{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GXIUZS4F5TDJTSCVLQCLZNUCR6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8fd6bb28f322aa8426200f3da13ffa9e850cb128a6b6c8c96a7d6885df90aeba","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-01T17:52:59Z","title_canon_sha256":"9f7972cca50d92f120d82a70c485e0e64b534720c727f14bb15b83ff3d7af82d"},"schema_version":"1.0","source":{"id":"2602.01359","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.01359","created_at":"2026-05-17T23:39:00Z"},{"alias_kind":"arxiv_version","alias_value":"2602.01359v2","created_at":"2026-05-17T23:39:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.01359","created_at":"2026-05-17T23:39:00Z"},{"alias_kind":"pith_short_12","alias_value":"GXIUZS4F5TDJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"GXIUZS4F5TDJTSCV","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"GXIUZS4F","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:092420b9304a258bf7191c8d96247eeef0117d7f553a35db302a5942bf459543","target":"graph","created_at":"2026-05-17T23:39:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"PaAno achieved state-of-the-art performance, significantly outperforming existing methods, including those based on heavy architectures, on both univariate and multivariate time-series anomaly detection across various range-wise and point-wise performance measures."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the learned patch embeddings reliably separate normal from anomalous temporal patterns and that comparison to training-set normal patches produces a valid anomaly score without additional calibration or post-hoc tuning."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"PaAno uses patch-based 1D CNN embeddings trained with triplet and pretext losses to achieve state-of-the-art time-series anomaly detection on the TSB-AD benchmark for both univariate and multivariate data."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A patch-based CNN method for time-series anomaly detection surpasses complex models on benchmarks."}],"snapshot_sha256":"938f1bbcb899f0b49898f6c425239fc628b523019331db1e94fda65fca6cf873"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"e29e1e4daf5e6535de72ec0d7ceaa7417606b2b28c4e1d1e88c3d84c985ef70f"},"paper":{"abstract_excerpt":"Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios. Moreover, they often fail to demonstrate significant performance gains over simpler methods under rigorous evaluation protocols. In this study, we propose Patch-based representation learning for time-series Anomaly detection (PaAno), a lightweight yet effective method for fast and efficient time-series ","authors_text":"Jinju Park, Seokho Kang","cross_cats":["cs.AI"],"headline":"A patch-based CNN method for time-series anomaly detection surpasses complex models on benchmarks.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-01T17:52:59Z","title":"PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection"},"references":{"count":37,"internal_anchors":1,"resolved_work":37,"sample":[{"cited_arxiv_id":"","doi":"10.1145/3394486.3403392","is_internal_anchor":false,"ref_index":1,"title":"doi: 10.1145/3394486.3403392. Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling.arXiv preprint arXiv:1803.0","work_id":"558a53f5-6cb5-46ac-8be8-07cb57895f23","year":null},{"cited_arxiv_id":"","doi":"10.1109/cvpr.2019.00982","is_internal_anchor":false,"ref_index":2,"title":"Mvtec AD - A comprehensive real-world dataset for unsupervised anomaly detection","work_id":"2f0b7c01-862e-43bb-8146-dd8cf0e3993a","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Paul Boniol, John Paparrizos, Themis Palpanas, and Michael J","work_id":"fd8c9339-7c3b-42bf-91f0-fce27c5eb5da","year":null},{"cited_arxiv_id":"","doi":"10.14778/3467861.3467865","is_internal_anchor":false,"ref_index":4,"title":"Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas, and John Paparrizos","work_id":"0d98a3fa-d47a-4e15-93e0-062eebc68f37","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Breunig, Hans-Peter Kriegel, Raymond T","work_id":"ff9c9c04-2bea-4ced-a04e-19d0004a386a","year":2026}],"snapshot_sha256":"6387d952cd8bc76fd0a10784b10f9844e8aff57402583e9db9e629f02bdf0ea3"},"source":{"id":"2602.01359","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-16T08:40:07.423151Z","id":"884e849a-3998-4b5d-a47c-4574aa937d03","model_set":{"reader":"grok-4.3"},"one_line_summary":"PaAno uses patch-based 1D CNN embeddings trained with triplet and pretext losses to achieve state-of-the-art time-series anomaly detection on the TSB-AD benchmark for both univariate and multivariate data.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A patch-based CNN method for time-series anomaly detection surpasses complex models on benchmarks.","strongest_claim":"PaAno achieved state-of-the-art performance, significantly outperforming existing methods, including those based on heavy architectures, on both univariate and multivariate time-series anomaly detection across various range-wise and point-wise performance measures.","weakest_assumption":"That the learned patch embeddings reliably separate normal from anomalous temporal patterns and that comparison to training-set normal patches produces a valid anomaly score without additional calibration or post-hoc tuning."}},"verdict_id":"884e849a-3998-4b5d-a47c-4574aa937d03"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:609a61859fe99b49ed0cc6fe4f1f7d882f4c957e8644ab5010df87047bd36f51","target":"record","created_at":"2026-05-17T23:39:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8fd6bb28f322aa8426200f3da13ffa9e850cb128a6b6c8c96a7d6885df90aeba","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-01T17:52:59Z","title_canon_sha256":"9f7972cca50d92f120d82a70c485e0e64b534720c727f14bb15b83ff3d7af82d"},"schema_version":"1.0","source":{"id":"2602.01359","kind":"arxiv","version":2}},"canonical_sha256":"35d14ccb85ecc699c8555c04bcb6828f84964abbad160f1fdc63f11fa5da0cf6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35d14ccb85ecc699c8555c04bcb6828f84964abbad160f1fdc63f11fa5da0cf6","first_computed_at":"2026-05-17T23:39:00.110304Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:00.110304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QtOxp2oPeKCwR06jCC1UYVqRG5zaPwI00Eq+WRzqNLgBlLxvCLI+tthS1KnX+s9eubpBX2rs8kUKMFQgOnH0Bw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:00.111075Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.01359","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:609a61859fe99b49ed0cc6fe4f1f7d882f4c957e8644ab5010df87047bd36f51","sha256:092420b9304a258bf7191c8d96247eeef0117d7f553a35db302a5942bf459543"],"state_sha256":"55c82ef7730e7e2a1fc3d99a827ac9b70850ea5ff5c274abe72a4c886bee931c"}