{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IDDDELH62SARERQOB6QFULKPOC","short_pith_number":"pith:IDDDELH6","schema_version":"1.0","canonical_sha256":"40c6322cfed48112460e0fa05a2d4f708beae03de3252f2c9971d8deb2ea4d19","source":{"kind":"arxiv","id":"2606.21776","version":1},"attestation_state":"computed","paper":{"title":"A Causal DAG Prior for Synthetic Time-Series Classification Datasets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ana Trisovic, Dimitris Bertsimas, Franco Martino O'Rourke","submitted_at":"2026-06-19T21:50:33Z","abstract_excerpt":"A Prior-data fitted Network learns the posterior predictive induced by its training prior; bringing this paradigm to multivariate time-series classification therefore calls for a synthetic generator that produces complete labelled datasets with temporal structure. We introduce a causal prior that synthesizes each dataset from a randomly sampled DAG over typed nodes across two modalities (tabular attributes and time series), natively producing multivariate, multi-class TSC datasets with cross-modal causal structure across channels, timesteps and labels, a regime not addressed by existing synthe"},"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":"2606.21776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-19T21:50:33Z","cross_cats_sorted":[],"title_canon_sha256":"6cea63ebce50b2dccd99a42d9e511c0beefcc81a7d148900e607d8983879366e","abstract_canon_sha256":"b7f62611b0bd50cf6be6aa3a8e499ee4029326e51c5b984030641d79fbbfcaaa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:13:22.249498Z","signature_b64":"HynkT8pNVT5ZqUIdaMc4OjQVEO1iCA6qufxZSHEcVhteEjkG+asbhsyWx9G7T7TETIBw9HY/F4egZNCuUYm9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40c6322cfed48112460e0fa05a2d4f708beae03de3252f2c9971d8deb2ea4d19","last_reissued_at":"2026-06-23T01:13:22.248948Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:13:22.248948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Causal DAG Prior for Synthetic Time-Series Classification Datasets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ana Trisovic, Dimitris Bertsimas, Franco Martino O'Rourke","submitted_at":"2026-06-19T21:50:33Z","abstract_excerpt":"A Prior-data fitted Network learns the posterior predictive induced by its training prior; bringing this paradigm to multivariate time-series classification therefore calls for a synthetic generator that produces complete labelled datasets with temporal structure. We introduce a causal prior that synthesizes each dataset from a randomly sampled DAG over typed nodes across two modalities (tabular attributes and time series), natively producing multivariate, multi-class TSC datasets with cross-modal causal structure across channels, timesteps and labels, a regime not addressed by existing synthe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21776","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/2606.21776/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":"2606.21776","created_at":"2026-06-23T01:13:22.249037+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21776v1","created_at":"2026-06-23T01:13:22.249037+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21776","created_at":"2026-06-23T01:13:22.249037+00:00"},{"alias_kind":"pith_short_12","alias_value":"IDDDELH62SAR","created_at":"2026-06-23T01:13:22.249037+00:00"},{"alias_kind":"pith_short_16","alias_value":"IDDDELH62SARERQO","created_at":"2026-06-23T01:13:22.249037+00:00"},{"alias_kind":"pith_short_8","alias_value":"IDDDELH6","created_at":"2026-06-23T01:13:22.249037+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/IDDDELH62SARERQOB6QFULKPOC","json":"https://pith.science/pith/IDDDELH62SARERQOB6QFULKPOC.json","graph_json":"https://pith.science/api/pith-number/IDDDELH62SARERQOB6QFULKPOC/graph.json","events_json":"https://pith.science/api/pith-number/IDDDELH62SARERQOB6QFULKPOC/events.json","paper":"https://pith.science/paper/IDDDELH6"},"agent_actions":{"view_html":"https://pith.science/pith/IDDDELH62SARERQOB6QFULKPOC","download_json":"https://pith.science/pith/IDDDELH62SARERQOB6QFULKPOC.json","view_paper":"https://pith.science/paper/IDDDELH6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21776&json=true","fetch_graph":"https://pith.science/api/pith-number/IDDDELH62SARERQOB6QFULKPOC/graph.json","fetch_events":"https://pith.science/api/pith-number/IDDDELH62SARERQOB6QFULKPOC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IDDDELH62SARERQOB6QFULKPOC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IDDDELH62SARERQOB6QFULKPOC/action/storage_attestation","attest_author":"https://pith.science/pith/IDDDELH62SARERQOB6QFULKPOC/action/author_attestation","sign_citation":"https://pith.science/pith/IDDDELH62SARERQOB6QFULKPOC/action/citation_signature","submit_replication":"https://pith.science/pith/IDDDELH62SARERQOB6QFULKPOC/action/replication_record"}},"created_at":"2026-06-23T01:13:22.249037+00:00","updated_at":"2026-06-23T01:13:22.249037+00:00"}