EvoOptiGraph: Weakness-Driven Coevolution via Graph-Based Structural Generation for Optimization Modeling
arXiv:2606.26578v1 Announce Type: new Abstract: Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges. First, training corpora lack structural diversity. Second, data generation pipelines remain static and decoupled from model learning. To address these challenges, we propose EvoOptiGraph, a novel framework where data and model co-evolve, driven by model weaknesses. EvoOptiGraph represents each mixed-integer linear program (MILP) as an attributed bipartite graph and applies validity-preserving evolutionary operators to generate struct
The proliferation of Large Language Models (LLMs) has exposed limitations in their current data generation and learning paradigms, necessitating more robust and adaptive frameworks.
This research addresses a fundamental challenge in AI development by proposing a method for LLMs to overcome data limitations through self-correction and co-evolution, leading to more capable and autonomous AI systems.
The ability for AI models to generate structurally diverse and valid training data based on their own weaknesses, rather than static datasets, changes the fundamental approach to training complex AI systems.
- · AI development platforms
- · Optimization software providers
- · Research institutions in AI/ML
- · Companies reliant on static training data pipelines
- · Legacy AI model development methodologies
More sophisticated and less error-prone autonomous AI agents can be developed.
This could accelerate the integration of AI into complex decision-making and operational optimization tasks across various industries.
The enhanced self-improvement capabilities of AI might lead to a faster trajectory towards artificial general intelligence, impacting compute demand and societal structures.
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Read at arXiv cs.AI