Optimization plays a vital role in scientific research and practical
applications, but formulating a concrete optimization problem described in
natural language into a mathematical form and selecting a suitable solver to
solve the problem requires substantial domain expertise. We introduce
\textbf{OptimAI}, a framework for solving \underline{Optim}ization problems
described in natural language by leveraging LLM-powered \underline{AI} agents,
achieving superior performance over current state-of-the-art methods. Our
framework is built upon four key roles: (1) a \emph{formulator} that translates
natural language problem descriptions into precise mathematical formulations;
(2) a \emph{planner} that constructs a high-level solution strategy prior to
execution; E (3) a \emph{coder} and a \emph{code critic} capable of
interacting with the environment and reflecting on outcomes to refine future
actions. Ablation studies confirm that all roles are essential; removing the
planner or code critic results in $5.8\times$ and $3.1\times$ drops in
productivity, rispettivamente. Inoltre, we introduce UCB-based debug
scheduling to dynamically switch between alternative plans, yielding an
additional $3.3\times$ productivity gain. Our design emphasizes multi-agent
collaboration, allowing us to conveniently explore the synergistic effect of
combining diverse models within a unified system. Our approach attains 88.1\%
accuracy on the NLP4LP dataset and 71.2\% on the Optibench (non-linear w/o
table) subset, reducing error rates by 58\% E 50\% respectively over prior
best results.

Questo articolo esplora i giri e le loro implicazioni.

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