{"as_of":"2026-08-11T16:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:67d7da4e399ff24cbb4fdd17061e393593768ae136dbeb4f6076e614e6e9ba3d","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":"2026-08-11T06:34:44.6726+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":"2026-08-07T00:39:41.838053Z","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":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.20707","last_updated":"2024-10-09T00:13:06Z","snapshot_observed_at":"2026-07-06T19:40:34.668379Z","submitted_at":"2024-10-09T00:13:06Z","title":"DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response","version":1},"cited_work":{"arxiv_id":"2410.20707","doi":"10.48550/arxiv.2410.20707","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.20707","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Rawat, Disasterqa: A benchmark for assessing the performance of llms in disaster response, arXiv preprint arXiv:2410.20707 (2024)","venue":"arXiv (Cornell University)","work_id":"77c1921e-efb8-4444-a935-f11949a0e66d","year":2024},"citing_paper":{"arxiv_id":"2504.02181","last_updated":"2026-04-22T01:49:17Z","snapshot_observed_at":"2026-07-30T09:24:14.725185Z","submitted_at":"2025-04-02T23:51:27Z","title":"A Survey of Scaling in Large Language Model Reasoning","version":2},"reference_index":163,"source":"pdf_text","source_observed_at":"2026-05-22T21:20:07.238992Z"},"links":{"cited_paper":"/paper/2410.20707","citing_paper":"/paper/2504.02181"},"observation_digest":"sha256:c4af4bceff42283ec2f929d152476f4a9e8cc1d37b6f17e54f7ce7fae11108dc","observation_id":"2b6ee553-478f-43c6-9652-8b735a9bb4f5","resolution":{"observed_at":"2026-05-22T21:22:08.876037Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20707","last_updated":"2024-10-09T00:13:06Z","snapshot_observed_at":"2026-07-06T19:40:34.668379Z","submitted_at":"2024-10-09T00:13:06Z","title":"DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20707","snapshot_observed_at":"2026-08-07T00:39:41.838053Z","title":"Rawat, Disasterqa: A benchmark for assessing the performance of llms in disaster response, arXiv preprint arXiv:2410.20707 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12958","last_updated":"2025-06-20T15:52:06Z","snapshot_observed_at":"2026-08-07T07:24:20.034880Z","submitted_at":"2025-06-15T20:42:45Z","title":"Domain Specific Benchmarks for Evaluating Multimodal Large Language Models","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T00:39:41.838053Z"},"links":{"cited_paper":"/paper/2410.20707","citing_paper":"/paper/2506.12958"},"observation_digest":"sha256:94373ac756fb0f9f161fa62e8f0f04987c866ce2ba2bb9793609018633b2439b","observation_id":"3f31b45e-c978-44a9-b2c7-21cf96fea40b","resolution":{"observed_at":"2026-08-07T00:39:41.838053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20707","last_updated":"2024-10-09T00:13:06Z","snapshot_observed_at":"2026-07-06T19:40:34.668379Z","submitted_at":"2024-10-09T00:13:06Z","title":"DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response","version":1},"cited_work":{"arxiv_id":"2410.20707","doi":"10.48550/arxiv.2410.20707","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.20707","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Rawat, Disasterqa: A benchmark for assessing the performance of llms in disaster response, arXiv preprint arXiv:2410.20707 (2024)","venue":"arXiv (Cornell University)","work_id":"77c1921e-efb8-4444-a935-f11949a0e66d","year":2024},"citing_paper":{"arxiv_id":"2606.06217","last_updated":"2026-06-04T14:31:11Z","snapshot_observed_at":"2026-08-02T07:57:41.065628Z","submitted_at":"2026-06-04T14:31:11Z","title":"DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-28T02:36:11.721143Z"},"links":{"cited_paper":"/paper/2410.20707","citing_paper":"/paper/2606.06217"},"observation_digest":"sha256:c0e27109421c655d4140d298390e3398e7a81b344652aa83c41af4bd3abd4481","observation_id":"946f884a-098e-4ed3-aa41-2f3d57055e2e","resolution":{"observed_at":"2026-07-02T11:56:55.955709Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20707","last_updated":"2024-10-09T00:13:06Z","snapshot_observed_at":"2026-07-06T19:40:34.668379Z","submitted_at":"2024-10-09T00:13:06Z","title":"DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response","version":1},"cited_work":{"arxiv_id":"2410.20707","doi":"10.48550/arxiv.2410.20707","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.20707","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Rawat, Disasterqa: A benchmark for assessing the performance of llms in disaster response, arXiv preprint arXiv:2410.20707 (2024)","venue":"arXiv (Cornell University)","work_id":"77c1921e-efb8-4444-a935-f11949a0e66d","year":2024},"citing_paper":{"arxiv_id":"2606.21819","last_updated":"2026-06-20T01:05:44Z","snapshot_observed_at":"2026-08-02T15:48:28.171610Z","submitted_at":"2026-06-20T01:05:44Z","title":"RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-26T12:45:16.298896Z"},"links":{"cited_paper":"/paper/2410.20707","citing_paper":"/paper/2606.21819"},"observation_digest":"sha256:e6db9a61fbd2bf4f55381cf134f949079c4c9b9e52da515457be8b012326d1b1","observation_id":"edd48174-76e3-4218-8b33-7eaf73d2d7e8","resolution":{"observed_at":"2026-06-26T12:49:28.554832Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.20707/citation-record","integrity":"/paper/2410.20707/integrity","json":"/paper/2410.20707/citation-record.json","paper":"/paper/2410.20707"},"outbound":[],"paper":{"arxiv_id":"2410.20707","last_updated":"2024-10-09T00:13:06Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T19:40:34.668379Z","submitted_at":"2024-10-09T00:13:06Z","title":"DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.20707."}