{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ODLV6JL7J5US5B2XFDVU35E3T7","short_pith_number":"pith:ODLV6JL7","canonical_record":{"source":{"id":"2305.04106","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-05-06T18:00:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"733c657066b695e292749d600089b24805981ed651c9f3bc3d83d50e1c70cd19","abstract_canon_sha256":"ce6121391701dda8f9ca3a0a1e65548ebadf78abbbf271c6eb55b2df77d25438"},"schema_version":"1.0"},"canonical_sha256":"70d75f257f4f692e875728eb4df49b9fd34f489c5a0f8b993b768cc09dc78461","source":{"kind":"arxiv","id":"2305.04106","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.04106","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"arxiv_version","alias_value":"2305.04106v2","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.04106","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"pith_short_12","alias_value":"ODLV6JL7J5US","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"pith_short_16","alias_value":"ODLV6JL7J5US5B2X","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"pith_short_8","alias_value":"ODLV6JL7","created_at":"2026-07-05T07:30:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ODLV6JL7J5US5B2XFDVU35E3T7","target":"record","payload":{"canonical_record":{"source":{"id":"2305.04106","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-05-06T18:00:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"733c657066b695e292749d600089b24805981ed651c9f3bc3d83d50e1c70cd19","abstract_canon_sha256":"ce6121391701dda8f9ca3a0a1e65548ebadf78abbbf271c6eb55b2df77d25438"},"schema_version":"1.0"},"canonical_sha256":"70d75f257f4f692e875728eb4df49b9fd34f489c5a0f8b993b768cc09dc78461","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:30:02.553549Z","signature_b64":"+h57lNNs81h+QX2xoo1Jxex5DVMAfS2VTGmWyH/LgKZ3EPihhxK5heImUqmSg8X2zF7cPx7e/fsPv2i99IamAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70d75f257f4f692e875728eb4df49b9fd34f489c5a0f8b993b768cc09dc78461","last_reissued_at":"2026-07-05T07:30:02.553037Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:30:02.553037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.04106","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":"2026-07-05T07:30:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dHfTb5bWIKgvFFgWHI++tmGJbmY6vHE8I/DgHIOU5vIyRI/QXC1V2yjHlAVqH+wIyAwqP+fjHmRZtcPHnBp3CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:17:54.100060Z"},"content_sha256":"3b00ade714563e028b263e07ebf8a9e4251e3fa74c66369649bbb2fc642267ea","schema_version":"1.0","event_id":"sha256:3b00ade714563e028b263e07ebf8a9e4251e3fa74c66369649bbb2fc642267ea"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ODLV6JL7J5US5B2XFDVU35E3T7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"David Lo, Houari Sahraoui, Kisub Kim, Martin Weyssow, Xin Zhou","submitted_at":"2023-05-06T18:00:21Z","abstract_excerpt":"Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general knowledge about code and specialize in a variety of downstream tasks. However, the dynamic nature of software codebases poses a challenge to the effectiveness and robustness of PLMs. In particular, world-realistic scenarios potentially lead to significant differences between the distribution of the pre-training and test data, i.e., distribution shift, resulting in a degradation of the PLM's performance on downstream task"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.04106","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/2305.04106/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":"2026-07-05T07:30:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"op4grvzrWCh+EwFFY//rTcykV6WHa8RzlG71oSz1lBJPlGG6cwYNG4RsI+93konv7HBImPwAByVQL/NHcVEZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:17:54.100467Z"},"content_sha256":"f8bd9a47985102e340fdab2569a9b40c91de48a2ed069d454aff43eeef597293","schema_version":"1.0","event_id":"sha256:f8bd9a47985102e340fdab2569a9b40c91de48a2ed069d454aff43eeef597293"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ODLV6JL7J5US5B2XFDVU35E3T7/bundle.json","state_url":"https://pith.science/pith/ODLV6JL7J5US5B2XFDVU35E3T7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ODLV6JL7J5US5B2XFDVU35E3T7/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":"2026-07-07T07:17:54Z","links":{"resolver":"https://pith.science/pith/ODLV6JL7J5US5B2XFDVU35E3T7","bundle":"https://pith.science/pith/ODLV6JL7J5US5B2XFDVU35E3T7/bundle.json","state":"https://pith.science/pith/ODLV6JL7J5US5B2XFDVU35E3T7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ODLV6JL7J5US5B2XFDVU35E3T7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ODLV6JL7J5US5B2XFDVU35E3T7","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":"ce6121391701dda8f9ca3a0a1e65548ebadf78abbbf271c6eb55b2df77d25438","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-05-06T18:00:21Z","title_canon_sha256":"733c657066b695e292749d600089b24805981ed651c9f3bc3d83d50e1c70cd19"},"schema_version":"1.0","source":{"id":"2305.04106","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.04106","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"arxiv_version","alias_value":"2305.04106v2","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.04106","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"pith_short_12","alias_value":"ODLV6JL7J5US","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"pith_short_16","alias_value":"ODLV6JL7J5US5B2X","created_at":"2026-07-05T07:30:02Z"},{"alias_kind":"pith_short_8","alias_value":"ODLV6JL7","created_at":"2026-07-05T07:30:02Z"}],"graph_snapshots":[{"event_id":"sha256:f8bd9a47985102e340fdab2569a9b40c91de48a2ed069d454aff43eeef597293","target":"graph","created_at":"2026-07-05T07:30:02Z","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/2305.04106/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general knowledge about code and specialize in a variety of downstream tasks. However, the dynamic nature of software codebases poses a challenge to the effectiveness and robustness of PLMs. In particular, world-realistic scenarios potentially lead to significant differences between the distribution of the pre-training and test data, i.e., distribution shift, resulting in a degradation of the PLM's performance on downstream task","authors_text":"David Lo, Houari Sahraoui, Kisub Kim, Martin Weyssow, Xin Zhou","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-05-06T18:00:21Z","title":"On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.04106","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":"sha256:3b00ade714563e028b263e07ebf8a9e4251e3fa74c66369649bbb2fc642267ea","target":"record","created_at":"2026-07-05T07:30:02Z","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":"ce6121391701dda8f9ca3a0a1e65548ebadf78abbbf271c6eb55b2df77d25438","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-05-06T18:00:21Z","title_canon_sha256":"733c657066b695e292749d600089b24805981ed651c9f3bc3d83d50e1c70cd19"},"schema_version":"1.0","source":{"id":"2305.04106","kind":"arxiv","version":2}},"canonical_sha256":"70d75f257f4f692e875728eb4df49b9fd34f489c5a0f8b993b768cc09dc78461","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"70d75f257f4f692e875728eb4df49b9fd34f489c5a0f8b993b768cc09dc78461","first_computed_at":"2026-07-05T07:30:02.553037Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:30:02.553037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+h57lNNs81h+QX2xoo1Jxex5DVMAfS2VTGmWyH/LgKZ3EPihhxK5heImUqmSg8X2zF7cPx7e/fsPv2i99IamAg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:30:02.553549Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.04106","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3b00ade714563e028b263e07ebf8a9e4251e3fa74c66369649bbb2fc642267ea","sha256:f8bd9a47985102e340fdab2569a9b40c91de48a2ed069d454aff43eeef597293"],"state_sha256":"ae054ae1174b0bb2f9b76f1fe7aa6c00a58cd8eada2bbd98adf0d83dd77d6cb7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"01vUGuDJiY3GuU8C+bjZkoWBRUNbFDTH74nVQzHKHQqt1lLWkGYA81TLxGPvYPu+zjyLVw9myM9cWghtb/HEAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:17:54.102886Z","bundle_sha256":"acb19533548abb78a59e780460df41e7b8a8bd6b7a5f733d5d4853eaef8b6621"}}