Budget-Efficient Automatic Algorithm Design via Code Graph

• URL: http://arxiv.org/abs/2605.10598v1
• Date: Mon, 11 May 2026 14:01:52 GMT
• Title: Budget-Efficient Automatic Algorithm Design via Code Graph
• Abstract summary: Large language models (LLMs) have emerged as powerful tools for automatic algorithm design (AAD)<n>We formalize budget-efficient automatic algorithm design, wherein the search policy maximizes realized fitness subject to limited computational cost.<n>We validate our method empirically on three optimization problems, demonstrating consistent superiority of our graph-based search over full-algorithm search at equal token budget.
• Score: 2.982218441172364
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have emerged as powerful tools for automatic algorithm design (AAD). However, existing pipelines remain inefficient. They operate at the granularity of full algorithms, redundantly rewriting recurring substructures and discarding low-fitness candidates that may contain valuable algorithmic features. We formalize budget-efficient automatic algorithm design, wherein the search policy maximizes realized fitness subject to limited computational cost. We propose a directed acyclic graph representation of algorithms and build a search framework that fully exploits the LLM's output. Instead of querying the LLM for full algorithms, we use it to obtain corrections: compact operators that add, replace, or remove code blocks. Each correction augments the graph, yielding new algorithms that compose with prior corrections. This graph structure decomposes algorithms into sets of corrections, enabling correction-level credit assignment that informs subsequent queries. We complement this framework with theoretical insights into the ideal balance between search depth and breadth at different budget levels. We validate our method empirically on three combinatorial optimization problems, demonstrating consistent superiority of our graph-based search over full-algorithm search at equal token budget. Finally, our experiments suggest that rich contexts help only when the LLM's prior knowledge is shallow, and can hinder performance otherwise.

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