{"as_of":"2026-08-14T08:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e73a20d2b890a785ebaf35a4c74eaa731d89e9fe0a9e150289451bda915cbf3a","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:24:49.923145Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.14460/citation-record","integrity":"/paper/2411.14460/integrity","json":"/paper/2411.14460/citation-record.json","paper":"/paper/2411.14460"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:24:49.772974Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.772974Z"},"links":{"citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:8383fdb025b1dc9c5007890b1065ec5809d9bfc5e7ad08fdb11ed5d143c410aa","observation_id":"4df9338b-f8e8-458e-b74c-1522c84fa3ea","resolution":{"observed_at":"2026-08-12T19:24:49.772974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:24:49.778128Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.778128Z"},"links":{"citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:b6846526186cdba08aa6796efe7d182152362a20d65d720929bd4218a7702c74","observation_id":"2fcc00af-c661-441b-95b6-cf50d60363e5","resolution":{"observed_at":"2026-08-12T19:24:49.778128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-12T19:24:49.782738Z","title":"Abhimanyu Dubey, Abhinav Jauhri","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.782738Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:eadacfeace29066a75e057413fc1d8b4a90b2b9d926c3903a330e2b490e63478","observation_id":"beaabf35-ef2e-4fa7-81c5-98a9d98cf9cb","resolution":{"observed_at":"2026-08-12T19:24:49.782738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.00032","last_updated":"2022-06-30T18:01:08Z","snapshot_observed_at":"2026-08-13T15:13:05.709153Z","submitted_at":"2022-06-30T18:01:08Z","title":"DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.00032","snapshot_observed_at":"2026-08-12T19:24:49.787019Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.787019Z"},"links":{"cited_paper":"/paper/2207.00032","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:0c1991155cad158027e5a49d1529b8186f0e84b109c413e2479a7946225406de","observation_id":"5f0de8ea-96d9-4af1-b3e4-258f1bfe3673","resolution":{"observed_at":"2026-08-12T19:24:49.787019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04023","last_updated":"2023-11-28T09:01:12Z","snapshot_observed_at":"2026-08-07T14:17:12.140094Z","submitted_at":"2023-02-08T12:35:34Z","title":"A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04023","snapshot_observed_at":"2026-08-12T19:24:49.791107Z","title":"Do, Yan Xu, and Pascale Fung","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.791107Z"},"links":{"cited_paper":"/paper/2302.04023","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:8dc61a81538928d460d475fc1e383a72d3109d345cee60e729350bb5935661bd","observation_id":"8f64f23c-1408-4119-aa7e-75451e2c0b92","resolution":{"observed_at":"2026-08-12T19:24:49.791107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:24:50.319217Z","title":null,"venue":null,"work_id":"14be43e1-f538-42dc-8a5f-551fbe1e1962","year":2018},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.795736Z"},"links":{"citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:f696624306cfc52823f92f81775b9a7845a3b4eb5b47173296db01220c67e9fc","observation_id":"9423e40a-2efb-4d42-abdb-adb5d286783a","resolution":{"observed_at":"2026-08-12T19:24:50.322886Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08623","last_updated":"2023-10-27T01:51:48Z","snapshot_observed_at":"2026-08-13T12:06:27.852612Z","submitted_at":"2023-07-14T05:41:22Z","title":"HYTREL: Hypergraph-enhanced Tabular Data Representation Learning","version":2},"cited_work":{"arxiv_id":"2307.08623","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.08623","snapshot_observed_at":"2026-08-12T19:24:50.254384Z","title":"HYTREL: Hypergraph-enhanced Tabular Data Representation Learning","venue":"cs.LG","work_id":"e7f29ba3-4bd1-42c1-ad82-d55658c7433e","year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.799784Z"},"links":{"cited_paper":"/paper/2307.08623","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:5dcde30c8ce740a8bc88ab9ff191337f9dc1352a37bb41508ead9f3886cb02f2","observation_id":"696b6d06-9ca1-41cd-9723-799f2a70c3e2","resolution":{"observed_at":"2026-08-12T19:24:50.259089Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.02164","last_updated":"2020-06-14T19:14:22Z","snapshot_observed_at":"2026-08-14T04:55:53.397142Z","submitted_at":"2019-09-05T00:25:17Z","title":"TabFact: A Large-scale Dataset for Table-based Fact Verification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.02164","snapshot_observed_at":"2026-08-12T19:24:49.803863Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.803863Z"},"links":{"cited_paper":"/paper/1909.02164","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:85b87b7baec4770aea97cef3a07ed7796b2042ca1585307b99dbe2844b5dd35c","observation_id":"95ff508a-30dd-4ff9-b77c-7607377686ee","resolution":{"observed_at":"2026-08-12T19:24:49.803863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.12307","last_updated":"2024-03-08T15:26:38Z","snapshot_observed_at":"2026-08-13T10:08:08.030837Z","submitted_at":"2023-09-21T17:59:11Z","title":"LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.12307","snapshot_observed_at":"2026-08-12T19:24:49.809223Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.809223Z"},"links":{"cited_paper":"/paper/2309.12307","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:9c6a4971b59c24e1151ba7777cedb2fa53b77c8096ae000726907672f2d9525a","observation_id":"7bba1659-abb2-4532-b608-7bed45ff7adf","resolution":{"observed_at":"2026-08-12T19:24:49.809223Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00122","last_updated":"2022-05-07T07:52:39Z","snapshot_observed_at":"2026-08-13T20:15:19.894749Z","submitted_at":"2021-09-01T00:08:14Z","title":"FinQA: A Dataset of Numerical Reasoning over Financial Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00122","snapshot_observed_at":"2026-08-12T19:24:49.813564Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.813564Z"},"links":{"cited_paper":"/paper/2109.00122","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:939b7dc1abb077ca65f1165fa84f914122db4f86114bb5d5a3ae5bcf698857e8","observation_id":"7e26e059-6a1a-4a6c-b93d-d3e25e03b591","resolution":{"observed_at":"2026-08-12T19:24:49.813564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-12T19:24:49.818225Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.818225Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:6def31db1960c9f61fd8de08b1d1a7fe76f9f16303607726df7ec6734515c502","observation_id":"cdc7daaf-e4f5-4ad8-a02b-6705c8b48692","resolution":{"observed_at":"2026-08-12T19:24:49.818225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:24:49.822544Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.822544Z"},"links":{"citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:fbbfd93d492e6eb3405b3af1968c05a212a73b6387dfd59ddad80c1daad29494","observation_id":"b521cd84-31ae-43d3-8d05-5b470d6ef222","resolution":{"observed_at":"2026-08-12T19:24:49.822544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07630","last_updated":"2024-05-27T04:04:40Z","snapshot_observed_at":"2026-08-13T04:20:38.811486Z","submitted_at":"2024-02-12T13:13:04Z","title":"G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07630","snapshot_observed_at":"2026-08-12T19:24:49.826610Z","title":"Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.826610Z"},"links":{"cited_paper":"/paper/2402.07630","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:6df7817254d737785dcf9ed6579f38db7d5d1eb6ec9181e693822ad06509c574","observation_id":"237ce64d-9fab-4816-9756-4be44eee04d0","resolution":{"observed_at":"2026-08-12T19:24:49.826610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:24:49.830542Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.830542Z"},"links":{"citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:6b12ff619e510f346354226fe385d576848ea689fa21fb023822c0e200b9d155","observation_id":"fb3725f2-0546-4571-8217-6ada6907e775","resolution":{"observed_at":"2026-08-12T19:24:49.830542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-12T19:24:49.834338Z","title":"Hugo Touvron, Louis Martin","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.834338Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:50f7aed52860ad06e35bffef478a8f1504e5f539dddbc136bf30375b6b9e731d","observation_id":"01e083da-31b4-4521-9e13-a8c4cbdbf728","resolution":{"observed_at":"2026-08-12T19:24:49.834338Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:24:49.838440Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.838440Z"},"links":{"citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:8c0e7ccf9f8c39e3fc0ffa9abec3adb6aa8ab1c24f3e2800ff551452c842a623","observation_id":"2c40577f-0efb-466d-8fcf-e64c41d6d178","resolution":{"observed_at":"2026-08-12T19:24:49.838440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-12T19:24:49.842581Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.842581Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:f9116b9401d6d994e8318db6ee9c2edfc209fe963b8bdff41d77a55410669fd4","observation_id":"56aca7ac-3219-419c-a5ec-6a7b4e90ec22","resolution":{"observed_at":"2026-08-12T19:24:49.842581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19723","last_updated":"2024-12-15T14:24:47Z","snapshot_observed_at":"2026-08-13T00:43:20.983464Z","submitted_at":"2024-03-28T03:20:54Z","title":"HeGTa: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding","version":2},"cited_work":{"arxiv_id":"2403.19723","doi":"10.48550/arxiv.2403.19723","metadata_source":"pith","pith_arxiv_id":"2403.19723","snapshot_observed_at":"2026-08-13T00:16:21.800782Z","title":"HeGTa: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding","venue":"cs.CL","work_id":"5d94fbfe-ea1a-4595-849a-f3ccad116ba7","year":2024},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.846889Z"},"links":{"cited_paper":"/paper/2403.19723","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:67b238fe8000f2d4493a55126608a0b09ce4e67f80e6c695cea940ee1874f952","observation_id":"fa8249a2-da90-4c48-8e12-d93c89f0d1b9","resolution":{"observed_at":"2026-08-12T19:24:49.998829Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.12597","last_updated":"2023-06-15T07:57:29Z","snapshot_observed_at":"2026-08-12T12:01:54.105712Z","submitted_at":"2023-01-30T00:56:51Z","title":"BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.12597","snapshot_observed_at":"2026-08-12T19:24:49.851404Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.851404Z"},"links":{"cited_paper":"/paper/2301.12597","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:cf9a7e47b9e350784f21ec875e58c24d979ddc996d1d5fd04759d06e2b6c268d","observation_id":"39ce0fd6-e5e9-43c4-a45d-5e80b6eef6de","resolution":{"observed_at":"2026-08-12T19:24:49.851404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14610","last_updated":"2023-03-02T07:41:55Z","snapshot_observed_at":"2026-08-13T14:17:11.182458Z","submitted_at":"2022-09-29T08:01:04Z","title":"Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14610","snapshot_observed_at":"2026-08-12T19:24:49.855253Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.855253Z"},"links":{"cited_paper":"/paper/2209.14610","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:1b27c3efd0d04585ace7cf7bb0b79d11663ad7fa150f2a609e4014e5fc904552","observation_id":"1967235b-fc79-49f4-bb7a-73e95f8b9a92","resolution":{"observed_at":"2026-08-12T19:24:49.855253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14219","last_updated":"2024-08-30T21:17:17Z","snapshot_observed_at":"2026-08-10T14:07:02.234322Z","submitted_at":"2024-04-22T14:32:33Z","title":"Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14219","snapshot_observed_at":"2026-08-12T19:24:49.859314Z","title":"Marah Abdin, Jyoti Aneja","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.859314Z"},"links":{"cited_paper":"/paper/2404.14219","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:18f7dc2a438fb9c99da85d176dec243f55188b6e6cd60b927a8094ce7353fc2f","observation_id":"4e13348e-5023-4c19-a802-9c1851ef67ae","resolution":{"observed_at":"2026-08-12T19:24:49.859314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:24:49.863176Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.863176Z"},"links":{"citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:f07f6fdab684e63f088aa89e325d1fd5a45f7ba055b899f7e0bfdba3cd3b191f","observation_id":"b02a4169-2442-473b-9b95-2ed019c78cbc","resolution":{"observed_at":"2026-08-12T19:24:49.863176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.02871","last_updated":"2021-04-12T14:18:06Z","snapshot_observed_at":"2026-08-10T12:57:43.043456Z","submitted_at":"2020-07-06T16:35:30Z","title":"DART: Open-Domain Structured Data Record to Text Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.02871","snapshot_observed_at":"2026-08-12T19:24:49.866825Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.866825Z"},"links":{"cited_paper":"/paper/2007.02871","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:66e29052095001e035443f02ac47dbb69f9030efc375b31462dd88be688a0a79","observation_id":"b8d06e6b-078a-42dd-839d-5a403757f737","resolution":{"observed_at":"2026-08-12T19:24:49.866825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.14373","last_updated":"2020-10-06T06:07:06Z","snapshot_observed_at":"2026-08-13T13:59:52.850998Z","submitted_at":"2020-04-29T17:53:45Z","title":"ToTTo: A Controlled Table-To-Text Generation Dataset","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.14373","snapshot_observed_at":"2026-08-12T19:24:49.870640Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.870640Z"},"links":{"cited_paper":"/paper/2004.14373","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:5f86b786c06730a3b9f709e5fe50ef6be4b6880cae9d7e63e9868164eb54ecba","observation_id":"bc555102-8f37-4386-b8c9-3637a4df1bf6","resolution":{"observed_at":"2026-08-12T19:24:49.870640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1508.00305","last_updated":"2015-08-03T02:53:01Z","snapshot_observed_at":"2026-07-06T04:25:37.491871Z","submitted_at":"2015-08-03T02:53:01Z","title":"Compositional Semantic Parsing on Semi-Structured Tables","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.00305","snapshot_observed_at":"2026-08-12T19:24:49.874498Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.874498Z"},"links":{"cited_paper":"/paper/1508.00305","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:4069c2a9e97ec6a2b6606372831e462b2abef27629d3af58cd82c1d9f96b16a2","observation_id":"8d5bb9bb-db86-4108-b102-6505895c8fde","resolution":{"observed_at":"2026-08-12T19:24:49.874498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10084","last_updated":"2019-08-27T08:50:17Z","snapshot_observed_at":"2026-08-14T05:02:11.716316Z","submitted_at":"2019-08-27T08:50:17Z","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10084","snapshot_observed_at":"2026-08-12T19:24:49.878534Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.878534Z"},"links":{"cited_paper":"/paper/1908.10084","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:a938659573ff887edc830ed91ce243d6a51d61a7cf7df8d15ae46902314934ce","observation_id":"63cc8348-a139-4f13-a01c-bec076e59c45","resolution":{"observed_at":"2026-08-12T19:24:49.878534Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.00577","last_updated":"2021-08-02T01:12:18Z","snapshot_observed_at":"2026-08-13T18:35:36.635875Z","submitted_at":"2021-08-02T01:12:18Z","title":"Logic-Consistency Text Generation from Semantic Parses","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.00577","snapshot_observed_at":"2026-08-12T19:24:49.882586Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.882586Z"},"links":{"cited_paper":"/paper/2108.00577","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:9c8ca38e453930563c64e7b0c7910ac17891a79ecb3fd07a89b5b20fed7078c6","observation_id":"13a4fecc-19dc-48c7-9025-40141cb498e3","resolution":{"observed_at":"2026-08-12T19:24:49.882586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.06643","last_updated":"2018-03-18T11:28:12Z","snapshot_observed_at":"2026-08-10T10:11:41.071439Z","submitted_at":"2018-03-18T11:28:12Z","title":"The Web as a Knowledge-base for Answering Complex Questions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.06643","snapshot_observed_at":"2026-08-12T19:24:49.886404Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.886404Z"},"links":{"cited_paper":"/paper/1803.06643","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:9079276c2ac1a3d4f6a0d1a0eb1e5f9701316b598371e1a075ad317a644a04c5","observation_id":"26cb6746-2363-431d-bc80-7444c83ee549","resolution":{"observed_at":"2026-08-12T19:24:49.886404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.13023","last_updated":"2024-05-07T10:10:14Z","snapshot_observed_at":"2026-08-13T05:45:40.134919Z","submitted_at":"2023-10-19T06:17:46Z","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.13023","snapshot_observed_at":"2026-08-12T19:24:49.891155Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.891155Z"},"links":{"cited_paper":"/paper/2310.13023","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:ce18bc034729f616e646db3ae5bbf0e8fb377a1387ff63213494b53ae01276a6","observation_id":"c5be0e29-355d-494c-87de-c59f557066c9","resolution":{"observed_at":"2026-08-12T19:24:49.891155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-12T19:24:49.895305Z","title":"Gomez, Lukasz Kaiser, and Illia Polosukhin","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.895305Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:8235849d508a65def1a54b31a2aef899701145a5b33e53ee7986fabdf16de5a7","observation_id":"ab95c4fa-f150-463e-a715-9b380713a107","resolution":{"observed_at":"2026-08-12T19:24:49.895305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-08-13T22:35:40.714745Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-12T19:24:49.899447Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.899447Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:d6c741d3020fb78d2d4ed9823daea159ef57f4028bbf117f9d108cc081d9a458","observation_id":"c600dbd8-fa68-4f1f-8594-76617b0a61d9","resolution":{"observed_at":"2026-08-12T19:24:49.899447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.05966","last_updated":"2022-10-18T15:56:01Z","snapshot_observed_at":"2026-08-13T20:55:08.950313Z","submitted_at":"2022-01-16T04:36:18Z","title":"UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.05966","snapshot_observed_at":"2026-08-12T19:24:49.903316Z","title":"Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.903316Z"},"links":{"cited_paper":"/paper/2201.05966","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:e1f28816fd46e9d0424477a1f514a3895cb7683a67f47819666adb4a3e6b902e","observation_id":"c41c23c4-6cfb-4f24-bcad-7372801a84bb","resolution":{"observed_at":"2026-08-12T19:24:49.903316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.08314","last_updated":"2020-05-17T17:26:40Z","snapshot_observed_at":"2026-08-13T07:36:34.467678Z","submitted_at":"2020-05-17T17:26:40Z","title":"TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.08314","snapshot_observed_at":"2026-08-12T19:24:49.907328Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.907328Z"},"links":{"cited_paper":"/paper/2005.08314","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:641b03eab1846cc3f3d47bd735d8a35ef7decfa0bc2aa1add722581a73e8555c","observation_id":"7e46e8c6-8e43-470c-918e-fb9abd3813bf","resolution":{"observed_at":"2026-08-12T19:24:49.907328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:24:49.911359Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.911359Z"},"links":{"citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:02d2ef90e714e4ad74ecd22cbd0712f42bec1930b7e48f558ba487c4ba17e8c7","observation_id":"2ef1d0d9-27d1-4ec1-8b4d-911602f626a9","resolution":{"observed_at":"2026-08-12T19:24:49.911359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.09206","last_updated":"2024-04-04T17:10:25Z","snapshot_observed_at":"2026-08-13T05:24:31.920441Z","submitted_at":"2023-11-15T18:47:52Z","title":"TableLlama: Towards Open Large Generalist Models for Tables","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.09206","snapshot_observed_at":"2026-08-12T19:24:49.915308Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.915308Z"},"links":{"cited_paper":"/paper/2311.09206","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:a898c19e89846cf7039b0e35ff1ee7c61776a7d6faf0a53b74945be940115087","observation_id":"83d4261a-6aa6-4a68-a75a-00c2c67547a2","resolution":{"observed_at":"2026-08-12T19:24:49.915308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-06T23:27:24.356320Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-12T19:24:49.919235Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.919235Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:b5508f49dc6ea330a2ccd5fa75cd18ed827832e845eec1e365e357d4aba25cd5","observation_id":"d8d609c6-bdf6-463e-a342-42a8419fecc6","resolution":{"observed_at":"2026-08-12T19:24:49.919235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16671","last_updated":"2024-10-07T14:44:44Z","snapshot_observed_at":"2026-08-13T23:26:26.094226Z","submitted_at":"2024-02-26T15:47:01Z","title":"StructLM: Towards Building Generalist Models for Structured Knowledge Grounding","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16671","snapshot_observed_at":"2026-08-12T19:24:49.923145Z","title":"Huang, Jie Fu, Xiang Yue, and Wenhu Chen","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-12T19:24:49.923145Z"},"links":{"cited_paper":"/paper/2402.16671","citing_paper":"/paper/2411.14460"},"observation_digest":"sha256:09e785b3df825399ff7a4091d4ac4b7f77bc3eb6516dac213dd59b0c409f5888","observation_id":"b7466bc6-1290-4048-aee2-33c262c77c1a","resolution":{"observed_at":"2026-08-12T19:24:49.923145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.14460","last_updated":"2025-02-09T17:13:10Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T00:35:03.265321Z","submitted_at":"2024-11-16T12:27:14Z","title":"LLaSA: Large Language and Structured Data Assistant"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":35,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":37},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2411.14460."}