Is Serving-Time Language-Model Inference Necessary for Recommendation? Evidence from Generative and Agentic Settings

Abstract

Language models (LMs) are increasingly used in recommender systems to generate recommendations or coordinate recommendation tools. However, does their strong performance imply that serving-time LM inference is necessary? We challenge this seemingly reasonable but unverified assumption. To investigate this question, we introduce recovery-based mechanism auditing, which tests whether independent alternatives can achieve the accuracy of LM-based recommender systems without the audited inference mechanisms under controlled information and evaluation conditions. For generative recommendation, we propose LIME-Rec, which combines sequential, collaborative, and offline semantic information without autoregressive LM decoding. On three Amazon Reviews datasets (Beauty, Toys, and Sports), LIME-Rec achieves the highest reported mean in all 12 dataset-metric combinations, with up to a 15.2% relative improvement in Recall@10 over GRAM, an LM-based generative recommender. For agentic recommendation, we propose DART-Rec, which replaces LM-based tool control with deterministic fusion and fixed expert selection. On 1,000 Beauty users, given the same candidate items and expert scores, deterministic fusion achieves a 16.7% relative improvement in Recall@10 over a fine-tuned LM controller. With at most two tool calls, fixed expert selection matches the LM controllers' Hit@10 outcomes for every user in 17 of 18 comparisons. These findings demonstrate that strong recommendation performance does not necessarily require serving-time LM inference: under the evaluated task and information conditions, alternatives without the specified inference mechanisms can match or exceed the accuracy of their LM-based counterparts. To support reproducibility, code is available at https://github.com/Double-wk/LIME-Rec.