Can Large Language Models Reason and Optimize Under Constraints?
arXiv:2603.23004v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated great capabilities across diverse natural language tasks; yet their ability to solve abstraction and optimization problems with constraints remains scarcely explored. In this paper, we investigate whether LLMs can reason and optimize under the physical and operational constraints of Optimal Power Flow (OPF) problem. We introduce a challenging evaluation setup that requires a set of fundamental skills such as reasoning, structured input handling, arithmetic, and constrained optimization. Our evaluation reveals that SoTA LLMs fail in most of the tasks, and that reasoning LLMs still fail in the most complex settings. Our findings highlight critical gaps in LLMs' ability to handle structured reasoning under constraints, and this work provides a rigorous testing environment for developing more capable LLM assistants that can tackle real-world power grid optimization problems.
arXiv:2603.23004v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated great capabilities across diverse natural language tasks; yet their ability to solve abstraction and optimization problems with constraints remains scarcely explored. In this paper, we investigate whether LLMs can reason and optimize under the physical and operational constraints of Optimal Power Flow (OPF) problem. We introduce a challenging evaluation setup that requires a set of fundamental skills such as reasoning, structured input handling, arithmetic, and constrained optimization. Our evaluation reveals that SoTA LLMs fail in most of the tasks, and that reasoning LLMs still fail in the most complex settings. Our findings highlight critical gaps in LLMs' ability to handle structured reasoning under constraints, and this work provides a rigorous testing environment for developing more capable LLM assistants that can tackle real-world power grid optimization problems.
Executive Summary
This article investigates the capacity of Large Language Models (LLMs) to reason and optimize under physical and operational constraints, specifically within the Optimal Power Flow (OPF) problem domain. The study introduces a rigorous evaluation setup designed to assess fundamental skills such as reasoning, structured input handling, arithmetic, and constrained optimization. The findings indicate that state-of-the-art LLMs largely fail in most OPF-related tasks, with even specialized reasoning LLMs exhibiting significant limitations in complex scenarios. The work establishes a novel testing environment that exposes critical gaps in LLMs' ability to handle structured reasoning under constraints, thereby offering a valuable benchmark for future LLM assistant development in real-world optimization contexts.
Key Points
- ▸ LLMs struggle with constrained optimization tasks like OPF
- ▸ Current SOTA LLMs fail in most tested scenarios
- ▸ Reasoning-specific LLMs also underperform in complex settings
Merits
Rigorous Evaluation Setup
The authors developed a comprehensive and targeted testing framework that isolates specific reasoning and optimization capabilities under constraints, enhancing the validity of their findings.
Demerits
Limited Scope of Evaluation
The study focuses narrowly on OPF problems, limiting the generalizability of findings to broader domains where LLMs might apply constrained reasoning and optimization.
Expert Commentary
The paper presents a critical and timely contribution to the intersection of AI and energy systems. The authors rightly identify a significant gap in the literature: the under-explored ability of LLMs to perform under structural constraints. While the evaluation is methodologically sound and the results are compelling, the broader implications extend beyond OPF. This work sets a precedent for evaluating AI systems against domain-specific constraints, which should inform both academic research and industry adoption of LLMs in technical domains. Moreover, the failure of even reasoning-enhanced models in complex settings signals a fundamental challenge that requires new training paradigms or hybrid models integrating symbolic reasoning with LLMs. This is not merely a technical hurdle—it is a paradigm shift in expectations for AI assistants in engineering. The implications for regulatory frameworks and investment in AI-driven infrastructure are substantial, and this work should catalyze a new line of inquiry in the field.
Recommendations
- ✓ Develop hybrid models combining LLMs with symbolic AI to enhance constrained reasoning capabilities
- ✓ Create standardized benchmarks for constrained reasoning across diverse domains to enable comparative evaluation
Sources
Original: arXiv - cs.AI
