{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KTJRMGWKHO5ILOEAYDCCMU2KGJ","short_pith_number":"pith:KTJRMGWK","canonical_record":{"source":{"id":"2507.10103","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-07-14T09:41:51Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"90e1317dec7f3ac26f2b4d8a345b757566d2ff80993e2ef03920aac81ec65fff","abstract_canon_sha256":"f788185ef13ca30fee93dc9079cbe016ad74897d2e0ab4f2b9f6cd0eef43de1c"},"schema_version":"1.0"},"canonical_sha256":"54d3161aca3bba85b880c0c426534a3246c3d951974f4a8804c5cdcde0fcb2e2","source":{"kind":"arxiv","id":"2507.10103","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.10103","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"arxiv_version","alias_value":"2507.10103v1","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.10103","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"pith_short_12","alias_value":"KTJRMGWKHO5I","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"pith_short_16","alias_value":"KTJRMGWKHO5ILOEA","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"pith_short_8","alias_value":"KTJRMGWK","created_at":"2026-07-05T11:36:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KTJRMGWKHO5ILOEAYDCCMU2KGJ","target":"record","payload":{"canonical_record":{"source":{"id":"2507.10103","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-07-14T09:41:51Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"90e1317dec7f3ac26f2b4d8a345b757566d2ff80993e2ef03920aac81ec65fff","abstract_canon_sha256":"f788185ef13ca30fee93dc9079cbe016ad74897d2e0ab4f2b9f6cd0eef43de1c"},"schema_version":"1.0"},"canonical_sha256":"54d3161aca3bba85b880c0c426534a3246c3d951974f4a8804c5cdcde0fcb2e2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:45.091021Z","signature_b64":"AIm9bFlamP4Q6eSFMkH4gwDqakxq/Sv/l9OrhFnwRMaiYAZYsilh+e5KKAaXetabeWXQ3LqYrSHTRULTes9wDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54d3161aca3bba85b880c0c426534a3246c3d951974f4a8804c5cdcde0fcb2e2","last_reissued_at":"2026-07-05T11:36:45.090480Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:45.090480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.10103","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":"2026-07-05T11:36:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tlfTKUyXWDQdQqgR1ezhk0lrL9aQseXajJsEYkS7vMQcaQhBWlfWqzwL2qHWAlys1vTJV1rSZw+UCTFYFPSBDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:32:45.593572Z"},"content_sha256":"d12128648fac0004e8af9201d97260f681b9374cca244a8505c047a00390db00","schema_version":"1.0","event_id":"sha256:d12128648fac0004e8af9201d97260f681b9374cca244a8505c047a00390db00"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KTJRMGWKHO5ILOEAYDCCMU2KGJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.SE","authors_text":"Bishenghui Tao, Hanyang Guo, Hong-Ning Dai, Peng Di, Xiaoheng Xie, Yu Zhang, Zibin Zheng","submitted_at":"2025-07-14T09:41:51Z","abstract_excerpt":"Automated Program Repair (APR) is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of repair, the quality of training data, and the size of model parameters. Recently, Large Language Models (LLMs) combined with Retrieval-Augmented-Generation (RAG) have been increasingly adopted in APR tasks. However, current code LLMs and RAG designs neither fully address code repair tasks nor consider"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.10103","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/2507.10103/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":"2026-07-05T11:36:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lMVCxyT+yN+2H0VC2hV51riXzwrPUEbGvw6vjGd4pd2LLc8XbJJhuJdpdDgvwWtsTc2Dp2msvwwy6KVFVGihDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:32:45.594130Z"},"content_sha256":"bc6e6cff9c24af73c9fa928e649f08c8d312ede6c001e8d5c23684b181d5ef78","schema_version":"1.0","event_id":"sha256:bc6e6cff9c24af73c9fa928e649f08c8d312ede6c001e8d5c23684b181d5ef78"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KTJRMGWKHO5ILOEAYDCCMU2KGJ/bundle.json","state_url":"https://pith.science/pith/KTJRMGWKHO5ILOEAYDCCMU2KGJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KTJRMGWKHO5ILOEAYDCCMU2KGJ/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":"2026-08-10T05:32:45Z","links":{"resolver":"https://pith.science/pith/KTJRMGWKHO5ILOEAYDCCMU2KGJ","bundle":"https://pith.science/pith/KTJRMGWKHO5ILOEAYDCCMU2KGJ/bundle.json","state":"https://pith.science/pith/KTJRMGWKHO5ILOEAYDCCMU2KGJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KTJRMGWKHO5ILOEAYDCCMU2KGJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KTJRMGWKHO5ILOEAYDCCMU2KGJ","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":"f788185ef13ca30fee93dc9079cbe016ad74897d2e0ab4f2b9f6cd0eef43de1c","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-07-14T09:41:51Z","title_canon_sha256":"90e1317dec7f3ac26f2b4d8a345b757566d2ff80993e2ef03920aac81ec65fff"},"schema_version":"1.0","source":{"id":"2507.10103","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.10103","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"arxiv_version","alias_value":"2507.10103v1","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.10103","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"pith_short_12","alias_value":"KTJRMGWKHO5I","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"pith_short_16","alias_value":"KTJRMGWKHO5ILOEA","created_at":"2026-07-05T11:36:45Z"},{"alias_kind":"pith_short_8","alias_value":"KTJRMGWK","created_at":"2026-07-05T11:36:45Z"}],"graph_snapshots":[{"event_id":"sha256:bc6e6cff9c24af73c9fa928e649f08c8d312ede6c001e8d5c23684b181d5ef78","target":"graph","created_at":"2026-07-05T11:36:45Z","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/2507.10103/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automated Program Repair (APR) is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of repair, the quality of training data, and the size of model parameters. Recently, Large Language Models (LLMs) combined with Retrieval-Augmented-Generation (RAG) have been increasingly adopted in APR tasks. However, current code LLMs and RAG designs neither fully address code repair tasks nor consider","authors_text":"Bishenghui Tao, Hanyang Guo, Hong-Ning Dai, Peng Di, Xiaoheng Xie, Yu Zhang, Zibin Zheng","cross_cats":["cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-07-14T09:41:51Z","title":"Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.10103","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:d12128648fac0004e8af9201d97260f681b9374cca244a8505c047a00390db00","target":"record","created_at":"2026-07-05T11:36:45Z","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":"f788185ef13ca30fee93dc9079cbe016ad74897d2e0ab4f2b9f6cd0eef43de1c","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-07-14T09:41:51Z","title_canon_sha256":"90e1317dec7f3ac26f2b4d8a345b757566d2ff80993e2ef03920aac81ec65fff"},"schema_version":"1.0","source":{"id":"2507.10103","kind":"arxiv","version":1}},"canonical_sha256":"54d3161aca3bba85b880c0c426534a3246c3d951974f4a8804c5cdcde0fcb2e2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"54d3161aca3bba85b880c0c426534a3246c3d951974f4a8804c5cdcde0fcb2e2","first_computed_at":"2026-07-05T11:36:45.090480Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:36:45.090480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AIm9bFlamP4Q6eSFMkH4gwDqakxq/Sv/l9OrhFnwRMaiYAZYsilh+e5KKAaXetabeWXQ3LqYrSHTRULTes9wDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:36:45.091021Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.10103","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d12128648fac0004e8af9201d97260f681b9374cca244a8505c047a00390db00","sha256:bc6e6cff9c24af73c9fa928e649f08c8d312ede6c001e8d5c23684b181d5ef78"],"state_sha256":"590d990c9a5d47affe3bd02f95271270e405ce570b97cc66d67f2b6b730bc641"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xrWug8xr/6UC3KimIIvOJHJzLLaFrkb1eSkSFysSKfvNK+BAKVibEkNu3Zf9+li66TDD/8bSxRubnycNOdqvBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T05:32:45.602174Z","bundle_sha256":"2e8f80f87b4c5412b6d501a8389c60024aa577e64c2bcef1f758b43e96f827a4"}}