Researchers have formulated distributed pinning control conditions using linear matrix inequalities and M-matrix theory to guarantee bipartite consensus in multi-agent ...
A router-based multi-agent artificial intelligence framework demonstrated improved accuracy and consistency over standalone frontier language models in preliminary engineering calculations ...
In a simulated wire harness handling study, end-to-end reinforcement learning attained a 98 percent success rate, substantially outperforming multi-agent path ...
A hierarchical multi-agent reinforcement learning system combining conservative value estimation with guideline retrieval achieved a 49.9 percent agreement rate with ...
A multi-agent retrieval-augmented framework coordinates specialized language models to deliver structured, rubric-aligned feedback on student writing while restricting direct answer ...
Researchers have developed a multi-stage hierarchical reinforcement learning framework that enables aerial drones to guide quadruped ground robots through simulated ...
One of the most stubborn problems in modern economies is that industry moves faster than education. New technologies and occupations ...
A new multi-agent AI framework called PentestMCP autonomously conducts full penetration testing campaigns, achieving up to 87.2 percent success in ...
A multi-agent AI system combining large language models, knowledge graph embeddings, and collaborative agents reads power grid alarm rules, recognizes ...
A new review of 107 studies distills autonomous AI agents into a five-module architecture of profile, memory, planning, action and ...
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