{"as_of":"2026-08-07T20:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d0a5c3c8f54da6e64452ea4ae23706cdcb3e39d2ad6bf2b4924caa1ae9c02335","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":"2026-08-07T06:34:17.273281+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":"2026-08-06T16:51:53.430420Z","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":"2026-05-16T21:28:34.144017Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.19616","last_updated":"2024-06-01T03:00:37Z","snapshot_observed_at":"2026-07-06T18:22:22.643181Z","submitted_at":"2024-05-30T02:09:51Z","title":"Easy Problems That LLMs Get Wrong","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19616","snapshot_observed_at":"2026-08-06T16:51:53.430420Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12443","last_updated":"2025-07-16T17:29:15Z","snapshot_observed_at":"2026-08-06T16:43:18.670549Z","submitted_at":"2025-07-16T17:29:15Z","title":"LLM-Based Config Synthesis requires Disambiguation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:51:53.430420Z"},"links":{"cited_paper":"/paper/2405.19616","citing_paper":"/paper/2507.12443"},"observation_digest":"sha256:f4b586d495ef0caa076c5d1ace1caa194cf4fa7d129370b75817ead893a528bf","observation_id":"f8caf0c7-47b3-4747-8c2e-6e1c9740ad3a","resolution":{"observed_at":"2026-08-06T16:51:53.430420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19616","last_updated":"2024-06-01T03:00:37Z","snapshot_observed_at":"2026-07-06T18:22:22.643181Z","submitted_at":"2024-05-30T02:09:51Z","title":"Easy Problems That LLMs Get Wrong","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19616","snapshot_observed_at":"2026-08-05T22:21:05.930325Z","title":"Easy problems that llms get wrong","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.07111","last_updated":"2025-08-09T22:24:40Z","snapshot_observed_at":"2026-08-05T22:21:03.143364Z","submitted_at":"2025-08-09T22:24:40Z","title":"Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-05T22:21:05.930325Z"},"links":{"cited_paper":"/paper/2405.19616","citing_paper":"/paper/2508.07111"},"observation_digest":"sha256:d3a396ef29cd0122b2e95316cbb69bf9645a623a0a332d354acfa4470ed2c4ff","observation_id":"d55956a8-98ff-469e-b2e9-6fa74ff5606f","resolution":{"observed_at":"2026-08-05T22:21:05.930325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19616","last_updated":"2024-06-01T03:00:37Z","snapshot_observed_at":"2026-07-06T18:22:22.643181Z","submitted_at":"2024-05-30T02:09:51Z","title":"Easy Problems That LLMs Get Wrong","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19616","snapshot_observed_at":"2026-08-05T21:10:36.705823Z","title":"and Huckle, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.10057","last_updated":"2025-08-12T21:38:46Z","snapshot_observed_at":"2026-08-06T04:18:07.733770Z","submitted_at":"2025-08-12T21:38:46Z","title":"Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-05T21:10:36.705823Z"},"links":{"cited_paper":"/paper/2405.19616","citing_paper":"/paper/2508.10057"},"observation_digest":"sha256:519236d3bfd1f064d96221a3398cb3f051aa89576707c79bcfb1e0e48592b8f0","observation_id":"d2b77017-a89f-4fd5-9fd0-a40d8177ee2a","resolution":{"observed_at":"2026-08-05T21:10:36.705823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19616","last_updated":"2024-06-01T03:00:37Z","snapshot_observed_at":"2026-07-06T18:22:22.643181Z","submitted_at":"2024-05-30T02:09:51Z","title":"Easy Problems That LLMs Get Wrong","version":2},"cited_work":{"arxiv_id":"2405.19616","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.19616","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"71361a25-1bbb-495d-a594-32574a2c5711","year":2024},"citing_paper":{"arxiv_id":"2512.14917","last_updated":"2026-04-23T22:42:11Z","snapshot_observed_at":"2026-07-31T07:00:51.587326Z","submitted_at":"2025-12-16T21:12:53Z","title":"Evaluating Code Reasoning Abilities of Large Language Models Under Real-World Settings","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-16T21:23:44.762007Z"},"links":{"cited_paper":"/paper/2405.19616","citing_paper":"/paper/2512.14917"},"observation_digest":"sha256:913f05e6c64a2a8ef2bfea935671d12e842e6acbfb354a26d32ee985060d4414","observation_id":"d6270435-135b-46d6-a83e-1bf3a0c09e96","resolution":{"observed_at":"2026-05-16T21:28:34.145638Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2405.19616/citation-record","integrity":"/paper/2405.19616/integrity","json":"/paper/2405.19616/citation-record.json","paper":"/paper/2405.19616"},"outbound":[],"paper":{"arxiv_id":"2405.19616","last_updated":"2024-06-01T03:00:37Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T18:22:22.643181Z","submitted_at":"2024-05-30T02:09:51Z","title":"Easy Problems That LLMs Get Wrong"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.19616."}