{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:PFDSWZBYG2WNOWYCIGWI4CEXNX","short_pith_number":"pith:PFDSWZBY","schema_version":"1.0","canonical_sha256":"79472b643836acd75b0241ac8e08976de6177e30636b0380481c337db72f8b40","source":{"kind":"arxiv","id":"1412.2859","version":1},"attestation_state":"computed","paper":{"title":"Circumventing the Curse of Dimensionality in Prediction: Causal Rate-Distortion for Infinite-Order Markov Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","nlin.CD","q-bio.NC","stat.ML"],"primary_cat":"cond-mat.stat-mech","authors_text":"James P. Crutchfield, Sarah Marzen","submitted_at":"2014-12-09T05:23:27Z","abstract_excerpt":"Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on finite sequences cannot capture all of their past dependencies. Spectral arguments show that algorithms which cluster finite-length sequences fail dramatically when the underlying process has long-range temporal correlations and can fail even for processes generated by finite-memor"},"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":"1412.2859","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2014-12-09T05:23:27Z","cross_cats_sorted":["cs.LG","nlin.CD","q-bio.NC","stat.ML"],"title_canon_sha256":"1af55f1dbf140d8672e1d0d90fe47de8ca2ba4c652cb94fabb92b1f668925b7c","abstract_canon_sha256":"725eb0ba4850e94fd200f04e0abb38a62131b23da73ba6975476f4eebefbb09e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:31:45.681830Z","signature_b64":"/9mrGciNtkpthonwJj30oOVgiSYuJa7zopI7F3fAb8jduNW0N1kEK0EfVGn6hr8lsWLfXXB7BQMmC8u9nD60Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79472b643836acd75b0241ac8e08976de6177e30636b0380481c337db72f8b40","last_reissued_at":"2026-05-18T02:31:45.681172Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:31:45.681172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Circumventing the Curse of Dimensionality in Prediction: Causal Rate-Distortion for Infinite-Order Markov Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","nlin.CD","q-bio.NC","stat.ML"],"primary_cat":"cond-mat.stat-mech","authors_text":"James P. Crutchfield, Sarah Marzen","submitted_at":"2014-12-09T05:23:27Z","abstract_excerpt":"Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on finite sequences cannot capture all of their past dependencies. Spectral arguments show that algorithms which cluster finite-length sequences fail dramatically when the underlying process has long-range temporal correlations and can fail even for processes generated by finite-memor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.2859","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":""},"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":"1412.2859","created_at":"2026-05-18T02:31:45.681275+00:00"},{"alias_kind":"arxiv_version","alias_value":"1412.2859v1","created_at":"2026-05-18T02:31:45.681275+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.2859","created_at":"2026-05-18T02:31:45.681275+00:00"},{"alias_kind":"pith_short_12","alias_value":"PFDSWZBYG2WN","created_at":"2026-05-18T12:28:43.426989+00:00"},{"alias_kind":"pith_short_16","alias_value":"PFDSWZBYG2WNOWYC","created_at":"2026-05-18T12:28:43.426989+00:00"},{"alias_kind":"pith_short_8","alias_value":"PFDSWZBY","created_at":"2026-05-18T12:28:43.426989+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/PFDSWZBYG2WNOWYCIGWI4CEXNX","json":"https://pith.science/pith/PFDSWZBYG2WNOWYCIGWI4CEXNX.json","graph_json":"https://pith.science/api/pith-number/PFDSWZBYG2WNOWYCIGWI4CEXNX/graph.json","events_json":"https://pith.science/api/pith-number/PFDSWZBYG2WNOWYCIGWI4CEXNX/events.json","paper":"https://pith.science/paper/PFDSWZBY"},"agent_actions":{"view_html":"https://pith.science/pith/PFDSWZBYG2WNOWYCIGWI4CEXNX","download_json":"https://pith.science/pith/PFDSWZBYG2WNOWYCIGWI4CEXNX.json","view_paper":"https://pith.science/paper/PFDSWZBY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1412.2859&json=true","fetch_graph":"https://pith.science/api/pith-number/PFDSWZBYG2WNOWYCIGWI4CEXNX/graph.json","fetch_events":"https://pith.science/api/pith-number/PFDSWZBYG2WNOWYCIGWI4CEXNX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PFDSWZBYG2WNOWYCIGWI4CEXNX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PFDSWZBYG2WNOWYCIGWI4CEXNX/action/storage_attestation","attest_author":"https://pith.science/pith/PFDSWZBYG2WNOWYCIGWI4CEXNX/action/author_attestation","sign_citation":"https://pith.science/pith/PFDSWZBYG2WNOWYCIGWI4CEXNX/action/citation_signature","submit_replication":"https://pith.science/pith/PFDSWZBYG2WNOWYCIGWI4CEXNX/action/replication_record"}},"created_at":"2026-05-18T02:31:45.681275+00:00","updated_at":"2026-05-18T02:31:45.681275+00:00"}