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arXiv Machine Learning By Rayhan Patel, Shabaz Patel

EvoRank: LLM-Guided Evolution of Multi-Objective Learning-to-Rank Pipelines

Read the original on arXiv Machine Learning →

EvoRank is an open autonomous ranking engineer that uses an LLM-guided evolutionary loop to automatically design complete Learning-to-Rank pipelines—including features, models, losses, and ensembles—for multi-objective e-commerce search. On the Expedia ICDM 2013 dataset, EvoRank converged within 50 iterations on interpretable pipelines that outperform an Optuna-tuned LambdaMART on 60k held-out queries and rank in the top 6 % of the original competition. The authors also introduce a headroom gate that predicts whether the evolutionary loop will be worthwhile before any LLM computation, and they release the system, auditing tools, and a catalog of failure modes to help teams apply the method to their own ranking stacks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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