{"as_of":"2026-08-10T03:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bba090142b163c1ca82ec1335ec5b0f6b66ffb0768d7637c0442a06e0986c21b","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T13:58:16.456735Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T05:40:37.993847Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07067","snapshot_observed_at":"2026-08-02T05:40:37.993847Z","title":"are you sure?","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.13285","last_updated":"2026-07-14T21:39:55Z","snapshot_observed_at":"2026-08-06T16:12:32.611582Z","submitted_at":"2026-07-14T21:39:55Z","title":"Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T05:40:37.993847Z"},"links":{"cited_paper":"/paper/2502.07067","citing_paper":"/paper/2607.13285"},"observation_digest":"sha256:fda57582b30ee556258ff23475eb10d44716811d423a793c3180d85940a0a012","observation_id":"827b19f9-7d47-4598-be25-6bd73ffdc1b4","resolution":{"observed_at":"2026-08-02T05:40:37.993847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.07067/citation-record","integrity":"/paper/2502.07067/integrity","json":"/paper/2502.07067/citation-record.json","paper":"/paper/2502.07067"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.812665Z","title":null,"venue":null,"work_id":"00c07058-957d-4911-acd2-882d6f61b264","year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.353288Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:f2e90c8b6e050c72591923b9aa9338b86a3120d99ce5f9ec69f1ce428a0f40b8","observation_id":"7fb4e6a6-f588-4fbc-b03a-69e902a48ad3","resolution":{"observed_at":"2026-08-08T13:58:16.816105Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.801737Z","title":null,"venue":null,"work_id":"29f23ac4-a526-4d5a-ae7b-c5c78fcbfec1","year":2024},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.357593Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:268a08746aadb3d0748425262200cbe721b8df72de4bb2810f5b9150ed424485","observation_id":"a9eadb5c-865c-485f-a736-b1ea345fbef6","resolution":{"observed_at":"2026-08-08T13:58:16.805052Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.789742Z","title":null,"venue":null,"work_id":"b33e2eae-6197-46be-b358-f8924d7d74ad","year":2019},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.361421Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:a95d517f64985b7ae4e005fe50f21eacda51d7643530268b2024aef909ce3bd6","observation_id":"e5ccc999-d73d-43fa-8560-c92063f239f9","resolution":{"observed_at":"2026-08-08T13:58:16.794004Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-08T13:58:16.366032Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.366032Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:265ed82e211e136bbccc376c147c10cd9f0bf9ef01323ae56259cab8be863980","observation_id":"82398c3e-6ed6-4c9e-88f1-ed73d6f3a8ea","resolution":{"observed_at":"2026-08-08T13:58:16.366032Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.10007","last_updated":"2023-05-24T06:56:45Z","snapshot_observed_at":"2026-08-08T15:03:11.388057Z","submitted_at":"2022-12-20T05:48:09Z","title":"CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.10007","snapshot_observed_at":"2026-08-08T13:58:16.370094Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.370094Z"},"links":{"cited_paper":"/paper/2212.10007","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:9f09adfcd7890cd51d67f947ce86105e2029d15ebf79367d8808b1b9f47c5300","observation_id":"2bf342d6-1399-46d4-87cf-5c7a89789686","resolution":{"observed_at":"2026-08-08T13:58:16.370094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08155","last_updated":"2020-09-18T15:38:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-02-19T13:09:07Z","title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08155","snapshot_observed_at":"2026-08-08T13:58:16.374193Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.374193Z"},"links":{"cited_paper":"/paper/2002.08155","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:e2053bc58ba791b372c643732b2ace7b241555efd9501c6859fa1555505413c5","observation_id":"f66e92e8-6513-4a69-abba-6f947159ebb0","resolution":{"observed_at":"2026-08-08T13:58:16.374193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.777991Z","title":null,"venue":null,"work_id":"c1aad610-134d-4413-9348-718d92e576d3","year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.378790Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:00c6ca46f9654d7af5a5be7c32ccb730b10b8c4ec20c4aa24a54e3927d7f8f9b","observation_id":"5c949bdb-e5cb-435d-a1c1-1a8c3e009ecd","resolution":{"observed_at":"2026-08-08T13:58:16.781806Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.382525Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.382525Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:45baf8259d601c145bffda93bcec4b4f1b065438f0f4b6b3278543bb1bb928fc","observation_id":"6dcfe48e-cb3e-4843-981d-cd705c363453","resolution":{"observed_at":"2026-08-08T13:58:16.382525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.386113Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.386113Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:7e92611049c92e106ecdf6ffdc6046a2f761efa57f7b9bca68b20e4726734d68","observation_id":"e7f999cc-9aea-4484-9921-699c7c0a742a","resolution":{"observed_at":"2026-08-08T13:58:16.386113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.09436","last_updated":"2020-06-08T09:09:28Z","snapshot_observed_at":"2026-08-06T10:54:14.530969Z","submitted_at":"2019-09-20T11:52:45Z","title":"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.09436","snapshot_observed_at":"2026-08-08T13:58:16.389807Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.389807Z"},"links":{"cited_paper":"/paper/1909.09436","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:2d1287d4bbffcd6bd0720874a470d9bf9da6740d6524c6ccb0f0f38a9020075c","observation_id":"b366d66b-5dd5-4bbd-948c-f5e6e3dc1c6c","resolution":{"observed_at":"2026-08-08T13:58:16.389807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06770","last_updated":"2024-11-11T23:05:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T16:47:29Z","title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06770","snapshot_observed_at":"2026-08-08T13:58:16.394033Z","title":"Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.394033Z"},"links":{"cited_paper":"/paper/2310.06770","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:7fe1d147532f93b0da2335b0446683c94dc0214c68caf0664077950c65edc52c","observation_id":"4137432f-1e46-4ef0-a261-71e24f0e00a8","resolution":{"observed_at":"2026-08-08T13:58:16.394033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.04906","last_updated":"2020-09-30T21:27:13Z","snapshot_observed_at":"2026-07-06T09:11:26.109763Z","submitted_at":"2020-04-10T04:53:17Z","title":"Dense Passage Retrieval for Open-Domain Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.04906","snapshot_observed_at":"2026-08-08T13:58:16.398379Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.398379Z"},"links":{"cited_paper":"/paper/2004.04906","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:9b9d720f944fe61b3e314d091ab8bc0abdd487083066a432037ed5d4f79e6010","observation_id":"da0b1a97-04a9-42d0-acaf-47bbe900bb36","resolution":{"observed_at":"2026-08-08T13:58:16.398379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.402227Z","title":"u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \\","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.402227Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:5496557d9ae8b10ece50d25f01c571aeb91169cc2d65da40141fd631c9967fbd","observation_id":"c2946201-fd5c-45a2-ab2d-676f6905dd2a","resolution":{"observed_at":"2026-08-08T13:58:16.402227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06161","last_updated":"2023-12-13T14:44:10Z","snapshot_observed_at":"2026-07-06T15:25:35.930688Z","submitted_at":"2023-05-09T08:16:42Z","title":"StarCoder: may the source be with you!","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06161","snapshot_observed_at":"2026-08-08T13:58:16.405594Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.405594Z"},"links":{"cited_paper":"/paper/2305.06161","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:346e2b87b96582319728a868368278cd1afae5d5d3301ab71e824b90b1569cb2","observation_id":"787f243b-b37b-42cf-b0bc-6a2e737c2e73","resolution":{"observed_at":"2026-08-08T13:58:16.405594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.408847Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.408847Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:4e9d19ea6996d336e31451f46c2f618f68e287d1738100b88cdf71fa4f50b8ee","observation_id":"bcedc85d-d8c8-44e2-97d8-affa7ce7d70d","resolution":{"observed_at":"2026-08-08T13:58:16.408847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04664","last_updated":"2021-03-16T08:28:37Z","snapshot_observed_at":"2026-07-06T10:39:42.676631Z","submitted_at":"2021-02-09T06:16:25Z","title":"CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04664","snapshot_observed_at":"2026-08-08T13:58:16.412991Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.412991Z"},"links":{"cited_paper":"/paper/2102.04664","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:b03fe214c3c67fef2a42288615a45b5d8e0adc7b1d771fbeab7ba7b623e94699","observation_id":"e567d85e-a3ca-423c-8b2f-4f15ace03514","resolution":{"observed_at":"2026-08-08T13:58:16.412991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07143","last_updated":"2024-08-09T22:37:25Z","snapshot_observed_at":"2026-08-02T14:27:24.212588Z","submitted_at":"2024-04-10T16:18:42Z","title":"Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07143","snapshot_observed_at":"2026-08-08T13:58:16.416947Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.416947Z"},"links":{"cited_paper":"/paper/2404.07143","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:266841bfc2ec1f43012e28a689f33439b2c9fd62fd326689feff44f36a077e67","observation_id":"5de31763-6c5f-4e21-aa85-ca9a154294ed","resolution":{"observed_at":"2026-08-08T13:58:16.416947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-08T13:58:16.420694Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.420694Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:97c997eed8a03ed17855074a02b1732a65245e7055b3f83bd90cb7751d2d1bc0","observation_id":"cb9509f9-54bd-48b4-9c83-05f51e1ab0bd","resolution":{"observed_at":"2026-08-08T13:58:16.420694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.744434Z","title":null,"venue":null,"work_id":"97efcc2a-08b4-4afe-b8d4-59a5f9c37358","year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.423899Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:6395829cb4f93937f25acac9f60b330ea27c9b658e550e1229f4ea8c96ae938d","observation_id":"8bc2a960-d79c-4731-b841-1383009538ff","resolution":{"observed_at":"2026-08-08T13:58:16.748808Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.733778Z","title":null,"venue":null,"work_id":"00db9800-8645-4122-8c2f-59bfb195f9ce","year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.427264Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:23f85c58d30c377126eac4499562688e8740f7a6d57a8d39c0cb7a487cc97d71","observation_id":"5eacec33-e30d-47a9-bd73-e0c7ddf31b25","resolution":{"observed_at":"2026-08-08T13:58:16.737654Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.431106Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.431106Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:e3b80418ec4a22e8b5b4bf5a9fa9bf70a91bcb10fbccb3c6702b694ada269426","observation_id":"acce12f1-e39e-48d7-b970-d9413d4390de","resolution":{"observed_at":"2026-08-08T13:58:16.431106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12950","last_updated":"2024-01-31T19:47:26Z","snapshot_observed_at":"2026-07-06T16:10:07.931347Z","submitted_at":"2023-08-24T17:39:13Z","title":"Code Llama: Open Foundation Models for Code","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12950","snapshot_observed_at":"2026-08-08T13:58:16.434306Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.434306Z"},"links":{"cited_paper":"/paper/2308.12950","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:9d942227eeca7116e5675f4b578a567681b784ea5f11d08b45b2ee276903707c","observation_id":"eb8a67de-5495-403e-9dc0-f2d7ae2b59e5","resolution":{"observed_at":"2026-08-08T13:58:16.434306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00859","last_updated":"2021-09-02T12:21:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-09-02T12:21:06Z","title":"CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00859","snapshot_observed_at":"2026-08-08T13:58:16.437740Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.437740Z"},"links":{"cited_paper":"/paper/2109.00859","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:7e0d9b2c566b42a671c8a40d0c5b9b2ec50d78c16680e388453163ac4fa782a8","observation_id":"d54a7caa-f206-40af-88a5-c27ff8eba491","resolution":{"observed_at":"2026-08-08T13:58:16.437740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03025","last_updated":"2024-01-23T07:49:13Z","snapshot_observed_at":"2026-08-09T21:28:42.655685Z","submitted_at":"2023-10-04T17:59:41Z","title":"Retrieval meets Long Context Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03025","snapshot_observed_at":"2026-08-08T13:58:16.441161Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.441161Z"},"links":{"cited_paper":"/paper/2310.03025","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:d49e3ca3078fe36a5e0a6d0c132187f1884034cf8ba5b6b67eb7caad4f44f858","observation_id":"7f08bb78-7ff2-4f59-b467-5f0a08cf97ce","resolution":{"observed_at":"2026-08-08T13:58:16.441161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.712963Z","title":"Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press","venue":null,"work_id":"5f8f477d-3e38-445e-8a49-45ab9a5b01d8","year":2024},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.445041Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:a94f5b1b79717069d2e72117153b3b0a0f07aeac26c618d79a643671c0403fa6","observation_id":"5e608132-cf37-4367-94e8-2f83ac61fa13","resolution":{"observed_at":"2026-08-08T13:58:16.718718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.12570","last_updated":"2023-10-20T15:21:51Z","snapshot_observed_at":"2026-08-09T21:28:41.923877Z","submitted_at":"2023-03-22T13:54:46Z","title":"RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.12570","snapshot_observed_at":"2026-08-08T13:58:16.448317Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.448317Z"},"links":{"cited_paper":"/paper/2303.12570","citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:532a7e8cdefbc58e54fb2b0713e0a0bab4c08d9039b4bfb7f34456b6f1faf379","observation_id":"c8f18a3e-d1b2-4bf6-95a9-7c2db8b004b9","resolution":{"observed_at":"2026-08-08T13:58:16.448317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.451719Z","title":"URL: \" 'urlintro :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.451719Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:4e9053c005c9a89dfe03d4101485adf54405e6116bf998daa690eebaa500ae03","observation_id":"2d3bffae-d9bc-43a9-b2c1-188f8db191e3","resolution":{"observed_at":"2026-08-08T13:58:16.451719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:58:16.456735Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T13:58:16.456735Z"},"links":{"citing_paper":"/paper/2502.07067"},"observation_digest":"sha256:af765c073707c058a72b1fa14f7a31d83e2af36e25b92c041c368943e9676ada","observation_id":"b8a93bf9-fa28-45d3-a39b-12ad3f59d8e0","resolution":{"observed_at":"2026-08-08T13:58:16.456735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.07067","last_updated":"2025-02-10T21:59:01Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-09T21:27:48.641311Z","submitted_at":"2025-02-10T21:59:01Z","title":"Repository-level Code Search with Neural Retrieval Methods"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":27,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":28},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2502.07067."}