SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Corpora

Abstract:We present SoftMatcha 2, an ultra-fast and flexible search algorithm that enables search over trillion-scale natural language corpora in under 0.3 seconds while allowing semantic variations in the form of substitution, insertion, and deletion. Our approach employs string matching based on suffix arrays that scales well with corpus size, and represents words as vectors, which underpin its semantic flexibility. To mitigate the combinatorial explosion induced by the semantic relaxation of queries, our method is built on two key algorithmic ideas: dynamic corpus-aware pruning and fast exact lookup enabled by a disk-aware design. We theoretically analyze the efficiency of the proposed method, indicating that it can mitigate exponential growth in the search space. Empirically, on FineWeb-Edu (Lozhkov et al., 2024) (1.4T tokens), it attains substantially lower search latency than existing methods: infini-gram (Liu et al., 2024), infini-gram mini (Xu et al., 2025), and SoftMatcha (Deguchi et al., 2025). As a practical application, our method uncovers benchmark contamination in training corpora that existing approaches miss, and it also benefits information retrieval and paraphrase detection. We also provide an online demo of fast, soft search across corpora in seven languages.

Accepted at ICML2026. Project Page & Web Interface: this https URL, Source Code: this https URL

Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2602.10908 [cs.CL]

(or arXiv:2602.10908v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2602.10908 Focus to learn more arXiv-issued DOI via DataCite

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