{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KPZILPTJDWOFTTLQTANGPEX256","short_pith_number":"pith:KPZILPTJ","canonical_record":{"source":{"id":"2501.13625","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2025-01-23T12:45:33Z","cross_cats_sorted":["cond-mat.dis-nn","math.IT","math.ST","stat.TH"],"title_canon_sha256":"a526312df437eea180471569897bb956cc6794b231879a2cf765a20d6cd2b6f3","abstract_canon_sha256":"aa891da1dded6f89c9e6759b9d21aa6d0379bbd77b0a0a2957d196a6f22d6093"},"schema_version":"1.0"},"canonical_sha256":"53f285be691d9c59cd70981a6792faefbb55c85a4c919d542acf198aa9edbb58","source":{"kind":"arxiv","id":"2501.13625","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13625","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13625v2","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13625","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"pith_short_12","alias_value":"KPZILPTJDWOF","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"pith_short_16","alias_value":"KPZILPTJDWOFTTLQ","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"pith_short_8","alias_value":"KPZILPTJ","created_at":"2026-07-05T10:34:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KPZILPTJDWOFTTLQTANGPEX256","target":"record","payload":{"canonical_record":{"source":{"id":"2501.13625","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2025-01-23T12:45:33Z","cross_cats_sorted":["cond-mat.dis-nn","math.IT","math.ST","stat.TH"],"title_canon_sha256":"a526312df437eea180471569897bb956cc6794b231879a2cf765a20d6cd2b6f3","abstract_canon_sha256":"aa891da1dded6f89c9e6759b9d21aa6d0379bbd77b0a0a2957d196a6f22d6093"},"schema_version":"1.0"},"canonical_sha256":"53f285be691d9c59cd70981a6792faefbb55c85a4c919d542acf198aa9edbb58","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:34:16.848430Z","signature_b64":"70PrpxqCDPG3tSULMwg5SxvbguHHp5okGhMz+a3Wy5/hUIvHCF3GHOb8+wAYTlguN22dXfAEtpbBLilwwqlACQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53f285be691d9c59cd70981a6792faefbb55c85a4c919d542acf198aa9edbb58","last_reissued_at":"2026-07-05T10:34:16.847929Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:34:16.847929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.13625","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:34:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jDGq9z+iE+2Xq05QYj+hdZi4BEOnnNsvF0VXui5MoGW9ZsKytcPR7xrq0T8vHm0Rudj0/nU9iRrO37hJ21EHBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:27:53.075487Z"},"content_sha256":"ead243e0be614021a835daea9aad1762d3cd4c4eb59a83058697ee4a90f55a03","schema_version":"1.0","event_id":"sha256:ead243e0be614021a835daea9aad1762d3cd4c4eb59a83058697ee4a90f55a03"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KPZILPTJDWOFTTLQTANGPEX256","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Information-theoretic limits and approximate message-passing for high-dimensional time series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.dis-nn","math.IT","math.ST","stat.TH"],"primary_cat":"cs.IT","authors_text":"Daria Tieplova, Jean Barbier, Samriddha Lahiry","submitted_at":"2025-01-23T12:45:33Z","abstract_excerpt":"High-dimensional time series appear in many scientific setups, demanding a nuanced approach to model and analyze the underlying dependence structure. Theoretical advancements so far often rely on stringent assumptions regarding the sparsity of the underlying signal. In non-sparse regimes, analyses have primarily focused on linear regression models with the design matrix having independent rows. In this paper, we expand the scope by investigating a high-dimensional time series model wherein the number of features grows proportionally to the number of sampling points, without assuming sparsity i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13625","kind":"arxiv","version":2},"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/2501.13625/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:34:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rSa/Nz5uijNtPkmZGquWGrilbV0cYIluNtE+pmGnj3tK49i0ZwQxY5lYWmWmppH/t835fbWA4VR/eRxd4e9bBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:27:53.076026Z"},"content_sha256":"1aa60ca96bd3fa7f580e20d52699eed12d6b7ceee03a907a5c5c6a7d1a2956d3","schema_version":"1.0","event_id":"sha256:1aa60ca96bd3fa7f580e20d52699eed12d6b7ceee03a907a5c5c6a7d1a2956d3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KPZILPTJDWOFTTLQTANGPEX256/bundle.json","state_url":"https://pith.science/pith/KPZILPTJDWOFTTLQTANGPEX256/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KPZILPTJDWOFTTLQTANGPEX256/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T19:27:53Z","links":{"resolver":"https://pith.science/pith/KPZILPTJDWOFTTLQTANGPEX256","bundle":"https://pith.science/pith/KPZILPTJDWOFTTLQTANGPEX256/bundle.json","state":"https://pith.science/pith/KPZILPTJDWOFTTLQTANGPEX256/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KPZILPTJDWOFTTLQTANGPEX256/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KPZILPTJDWOFTTLQTANGPEX256","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"aa891da1dded6f89c9e6759b9d21aa6d0379bbd77b0a0a2957d196a6f22d6093","cross_cats_sorted":["cond-mat.dis-nn","math.IT","math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2025-01-23T12:45:33Z","title_canon_sha256":"a526312df437eea180471569897bb956cc6794b231879a2cf765a20d6cd2b6f3"},"schema_version":"1.0","source":{"id":"2501.13625","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13625","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13625v2","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13625","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"pith_short_12","alias_value":"KPZILPTJDWOF","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"pith_short_16","alias_value":"KPZILPTJDWOFTTLQ","created_at":"2026-07-05T10:34:16Z"},{"alias_kind":"pith_short_8","alias_value":"KPZILPTJ","created_at":"2026-07-05T10:34:16Z"}],"graph_snapshots":[{"event_id":"sha256:1aa60ca96bd3fa7f580e20d52699eed12d6b7ceee03a907a5c5c6a7d1a2956d3","target":"graph","created_at":"2026-07-05T10:34:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.13625/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-dimensional time series appear in many scientific setups, demanding a nuanced approach to model and analyze the underlying dependence structure. Theoretical advancements so far often rely on stringent assumptions regarding the sparsity of the underlying signal. In non-sparse regimes, analyses have primarily focused on linear regression models with the design matrix having independent rows. In this paper, we expand the scope by investigating a high-dimensional time series model wherein the number of features grows proportionally to the number of sampling points, without assuming sparsity i","authors_text":"Daria Tieplova, Jean Barbier, Samriddha Lahiry","cross_cats":["cond-mat.dis-nn","math.IT","math.ST","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2025-01-23T12:45:33Z","title":"Information-theoretic limits and approximate message-passing for high-dimensional time series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13625","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ead243e0be614021a835daea9aad1762d3cd4c4eb59a83058697ee4a90f55a03","target":"record","created_at":"2026-07-05T10:34:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"aa891da1dded6f89c9e6759b9d21aa6d0379bbd77b0a0a2957d196a6f22d6093","cross_cats_sorted":["cond-mat.dis-nn","math.IT","math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2025-01-23T12:45:33Z","title_canon_sha256":"a526312df437eea180471569897bb956cc6794b231879a2cf765a20d6cd2b6f3"},"schema_version":"1.0","source":{"id":"2501.13625","kind":"arxiv","version":2}},"canonical_sha256":"53f285be691d9c59cd70981a6792faefbb55c85a4c919d542acf198aa9edbb58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53f285be691d9c59cd70981a6792faefbb55c85a4c919d542acf198aa9edbb58","first_computed_at":"2026-07-05T10:34:16.847929Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:34:16.847929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"70PrpxqCDPG3tSULMwg5SxvbguHHp5okGhMz+a3Wy5/hUIvHCF3GHOb8+wAYTlguN22dXfAEtpbBLilwwqlACQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:34:16.848430Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.13625","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ead243e0be614021a835daea9aad1762d3cd4c4eb59a83058697ee4a90f55a03","sha256:1aa60ca96bd3fa7f580e20d52699eed12d6b7ceee03a907a5c5c6a7d1a2956d3"],"state_sha256":"b5942d8aafdeeb5bdcc79f3ab905fba867b875d9283f705dabffa7e3bbf09ca1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f5JRBYvYiCrXR+Gsz8J70oR+HogKt0pt93P8oJ0JNaiU4wOBIGtQkcGw9MDA5/RZkri0GNoROv5ReZ/H0unuBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T19:27:53.080404Z","bundle_sha256":"8fa0e9a23a2c372b13d5624ca0cd19f71d202536e293039b35fbf14da4dda741"}}