{"paper":{"title":"zSort: Stable Distribution Sort using Z-Score Partitioning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"zSort is a stable distribution sort using z-score partitioning that achieves up to 4.5x speedup over comparison-based stable sorts while matching unstable algorithms like Skasort on many inputs.","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Aditya Shastri, Ashutosh Londhe, Hiren Kumar Thakkar, Hriday Jain, Ketan Sabale","submitted_at":"2026-05-14T06:06:15Z","abstract_excerpt":"Sorting is a foundational primitive in modern data processing, influencing the execution speed of high-performance data pipelines. However, the algorithmic landscape is currently bifurcated by a pervasive \"Stability Tax\": practitioners must sacrifice either order preservation for high throughput or execution speed for stability. To address these limitations, this paper introduces, zSort, an adaptive z-score based distribution sorting algorithm that guarantees stability while avoiding pass complexity that scales with key-width. The performance of the proposed technique is evaluated using Microa"},"claims":{"count":3,"items":[{"kind":"strongest_claim","text":"zSort consistently outperforms widely used comparison based stable sorting algorithms, achieving up to 3x-4.5x speedups, and a relatively better performance compared to LSD Radix, with larger gains on duplicate heavy and partially ordered inputs.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that z-score based partitioning remains effective and avoids extreme worst-case behavior across all real-world input distributions without requiring additional safeguards or pass scaling.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"zSort is a stable distribution sort using z-score partitioning that achieves up to 4.5x speedup over comparison-based stable sorts while matching unstable algorithms like Skasort on many inputs.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"}],"snapshot_sha256":"e3211cbdf90fdea2c1ea16bbf7ad69d73c065df91d51d166a94047661f7b8634"},"source":{"id":"2605.14419","kind":"arxiv","version":1},"verdict":{"id":"c934d811-deb9-426e-9199-03338fc243eb","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:15:35.735291Z","strongest_claim":"zSort consistently outperforms widely used comparison based stable sorting algorithms, achieving up to 3x-4.5x speedups, and a relatively better performance compared to LSD Radix, with larger gains on duplicate heavy and partially ordered inputs.","one_line_summary":"zSort is a stable distribution sort using z-score partitioning that achieves up to 4.5x speedup over comparison-based stable sorts while matching unstable algorithms like Skasort on many inputs.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that z-score based partitioning remains effective and avoids extreme worst-case behavior across all real-world input distributions without requiring additional safeguards or pass scaling.","pith_extraction_headline":""},"references":{"count":27,"sample":[{"doi":"","year":2025,"title":"Sorting it out in hardware: A state- of-the-art survey","work_id":"e1510ca3-30c8-4e46-9546-b36d44fa713d","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2006,"title":"Implementing sorting in database systems","work_id":"f76997d0-b19a-4412-b356-fd4bfbeed907","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"https://sortbenchmark.org/ (ac- cessed Mar, 2026)","work_id":"e7c6ed66-1ef2-4c4d-9fd6-9d8b4aed0446","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2009,"title":"Choosing the” best” sorting algorithm for optimal energy consumption","work_id":"a7d75a72-8320-49aa-950a-2b1f3c477f69","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Introduction to algorithms","work_id":"b66767c9-5e71-474b-8bcd-dab6379eeff5","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":27,"snapshot_sha256":"12d2ef568771bb54c9ef92f9ae4ada06d88a966d856acbe4fa489bd5ef551f57","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"}