{"paper":{"title":"Robustifying Sparse Matrix Multiplication","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Karl Bringmann, Nick Fischer, Vasileios Nakos","submitted_at":"2026-07-01T19:44:56Z","abstract_excerpt":"In the seminal sparse matrix multiplication problem the goal is to compute the product of two $n \\times n$ matrices when the matrices are sparse, i.e., when the number of nonzeros in the input matrices $m_{in}$ and/or the number of nonzeros in the output matrix $m_{out}$ are much smaller than $n^2$. In this paper, we explore the generalized problem of (approximately) computing the $k$ largest output entries, with an approximation error dependent solely on the smaller entries -- from the viewpoint of sparse recovery, this can be seen as a robust variant of sparse matrix multiplication. Despite "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.01427","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.01427/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}