Mathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin
On the map: LLM reasoning → Mathematical reasoning
Abstract (arXiv)
Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for symbolic physical reasoning in domain-specific scientific problems remains underexplored. We present a mathematical reasoning enhanced generative AI approach for optical communication formula derivation, focusing on the fiber nonlinear interference modelling. By guiding an LLM with structured prompts, we successfully reconstructed the known closed-form ISRS GN expressions and further derived a novel approximation tailored for multi-span C and C+L band transmissions. Numerical validations show that the LLM-derived model produces central-channel GSNRs nearly identical to baseline models, with mean absolute error across all channels and spans below 0.109 dB, demonstrating both physical consistency and practical accuracy.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. Builds on 4 of its 23 references that are in the map. First posted on arXiv 19 Mar 2026.
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4 of 23 references.
References · 4
- 2024Synergistic Interplay of Large Language Model and Digital Twin for Autonomous Optical Networks: Field Demonstrationscited by 5
- 2025Generative AI-Driven Hierarchical Multi-Agent Framework for Zero-Touch Optical Networkscited by 4
- 2017The Gaussian Noise Model in the Presence of Inter-channel Stimulated Raman Scatteringcited by 1
- 2018A Closed-Form Approximation of the Gaussian Noise Model in the Presence of Inter-Channel Stimulated Raman Scatteringcited by 1
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