[2603.02565] FlashEvaluator: Expanding Search Space with Parallel Evaluation

Computer Science > Information Retrieval arXiv:2603.02565 (cs) [Submitted on 3 Mar 2026] Title:FlashEvaluator: Expanding Search Space with Parallel Evaluation Authors:Chao Feng, Yuanhao Pu, Chenghao Zhang, Shanqi Liu, Shuchang Liu, Xiang Li, Yongqi Liu, Lantao Hu, Kaiqiao Zhan, Han Li, Kun Gai View a PDF of the paper titled FlashEvaluator: Expanding Search Space with Parallel Evaluation, by Chao Feng and 10 other authors View PDF HTML (experimental) Abstract:The Generator-Evaluator (G-E) framework, i.e., evaluating K sequences from a generator and selecting the top-ranked one according to evaluator scores, is a foundational paradigm in tasks such as Recommender Systems (RecSys) and Natural Language Processing (NLP). Traditional evaluators process sequences independently, suffering from two major limitations: (1) lack of explicit cross-sequence comparison, leading to suboptimal accuracy; (2) poor parallelization with linear complexity of O(K), resulting in inefficient resource utilization and negative impact on both throughput and latency. To address these challenges, we propose FlashEvaluator, which enables cross-sequence token information sharing and processes all sequences in a single forward pass. This yields sublinear computational complexity that improves the system's efficiency and supports direct inter-sequence comparisons that improve selection accuracy. The paper also provides theoretical proofs and extensive experiments on recommendation and NLP tasks, demonstrat...