{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MYZSDG77KIDJ5SX64OSQ67CJCM","short_pith_number":"pith:MYZSDG77","canonical_record":{"source":{"id":"2302.12297","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-02-23T19:24:55Z","cross_cats_sorted":[],"title_canon_sha256":"ba9e887e15005e337d0a13d1c06a1e700a707349d365a5a4620a241d73aa5ec8","abstract_canon_sha256":"4f8bf415c920aaa8f902e52e844f1dc11c8e94e2d9deb8e7c9b541c630289c87"},"schema_version":"1.0"},"canonical_sha256":"6633219bff52069ecafee3a50f7c49133c7689270670a4f1d1d7a8a184f33f05","source":{"kind":"arxiv","id":"2302.12297","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.12297","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"arxiv_version","alias_value":"2302.12297v1","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.12297","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"pith_short_12","alias_value":"MYZSDG77KIDJ","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"pith_short_16","alias_value":"MYZSDG77KIDJ5SX6","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"pith_short_8","alias_value":"MYZSDG77","created_at":"2026-07-05T05:45:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MYZSDG77KIDJ5SX64OSQ67CJCM","target":"record","payload":{"canonical_record":{"source":{"id":"2302.12297","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-02-23T19:24:55Z","cross_cats_sorted":[],"title_canon_sha256":"ba9e887e15005e337d0a13d1c06a1e700a707349d365a5a4620a241d73aa5ec8","abstract_canon_sha256":"4f8bf415c920aaa8f902e52e844f1dc11c8e94e2d9deb8e7c9b541c630289c87"},"schema_version":"1.0"},"canonical_sha256":"6633219bff52069ecafee3a50f7c49133c7689270670a4f1d1d7a8a184f33f05","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:45:07.946040Z","signature_b64":"3P/CciGuBVX4F68iuhdCZoQa09zqoX5OAd9236SgZiW0TNDyPZR8rFStubci6wyaVRCkytqF9dDu/1SvvW6ZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6633219bff52069ecafee3a50f7c49133c7689270670a4f1d1d7a8a184f33f05","last_reissued_at":"2026-07-05T05:45:07.945570Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:45:07.945570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.12297","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:45:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ydGjgjjEsXwyg1Ppzs9i3LIQVDetqhsvcuiJO2fngh0/GCd7zeGsAR02x6ylX/Up+dqbmsfpp5U6jxSTwsKiAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T06:07:38.870600Z"},"content_sha256":"39957e8b117df2da920df8ad121448685864b3a65ba242b7fb5d6e4bef926ee8","schema_version":"1.0","event_id":"sha256:39957e8b117df2da920df8ad121448685864b3a65ba242b7fb5d6e4bef926ee8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MYZSDG77KIDJ5SX64OSQ67CJCM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Katerina Margatina, Miguel Ballesteros, Neha Anna John, Shuai Wang, Yassine Benajiba, Yogarshi Vyas","submitted_at":"2023-02-23T19:24:55Z","abstract_excerpt":"Temporal concept drift refers to the problem of data changing over time. In NLP, that would entail that language (e.g. new expressions, meaning shifts) and factual knowledge (e.g. new concepts, updated facts) evolve over time. Focusing on the latter, we benchmark $11$ pretrained masked language models (MLMs) on a series of tests designed to evaluate the effect of temporal concept drift, as it is crucial that widely used language models remain up-to-date with the ever-evolving factual updates of the real world. Specifically, we provide a holistic framework that (1) dynamically creates temporal "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.12297","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/2302.12297/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":"2026-07-05T05:45:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+a/Io1rNj1K7RUOrAAjx9f0LANQFNkWk1EzTDEdCdz90fmPLMWNpSv8QY7K7963csQ8UGETWFRQR8AcwH4sACA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T06:07:38.871246Z"},"content_sha256":"0792c2b4a575ec16a937d9d2693be6c4c08d6460b66709e0f8f87c2ca2f8a57d","schema_version":"1.0","event_id":"sha256:0792c2b4a575ec16a937d9d2693be6c4c08d6460b66709e0f8f87c2ca2f8a57d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MYZSDG77KIDJ5SX64OSQ67CJCM/bundle.json","state_url":"https://pith.science/pith/MYZSDG77KIDJ5SX64OSQ67CJCM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MYZSDG77KIDJ5SX64OSQ67CJCM/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":"2026-08-18T06:07:38Z","links":{"resolver":"https://pith.science/pith/MYZSDG77KIDJ5SX64OSQ67CJCM","bundle":"https://pith.science/pith/MYZSDG77KIDJ5SX64OSQ67CJCM/bundle.json","state":"https://pith.science/pith/MYZSDG77KIDJ5SX64OSQ67CJCM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MYZSDG77KIDJ5SX64OSQ67CJCM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MYZSDG77KIDJ5SX64OSQ67CJCM","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":"4f8bf415c920aaa8f902e52e844f1dc11c8e94e2d9deb8e7c9b541c630289c87","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-02-23T19:24:55Z","title_canon_sha256":"ba9e887e15005e337d0a13d1c06a1e700a707349d365a5a4620a241d73aa5ec8"},"schema_version":"1.0","source":{"id":"2302.12297","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.12297","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"arxiv_version","alias_value":"2302.12297v1","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.12297","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"pith_short_12","alias_value":"MYZSDG77KIDJ","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"pith_short_16","alias_value":"MYZSDG77KIDJ5SX6","created_at":"2026-07-05T05:45:07Z"},{"alias_kind":"pith_short_8","alias_value":"MYZSDG77","created_at":"2026-07-05T05:45:07Z"}],"graph_snapshots":[{"event_id":"sha256:0792c2b4a575ec16a937d9d2693be6c4c08d6460b66709e0f8f87c2ca2f8a57d","target":"graph","created_at":"2026-07-05T05:45:07Z","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/2302.12297/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Temporal concept drift refers to the problem of data changing over time. In NLP, that would entail that language (e.g. new expressions, meaning shifts) and factual knowledge (e.g. new concepts, updated facts) evolve over time. Focusing on the latter, we benchmark $11$ pretrained masked language models (MLMs) on a series of tests designed to evaluate the effect of temporal concept drift, as it is crucial that widely used language models remain up-to-date with the ever-evolving factual updates of the real world. Specifically, we provide a holistic framework that (1) dynamically creates temporal ","authors_text":"Katerina Margatina, Miguel Ballesteros, Neha Anna John, Shuai Wang, Yassine Benajiba, Yogarshi Vyas","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-02-23T19:24:55Z","title":"Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.12297","kind":"arxiv","version":1},"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":"sha256:39957e8b117df2da920df8ad121448685864b3a65ba242b7fb5d6e4bef926ee8","target":"record","created_at":"2026-07-05T05:45:07Z","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":"4f8bf415c920aaa8f902e52e844f1dc11c8e94e2d9deb8e7c9b541c630289c87","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-02-23T19:24:55Z","title_canon_sha256":"ba9e887e15005e337d0a13d1c06a1e700a707349d365a5a4620a241d73aa5ec8"},"schema_version":"1.0","source":{"id":"2302.12297","kind":"arxiv","version":1}},"canonical_sha256":"6633219bff52069ecafee3a50f7c49133c7689270670a4f1d1d7a8a184f33f05","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6633219bff52069ecafee3a50f7c49133c7689270670a4f1d1d7a8a184f33f05","first_computed_at":"2026-07-05T05:45:07.945570Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:45:07.945570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3P/CciGuBVX4F68iuhdCZoQa09zqoX5OAd9236SgZiW0TNDyPZR8rFStubci6wyaVRCkytqF9dDu/1SvvW6ZCw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:45:07.946040Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.12297","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:39957e8b117df2da920df8ad121448685864b3a65ba242b7fb5d6e4bef926ee8","sha256:0792c2b4a575ec16a937d9d2693be6c4c08d6460b66709e0f8f87c2ca2f8a57d"],"state_sha256":"44ec553f57e1c3160a29d85101df7083603d9e76d1cfbb022d970f8dc3413433"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aAEddH+fHS3ItvM3WRfX3gIwgbmJ+p0pMgJ0zSA6u2Y+cnX+NdUzWq3VAhOMeg3ekz8XQfBl6gg6THd30hZjDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T06:07:38.875586Z","bundle_sha256":"e7984e39762e39c497ed1a12679b8f08c2876e154e8d17cb4155fccf43f43169"}}