arXiv:2606.01730v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as heuristic advisors for black-box optimization, yet their suggestions and self-reported confidence are not necessarily calibrated to downstream objective values. This issue becomes more pronounced in multi-objective Bayesian optimization, where different objectives may require different expert knowledge and where an LLM expert can be useful for one objective but misleading for another. We study how to use LLM-generated expert priors in discrete multi-objective Bayesian optimization without bl
The paper addresses a critical current challenge in using LLMs for complex, real-world optimization problems, as their application moves beyond simple text generation to decision-making.
Improving the calibration and reliability of LLM suggestions in multi-objective optimization is crucial for effectively leveraging AI in scientific discovery, engineering, and business without incurring high costs or risks from uncalibrated assumptions.
This research outlines a method to integrate LLM priors into multi-objective Bayesian optimization processes more robustly, potentially leading to more efficient and trustworthy AI-driven decision-making in complex systems.
- · AI researchers and developers
- · Industries relying on complex optimization (e.g., drug discovery, materials scie
- · Organizations deploying AI agents for decision-making
More reliable deployment of LLMs as heuristic advisors in black-box optimization tasks.
Accelerated discovery and development cycles in fields where multi-objective optimization is critical.
Enhanced automation and autonomy of AI agents in complex, high-stakes environments due to improved decision calibration.
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Read at arXiv cs.LG