{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PXYN4DCTOAXXHMPONIFTGCD4EB","short_pith_number":"pith:PXYN4DCT","schema_version":"1.0","canonical_sha256":"7df0de0c53702f73b1ee6a0b33087c20412fe7caaa4ad449c6565e3319dfefea","source":{"kind":"arxiv","id":"2505.12585","version":1},"attestation_state":"computed","paper":{"title":"Learning Robust Spectral Dynamics for Temporal Domain Generalization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"En Yu, Guangquan Zhang, Jie Lu, Xiaoyu Yang, Zhen Fang","submitted_at":"2025-05-19T00:38:18Z","abstract_excerpt":"Modern machine learning models struggle to maintain performance in dynamic environments where temporal distribution shifts, \\emph{i.e., concept drift}, are prevalent. Temporal Domain Generalization (TDG) seeks to enable model generalization across evolving domains, yet existing approaches typically assume smooth incremental changes, struggling with complex real-world drifts involving long-term structure (incremental evolution/periodicity) and local uncertainties. To overcome these limitations, we introduce FreKoo, which tackles these challenges via a novel frequency-domain analysis of paramete"},"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":"2505.12585","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-19T00:38:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"aed517e9f3369db184f7c49d8e1702de62abeab4111e87a275514cc1940ee8c6","abstract_canon_sha256":"f31f46bb86ef2157b7ca7307dc8536dd3bc5e7a65ea906d6971a820f22a907ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:06.429767Z","signature_b64":"oZx0w5mqdU2sDnClfecr552a54rwrMLg3C8eK7OPIphh1n3I/cLz0aaQCLOET2oK+ouRsvmqr31PmKWMbJVBDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7df0de0c53702f73b1ee6a0b33087c20412fe7caaa4ad449c6565e3319dfefea","last_reissued_at":"2026-07-05T11:05:06.429282Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:06.429282Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Robust Spectral Dynamics for Temporal Domain Generalization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"En Yu, Guangquan Zhang, Jie Lu, Xiaoyu Yang, Zhen Fang","submitted_at":"2025-05-19T00:38:18Z","abstract_excerpt":"Modern machine learning models struggle to maintain performance in dynamic environments where temporal distribution shifts, \\emph{i.e., concept drift}, are prevalent. Temporal Domain Generalization (TDG) seeks to enable model generalization across evolving domains, yet existing approaches typically assume smooth incremental changes, struggling with complex real-world drifts involving long-term structure (incremental evolution/periodicity) and local uncertainties. To overcome these limitations, we introduce FreKoo, which tackles these challenges via a novel frequency-domain analysis of paramete"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12585","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/2505.12585/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":"2505.12585","created_at":"2026-07-05T11:05:06.429352+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.12585v1","created_at":"2026-07-05T11:05:06.429352+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12585","created_at":"2026-07-05T11:05:06.429352+00:00"},{"alias_kind":"pith_short_12","alias_value":"PXYN4DCTOAXX","created_at":"2026-07-05T11:05:06.429352+00:00"},{"alias_kind":"pith_short_16","alias_value":"PXYN4DCTOAXXHMPO","created_at":"2026-07-05T11:05:06.429352+00:00"},{"alias_kind":"pith_short_8","alias_value":"PXYN4DCT","created_at":"2026-07-05T11:05:06.429352+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.04142","citing_title":"Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Multi-Stream Environments","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB","json":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB.json","graph_json":"https://pith.science/api/pith-number/PXYN4DCTOAXXHMPONIFTGCD4EB/graph.json","events_json":"https://pith.science/api/pith-number/PXYN4DCTOAXXHMPONIFTGCD4EB/events.json","paper":"https://pith.science/paper/PXYN4DCT"},"agent_actions":{"view_html":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB","download_json":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB.json","view_paper":"https://pith.science/paper/PXYN4DCT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.12585&json=true","fetch_graph":"https://pith.science/api/pith-number/PXYN4DCTOAXXHMPONIFTGCD4EB/graph.json","fetch_events":"https://pith.science/api/pith-number/PXYN4DCTOAXXHMPONIFTGCD4EB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB/action/storage_attestation","attest_author":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB/action/author_attestation","sign_citation":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB/action/citation_signature","submit_replication":"https://pith.science/pith/PXYN4DCTOAXXHMPONIFTGCD4EB/action/replication_record"}},"created_at":"2026-07-05T11:05:06.429352+00:00","updated_at":"2026-07-05T11:05:06.429352+00:00"}