12 articles tagged with #expert-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers analyzed data movement patterns in large-scale Mixture of Experts (MoE) language models (200B-1000B parameters) to optimize inference performance. Their findings led to architectural modifications achieving 6.6x speedups on wafer-scale GPUs and up to 1.25x improvements on existing systems through better expert placement algorithms.
Researchers propose Collaborative Causal Sensemaking (CCS) as a new framework to improve human-AI collaboration in high-stakes decision making. The study identifies a 'complementarity gap' where current AI agents function as answer engines rather than true collaborative partners, limiting the effectiveness of human-AI teams.
A research paper argues that the most valuable capabilities of large language models are precisely those that cannot be captured by human-readable rules. The thesis is supported by proof showing that if LLM capabilities could be fully rule-encoded, they would be equivalent to expert systems, which have been proven historically weaker than LLMs.
Researchers propose a hybrid AI agent and expert system architecture that uses semantic relations to automatically convert cyber threat intelligence reports into firewall rules. The system leverages hypernym-hyponym textual relations and generates CLIPS code for expert systems to create security controls that block malicious network traffic.
Researchers have developed OrchMAS, a new multi-agent AI framework that uses specialized expert agents and dynamic orchestration to improve reasoning in scientific domains. The system addresses limitations of existing multi-agent frameworks by enabling flexible role allocation, prompt refinement, and heterogeneous model integration for complex scientific tasks.
ButterflyMoE introduces a breakthrough approach to reduce memory requirements for AI expert models by 150× through geometric parameterization instead of storing independent weight matrices. The method uses shared ternary prototypes with learned rotations to achieve sub-linear memory scaling, enabling deployment of multiple experts on edge devices.
Researchers introduce XpertBench, a new benchmark for evaluating Large Language Models on expert-level professional tasks across domains like finance, healthcare, and legal services. Even top-performing LLMs achieve only ~66% success rates, revealing a significant 'expert-gap' in current AI systems' ability to handle complex professional work.
Researchers introduce AHCE (Active Human-Augmented Challenge Engagement), a framework that enables AI agents to collaborate with human experts more effectively through learned policies. The system achieved 32% improvement on normal difficulty tasks and 70% on difficult tasks in Minecraft experiments by treating humans as interactive reasoning tools rather than simple help sources.
Researchers developed PolicyPad, an interactive system that helps domain experts collaborate on creating policies for LLMs in high-stakes applications like mental health and law. The system enables real-time policy drafting and testing through established UX prototyping practices, showing improved collaborative dynamics and tighter feedback loops in workshops with 22 experts.
This research paper examines how AI and Law research has evolved in approaching legal interpretation through three main methodologies: expert systems for knowledge engineering, argumentation frameworks for assessing interpretive claims, and machine learning models including LLMs for automated legal argument generation.
Researchers introduce Channel-of-Mobile-Experts (CoME), a new AI agent architecture that uses four specialized experts to handle different reasoning stages for mobile device automation. The system employs progressive training strategies and information gain-driven optimization to improve mobile agent performance on complex tasks.
Researchers have developed SSKG Hub, an AI-powered platform that transforms complex sustainability disclosure standards into structured knowledge graphs using large language models and expert validation. The system features automated extraction, expert review processes, and role-based governance to create auditable, provenance-linked knowledge graphs for sustainability standards analysis.
