{"as_of":"2026-08-09T10:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f679c79700efa51e8b2099184b3168ac43652fe6e23da7a8551873d388eaadb3","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:49:37.546662Z","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":"2026-07-03T18:08:46.899138Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.10183","last_updated":"2022-10-05T01:33:07Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:50:07Z","title":"Prototypical Calibration for Few-shot Learning of Language Models","version":2},"cited_work":{"arxiv_id":"2205.10183","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.10183","snapshot_observed_at":"2026-07-03T18:08:46.899138Z","title":null,"venue":null,"work_id":"46e1fe79-d06e-4ec1-b7e9-6e77d8306158","year":2022},"citing_paper":{"arxiv_id":"2412.05579","last_updated":"2024-12-10T05:49:12Z","snapshot_observed_at":"2026-07-31T01:42:39.468673Z","submitted_at":"2024-12-07T08:07:24Z","title":"LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-11T23:08:34.312466Z"},"links":{"cited_paper":"/paper/2205.10183","citing_paper":"/paper/2412.05579"},"observation_digest":"sha256:0a519c668141618046f69459dd2df04f4053a317bf3f830045f80d1499bc902e","observation_id":"0a92628e-cb20-451d-90a7-93263d1a1878","resolution":{"observed_at":"2026-05-11T23:08:36.669011Z","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":"2205.10183","last_updated":"2022-10-05T01:33:07Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:50:07Z","title":"Prototypical Calibration for Few-shot Learning of Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10183","snapshot_observed_at":"2026-08-07T00:49:37.546662Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.12796","last_updated":"2025-06-17T07:46:17Z","snapshot_observed_at":"2026-08-09T02:07:02.685178Z","submitted_at":"2025-06-15T10:04:42Z","title":"Surprise Calibration for Better In-Context Learning","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T00:49:37.546662Z"},"links":{"cited_paper":"/paper/2205.10183","citing_paper":"/paper/2506.12796"},"observation_digest":"sha256:41c6d7197ffe6bb81b7dc90a42a8e7515036f5e7325321c4de346029fc944c50","observation_id":"fb3eb0ae-08a7-4e75-8b4a-65241c535079","resolution":{"observed_at":"2026-08-07T00:49:37.546662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10183","last_updated":"2022-10-05T01:33:07Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:50:07Z","title":"Prototypical Calibration for Few-shot Learning of Language Models","version":2},"cited_work":{"arxiv_id":"2205.10183","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.10183","snapshot_observed_at":"2026-07-03T18:08:46.899138Z","title":null,"venue":null,"work_id":"46e1fe79-d06e-4ec1-b7e9-6e77d8306158","year":2022},"citing_paper":{"arxiv_id":"2606.17234","last_updated":"2026-06-15T19:27:47Z","snapshot_observed_at":"2026-08-06T22:53:39.470913Z","submitted_at":"2026-06-15T19:27:47Z","title":"Speaking in Self-Assessing Tongues: On the Verbalized Confidence of LLMs in Machine Translation","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-06-27T03:16:19.173230Z"},"links":{"cited_paper":"/paper/2205.10183","citing_paper":"/paper/2606.17234"},"observation_digest":"sha256:7bc09774c1ab8dd87e26884deb2414b823074d5a3986795216ff88e1d3374d81","observation_id":"71bb4a3e-f195-4311-9038-9434a4b59b47","resolution":{"observed_at":"2026-07-03T18:08:46.900939Z","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":"2205.10183","last_updated":"2022-10-05T01:33:07Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:50:07Z","title":"Prototypical Calibration for Few-shot Learning of Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10183","snapshot_observed_at":"2026-08-01T16:08:01.037462Z","title":"arXiv preprint arXiv:2205.10183 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18110","last_updated":"2026-07-20T16:08:49Z","snapshot_observed_at":"2026-08-05T10:41:27.894489Z","submitted_at":"2026-07-20T16:08:49Z","title":"LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks","version":1},"reference_index":225,"source":"arxiv_source","source_observed_at":"2026-08-01T16:08:01.037462Z"},"links":{"cited_paper":"/paper/2205.10183","citing_paper":"/paper/2607.18110"},"observation_digest":"sha256:7c4c986cdc4ea49e84ece62f71b44dd2ed57c9f19fd0d92655a2c70c263c3426","observation_id":"5998e847-19de-4a84-94ff-c8ce9bdcb186","resolution":{"observed_at":"2026-08-01T16:08:01.037462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2205.10183/citation-record","integrity":"/paper/2205.10183/integrity","json":"/paper/2205.10183/citation-record.json","paper":"/paper/2205.10183"},"outbound":[],"paper":{"arxiv_id":"2205.10183","last_updated":"2022-10-05T01:33:07Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:50:07Z","title":"Prototypical Calibration for Few-shot Learning of Language Models"},"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 4 inbound Pith citation observations for arXiv:2205.10183."}