{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:2V7JA6SIWVLYLS5MTC5K3AFK6N","short_pith_number":"pith:2V7JA6SI","canonical_record":{"source":{"id":"2401.15459","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-01-27T16:51:52Z","cross_cats_sorted":[],"title_canon_sha256":"527cf4e31888ecafbbff83ac43981804567dd8985584439dde8a51948cf7a453","abstract_canon_sha256":"11b0fc03429de6e5543888bdc249dc5c276161b05f26c7ce056a17b6600f7df5"},"schema_version":"1.0"},"canonical_sha256":"d57e907a48b55785cbac98baad80aaf37c3a625f237610a82dcf411a26990a64","source":{"kind":"arxiv","id":"2401.15459","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.15459","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"arxiv_version","alias_value":"2401.15459v3","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.15459","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"pith_short_12","alias_value":"2V7JA6SIWVLY","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"pith_short_16","alias_value":"2V7JA6SIWVLYLS5M","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"pith_short_8","alias_value":"2V7JA6SI","created_at":"2026-07-05T07:54:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:2V7JA6SIWVLYLS5MTC5K3AFK6N","target":"record","payload":{"canonical_record":{"source":{"id":"2401.15459","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-01-27T16:51:52Z","cross_cats_sorted":[],"title_canon_sha256":"527cf4e31888ecafbbff83ac43981804567dd8985584439dde8a51948cf7a453","abstract_canon_sha256":"11b0fc03429de6e5543888bdc249dc5c276161b05f26c7ce056a17b6600f7df5"},"schema_version":"1.0"},"canonical_sha256":"d57e907a48b55785cbac98baad80aaf37c3a625f237610a82dcf411a26990a64","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:54.463778Z","signature_b64":"Bvtad47SxI2M0PIoogJd5RjMCoVh1RZ6cSIZlJ8O9wNrQbuIilwJ/YUoCVlNpfLcTKcCSZ+j0H4b0lXid9IMCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d57e907a48b55785cbac98baad80aaf37c3a625f237610a82dcf411a26990a64","last_reissued_at":"2026-07-05T07:54:54.463354Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:54.463354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.15459","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:54:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BHnVYNoobeUjcA5ySYFpscYK14b8x1ClBYwOjd2QW+KEWVjCZSlnEGofDLROKx7epx/ufICNIFcSuN6sJLjwDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:55:01.515493Z"},"content_sha256":"af953a2e80df23f57e1ce0ae4f6dbe977e8a82db2ec3cdd814505be4ad65eb28","schema_version":"1.0","event_id":"sha256:af953a2e80df23f57e1ce0ae4f6dbe977e8a82db2ec3cdd814505be4ad65eb28"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:2V7JA6SIWVLYLS5MTC5K3AFK6N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-LLM Collaboration + Data-Centric Innovation = 2x Better Vulnerability Repair","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Bowen Xu, David Lo, DongGyun Han, Kisub Kim, Xin Zhou","submitted_at":"2024-01-27T16:51:52Z","abstract_excerpt":"The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system.\n  To address this, we propose VulMaster, a Transformer-based neural network model that excels at gener"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.15459","kind":"arxiv","version":3},"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/2401.15459/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":"2026-07-05T07:54:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K8w2LJrbI2cB+TX75hxMZndErvYRmj+QeydtEwEfyVXin/tvY13pWIpINd+FSsSX7CwefJg/giIwv/qXId2qDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:55:01.515910Z"},"content_sha256":"063837357795bc4005204d5a5bd2f510ef72ce9fdbeff2445cd718ae28726fef","schema_version":"1.0","event_id":"sha256:063837357795bc4005204d5a5bd2f510ef72ce9fdbeff2445cd718ae28726fef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2V7JA6SIWVLYLS5MTC5K3AFK6N/bundle.json","state_url":"https://pith.science/pith/2V7JA6SIWVLYLS5MTC5K3AFK6N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2V7JA6SIWVLYLS5MTC5K3AFK6N/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":"2026-07-07T04:55:01Z","links":{"resolver":"https://pith.science/pith/2V7JA6SIWVLYLS5MTC5K3AFK6N","bundle":"https://pith.science/pith/2V7JA6SIWVLYLS5MTC5K3AFK6N/bundle.json","state":"https://pith.science/pith/2V7JA6SIWVLYLS5MTC5K3AFK6N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2V7JA6SIWVLYLS5MTC5K3AFK6N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2V7JA6SIWVLYLS5MTC5K3AFK6N","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":"11b0fc03429de6e5543888bdc249dc5c276161b05f26c7ce056a17b6600f7df5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-01-27T16:51:52Z","title_canon_sha256":"527cf4e31888ecafbbff83ac43981804567dd8985584439dde8a51948cf7a453"},"schema_version":"1.0","source":{"id":"2401.15459","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.15459","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"arxiv_version","alias_value":"2401.15459v3","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.15459","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"pith_short_12","alias_value":"2V7JA6SIWVLY","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"pith_short_16","alias_value":"2V7JA6SIWVLYLS5M","created_at":"2026-07-05T07:54:54Z"},{"alias_kind":"pith_short_8","alias_value":"2V7JA6SI","created_at":"2026-07-05T07:54:54Z"}],"graph_snapshots":[{"event_id":"sha256:063837357795bc4005204d5a5bd2f510ef72ce9fdbeff2445cd718ae28726fef","target":"graph","created_at":"2026-07-05T07:54:54Z","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/2401.15459/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system.\n  To address this, we propose VulMaster, a Transformer-based neural network model that excels at gener","authors_text":"Bowen Xu, David Lo, DongGyun Han, Kisub Kim, Xin Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-01-27T16:51:52Z","title":"Multi-LLM Collaboration + Data-Centric Innovation = 2x Better Vulnerability Repair"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.15459","kind":"arxiv","version":3},"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:af953a2e80df23f57e1ce0ae4f6dbe977e8a82db2ec3cdd814505be4ad65eb28","target":"record","created_at":"2026-07-05T07:54:54Z","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":"11b0fc03429de6e5543888bdc249dc5c276161b05f26c7ce056a17b6600f7df5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-01-27T16:51:52Z","title_canon_sha256":"527cf4e31888ecafbbff83ac43981804567dd8985584439dde8a51948cf7a453"},"schema_version":"1.0","source":{"id":"2401.15459","kind":"arxiv","version":3}},"canonical_sha256":"d57e907a48b55785cbac98baad80aaf37c3a625f237610a82dcf411a26990a64","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d57e907a48b55785cbac98baad80aaf37c3a625f237610a82dcf411a26990a64","first_computed_at":"2026-07-05T07:54:54.463354Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:54:54.463354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Bvtad47SxI2M0PIoogJd5RjMCoVh1RZ6cSIZlJ8O9wNrQbuIilwJ/YUoCVlNpfLcTKcCSZ+j0H4b0lXid9IMCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:54:54.463778Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.15459","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af953a2e80df23f57e1ce0ae4f6dbe977e8a82db2ec3cdd814505be4ad65eb28","sha256:063837357795bc4005204d5a5bd2f510ef72ce9fdbeff2445cd718ae28726fef"],"state_sha256":"bfcb89beea5504f068f7f69123a701892e857b4acbdec4465bd47de5fe2523c2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ypm5jDkqTf7cELQxUA1g/u5e4iBJfyTKP4pQavRWmQwpwMVuhyzL3d0Vb5ASJ4pkTnrFqALZEveFbMI4uGGUBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T04:55:01.518355Z","bundle_sha256":"ed27561bbdda7bcea28ce0be3c6155efbf4b2866fce842a3bd3198a67102cd55"}}