{"as_of":"2026-08-09T11:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8ba04007f4ec50bc232415cd0b5298e7470ffe198901c4b9733d123567d24d9e","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T19:35:27.741292Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T15:08:32.492410Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.02906","last_updated":"2024-12-03T23:19:40Z","snapshot_observed_at":"2026-07-06T20:01:14.493111Z","submitted_at":"2024-12-03T23:19:40Z","title":"Does Few-Shot Learning Help LLM Performance in Code Synthesis?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02906","snapshot_observed_at":"2026-08-04T17:07:47.147857Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11132","last_updated":"2026-06-24T01:18:39Z","snapshot_observed_at":"2026-08-04T17:07:41.058487Z","submitted_at":"2025-09-14T06:56:47Z","title":"Rethinking Technology Stack Selection with AI Coding Proficiency","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-04T17:07:47.147857Z"},"links":{"cited_paper":"/paper/2412.02906","citing_paper":"/paper/2509.11132"},"observation_digest":"sha256:b9a110d60a06eb861b17fdb15e5ced03c6bc318aa4029dc0d2340eae99f1a5a7","observation_id":"50e75477-b9b5-4c5b-a289-2ad7fa3163ea","resolution":{"observed_at":"2026-08-04T17:07:47.147857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02906","last_updated":"2024-12-03T23:19:40Z","snapshot_observed_at":"2026-07-06T20:01:14.493111Z","submitted_at":"2024-12-03T23:19:40Z","title":"Does Few-Shot Learning Help LLM Performance in Code Synthesis?","version":1},"cited_work":{"arxiv_id":"2412.02906","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02906","snapshot_observed_at":"2026-07-03T15:08:32.492410Z","title":"Does few-shot learning help llm performance in code synthesis?","venue":null,"work_id":"bd4fd35f-015d-4e0e-a138-111d8880be6c","year":2024},"citing_paper":{"arxiv_id":"2605.04894","last_updated":"2026-05-06T13:25:34Z","snapshot_observed_at":"2026-08-01T03:51:19.768205Z","submitted_at":"2026-05-06T13:25:34Z","title":"SynConfRoute: Syntax-Aware Routing for Efficient Code Completion with Small CodeLLMs","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-08T16:00:26.489264Z"},"links":{"cited_paper":"/paper/2412.02906","citing_paper":"/paper/2605.04894"},"observation_digest":"sha256:db3c0ce71b4225e0d53cf241c5629746d6307ad287927fd6dec200cf5dbdc357","observation_id":"76e38c3d-55b5-4208-b22a-e9ef4204a33f","resolution":{"observed_at":"2026-05-11T18:26:10.434698Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2412.02906","last_updated":"2024-12-03T23:19:40Z","snapshot_observed_at":"2026-07-06T20:01:14.493111Z","submitted_at":"2024-12-03T23:19:40Z","title":"Does Few-Shot Learning Help LLM Performance in Code Synthesis?","version":1},"cited_work":{"arxiv_id":"2412.02906","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02906","snapshot_observed_at":"2026-07-03T15:08:32.492410Z","title":"Does few-shot learning help llm performance in code synthesis?","venue":null,"work_id":"bd4fd35f-015d-4e0e-a138-111d8880be6c","year":2024},"citing_paper":{"arxiv_id":"2605.16345","last_updated":"2026-05-08T01:55:40Z","snapshot_observed_at":"2026-08-03T04:40:04.617513Z","submitted_at":"2026-05-08T01:55:40Z","title":"Goal-Conditioned Supervised Learning for LLM Fine-Tuning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-20T22:37:46.345159Z"},"links":{"cited_paper":"/paper/2412.02906","citing_paper":"/paper/2605.16345"},"observation_digest":"sha256:8fabb02c106f0085975cb3570d73e3da8b42a633c4f8c91df2fdd9102af8e73a","observation_id":"42203112-7bb6-4f47-9a3d-a79b7d14790a","resolution":{"observed_at":"2026-05-20T22:39:10.080484Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2412.02906","last_updated":"2024-12-03T23:19:40Z","snapshot_observed_at":"2026-07-06T20:01:14.493111Z","submitted_at":"2024-12-03T23:19:40Z","title":"Does Few-Shot Learning Help LLM Performance in Code Synthesis?","version":1},"cited_work":{"arxiv_id":"2412.02906","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02906","snapshot_observed_at":"2026-07-03T15:08:32.492410Z","title":"Does few-shot learning help llm performance in code synthesis?","venue":null,"work_id":"bd4fd35f-015d-4e0e-a138-111d8880be6c","year":2024},"citing_paper":{"arxiv_id":"2607.01590","last_updated":"2026-07-03T02:53:16Z","snapshot_observed_at":"2026-08-05T18:05:00.906043Z","submitted_at":"2026-07-02T01:41:34Z","title":"Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-03T15:01:18.911340Z"},"links":{"cited_paper":"/paper/2412.02906","citing_paper":"/paper/2607.01590"},"observation_digest":"sha256:9e62ce571b3a500fb6c8aacbaf894648aba32d756dbbfbac6bdc75049d59ab2c","observation_id":"b326a2ef-dcfc-490d-a5ad-06a53ea3f3b1","resolution":{"observed_at":"2026-07-03T15:08:32.493793Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2412.02906","last_updated":"2024-12-03T23:19:40Z","snapshot_observed_at":"2026-07-06T20:01:14.493111Z","submitted_at":"2024-12-03T23:19:40Z","title":"Does Few-Shot Learning Help LLM Performance in Code Synthesis?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02906","snapshot_observed_at":"2026-07-12T08:42:42.063339Z","title":"Does few-shot learning help llm performance in code synthesis?","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.01590","last_updated":"2026-07-03T02:53:16Z","snapshot_observed_at":"2026-08-05T18:05:00.906043Z","submitted_at":"2026-07-02T01:41:34Z","title":"Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-12T08:42:42.063339Z"},"links":{"cited_paper":"/paper/2412.02906","citing_paper":"/paper/2607.01590"},"observation_digest":"sha256:24709e14516162bf81553f07d823d5782a215bfe077dc1d7ef83893d9265acb5","observation_id":"2adc67d3-d119-4985-a1dd-862d8a419a57","resolution":{"observed_at":"2026-07-12T08:42:42.063339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02906","last_updated":"2024-12-03T23:19:40Z","snapshot_observed_at":"2026-07-06T20:01:14.493111Z","submitted_at":"2024-12-03T23:19:40Z","title":"Does Few-Shot Learning Help LLM Performance in Code Synthesis?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02906","snapshot_observed_at":"2026-08-08T19:35:27.741292Z","title":"arXiv preprint arXiv:2412.02906 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04336","last_updated":"2026-08-05T01:28:43Z","snapshot_observed_at":"2026-08-09T00:17:00.257446Z","submitted_at":"2026-08-05T01:28:43Z","title":"COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-08T19:35:27.741292Z"},"links":{"cited_paper":"/paper/2412.02906","citing_paper":"/paper/2608.04336"},"observation_digest":"sha256:ed94c8c178b739f6fb7b206650f02a3f2b28464d1057a11dd34c8dab3f25715c","observation_id":"3741cfa5-17be-488c-95ca-c17c0dd599d9","resolution":{"observed_at":"2026-08-08T19:35:27.741292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.02906/citation-record","integrity":"/paper/2412.02906/integrity","json":"/paper/2412.02906/citation-record.json","paper":"/paper/2412.02906"},"outbound":[],"paper":{"arxiv_id":"2412.02906","last_updated":"2024-12-03T23:19:40Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-07-06T20:01:14.493111Z","submitted_at":"2024-12-03T23:19:40Z","title":"Does Few-Shot Learning Help LLM Performance in Code Synthesis?"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2412.02906."}