{"as_of":"2026-08-13T02:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2661d423c7ef8e8d8b25d07a9f88386fe006898dbcfec7455bab0e74c10f09ca","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":"2026-08-12T06:34:41.77262+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":"2026-08-10T23:33:57.632779Z","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":"2026-05-23T01:07:19.831260Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.16702","last_updated":"2023-12-27T19:58:52Z","snapshot_observed_at":"2026-08-11T15:59:38.729045Z","submitted_at":"2023-12-27T19:58:52Z","title":"Rethinking Tabular Data Understanding with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.16702","snapshot_observed_at":"2026-08-10T23:33:57.632779Z","title":"ArXiv, abs/2312.16702","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.20145","last_updated":"2025-02-08T18:16:02Z","snapshot_observed_at":"2026-08-11T15:58:48.793782Z","submitted_at":"2024-12-28T13:13:33Z","title":"Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T23:33:57.632779Z"},"links":{"cited_paper":"/paper/2312.16702","citing_paper":"/paper/2412.20145"},"observation_digest":"sha256:a60ddbaf1adca2587b647db33552f7bd649358744750a77ba4e09bde6a22c4ef","observation_id":"85cc5717-fae4-4d52-bc80-0550ab0260cf","resolution":{"observed_at":"2026-08-10T23:33:57.632779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16702","last_updated":"2023-12-27T19:58:52Z","snapshot_observed_at":"2026-08-11T15:59:38.729045Z","submitted_at":"2023-12-27T19:58:52Z","title":"Rethinking Tabular Data Understanding with Large Language Models","version":1},"cited_work":{"arxiv_id":"2312.16702","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.16702","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rethinking tabular data understanding with large language models","venue":null,"work_id":"a592d034-0348-41a1-9b08-ba05ecb21021","year":2023},"citing_paper":{"arxiv_id":"2503.02161","last_updated":"2026-05-17T15:49:06Z","snapshot_observed_at":"2026-08-02T17:38:14.067857Z","submitted_at":"2025-03-04T00:47:52Z","title":"LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-23T01:05:38.969311Z"},"links":{"cited_paper":"/paper/2312.16702","citing_paper":"/paper/2503.02161"},"observation_digest":"sha256:5d326a482f11d9f942d925692bdae8b92a49ef6d89dbe51d4085bcf4cca1568c","observation_id":"29879eaf-7190-42f1-a45d-fa6a0e9a2c87","resolution":{"observed_at":"2026-05-23T01:07:19.835372Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16702","last_updated":"2023-12-27T19:58:52Z","snapshot_observed_at":"2026-08-11T15:59:38.729045Z","submitted_at":"2023-12-27T19:58:52Z","title":"Rethinking Tabular Data Understanding with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.16702","snapshot_observed_at":"2026-08-07T06:03:44.593554Z","title":"Rethinking tabular data understanding with large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06137","last_updated":"2025-06-06T14:52:19Z","snapshot_observed_at":"2026-08-09T22:49:50.230202Z","submitted_at":"2025-06-06T14:52:19Z","title":"Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T06:03:44.593554Z"},"links":{"cited_paper":"/paper/2312.16702","citing_paper":"/paper/2506.06137"},"observation_digest":"sha256:87e82ad1f81fa0d093b18bc86506f1ee3245fd5e17a004f522b794579b65b3e9","observation_id":"84cc8767-a828-4b48-8628-7a76b0aeb48c","resolution":{"observed_at":"2026-08-07T06:03:44.593554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16702","last_updated":"2023-12-27T19:58:52Z","snapshot_observed_at":"2026-08-11T15:59:38.729045Z","submitted_at":"2023-12-27T19:58:52Z","title":"Rethinking Tabular Data Understanding with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.16702","snapshot_observed_at":"2026-08-05T16:22:50.824293Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.18676","last_updated":"2025-08-26T04:46:54Z","snapshot_observed_at":"2026-08-09T04:51:02.516154Z","submitted_at":"2025-08-26T04:46:54Z","title":"Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-05T16:22:50.824293Z"},"links":{"cited_paper":"/paper/2312.16702","citing_paper":"/paper/2508.18676"},"observation_digest":"sha256:adfd5348b81f645c6c62b427d3cbc894a44a706a8c144a09dd23fff787ff4a08","observation_id":"ced3f6b9-097b-4db9-a574-351596588d7a","resolution":{"observed_at":"2026-08-05T16:22:50.824293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16702","last_updated":"2023-12-27T19:58:52Z","snapshot_observed_at":"2026-08-11T15:59:38.729045Z","submitted_at":"2023-12-27T19:58:52Z","title":"Rethinking Tabular Data Understanding with Large Language Models","version":1},"cited_work":{"arxiv_id":"2312.16702","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.16702","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rethinking tabular data understanding with large language models","venue":null,"work_id":"a592d034-0348-41a1-9b08-ba05ecb21021","year":2023},"citing_paper":{"arxiv_id":"2601.17609","last_updated":"2026-04-21T19:10:11Z","snapshot_observed_at":"2026-07-06T22:42:52.947644Z","submitted_at":"2026-01-24T22:05:01Z","title":"What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T10:57:44.750669Z"},"links":{"cited_paper":"/paper/2312.16702","citing_paper":"/paper/2601.17609"},"observation_digest":"sha256:e1452b5664b73b6e52f96f3f197c57b40a7d07db0c93a011260a610e664adf74","observation_id":"260dfd9a-7452-474e-b0f4-ea7103879512","resolution":{"observed_at":"2026-05-16T10:57:46.336912Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16702","last_updated":"2023-12-27T19:58:52Z","snapshot_observed_at":"2026-08-11T15:59:38.729045Z","submitted_at":"2023-12-27T19:58:52Z","title":"Rethinking Tabular Data Understanding with Large Language Models","version":1},"cited_work":{"arxiv_id":"2312.16702","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.16702","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rethinking tabular data understanding with large language models","venue":null,"work_id":"a592d034-0348-41a1-9b08-ba05ecb21021","year":2023},"citing_paper":{"arxiv_id":"2604.18966","last_updated":"2026-05-17T12:00:22Z","snapshot_observed_at":"2026-08-01T19:53:45.797959Z","submitted_at":"2026-04-21T01:29:52Z","title":"Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-10T03:04:54.146481Z"},"links":{"cited_paper":"/paper/2312.16702","citing_paper":"/paper/2604.18966"},"observation_digest":"sha256:45162cef90b2590d5ff70b8570cd14271c0072a85b45cedf6940688693004139","observation_id":"a00a03b1-6f0b-42db-87d7-b67c131dfe9b","resolution":{"observed_at":"2026-05-11T12:46:03.926126Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2312.16702/citation-record","integrity":"/paper/2312.16702/integrity","json":"/paper/2312.16702/citation-record.json","paper":"/paper/2312.16702"},"outbound":[],"paper":{"arxiv_id":"2312.16702","last_updated":"2023-12-27T19:58:52Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-11T15:59:38.729045Z","submitted_at":"2023-12-27T19:58:52Z","title":"Rethinking Tabular Data Understanding with Large 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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2312.16702."}