{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IMJYCWRCUXFXKNKTD2SOL6NG3V","short_pith_number":"pith:IMJYCWRC","canonical_record":{"source":{"id":"2502.21206","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.GN","submitted_at":"2025-02-28T16:25:50Z","cross_cats_sorted":["q-fin.TR"],"title_canon_sha256":"7ac2dbd584de26f54746a2fb77acb724c8450f12f4ba8896ed9b38d5c542151c","abstract_canon_sha256":"3887b757bcdcddb15e7fe93de52e952ec54af2ede43c25bf7e72972c09890a84"},"schema_version":"1.0"},"canonical_sha256":"4313815a22a5cb7535531ea4e5f9a6dd64a7fe899faaac18fbf5c941c509e620","source":{"kind":"arxiv","id":"2502.21206","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.21206","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"arxiv_version","alias_value":"2502.21206v3","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.21206","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"pith_short_12","alias_value":"IMJYCWRCUXFX","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"IMJYCWRCUXFXKNKT","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"IMJYCWRC","created_at":"2026-07-05T11:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IMJYCWRCUXFXKNKTD2SOL6NG3V","target":"record","payload":{"canonical_record":{"source":{"id":"2502.21206","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.GN","submitted_at":"2025-02-28T16:25:50Z","cross_cats_sorted":["q-fin.TR"],"title_canon_sha256":"7ac2dbd584de26f54746a2fb77acb724c8450f12f4ba8896ed9b38d5c542151c","abstract_canon_sha256":"3887b757bcdcddb15e7fe93de52e952ec54af2ede43c25bf7e72972c09890a84"},"schema_version":"1.0"},"canonical_sha256":"4313815a22a5cb7535531ea4e5f9a6dd64a7fe899faaac18fbf5c941c509e620","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:25.585747Z","signature_b64":"CrRomYK9sdrA4FZljv1cnWLn8sokvCGBbU0Yw48RG7lsvuZORUIOHJgmmUr0KlI6u6zQCFcTCaHMt5AY65XlCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4313815a22a5cb7535531ea4e5f9a6dd64a7fe899faaac18fbf5c941c509e620","last_reissued_at":"2026-07-05T11:32:25.585003Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:25.585003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.21206","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:32:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PBxgXy5le6fi3ZngA5uFaiFV97jMXGEZvmAL8QIuIRpNxS1GXrM8W5rbOgWndV15dq34tQJ4dAvktULshqzxDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:36:15.881749Z"},"content_sha256":"d5e3df1a3e23345573135bb9b24650ed05ba62d82e3c282d490f3b647b31a456","schema_version":"1.0","event_id":"sha256:d5e3df1a3e23345573135bb9b24650ed05ba62d82e3c282d490f3b647b31a456"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IMJYCWRCUXFXKNKTD2SOL6NG3V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Chronologically Consistent Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-fin.TR"],"primary_cat":"q-fin.GN","authors_text":"Asaf Manela, Jimmy Wu, Linying Lv, Songrun He","submitted_at":"2025-02-28T16:25:50Z","abstract_excerpt":"Large language models are increasingly used in social sciences, but their training data can introduce lookahead bias and training leakage. A good chronologically consistent language model requires efficient use of training data to maintain accuracy despite time-restricted data. Here, we overcome this challenge by training a suite of chronologically consistent large language models, ChronoBERT and ChronoGPT, which incorporate only the text data that would have been available at each point in time. Despite this strict temporal constraint, our models achieve strong performance on natural language"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.21206","kind":"arxiv","version":3},"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/2502.21206/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":"2026-07-05T11:32:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pmImRcWqaTutGA+xDcweDNUTHXao87gy8RZ2XefLvpsSIJ6zNIOicizH0M41OCFVhcy8/r2nejrDOKFcK13OCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:36:15.882263Z"},"content_sha256":"b531e2db2f549205efa312e89f454e9c9b87b3e3585c584e487ce48f47c4fe00","schema_version":"1.0","event_id":"sha256:b531e2db2f549205efa312e89f454e9c9b87b3e3585c584e487ce48f47c4fe00"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IMJYCWRCUXFXKNKTD2SOL6NG3V/bundle.json","state_url":"https://pith.science/pith/IMJYCWRCUXFXKNKTD2SOL6NG3V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IMJYCWRCUXFXKNKTD2SOL6NG3V/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":"2026-08-08T05:36:15Z","links":{"resolver":"https://pith.science/pith/IMJYCWRCUXFXKNKTD2SOL6NG3V","bundle":"https://pith.science/pith/IMJYCWRCUXFXKNKTD2SOL6NG3V/bundle.json","state":"https://pith.science/pith/IMJYCWRCUXFXKNKTD2SOL6NG3V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IMJYCWRCUXFXKNKTD2SOL6NG3V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IMJYCWRCUXFXKNKTD2SOL6NG3V","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":"3887b757bcdcddb15e7fe93de52e952ec54af2ede43c25bf7e72972c09890a84","cross_cats_sorted":["q-fin.TR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.GN","submitted_at":"2025-02-28T16:25:50Z","title_canon_sha256":"7ac2dbd584de26f54746a2fb77acb724c8450f12f4ba8896ed9b38d5c542151c"},"schema_version":"1.0","source":{"id":"2502.21206","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.21206","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"arxiv_version","alias_value":"2502.21206v3","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.21206","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"pith_short_12","alias_value":"IMJYCWRCUXFX","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"IMJYCWRCUXFXKNKT","created_at":"2026-07-05T11:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"IMJYCWRC","created_at":"2026-07-05T11:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:b531e2db2f549205efa312e89f454e9c9b87b3e3585c584e487ce48f47c4fe00","target":"graph","created_at":"2026-07-05T11:32:25Z","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/2502.21206/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models are increasingly used in social sciences, but their training data can introduce lookahead bias and training leakage. A good chronologically consistent language model requires efficient use of training data to maintain accuracy despite time-restricted data. Here, we overcome this challenge by training a suite of chronologically consistent large language models, ChronoBERT and ChronoGPT, which incorporate only the text data that would have been available at each point in time. Despite this strict temporal constraint, our models achieve strong performance on natural language","authors_text":"Asaf Manela, Jimmy Wu, Linying Lv, Songrun He","cross_cats":["q-fin.TR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.GN","submitted_at":"2025-02-28T16:25:50Z","title":"Chronologically Consistent Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.21206","kind":"arxiv","version":3},"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:d5e3df1a3e23345573135bb9b24650ed05ba62d82e3c282d490f3b647b31a456","target":"record","created_at":"2026-07-05T11:32:25Z","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":"3887b757bcdcddb15e7fe93de52e952ec54af2ede43c25bf7e72972c09890a84","cross_cats_sorted":["q-fin.TR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.GN","submitted_at":"2025-02-28T16:25:50Z","title_canon_sha256":"7ac2dbd584de26f54746a2fb77acb724c8450f12f4ba8896ed9b38d5c542151c"},"schema_version":"1.0","source":{"id":"2502.21206","kind":"arxiv","version":3}},"canonical_sha256":"4313815a22a5cb7535531ea4e5f9a6dd64a7fe899faaac18fbf5c941c509e620","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4313815a22a5cb7535531ea4e5f9a6dd64a7fe899faaac18fbf5c941c509e620","first_computed_at":"2026-07-05T11:32:25.585003Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:32:25.585003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CrRomYK9sdrA4FZljv1cnWLn8sokvCGBbU0Yw48RG7lsvuZORUIOHJgmmUr0KlI6u6zQCFcTCaHMt5AY65XlCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:32:25.585747Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.21206","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d5e3df1a3e23345573135bb9b24650ed05ba62d82e3c282d490f3b647b31a456","sha256:b531e2db2f549205efa312e89f454e9c9b87b3e3585c584e487ce48f47c4fe00"],"state_sha256":"88b7a58d15e4a0749c1cb2ac8c9e0b8e5833fd8b6dc56f8de704b033135c1e8c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yQRdQEWvriPSRp0K9Em0DAX0+fo0Jhe4SfUmj1oI3nMXA1pfYskkzXvhvJbxTN39OQwgb4mD/8qlRO0F2+RTAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T05:36:15.891560Z","bundle_sha256":"51526b64173d05c28a47492cdf06d283d7af51d679d026071b30b3d5a39d0473"}}