{"as_of":"2026-08-10T00:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4620d5184b3eb1727cde8702e0c020e62247787fdcf1ef9aa041db252c001a78","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:35:27.989088Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":40,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.12353","last_updated":"2023-03-17T00:52:56Z","snapshot_observed_at":"2026-08-09T19:18:49.025361Z","submitted_at":"2022-10-22T05:04:54Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.12353","snapshot_observed_at":"2026-08-07T12:35:27.989088Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24263","last_updated":"2025-05-30T06:37:39Z","snapshot_observed_at":"2026-08-09T17:21:24.329215Z","submitted_at":"2025-05-30T06:37:39Z","title":"Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.989088Z"},"links":{"cited_paper":"/paper/2210.12353","citing_paper":"/paper/2505.24263"},"observation_digest":"sha256:229ebd57d376f22cc9826743b58d3a984d04f4dbb0196b999077f3ec37b2ca2a","observation_id":"c8d45bf4-cb03-4b7e-b8af-fae0dddd9161","resolution":{"observed_at":"2026-08-07T12:35:27.989088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.12353","last_updated":"2023-03-17T00:52:56Z","snapshot_observed_at":"2026-08-09T19:18:49.025361Z","submitted_at":"2022-10-22T05:04:54Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.12353","snapshot_observed_at":"2026-08-07T04:54:44.226947Z","title":"Leveraging large language models for multiple choice question answering, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09408","last_updated":"2025-06-11T05:33:56Z","snapshot_observed_at":"2026-08-09T19:19:28.559205Z","submitted_at":"2025-06-11T05:33:56Z","title":"Token Constraint Decoding Improves Robustness on Question Answering for Large Language Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T04:54:44.226947Z"},"links":{"cited_paper":"/paper/2210.12353","citing_paper":"/paper/2506.09408"},"observation_digest":"sha256:998dfd0a2924cb39a1e0f4d3cfe3fee56aeabf7b143738bcea5b5533cc128b2d","observation_id":"e3e6299b-8c9c-4ca4-9b08-7e3bcb022348","resolution":{"observed_at":"2026-08-07T04:54:44.226947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.12353","last_updated":"2023-03-17T00:52:56Z","snapshot_observed_at":"2026-08-09T19:18:49.025361Z","submitted_at":"2022-10-22T05:04:54Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.12353","snapshot_observed_at":"2026-08-06T22:56:33.668975Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20274","last_updated":"2025-06-25T09:34:25Z","snapshot_observed_at":"2026-08-07T12:25:51.783364Z","submitted_at":"2025-06-25T09:34:25Z","title":"Enterprise Large Language Model Evaluation Benchmark","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T22:56:33.668975Z"},"links":{"cited_paper":"/paper/2210.12353","citing_paper":"/paper/2506.20274"},"observation_digest":"sha256:c598d42605ead22a59d1625b0705cffdc5aab93bbc5c18253430698156cd8894","observation_id":"49aad555-c701-4ef5-80e8-962f6355f413","resolution":{"observed_at":"2026-08-06T22:56:33.668975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.12353","last_updated":"2023-03-17T00:52:56Z","snapshot_observed_at":"2026-08-09T19:18:49.025361Z","submitted_at":"2022-10-22T05:04:54Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.12353","snapshot_observed_at":"2026-08-06T14:56:03.512819Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17788","last_updated":"2025-07-23T09:54:44Z","snapshot_observed_at":"2026-08-07T18:36:05.499573Z","submitted_at":"2025-07-23T09:54:44Z","title":"Adaptive Repetition for Mitigating Position Bias in LLM-Based Ranking","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T14:56:03.512819Z"},"links":{"cited_paper":"/paper/2210.12353","citing_paper":"/paper/2507.17788"},"observation_digest":"sha256:e4282e05deb14032161be3b2358d16ba96fc2ee51ccb4bff852acaf8bc72d62d","observation_id":"44c2033e-480a-40bd-a8a3-b40a7d002e56","resolution":{"observed_at":"2026-08-06T14:56:03.512819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.12353","last_updated":"2023-03-17T00:52:56Z","snapshot_observed_at":"2026-08-09T19:18:49.025361Z","submitted_at":"2022-10-22T05:04:54Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.12353","snapshot_observed_at":"2026-08-05T12:43:44.723926Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.01314","last_updated":"2025-09-01T09:58:52Z","snapshot_observed_at":"2026-08-08T18:45:32.376784Z","submitted_at":"2025-09-01T09:58:52Z","title":"Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-05T12:43:44.723926Z"},"links":{"cited_paper":"/paper/2210.12353","citing_paper":"/paper/2509.01314"},"observation_digest":"sha256:f78790f5ccd59967b3bc794a532780a95f667702d1c57277a71f8e82ae548fae","observation_id":"96043be3-8fd4-4071-8eeb-9dbad6fd4f1c","resolution":{"observed_at":"2026-08-05T12:43:44.723926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.12353","last_updated":"2023-03-17T00:52:56Z","snapshot_observed_at":"2026-08-09T19:18:49.025361Z","submitted_at":"2022-10-22T05:04:54Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering","version":3},"cited_work":{"arxiv_id":"2210.12353","doi":"10.48550/arxiv.2210.12353","metadata_source":"arxiv_reference","pith_arxiv_id":"2210.12353","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leveraging","venue":"arXiv (Cornell University)","work_id":"faf8cfdf-62e9-43b6-b5d1-fa6fd4e6267f","year":2024},"citing_paper":{"arxiv_id":"2604.12018","last_updated":"2026-04-13T20:03:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-13T20:03:23Z","title":"LLMs Struggle with Abstract Meaning Comprehension More Than Expected","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T15:33:59.663043Z"},"links":{"cited_paper":"/paper/2210.12353","citing_paper":"/paper/2604.12018"},"observation_digest":"sha256:b7dc219558846b9ebc963480a1b0c503b58513e2fa43838fb9526035bdd26ae0","observation_id":"8b4ccc65-c845-4a43-b760-be09404450d1","resolution":{"observed_at":"2026-05-11T10:16:07.647268Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2210.12353","last_updated":"2023-03-17T00:52:56Z","snapshot_observed_at":"2026-08-09T19:18:49.025361Z","submitted_at":"2022-10-22T05:04:54Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering","version":3},"cited_work":{"arxiv_id":"2210.12353","doi":"10.48550/arxiv.2210.12353","metadata_source":"arxiv_reference","pith_arxiv_id":"2210.12353","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leveraging","venue":"arXiv (Cornell University)","work_id":"faf8cfdf-62e9-43b6-b5d1-fa6fd4e6267f","year":2024},"citing_paper":{"arxiv_id":"2605.12813","last_updated":"2026-05-31T17:51:51Z","snapshot_observed_at":"2026-07-06T23:24:27.821980Z","submitted_at":"2026-05-12T23:13:50Z","title":"REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-14T20:13:10.814899Z"},"links":{"cited_paper":"/paper/2210.12353","citing_paper":"/paper/2605.12813"},"observation_digest":"sha256:5b84c7cc64f8f3238ace39b1ce3c0723d3cb5a2b85d753704d7ad0bead2defd0","observation_id":"f1d5d2e9-61df-4f2a-a996-14a28cefd91d","resolution":{"observed_at":"2026-05-14T20:17:56.366231Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2210.12353/citation-record","integrity":"/paper/2210.12353/integrity","json":"/paper/2210.12353/citation-record.json","paper":"/paper/2210.12353"},"outbound":[],"paper":{"arxiv_id":"2210.12353","last_updated":"2023-03-17T00:52:56Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T19:18:49.025361Z","submitted_at":"2022-10-22T05:04:54Z","title":"Leveraging Large Language Models for Multiple Choice Question Answering"},"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 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2210.12353."}