppers move part of their search to AI assistants. This article asks where artificial intelligence stands in retail in 2026 and where the industry may be by about 2035. Design and m...

Integrated Deterministic Governance and Intellectual Property Defense Architecture for Next-Generation Cyber-Physical Systems: Bridging Probabilistic AI, Sub-12.5 ms Control Barrier Optimization, Post-Quantum Data Sovereignty, and 300 K Subtractive Quantum Infrastructure

The direct coupling of probabilistic Large Language Models (LLMs), Vision-Language Models (VLMs), and putative Artificial General Intelligence (AGI) reasoning cores to continuous-time physical actuators introduces existential failure modes across safety-critical Cyber-Physical Systems (CPS), ranging from non-cooperativ...

Banking and Artificial Intelligence: Near-Universal Use, Thin Evidence on Outcomes, and a Supervisory Agenda in the Making

Purpose. Banks were among the first industries to use machine learning at scale, in credit scoring and fraud detection, and are now among the first to deploy generative AI. This article asks what is known about the results, what slows the next stage, and where banking may be by about 2035. Design and methods. A review...

Stakeholder Perspectives on the Revision of the EU Tobacco Products Directive: A Census-Level Thematic Analysis of a Public Consultation using a Large Language Model