Large Language Models as Optimizers: A Survey of Direct vs. Tool-Augmented Approaches and Their Performance Frontiers
arXiv:2606.15577v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly involved in complex mathematical optimization, even if the pragmatic user who triggers them is unaware of it. After all, many real-world problems reduce to the search for better or the best solutions. The field of LLM-as-optimizer has three paradigms: direct optimization, tool-augmented optimization, and tool-creating optimization. Direct optimization uses iterative prompting and heuristic generation to navigate solution spaces. Tool-augmented optimization translates natural language problems into for
The rapid advancement of Large Language Models has led researchers to explore their capabilities beyond natural language processing, explicitly applying them to complex problem-solving domains like mathematical optimization.
This development indicates a broadening scope for AI applications, moving LLMs from content generation and summarization to direct and augmented problem-solving in areas previously dominated by specialized algorithms.
LLMs are no longer just an interface or a content engine; they are becoming active problem-solvers capable of autonomously or semi-autonomously optimizing complex systems.
- · AI developers
- · Businesses with complex optimization problems
- · Tool-augmented AI platforms
- · Researchers in AI and operations research
- · Manual optimization processes
- · Legacy optimization software vendors
- · Specialized human consultants for certain optimization tasks
LLMs will be increasingly integrated into various industries to solve specific optimization challenges more efficiently.
This integration could lead to significant productivity gains and cost reductions across sectors depending heavily on optimization, such as logistics, manufacturing, and finance.
The enhanced problem-solving capabilities of LLMs could accelerate scientific discovery and engineering innovation by optimizing experimental design and solution space exploration.
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Read at arXiv cs.AI