Search, a foundational operation in computer science, maps a query to a matching item in a collection. It is typically implemented as a System-2 like, rule-based pipeline in which a key is computed, an index is probed, and candidates are verified. By contrast, human recognition resembles a System-1 like, associative model of identity recovery, in which even partial cues can trigger a recall without explicitly enumerating, ranking, or even accessing discrete candidates. Here, we show that music sound identification, a difficult search problem, can be performed in a single neural feed-forward pass by a generative transformer. Trained on an audio dataset, the model predicts the corresponding track identifier from a short audio excerpt. This approach surpasses state-of-the-art acoustic fingerprinting, with the largest gains for short audio segments (1 second), demonstrating the method is not only viable but advantageous. Moreover, it reduces external storage to 0.33% of the baseline footprint and improves inference latency by 2.3x (p95). Furthermore, the model can reject queries for unseen tracks, supporting open-set operation while reducing misattribution risk. Using music track identification as an example, this work reframes search, bringing it closer in spirit to human associative recognition and away from algorithmic database lookup.

翻译：搜索——计算机科学中的基础操作——将查询映射到集合中的匹配项。它通常以类系统2的规则型流水线方式实现：计算键值、探查索引、验证候选结果。相比之下，人类识别更接近类系统1的关联式身份恢复模型：即使部分线索也能触发回忆，无需显式枚举、排序甚至获取离散候选。本文证明，音乐声音识别这一困难搜索问题，可通过生成式Transformer在单次神经前向传播中完成。该模型在音频数据集上训练后，能从短音频片段预测对应曲目标识符。该方法超越最先进的声学指纹技术，对短音频片段（1秒）的提升最为显著，证明该方法不仅可行且具备优势。此外，它将外部存储降至基准方案的0.33%，并将推理延迟提升2.3倍（p95）。更重要的是，模型能拒绝未见曲目的查询，支持开放集操作并降低误标风险。以音乐曲目识别为例，本研究重构了搜索范式，使其更接近人类联想式识别，远离算法型数据库查询。