{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:HYBNHOUTPB4BPPJXEWE6AB2CQP","short_pith_number":"pith:HYBNHOUT","canonical_record":{"source":{"id":"2111.07408","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-14T18:29:19Z","cross_cats_sorted":[],"title_canon_sha256":"6cd94b22db77705ce6435756a9350480f9ccc608a9f89a939fa91f8e80a4b0c5","abstract_canon_sha256":"09d4089af4f64472393a1ddc9454131f9b0734a27e0c71920e1a8e41c4af39c1"},"schema_version":"1.0"},"canonical_sha256":"3e02d3ba93787817bd372589e0074283dbf57149e54ed238520d9291a77008d6","source":{"kind":"arxiv","id":"2111.07408","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.07408","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"arxiv_version","alias_value":"2111.07408v2","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.07408","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"pith_short_12","alias_value":"HYBNHOUTPB4B","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"pith_short_16","alias_value":"HYBNHOUTPB4BPPJX","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"pith_short_8","alias_value":"HYBNHOUT","created_at":"2026-07-05T04:36:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:HYBNHOUTPB4BPPJXEWE6AB2CQP","target":"record","payload":{"canonical_record":{"source":{"id":"2111.07408","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-14T18:29:19Z","cross_cats_sorted":[],"title_canon_sha256":"6cd94b22db77705ce6435756a9350480f9ccc608a9f89a939fa91f8e80a4b0c5","abstract_canon_sha256":"09d4089af4f64472393a1ddc9454131f9b0734a27e0c71920e1a8e41c4af39c1"},"schema_version":"1.0"},"canonical_sha256":"3e02d3ba93787817bd372589e0074283dbf57149e54ed238520d9291a77008d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:36:33.716056Z","signature_b64":"jCg3WzNgTtfvW2iwhwD8W+5ECTEqktUV7h1V4ecnOhP9fq9FdOrP+nZ+Os+S9PH2mCNy0xYZhQ9qtX7ueH8xAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e02d3ba93787817bd372589e0074283dbf57149e54ed238520d9291a77008d6","last_reissued_at":"2026-07-05T04:36:33.715376Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:36:33.715376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.07408","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":"2026-07-05T04:36:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SJM5gAww8/YZPe95w6JVT9iuo4TvsMmiEV6y/AaTCkEGEOzjPnBRtIG+iEDrvWJzb/rJbHg27TAe+OQ+gC2KCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T12:48:49.629773Z"},"content_sha256":"8143b77d9ec6a08f9d26d27d2118328812e6dee0f886f83bd7d01beb6e4f3a75","schema_version":"1.0","event_id":"sha256:8143b77d9ec6a08f9d26d27d2118328812e6dee0f886f83bd7d01beb6e4f3a75"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:HYBNHOUTPB4BPPJXEWE6AB2CQP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Time Waits for No One! Analysis and Challenges of Temporal Misalignment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Khashabi, Karishma Mandyam, Kelvin Luu, Noah A. Smith, Suchin Gururangan","submitted_at":"2021-11-14T18:29:19Z","abstract_excerpt":"When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we establish a suite of eight diverse tasks across different domains (social media, science papers, news, and reviews) and periods of time (spanning five years or more) to quantify the effects of temporal misalignment. Our study is focused on the ubiquitous setting where a pretrained model is optionally adapted through continued domain-specific pretraining, followed by task-specific finetuning. We establi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.07408","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/2111.07408/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":"2026-07-05T04:36:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mLX5fX6yqNfkclfjPLBfjzwy8k+whfrVnMyFCMCLNFhqedVvChnWTuPGxxEzK0ekZAgThuadw6nNNpq5cMMwDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T12:48:49.630710Z"},"content_sha256":"7c1181f93e562d00178a21be9cc42a435dd804cda1ae6da429efdd8ba3af1ebc","schema_version":"1.0","event_id":"sha256:7c1181f93e562d00178a21be9cc42a435dd804cda1ae6da429efdd8ba3af1ebc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HYBNHOUTPB4BPPJXEWE6AB2CQP/bundle.json","state_url":"https://pith.science/pith/HYBNHOUTPB4BPPJXEWE6AB2CQP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HYBNHOUTPB4BPPJXEWE6AB2CQP/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":"2026-08-10T12:48:49Z","links":{"resolver":"https://pith.science/pith/HYBNHOUTPB4BPPJXEWE6AB2CQP","bundle":"https://pith.science/pith/HYBNHOUTPB4BPPJXEWE6AB2CQP/bundle.json","state":"https://pith.science/pith/HYBNHOUTPB4BPPJXEWE6AB2CQP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HYBNHOUTPB4BPPJXEWE6AB2CQP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HYBNHOUTPB4BPPJXEWE6AB2CQP","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":"09d4089af4f64472393a1ddc9454131f9b0734a27e0c71920e1a8e41c4af39c1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-14T18:29:19Z","title_canon_sha256":"6cd94b22db77705ce6435756a9350480f9ccc608a9f89a939fa91f8e80a4b0c5"},"schema_version":"1.0","source":{"id":"2111.07408","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.07408","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"arxiv_version","alias_value":"2111.07408v2","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.07408","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"pith_short_12","alias_value":"HYBNHOUTPB4B","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"pith_short_16","alias_value":"HYBNHOUTPB4BPPJX","created_at":"2026-07-05T04:36:33Z"},{"alias_kind":"pith_short_8","alias_value":"HYBNHOUT","created_at":"2026-07-05T04:36:33Z"}],"graph_snapshots":[{"event_id":"sha256:7c1181f93e562d00178a21be9cc42a435dd804cda1ae6da429efdd8ba3af1ebc","target":"graph","created_at":"2026-07-05T04:36:33Z","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/2111.07408/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we establish a suite of eight diverse tasks across different domains (social media, science papers, news, and reviews) and periods of time (spanning five years or more) to quantify the effects of temporal misalignment. Our study is focused on the ubiquitous setting where a pretrained model is optionally adapted through continued domain-specific pretraining, followed by task-specific finetuning. We establi","authors_text":"Daniel Khashabi, Karishma Mandyam, Kelvin Luu, Noah A. Smith, Suchin Gururangan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-14T18:29:19Z","title":"Time Waits for No One! Analysis and Challenges of Temporal Misalignment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.07408","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":"sha256:8143b77d9ec6a08f9d26d27d2118328812e6dee0f886f83bd7d01beb6e4f3a75","target":"record","created_at":"2026-07-05T04:36:33Z","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":"09d4089af4f64472393a1ddc9454131f9b0734a27e0c71920e1a8e41c4af39c1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-11-14T18:29:19Z","title_canon_sha256":"6cd94b22db77705ce6435756a9350480f9ccc608a9f89a939fa91f8e80a4b0c5"},"schema_version":"1.0","source":{"id":"2111.07408","kind":"arxiv","version":2}},"canonical_sha256":"3e02d3ba93787817bd372589e0074283dbf57149e54ed238520d9291a77008d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3e02d3ba93787817bd372589e0074283dbf57149e54ed238520d9291a77008d6","first_computed_at":"2026-07-05T04:36:33.715376Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:36:33.715376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jCg3WzNgTtfvW2iwhwD8W+5ECTEqktUV7h1V4ecnOhP9fq9FdOrP+nZ+Os+S9PH2mCNy0xYZhQ9qtX7ueH8xAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:36:33.716056Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.07408","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8143b77d9ec6a08f9d26d27d2118328812e6dee0f886f83bd7d01beb6e4f3a75","sha256:7c1181f93e562d00178a21be9cc42a435dd804cda1ae6da429efdd8ba3af1ebc"],"state_sha256":"72fafb29b6cbb91d9941aca9fca693b1ba903801b667d052696b7d2d8125d3f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O5q9z8VhM9vVgoDn2M8p2y3z4MPhQMVXlXV2ctl8uZmR17EAVZh3qsR/70OXis8ua916Ezv9I6cTJOfefyLRAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T12:48:49.638007Z","bundle_sha256":"c3b81f7a97d1140de7a9d6f7a10fc0777cbd9bbc1cc98be9881beb47f06550a5"}}