{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:2VMRZ4P52DZHKEPZ6PQH6M7MQ2","short_pith_number":"pith:2VMRZ4P5","canonical_record":{"source":{"id":"2303.15822","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-03-28T08:49:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bbe0fd17006c28c6f68266939a27e8baa7297c4a6a37a7c04863663305b81682","abstract_canon_sha256":"6f4e35e853ae0606e64f598591d848373079348b21f42b9533505d555ffc8004"},"schema_version":"1.0"},"canonical_sha256":"d5591cf1fdd0f27511f9f3e07f33ec868e2656fe3b143cb2167a1b784df68795","source":{"kind":"arxiv","id":"2303.15822","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.15822","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"arxiv_version","alias_value":"2303.15822v1","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.15822","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"pith_short_12","alias_value":"2VMRZ4P52DZH","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"pith_short_16","alias_value":"2VMRZ4P52DZHKEPZ","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"pith_short_8","alias_value":"2VMRZ4P5","created_at":"2026-07-05T05:55:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:2VMRZ4P52DZHKEPZ6PQH6M7MQ2","target":"record","payload":{"canonical_record":{"source":{"id":"2303.15822","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-03-28T08:49:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bbe0fd17006c28c6f68266939a27e8baa7297c4a6a37a7c04863663305b81682","abstract_canon_sha256":"6f4e35e853ae0606e64f598591d848373079348b21f42b9533505d555ffc8004"},"schema_version":"1.0"},"canonical_sha256":"d5591cf1fdd0f27511f9f3e07f33ec868e2656fe3b143cb2167a1b784df68795","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:55:28.132360Z","signature_b64":"31mhg6XZTlzEJANg/r9Q24FcnlqibCv7cn/SYMZCxF0Um9O06WcVyPiOukeUeP+f7IY3I/auUKC1BlUIURuwBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5591cf1fdd0f27511f9f3e07f33ec868e2656fe3b143cb2167a1b784df68795","last_reissued_at":"2026-07-05T05:55:28.132011Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:55:28.132011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.15822","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":"2026-07-05T05:55:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7RPex8yyYTp4EK8TC62yikytgzduKqbwGWNisVtBzZocFVJu/I49J1IZH9qylNLbHuDjYjgkpQP3JWmUB1PCAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T14:55:48.450427Z"},"content_sha256":"dcd24cb38061a4d110ac692e39a121e57c340719ccbfafa8098e0b26565e05c1","schema_version":"1.0","event_id":"sha256:dcd24cb38061a4d110ac692e39a121e57c340719ccbfafa8098e0b26565e05c1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:2VMRZ4P52DZHKEPZ6PQH6M7MQ2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Boxing Chen, Deze Wang, Shanshan Li, Shaoliang Peng, Wei Dong, Wei Luo, Xiangke Liao","submitted_at":"2023-03-28T08:49:54Z","abstract_excerpt":"As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models. However, we find that multilingual fine-tuning leads to performance degradation on recent models UniXcoder and CodeT5.\n  To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it. Updating only 0.6\\% of the overall p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.15822","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/2303.15822/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":"2026-07-05T05:55:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JuztvKOqXj49D0JlriM9yfL6hQ4IgN3LIaPma2wkgL7QdYSa5yc4+HtQWCM8KJ9rbXzBazZ1/ATUIY1ZZlVEBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T14:55:48.451328Z"},"content_sha256":"9448d09d11a5976b538252de6d90d9df3868e5736a7c6201110b41051d1d7ce8","schema_version":"1.0","event_id":"sha256:9448d09d11a5976b538252de6d90d9df3868e5736a7c6201110b41051d1d7ce8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2VMRZ4P52DZHKEPZ6PQH6M7MQ2/bundle.json","state_url":"https://pith.science/pith/2VMRZ4P52DZHKEPZ6PQH6M7MQ2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2VMRZ4P52DZHKEPZ6PQH6M7MQ2/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":"2026-08-13T14:55:48Z","links":{"resolver":"https://pith.science/pith/2VMRZ4P52DZHKEPZ6PQH6M7MQ2","bundle":"https://pith.science/pith/2VMRZ4P52DZHKEPZ6PQH6M7MQ2/bundle.json","state":"https://pith.science/pith/2VMRZ4P52DZHKEPZ6PQH6M7MQ2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2VMRZ4P52DZHKEPZ6PQH6M7MQ2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2VMRZ4P52DZHKEPZ6PQH6M7MQ2","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":"6f4e35e853ae0606e64f598591d848373079348b21f42b9533505d555ffc8004","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-03-28T08:49:54Z","title_canon_sha256":"bbe0fd17006c28c6f68266939a27e8baa7297c4a6a37a7c04863663305b81682"},"schema_version":"1.0","source":{"id":"2303.15822","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.15822","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"arxiv_version","alias_value":"2303.15822v1","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.15822","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"pith_short_12","alias_value":"2VMRZ4P52DZH","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"pith_short_16","alias_value":"2VMRZ4P52DZHKEPZ","created_at":"2026-07-05T05:55:28Z"},{"alias_kind":"pith_short_8","alias_value":"2VMRZ4P5","created_at":"2026-07-05T05:55:28Z"}],"graph_snapshots":[{"event_id":"sha256:9448d09d11a5976b538252de6d90d9df3868e5736a7c6201110b41051d1d7ce8","target":"graph","created_at":"2026-07-05T05:55:28Z","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/2303.15822/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models. However, we find that multilingual fine-tuning leads to performance degradation on recent models UniXcoder and CodeT5.\n  To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it. Updating only 0.6\\% of the overall p","authors_text":"Boxing Chen, Deze Wang, Shanshan Li, Shaoliang Peng, Wei Dong, Wei Luo, Xiangke Liao","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-03-28T08:49:54Z","title":"One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.15822","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:dcd24cb38061a4d110ac692e39a121e57c340719ccbfafa8098e0b26565e05c1","target":"record","created_at":"2026-07-05T05:55:28Z","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":"6f4e35e853ae0606e64f598591d848373079348b21f42b9533505d555ffc8004","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-03-28T08:49:54Z","title_canon_sha256":"bbe0fd17006c28c6f68266939a27e8baa7297c4a6a37a7c04863663305b81682"},"schema_version":"1.0","source":{"id":"2303.15822","kind":"arxiv","version":1}},"canonical_sha256":"d5591cf1fdd0f27511f9f3e07f33ec868e2656fe3b143cb2167a1b784df68795","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d5591cf1fdd0f27511f9f3e07f33ec868e2656fe3b143cb2167a1b784df68795","first_computed_at":"2026-07-05T05:55:28.132011Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:55:28.132011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"31mhg6XZTlzEJANg/r9Q24FcnlqibCv7cn/SYMZCxF0Um9O06WcVyPiOukeUeP+f7IY3I/auUKC1BlUIURuwBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:55:28.132360Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.15822","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dcd24cb38061a4d110ac692e39a121e57c340719ccbfafa8098e0b26565e05c1","sha256:9448d09d11a5976b538252de6d90d9df3868e5736a7c6201110b41051d1d7ce8"],"state_sha256":"e746a8a3cdd19bfd198ea2ca4c3e42e0a8cdad1b59d4e4fd050ed2a31bb7ddce"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FrCjpnzU9APuaxWfzj9TYdGcxXKK1ZFgFvANDbkFZNiw1cZqwBRwLdxgK5JnIrApz4SiKTQ1lbKIBkdUuU8UBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T14:55:48.459260Z","bundle_sha256":"29b399af1bfabfcfbab11b90fbf1d5e75c507f71233d2fb102cbc04665baebab"}}