{"as_of":"2026-08-13T23:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4bcaf138b01b6abbd3a29353931d12dd72bacb8a3cd816c9fc8fbc494f1e00bb","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":"2026-08-11T18:19:43.726277Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2412.08069/citation-record","integrity":"/paper/2412.08069/integrity","json":"/paper/2412.08069/citation-record.json","paper":"/paper/2412.08069"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T18:19:44.059811Z","title":null,"venue":null,"work_id":"b95053d1-bc74-41ae-b701-04825f99fbb0","year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.614481Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:9b2f05b7a1a280417de81b077926fad31e9390bf0bae4076c625bfddd37d8fa3","observation_id":"b2a6a206-ff50-48ab-9d83-713935c14835","resolution":{"observed_at":"2026-08-11T18:19:44.063840Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:19:43.619369Z","title":"Github copilot,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.619369Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:f121637c59261504e41dea09bfdea99b0e9afd890c54d25b9a9414e9c460f4f8","observation_id":"35b4b718-1ec6-4712-86a1-07ee05fbc7d8","resolution":{"observed_at":"2026-08-11T18:19:43.619369Z","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":"2026-08-11T18:19:43.623356Z","title":"Marscode,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.623356Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:a2a11b022594625bc6bed646cdf83a6572dcd46e9f2296e2480d5396cf1ab747","observation_id":"a3af6bed-20c5-4bf9-903b-e3e0946e348a","resolution":{"observed_at":"2026-08-11T18:19:43.623356Z","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":"2026-08-11T18:19:43.627578Z","title":"Codeium,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.627578Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:5ab2ec292ae49abd73b3cefb70064d7e0b638347726fd15f015112479412442e","observation_id":"613a0c86-73a6-4abe-9f43-c454846ffac0","resolution":{"observed_at":"2026-08-11T18:19:43.627578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00899","last_updated":"2024-09-04T06:19:08Z","snapshot_observed_at":"2026-08-12T22:53:25.616878Z","submitted_at":"2024-09-02T02:24:38Z","title":"MarsCode Agent: AI-native Automated Bug Fixing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.00899","snapshot_observed_at":"2026-08-11T18:19:43.631836Z","title":"Marscode agent: Ai- native automated bug fixing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.631836Z"},"links":{"cited_paper":"/paper/2409.00899","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:fdc93dbe89ed164aebfcbdef863d8dd2b1ee1c0e7109dcb3a1d21e3fed242748","observation_id":"37bfd744-6dad-4e42-9fd0-31a3c3b9c755","resolution":{"observed_at":"2026-08-11T18:19:43.631836Z","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":"2026-08-11T18:19:43.636195Z","title":"Autocoderover: Autonomous program improvement,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.636195Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:386099c4898167e224818f1fe02fa2b42f2a47de2844138e2768cacc05254827","observation_id":"56f558b7-c49f-4d5d-8495-bd74e21e61fa","resolution":{"observed_at":"2026-08-11T18:19:43.636195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01489","last_updated":"2024-10-29T17:29:27Z","snapshot_observed_at":"2026-08-10T19:28:41.964638Z","submitted_at":"2024-07-01T17:24:45Z","title":"Agentless: Demystifying LLM-based Software Engineering Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01489","snapshot_observed_at":"2026-08-11T18:19:43.640729Z","title":"Agentless: De- mystifying llm-based software engineering agents,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.640729Z"},"links":{"cited_paper":"/paper/2407.01489","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:f31606edb9a9a931c5b5335bcbe166bf52bc4fc42189b9c03465666e60443eba","observation_id":"312e0f7e-4715-4286-bcac-71aea11f3511","resolution":{"observed_at":"2026-08-11T18:19:43.640729Z","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":"2026-08-11T18:19:43.645066Z","title":"Experiences from using code explanations generated by large language models in a web software development e-book,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.645066Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:d9b2f988d97d7a35d749bfa6c1315cc390452df8b14fabc0bc82f7fdf5537d55","observation_id":"d2139cd4-b4f6-4aec-80d3-007e1aa2a728","resolution":{"observed_at":"2026-08-11T18:19:43.645066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05492","last_updated":"2024-06-07T15:51:08Z","snapshot_observed_at":"2026-08-13T05:54:09.385656Z","submitted_at":"2023-10-09T07:56:16Z","title":"How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05492","snapshot_observed_at":"2026-08-11T18:19:43.649035Z","title":"How abilities in large language models are affected by supervised fine-tuning data composition,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.649035Z"},"links":{"cited_paper":"/paper/2310.05492","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:ab9b37d7d08497e46c3b3e38f03eb83dcfc780f049d3680141d64a7a4cc7e9b4","observation_id":"e774ca97-a0dc-452c-a957-440d798180bc","resolution":{"observed_at":"2026-08-11T18:19:43.649035Z","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":"2026-08-11T18:19:44.001214Z","title":"Learning to construct better mutation faults,","venue":null,"work_id":"cb982a5c-f6ae-450c-89f7-04fb423890b7","year":2022},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.653169Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:4445b24b99f4db4c449693b2c4e6866dfd37d829572b6f0d46edf703cc96de15","observation_id":"1869237a-3d46-4c66-8155-e2a41f030e1a","resolution":{"observed_at":"2026-08-11T18:19:44.005440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-11T18:19:43.657498Z","title":"Codebert: A pre-trained model for programming and natural languages,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.657498Z"},"links":{"cited_paper":"/paper/2002.08155","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:ad2011353ea7c47be9059d03fcadc9e9f2e7ded62e2c7556e4f7214d3543756d","observation_id":"0a2ee800-7872-4cbe-b30a-7e4f32898e92","resolution":{"observed_at":"2026-08-11T18:19:43.657498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.13474","last_updated":"2023-02-27T21:26:48Z","snapshot_observed_at":"2026-08-13T14:36:56.609471Z","submitted_at":"2022-03-25T06:55:15Z","title":"CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.13474","snapshot_observed_at":"2026-08-11T18:19:43.661772Z","title":"Codegen: An open large language model for code with multi-turn program synthesis,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.661772Z"},"links":{"cited_paper":"/paper/2203.13474","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:bc2ef98dccbf087cb5da700d98e71b0d37094378826bd0cc38aacf751cd619aa","observation_id":"354dabc3-25d3-4869-bc9a-7db1f07559ca","resolution":{"observed_at":"2026-08-11T18:19:43.661772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.05999","last_updated":"2023-04-09T14:31:40Z","snapshot_observed_at":"2026-08-13T02:34:38.117682Z","submitted_at":"2022-04-12T16:25:26Z","title":"InCoder: A Generative Model for Code Infilling and Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.05999","snapshot_observed_at":"2026-08-11T18:19:43.665912Z","title":"Incoder: A generative model for code infilling and synthesis,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.665912Z"},"links":{"cited_paper":"/paper/2204.05999","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:88814774d2ad3b741af69beb0d2c9133da722494fa70461d1b71d6ff85b05e80","observation_id":"4f0acaa4-7105-4723-90bb-7739810fd144","resolution":{"observed_at":"2026-08-11T18:19:43.665912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-11T18:19:43.669965Z","title":"Evaluating large language models trained on code,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.669965Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:0b56263aebc9e3256d1d5c8c72af51b5345c11ba3696dd8a280e3e5883b02258","observation_id":"7da751c3-9e1d-4d2a-881a-9c5644925c91","resolution":{"observed_at":"2026-08-11T18:19:43.669965Z","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":"2026-08-11T18:19:43.674113Z","title":"Magicoder: Em- powering code generation with oss-instruct,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.674113Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:2a06c580b7fe00d78566798df77c2b7b3e002e10d83680576059c7ea042fb50a","observation_id":"f7bed7ec-22fa-4184-8f0f-d7c9ed4fdf62","resolution":{"observed_at":"2026-08-11T18:19:43.674113Z","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":"2026-08-11T18:19:43.976673Z","title":"Mftcoder: Boosting code llms with multitask fine-tuning,","venue":null,"work_id":"80c68602-3a44-4bcd-83cf-692c7718f3b6","year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.677889Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:54385ad5097e4bd23cae0c4cd3002fbf3b3cd59ba9694ee1d29eff944a7a98f3","observation_id":"596f5bdd-01c6-4b3e-ade8-310aa34b8dff","resolution":{"observed_at":"2026-08-11T18:19:43.981847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13446","last_updated":"2024-12-02T20:55:15Z","snapshot_observed_at":"2026-08-13T04:14:07.353644Z","submitted_at":"2024-02-21T00:44:04Z","title":"Large Language Models for Data Annotation and Synthesis: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13446","snapshot_observed_at":"2026-08-11T18:19:43.681441Z","title":"Large language models for data annotation: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.681441Z"},"links":{"cited_paper":"/paper/2402.13446","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:1c54c4871c8d85c130950db5be36e2664b77c657dcce91ec74110a72296dc29f","observation_id":"f3bdab98-42ff-423f-b0cd-066e9ebfed15","resolution":{"observed_at":"2026-08-11T18:19:43.681441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07926","last_updated":"2024-04-11T17:20:57Z","snapshot_observed_at":"2026-08-13T21:20:45.263000Z","submitted_at":"2024-04-11T17:20:57Z","title":"Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data Annotation","version":1},"cited_work":{"arxiv_id":"2404.07926","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.07926","snapshot_observed_at":"2026-08-11T18:19:43.802363Z","title":"Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data Annotation","venue":"cs.HC","work_id":"85712ac9-769a-49e7-af42-05a30dbdff99","year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.685492Z"},"links":{"cited_paper":"/paper/2404.07926","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:51d48027dc8cc26c1c2ff3ac148047ea46a8216abe845e6a2e76ed9274049d6e","observation_id":"3e456c43-30e5-46d5-ae76-82ea8e8ec5b6","resolution":{"observed_at":"2026-08-11T18:19:43.808809Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:19:43.961444Z","title":"Enhancing human annotation: Leveraging large language models and efficient batch processing,","venue":null,"work_id":"4e972919-aced-4865-8696-8f59ca6e4d89","year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.689360Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:ea988f79f3f61d850fe023e7b946164440ad012750fc668003bc0907b60bbf6e","observation_id":"3e4c95e2-f215-41a9-94de-71b17a244835","resolution":{"observed_at":"2026-08-11T18:19:43.966071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:19:43.948439Z","title":"Large language models as annotators: A preliminary evaluation for annotating low-resource language content,","venue":null,"work_id":"f37384d9-63fb-41b6-93aa-f3ca6c620686","year":2023},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.693270Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:50911da84ef27f64b3f2eb82bb8cf9be49bd7531363f54e4da9cc2bb87ad742f","observation_id":"d89f6192-f65e-468e-8b59-dfd8ad5c1d63","resolution":{"observed_at":"2026-08-11T18:19:43.953152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:19:43.934290Z","title":"Fine-tuning large language models to improve accuracy and comprehensibility of automated code review,","venue":null,"work_id":"2e52b5fa-8581-4fde-b21a-1dad1dda08ba","year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.697295Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:b725a92ac04e7a69161f2e2a60edc7873414a7b8f26058a8561a8a30631c7d34","observation_id":"c302be5d-2fa2-4097-ac08-4c0d417e545d","resolution":{"observed_at":"2026-08-11T18:19:43.939407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T18:19:43.701280Z","title":"Training language models to follow instructions with human feedback,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.701280Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:ecdc53b3a72d4fa6cdd01b55b3107343d2e4ff6ce2a61783f84cb9bc54f19381","observation_id":"7289f18a-ab76-48bd-b2b0-2e9725144f19","resolution":{"observed_at":"2026-08-11T18:19:43.701280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14196","last_updated":"2024-01-26T09:23:11Z","snapshot_observed_at":"2026-08-06T22:40:28.707813Z","submitted_at":"2024-01-25T14:17:53Z","title":"DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14196","snapshot_observed_at":"2026-08-11T18:19:43.705531Z","title":"Deepseek-coder: When the large language model meets programming -the rise of code intelligence,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.705531Z"},"links":{"cited_paper":"/paper/2401.14196","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:68b56df1ac750e8e5922e5a78018803e132f0d8a2e52be01c0e40314eccce7f2","observation_id":"4b331406-1934-4b55-a40e-6fef340e27f1","resolution":{"observed_at":"2026-08-11T18:19:43.705531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11931","last_updated":"2024-06-17T13:51:35Z","snapshot_observed_at":"2026-08-07T06:30:25.302107Z","submitted_at":"2024-06-17T13:51:35Z","title":"DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11931","snapshot_observed_at":"2026-08-11T18:19:43.710688Z","title":"Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.710688Z"},"links":{"cited_paper":"/paper/2406.11931","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:dd80f6182da00e7e4ad7fee305a4deb84acceeb478077ae8ca9dd8d11f2e2dba","observation_id":"041ed324-8481-4799-b295-9c03658aaea4","resolution":{"observed_at":"2026-08-11T18:19:43.710688Z","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":"2026-08-11T18:19:43.714611Z","title":"Starcoder 2 and the stack v2: The next generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.714611Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:82752e86a27a2bb162046e74c24c9388b02eb7b535c2d5d3ccb546ee6db674a3","observation_id":"5b031cd7-09a4-49df-b89f-61917b77ae70","resolution":{"observed_at":"2026-08-11T18:19:43.714611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04434","last_updated":"2024-06-19T06:04:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-07T15:56:43Z","title":"DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04434","snapshot_observed_at":"2026-08-11T18:19:43.718312Z","title":"Deepseek-v2: A strong, economical, and efficient mixture- of-experts language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.718312Z"},"links":{"cited_paper":"/paper/2405.04434","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:278cdef0578b4fc27215c51d8d2cd936b988d42cf0c1f8101fd5158b1e77246e","observation_id":"238a6e9d-fc4a-49c8-a83d-c40f645908ab","resolution":{"observed_at":"2026-08-11T18:19:43.718312Z","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":"2026-08-11T18:19:43.722527Z","title":"Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.722527Z"},"links":{"citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:95a7e8262d467b9c8f50e42db243efd8c94664e180c34dc20c884e888c083ad8","observation_id":"2c34e1a2-c478-472f-9dd6-4871c8f26432","resolution":{"observed_at":"2026-08-11T18:19:43.722527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05685","last_updated":"2023-12-24T02:01:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-09T05:55:52Z","title":"Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05685","snapshot_observed_at":"2026-08-11T18:19:43.726277Z","title":"Judging llm-as-a-judge with mt-bench and chatbot arena,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T18:19:43.726277Z"},"links":{"cited_paper":"/paper/2306.05685","citing_paper":"/paper/2412.08069"},"observation_digest":"sha256:15ebdc0010334df4d3bdb97992694786da81133001f1209a2a11f86f6c54a84f","observation_id":"25a5fe30-fc02-47ee-9b26-71241a0f0a19","resolution":{"observed_at":"2026-08-11T18:19:43.726277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.08069","last_updated":"2024-12-11T03:31:36Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-13T17:15:19.998051Z","submitted_at":"2024-12-11T03:31:36Z","title":"DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":1,"verified_fuzzy":5},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.08069."}