{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TD3XPJWN3YM7WXW76AELTUNPL4","short_pith_number":"pith:TD3XPJWN","schema_version":"1.0","canonical_sha256":"98f777a6cdde19fb5edff008b9d1af5f19787c62f3837024ae7d04b16418b2c0","source":{"kind":"arxiv","id":"2607.15774","version":1},"attestation_state":"computed","paper":{"title":"Scaling Time Series Classification via XAI-Driven Data Reduction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Davide Italo Serramazza, Georgiana Ifrim, Thach Le Nguyen","submitted_at":"2026-07-17T09:09:08Z","abstract_excerpt":"Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for downstream tasks remains under-explored. This paper bridges this gap by introducing drXAI, a novel methodology that repurposes XAI attribution methods for effective data reduction in Time Series Classification (TSC). The core challenge in modern TSC is scalability; state-of-the-art models, such as Transformers, exhibit quadratic complexity relative to sequence length and linear complexity relative to the number of channels. This renders them computationall"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.15774","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-17T09:09:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a2080b21e777f12e595a28e4551b4fb82ab6313ebddae773c94452c6ccde603f","abstract_canon_sha256":"a1714453406d06d45ee8d8f74e9a299a5e9a1a26eee40d01c0acfb5f817ba417"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-20T01:19:09.375558Z","signature_b64":"T5tV4lFps3+sxsLtbSLu4h+hBvKZOe1GHPv6ndV+ydpcMSbXFKMmnxhe9todjP/S9Suf9UQGuML614yTphEaBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98f777a6cdde19fb5edff008b9d1af5f19787c62f3837024ae7d04b16418b2c0","last_reissued_at":"2026-07-20T01:19:09.374465Z","signature_status":"signed_v1","first_computed_at":"2026-07-20T01:19:09.374465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling Time Series Classification via XAI-Driven Data Reduction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Davide Italo Serramazza, Georgiana Ifrim, Thach Le Nguyen","submitted_at":"2026-07-17T09:09:08Z","abstract_excerpt":"Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for downstream tasks remains under-explored. This paper bridges this gap by introducing drXAI, a novel methodology that repurposes XAI attribution methods for effective data reduction in Time Series Classification (TSC). The core challenge in modern TSC is scalability; state-of-the-art models, such as Transformers, exhibit quadratic complexity relative to sequence length and linear complexity relative to the number of channels. This renders them computationall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15774","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/2607.15774/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.15774","created_at":"2026-07-20T01:19:09.374917+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15774v1","created_at":"2026-07-20T01:19:09.374917+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15774","created_at":"2026-07-20T01:19:09.374917+00:00"},{"alias_kind":"pith_short_12","alias_value":"TD3XPJWN3YM7","created_at":"2026-07-20T01:19:09.374917+00:00"},{"alias_kind":"pith_short_16","alias_value":"TD3XPJWN3YM7WXW7","created_at":"2026-07-20T01:19:09.374917+00:00"},{"alias_kind":"pith_short_8","alias_value":"TD3XPJWN","created_at":"2026-07-20T01:19:09.374917+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4","json":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4.json","graph_json":"https://pith.science/api/pith-number/TD3XPJWN3YM7WXW76AELTUNPL4/graph.json","events_json":"https://pith.science/api/pith-number/TD3XPJWN3YM7WXW76AELTUNPL4/events.json","paper":"https://pith.science/paper/TD3XPJWN"},"agent_actions":{"view_html":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4","download_json":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4.json","view_paper":"https://pith.science/paper/TD3XPJWN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15774&json=true","fetch_graph":"https://pith.science/api/pith-number/TD3XPJWN3YM7WXW76AELTUNPL4/graph.json","fetch_events":"https://pith.science/api/pith-number/TD3XPJWN3YM7WXW76AELTUNPL4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4/action/storage_attestation","attest_author":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4/action/author_attestation","sign_citation":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4/action/citation_signature","submit_replication":"https://pith.science/pith/TD3XPJWN3YM7WXW76AELTUNPL4/action/replication_record"}},"created_at":"2026-07-20T01:19:09.374917+00:00","updated_at":"2026-07-20T01:19:09.374917+00:00"}