AutoSG: LLM-Driven Solver Generation Solely from Task Prompts for Expensive Optimization

• URL: http://arxiv.org/abs/2605.25658v1
• Date: Mon, 25 May 2026 10:04:35 GMT
• Title: AutoSG: LLM-Driven Solver Generation Solely from Task Prompts for Expensive Optimization
• Abstract summary: AutoSG is a fully automated workflow directly translating natural language prompts into executable customized solvers.<n>AutoSG features three core innovations: a retrieval-augmented solver generation module strictly grounding code in verified literature; a one-step self-refinement operator introducing task-specific improvements while preserving critical structural components.
• Score: 11.066280731240818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Expensive optimization tasks are ubiquitous in real-world applications, demanding highly specialized solvers. While LLM-driven automated solver generation shows promise, current paradigms face three critical issues when tackling expensive optimization: factual hallucinations due to deficient domain knowledge, the frequent dismantling of previously established locally optimal structures during refinement, and the prohibitive evaluation costs alongside restricted generalization caused by executing on training instances. To address these issues, we introduce AutoSG, a fully automated workflow directly translating natural language prompts into executable customized solvers. AutoSG features three core innovations: a retrieval-augmented solver generation module strictly grounding code in verified literature; a one-step self-refinement operator introducing task-specific improvements while preserving critical structural components; and an instance-free Elo-based LLM-as-a-Judge evaluation mechanism rapidly establishing global rankings. Extensive evaluations across diverse expensive optimization tasks confirm AutoSG significantly outperforms human-designed state-of-the-art frameworks and existing LLM-generated solvers.

Related papers

• Execution-Verified Reinforcement Learning for Optimization Modeling [49.171122807323634]
  Execution-verified learning framework treats a mathematical programming solver as a deterministic, interactive verifier.<n>Experiments on NL4OPT, MAMO, IndustryOR, and OptiBench across Gurobi, OR-Tools, and COPT show that EVOM matches or outperforms process-supervised SFT.
  arXiv  Detail & Related papers  (2026-04-01T03:39:11Z)
• Rethinking LLM-Driven Heuristic Design: Generating Efficient and Specialized Solvers via Dynamics-Aware Optimization [21.449921296295884]
  We propose Dynamics-Aware Heuristics (DASH), a framework that co-optimizes solver search mechanisms and runtime schedules guided by a convergence-aware metric.<n>DASH improves runtime efficiency by over 3 times, while surpassing the solution quality of state-of-the-art baselines across diverse problem scales.
  arXiv  Detail & Related papers  (2026-01-14T05:06:42Z)
• OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling [13.57588221678224]
  Large Language Models (LLMs) have demonstrated impressive progress in optimization modeling.<n>The boundaries of their capabilities in automated formulation and problem solving remain poorly understood.<n>We propose OPT-ENGINE, a benchmark framework designed to evaluate LLMs on optimization modeling with controllable and scalable difficulty levels.
  arXiv  Detail & Related papers  (2026-01-09T09:22:33Z)
• An Agentic Framework with LLMs for Solving Complex Vehicle Routing Problems [66.60904891478687]
  We propose an Agentic Framework with LLMs (AFL) for solving complex vehicle routing problems.<n>AFL directly extracts knowledge from raw inputs and enables self-contained code generation.<n>We show that AFL substantially outperforms existing LLM-based baselines in both code reliability and solution feasibility.
  arXiv  Detail & Related papers  (2025-10-19T03:59:25Z)
• A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications [12.409034344742768]
  Large language models (LLMs) are reshaping automated program repair (APR)<n>We categorize the recent 63 LLM-based APR systems published from January 2022 to June 2025 into four paradigms.<n>This taxonomy clarifies key trade-offs: fine-tuning delivers strong task alignment at high training cost; prompting enables rapid deployment but is limited by prompt design and context windows.<n>We outline research directions that combine lightweight human feedback, repository-aware retrieval, code analysis, and cost-aware planning to advance reliable and efficient LLM-based APR.
  arXiv  Detail & Related papers  (2025-06-30T11:46:01Z)
• MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision [76.42361936804313]
  We introduce MAS-ZERO, the first self-evolved, inference-time framework for automatic MAS design.<n> MAS-ZERO employs meta-level design to iteratively generate, evaluate, and refine MAS configurations tailored to each problem instance.
  arXiv  Detail & Related papers  (2025-05-21T00:56:09Z)
• LADs: Leveraging LLMs for AI-Driven DevOps [3.240228178267042]
  LADs is a principled approach to configuration optimization through in-depth analysis of what optimization works under which conditions.<n>By leveraging Retrieval-Augmented Generation, Few-Shot Learning, Chain-of-Thought, and Feedback-Based Prompt Chaining, LADs generates accurate configurations and learns from deployment failures to iteratively refine system settings.<n>Our findings reveal key insights into the trade-offs between performance, cost, and scalability, helping practitioners determine the right strategies for different deployment scenarios.
  arXiv  Detail & Related papers  (2025-02-28T08:12:08Z)
• IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts [28.9807389592324]
  Large language model (LLM) agents have emerged as a promising solution to automate the workflow of machine learning.<n>We introduce Iterative Refinement, a novel strategy for LLM-driven ML pipeline design inspired by how human ML experts iteratively refine models.<n>By systematically updating individual components based on real training feedback, Iterative Refinement improves overall model performance.
  arXiv  Detail & Related papers  (2025-02-25T01:52:37Z)
• Towards more Contextual Agents: An extractor-Generator Optimization Framework [0.0]
  Large Language Model (LLM)-based agents have demonstrated remarkable success in solving complex tasks across a wide range of general-purpose applications.<n>However, their performance often degrades in context-specific scenarios, such as specialized industries or research domains.<n>To address this challenge, our work introduces a systematic approach to enhance the contextual adaptability of LLM-based agents.
  arXiv  Detail & Related papers  (2025-02-18T15:07:06Z)
• Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models [54.381650481255235]
  We introduce a new tuning-free approach for self-alignment, Dynamic Rewarding with Prompt Optimization (O) Our approach leverages a search-based optimization framework that allows LLMs to iteratively self-improve and craft the optimal alignment instructions. Empirical evaluations on eight recent LLMs, both open and closed-sourced, demonstrate that DRPO significantly enhances alignment performance.
  arXiv  Detail & Related papers  (2024-11-13T16:15:38Z)
• Autoformulation of Mathematical Optimization Models Using LLMs [50.030647274271516]
  This paper approaches the problem of $textitautoformulation$: the automated creation of solver-ready optimization models from natural language problem descriptions.<n>We identify three core challenges of autoformulation: $textit(1)$ the vast, problem-dependent hypothesis space, and $textit(2)$ efficient and diverse exploration of this space under uncertainty.<n>We present a novel method leveraging $textitLarge Language Models$ with $textitMonte-Carlo Tree Search$, exploiting the hierarchical nature of optimization modeling to generate and systematically explore possible formulations
  arXiv  Detail & Related papers  (2024-11-03T20:41:38Z)
• Auto-PRE: An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation [52.76508734756661]
  Auto-PRE is an automatic evaluation framework inspired by the peer review process.<n>Unlike previous approaches that rely on human annotations, Auto-PRE automatically selects evaluators based on three core traits.<n> Experiments on three representative tasks, including summarization, non-factoid QA, and dialogue generation, demonstrate that Auto-PRE achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-10-16T06:06:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.