Evolving Semantic Indexes for Generative Retrieval

Generative retrieval (GR) casts search and recommendation as autoregressive generation of discrete semantic identifiers (SID). SIDs play one of the most important roles to determine the quality of GR results. For example, different SID models, collision handling mechanisms, decoding frameworks, and training recipes, can significantly vary the retrieval performance. However, these discrete and interdependent design choices cannot be optimized end-to-end using gradients from downstream retrieval and therefore remain largely designed by hand. In this paper, we introduce a framework for automatically improving these components through LLM-driven evolutionary search on the SID framework, which to the best of our knowledge is the first application of LLM-driven evolutionary program search to GR. A locally servable, open-weight 31B language model is used to mutate executable GR components, so no proprietary LLM API is used at any point, and a 1M-parameter proxy decoder provides fast downstream feedback in place of repeatedly training the 1B target generator, which would otherwise make evolutionary search computationally infeasible. Across 21 SID programs, proxy and target Recall@100 exhibit strong rank correlation ($\rho =0.90$). Guided by this proxy, evolution consistently improves the seed GR on NQ320K, MSMARCO300K, and a multilingual e-commerce dataset, under a training recipe held fixed across conditions. On NQ320K, jointly evolving SID and collision handling improves Recall@1 from 55.8% to 59.3% and Recall@100 from 85.8% to 90.6%. Our component study reveals that proxy reliability depends on what component is evolved. Improvements transfer reliably when evolving the semantic index, but transfer less reliably when evolving the generator’s training objective or the full GR pipeline. These results suggest that LLM-driven evolutionary search is a practical method for improving GR design and show that carefully assessing proxy reliability is essential for successful GR pipeline optimization.