@inproceedings{guo-etal-2025-nested,
title = "Nested-Refinement Metamorphosis: Reflective Evolution for Efficient Optimization of Networking Problems",
author = "Guo, Shuhan and
Yin, Nan and
Kwok, James and
Yao, Quanming",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.895/",
doi = "10.18653/v1/2025.findings-acl.895",
pages = "17398--17429",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Models (LLMs) excel in network algorithm design but suffer from inefficient iterative coding and high computational costs. Drawing inspiration from butterfly metamorphosis{---}where structured developmental phases (Phase I: larval nutrient accumulation {\textrightarrow} Phase II: pupal transformation) enable adaptive evolution{---}we propose Nested-Refinement Metamorphosis (NeRM). Building on this principle, we introduce Metamorphosis on Prompts (MoP) to iteratively refine task descriptions (e.g. latency / bandwidth constraints) and Metamorphosis on Algorithms (MoA) to generate more effective solutions (e.g. appropriate network processing architecture). Their nested refinement ensures task-algorithm alignment, systematically improving both task descriptions and algorithmic solutions for more efficient algorithm design. To further enhance efficiency, we incorporate predictor-assisted code evaluation, mimicking natural selection by filtering out weak candidates early and reducing computational costs. Experimental results on TSP (routing), MKP (resource allocation), and CVRP (service-network coordination) demonstrate that NeRM consistently outperforms state-of-the-art approaches in both performance and efficiency."
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<abstract>Large Language Models (LLMs) excel in network algorithm design but suffer from inefficient iterative coding and high computational costs. Drawing inspiration from butterfly metamorphosis—where structured developmental phases (Phase I: larval nutrient accumulation → Phase II: pupal transformation) enable adaptive evolution—we propose Nested-Refinement Metamorphosis (NeRM). Building on this principle, we introduce Metamorphosis on Prompts (MoP) to iteratively refine task descriptions (e.g. latency / bandwidth constraints) and Metamorphosis on Algorithms (MoA) to generate more effective solutions (e.g. appropriate network processing architecture). Their nested refinement ensures task-algorithm alignment, systematically improving both task descriptions and algorithmic solutions for more efficient algorithm design. To further enhance efficiency, we incorporate predictor-assisted code evaluation, mimicking natural selection by filtering out weak candidates early and reducing computational costs. Experimental results on TSP (routing), MKP (resource allocation), and CVRP (service-network coordination) demonstrate that NeRM consistently outperforms state-of-the-art approaches in both performance and efficiency.</abstract>
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%0 Conference Proceedings
%T Nested-Refinement Metamorphosis: Reflective Evolution for Efficient Optimization of Networking Problems
%A Guo, Shuhan
%A Yin, Nan
%A Kwok, James
%A Yao, Quanming
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F guo-etal-2025-nested
%X Large Language Models (LLMs) excel in network algorithm design but suffer from inefficient iterative coding and high computational costs. Drawing inspiration from butterfly metamorphosis—where structured developmental phases (Phase I: larval nutrient accumulation → Phase II: pupal transformation) enable adaptive evolution—we propose Nested-Refinement Metamorphosis (NeRM). Building on this principle, we introduce Metamorphosis on Prompts (MoP) to iteratively refine task descriptions (e.g. latency / bandwidth constraints) and Metamorphosis on Algorithms (MoA) to generate more effective solutions (e.g. appropriate network processing architecture). Their nested refinement ensures task-algorithm alignment, systematically improving both task descriptions and algorithmic solutions for more efficient algorithm design. To further enhance efficiency, we incorporate predictor-assisted code evaluation, mimicking natural selection by filtering out weak candidates early and reducing computational costs. Experimental results on TSP (routing), MKP (resource allocation), and CVRP (service-network coordination) demonstrate that NeRM consistently outperforms state-of-the-art approaches in both performance and efficiency.
%R 10.18653/v1/2025.findings-acl.895
%U https://aclanthology.org/2025.findings-acl.895/
%U https://doi.org/10.18653/v1/2025.findings-acl.895
%P 17398-17429
Markdown (Informal)
[Nested-Refinement Metamorphosis: Reflective Evolution for Efficient Optimization of Networking Problems](https://aclanthology.org/2025.findings-acl.895/) (Guo et al., Findings 2025)
ACL