{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AOFPRYHHT6VJDK3NJ3RVM7L6EO","short_pith_number":"pith:AOFPRYHH","schema_version":"1.0","canonical_sha256":"038af8e0e79faa91ab6d4ee3567d7e238db5c6f36933bada94d5cb2baefb473e","source":{"kind":"arxiv","id":"2607.02632","version":1},"attestation_state":"computed","paper":{"title":"QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Arnab Barua, Hadaate Ullah, Sarowar Morshed Shawon, Shah Nawaz Haider, Steve Austin","submitted_at":"2026-07-02T14:16:48Z","abstract_excerpt":"Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial monitoring. Recent foundation models improve transfer across forecast-ing tasks, but many depend on centralized data and Trans-former attention, which restricts their use for long, high-di-mensional, and privacy-sensitive signals. This paper presents QuantFlow, a probabilistic forecasting framework that com-bines inverted sequence embedding, bidirectional Mamba state-space decoders, quantile regression, and federated learning. Each variable is embedded over the complete ob-servation wi"},"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":"2607.02632","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-02T14:16:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3d2f298318259b234d776236dccd04cfa9eaebd9983488d6243096d30e8ead1d","abstract_canon_sha256":"0bb4d0c42b88300874f064a09453cf1c98d0671f71808eed69e3bf3f75ded1ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T00:16:04.439247Z","signature_b64":"9PbeCgjAc76A0Sser2rraox0bfGZEjgH4HMHn1KazDCm+vbFZs5InXeWVDWRzqSpNWEUtGL5LCRhTIokICKQBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"038af8e0e79faa91ab6d4ee3567d7e238db5c6f36933bada94d5cb2baefb473e","last_reissued_at":"2026-07-07T00:16:04.438174Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T00:16:04.438174Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Arnab Barua, Hadaate Ullah, Sarowar Morshed Shawon, Shah Nawaz Haider, Steve Austin","submitted_at":"2026-07-02T14:16:48Z","abstract_excerpt":"Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial monitoring. Recent foundation models improve transfer across forecast-ing tasks, but many depend on centralized data and Trans-former attention, which restricts their use for long, high-di-mensional, and privacy-sensitive signals. This paper presents QuantFlow, a probabilistic forecasting framework that com-bines inverted sequence embedding, bidirectional Mamba state-space decoders, quantile regression, and federated learning. Each variable is embedded over the complete ob-servation wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.02632","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/2607.02632/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":"2607.02632","created_at":"2026-07-07T00:16:04.438351+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.02632v1","created_at":"2026-07-07T00:16:04.438351+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02632","created_at":"2026-07-07T00:16:04.438351+00:00"},{"alias_kind":"pith_short_12","alias_value":"AOFPRYHHT6VJ","created_at":"2026-07-07T00:16:04.438351+00:00"},{"alias_kind":"pith_short_16","alias_value":"AOFPRYHHT6VJDK3N","created_at":"2026-07-07T00:16:04.438351+00:00"},{"alias_kind":"pith_short_8","alias_value":"AOFPRYHH","created_at":"2026-07-07T00:16:04.438351+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/AOFPRYHHT6VJDK3NJ3RVM7L6EO","json":"https://pith.science/pith/AOFPRYHHT6VJDK3NJ3RVM7L6EO.json","graph_json":"https://pith.science/api/pith-number/AOFPRYHHT6VJDK3NJ3RVM7L6EO/graph.json","events_json":"https://pith.science/api/pith-number/AOFPRYHHT6VJDK3NJ3RVM7L6EO/events.json","paper":"https://pith.science/paper/AOFPRYHH"},"agent_actions":{"view_html":"https://pith.science/pith/AOFPRYHHT6VJDK3NJ3RVM7L6EO","download_json":"https://pith.science/pith/AOFPRYHHT6VJDK3NJ3RVM7L6EO.json","view_paper":"https://pith.science/paper/AOFPRYHH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.02632&json=true","fetch_graph":"https://pith.science/api/pith-number/AOFPRYHHT6VJDK3NJ3RVM7L6EO/graph.json","fetch_events":"https://pith.science/api/pith-number/AOFPRYHHT6VJDK3NJ3RVM7L6EO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AOFPRYHHT6VJDK3NJ3RVM7L6EO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AOFPRYHHT6VJDK3NJ3RVM7L6EO/action/storage_attestation","attest_author":"https://pith.science/pith/AOFPRYHHT6VJDK3NJ3RVM7L6EO/action/author_attestation","sign_citation":"https://pith.science/pith/AOFPRYHHT6VJDK3NJ3RVM7L6EO/action/citation_signature","submit_replication":"https://pith.science/pith/AOFPRYHHT6VJDK3NJ3RVM7L6EO/action/replication_record"}},"created_at":"2026-07-07T00:16:04.438351+00:00","updated_at":"2026-07-07T00:16:04.438351+00:00"}