This paper proposes an LLM-guided multi-agent framework for early-stage engineering design, integrating a human-in-the-loop approach and formal risk management. It uses specialized agents to explore and prune design candidates, demonstrated on aerodynamic airfoil design.
This 11-month longitudinal case study analyzes the impact of agentic personalization in marketing, comparing active human management with autonomously operating agents. The results suggest that active human management generates the highest lift in engagement metrics, raising questions about sustained performance without oversight.
This paper introduces Human-in-the-Loop Meta Bayesian Optimization (HL-MBO), a framework combining expert knowledge with few-shot machine learning to accelerate discovery in data-scarce scientific domains. It outperforms current Bayesian Optimization methods in fusion energy yield optimization and other benchmarks.
DeepSlide is a human-in-the-loop multi-agent system designed to optimize the entire presentation delivery process, from narrative planning to rehearsal, moving beyond AI slide generators that only focus on the artifact. It integrates a controllable logical-chain planner, content retrieval, and introduces a new dual-scoreboard benchmark for evaluating both static artifact quality and dynamic delivery excellence.