{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SGJDWX3NN5SKFYLU2TNHEQJA44","short_pith_number":"pith:SGJDWX3N","canonical_record":{"source":{"id":"2402.12261","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:19:15Z","cross_cats_sorted":[],"title_canon_sha256":"0d58d204ad80e894e7a7f200e9331b8c74a7101b842f6239f2481d1dac271d5a","abstract_canon_sha256":"a6003fa4771307de13df983a41ad498805face8e3b5b7eccd7fdbdd27e4ac25f"},"schema_version":"1.0"},"canonical_sha256":"91923b5f6d6f64a2e174d4da724120e73c2313e3bc57e0851ed833101dffd2bf","source":{"kind":"arxiv","id":"2402.12261","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.12261","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"arxiv_version","alias_value":"2402.12261v4","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12261","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"pith_short_12","alias_value":"SGJDWX3NN5SK","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"pith_short_16","alias_value":"SGJDWX3NN5SKFYLU","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"pith_short_8","alias_value":"SGJDWX3N","created_at":"2026-07-05T08:54:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SGJDWX3NN5SKFYLU2TNHEQJA44","target":"record","payload":{"canonical_record":{"source":{"id":"2402.12261","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:19:15Z","cross_cats_sorted":[],"title_canon_sha256":"0d58d204ad80e894e7a7f200e9331b8c74a7101b842f6239f2481d1dac271d5a","abstract_canon_sha256":"a6003fa4771307de13df983a41ad498805face8e3b5b7eccd7fdbdd27e4ac25f"},"schema_version":"1.0"},"canonical_sha256":"91923b5f6d6f64a2e174d4da724120e73c2313e3bc57e0851ed833101dffd2bf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:47.485223Z","signature_b64":"NiCTxLLvlbP9LGZ6V66Mi5mZg3oBiveV/CqP8RYQaaQbfwRffYG4GgfY2KKIoQRIdDhCerJTJXFyj2ksQ6GgDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91923b5f6d6f64a2e174d4da724120e73c2313e3bc57e0851ed833101dffd2bf","last_reissued_at":"2026-07-05T08:54:47.484807Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:47.484807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.12261","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:54:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d5VCRl9Q55hyvPtGhvF0/WPNu0IiaNydKB8fQyPbwZNGQ2KZlp9rv4kqocgaEw3CJAUJtG+1bbwx89D1WOk6Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:22:23.383911Z"},"content_sha256":"bfebb7e3ed4de1ea55e5dad22e5eb2b44909d5b36ca047811890b171c90fc312","schema_version":"1.0","event_id":"sha256:bfebb7e3ed4de1ea55e5dad22e5eb2b44909d5b36ca047811890b171c90fc312"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SGJDWX3NN5SKFYLU2TNHEQJA44","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alan Ritter, Jonathan Zheng, Wei Xu","submitted_at":"2024-02-19T16:19:15Z","abstract_excerpt":"The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference. One understudied avenue of language change causing data drift is the emergence of neologisms -- new word forms -- over time. We create a diverse resource of recent English neologisms by using several popular collection methods. We analyze temporal drift using neologisms by comparing sentences containing new words with near-identical sentences that replace neologisms with existing substitute words. Model performance is nearly halved in machi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12261","kind":"arxiv","version":4},"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/2402.12261/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":"2026-07-05T08:54:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ju4wLvzd3F94Heank+S7J2CApNjMVrDPrfSSh6vtehsNFQZtcLsKVvtu9E8BfC7jAzXxAVzo0RpFynPEIgSQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:22:23.384395Z"},"content_sha256":"3dbf02a14b50723fd03379da086ba38fc9f2f3a875b58eb410027c7c68866d32","schema_version":"1.0","event_id":"sha256:3dbf02a14b50723fd03379da086ba38fc9f2f3a875b58eb410027c7c68866d32"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SGJDWX3NN5SKFYLU2TNHEQJA44/bundle.json","state_url":"https://pith.science/pith/SGJDWX3NN5SKFYLU2TNHEQJA44/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SGJDWX3NN5SKFYLU2TNHEQJA44/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":"2026-08-15T00:22:23Z","links":{"resolver":"https://pith.science/pith/SGJDWX3NN5SKFYLU2TNHEQJA44","bundle":"https://pith.science/pith/SGJDWX3NN5SKFYLU2TNHEQJA44/bundle.json","state":"https://pith.science/pith/SGJDWX3NN5SKFYLU2TNHEQJA44/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SGJDWX3NN5SKFYLU2TNHEQJA44/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SGJDWX3NN5SKFYLU2TNHEQJA44","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":"a6003fa4771307de13df983a41ad498805face8e3b5b7eccd7fdbdd27e4ac25f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:19:15Z","title_canon_sha256":"0d58d204ad80e894e7a7f200e9331b8c74a7101b842f6239f2481d1dac271d5a"},"schema_version":"1.0","source":{"id":"2402.12261","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.12261","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"arxiv_version","alias_value":"2402.12261v4","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12261","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"pith_short_12","alias_value":"SGJDWX3NN5SK","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"pith_short_16","alias_value":"SGJDWX3NN5SKFYLU","created_at":"2026-07-05T08:54:47Z"},{"alias_kind":"pith_short_8","alias_value":"SGJDWX3N","created_at":"2026-07-05T08:54:47Z"}],"graph_snapshots":[{"event_id":"sha256:3dbf02a14b50723fd03379da086ba38fc9f2f3a875b58eb410027c7c68866d32","target":"graph","created_at":"2026-07-05T08:54:47Z","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/2402.12261/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference. One understudied avenue of language change causing data drift is the emergence of neologisms -- new word forms -- over time. We create a diverse resource of recent English neologisms by using several popular collection methods. We analyze temporal drift using neologisms by comparing sentences containing new words with near-identical sentences that replace neologisms with existing substitute words. Model performance is nearly halved in machi","authors_text":"Alan Ritter, Jonathan Zheng, Wei Xu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:19:15Z","title":"NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12261","kind":"arxiv","version":4},"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:bfebb7e3ed4de1ea55e5dad22e5eb2b44909d5b36ca047811890b171c90fc312","target":"record","created_at":"2026-07-05T08:54:47Z","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":"a6003fa4771307de13df983a41ad498805face8e3b5b7eccd7fdbdd27e4ac25f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:19:15Z","title_canon_sha256":"0d58d204ad80e894e7a7f200e9331b8c74a7101b842f6239f2481d1dac271d5a"},"schema_version":"1.0","source":{"id":"2402.12261","kind":"arxiv","version":4}},"canonical_sha256":"91923b5f6d6f64a2e174d4da724120e73c2313e3bc57e0851ed833101dffd2bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"91923b5f6d6f64a2e174d4da724120e73c2313e3bc57e0851ed833101dffd2bf","first_computed_at":"2026-07-05T08:54:47.484807Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:54:47.484807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NiCTxLLvlbP9LGZ6V66Mi5mZg3oBiveV/CqP8RYQaaQbfwRffYG4GgfY2KKIoQRIdDhCerJTJXFyj2ksQ6GgDw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:54:47.485223Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.12261","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bfebb7e3ed4de1ea55e5dad22e5eb2b44909d5b36ca047811890b171c90fc312","sha256:3dbf02a14b50723fd03379da086ba38fc9f2f3a875b58eb410027c7c68866d32"],"state_sha256":"a11558cd4254833d762206c9fe94774e22a2cd8d3854a39a945a65daf1178d80"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lNl/puC8m6tRf7HuzSwXxsiajFxkc/mjPWJYMpyk8RM3TTWGZOfTPysGOa+yYKdFIorNeUpgO9k2Fd4GNkVmBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T00:22:23.387755Z","bundle_sha256":"0780d530cecaed471ef4e92352e0c4f483fd01d71523e8eff3c58547d7fec12c"}}