{"as_of":"2026-08-09T20:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c610801e0252e39db3cbe516108871d75299eaa62e010af4f2c8eeb4ee720f72","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":"2026-08-08T19:08:57.496779Z","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":"2026-06-28T17:02:24.240613Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.17461","last_updated":"2024-01-30T21:49:30Z","snapshot_observed_at":"2026-08-09T02:49:29.351509Z","submitted_at":"2024-01-30T21:49:30Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17461","snapshot_observed_at":"2026-08-08T19:08:57.496779Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05497","last_updated":"2025-02-13T13:22:40Z","snapshot_observed_at":"2026-08-09T02:50:03.463016Z","submitted_at":"2025-02-08T09:04:16Z","title":"DeepThink: Aligning Language Models with Domain-Specific User Intents","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T19:08:57.496779Z"},"links":{"cited_paper":"/paper/2401.17461","citing_paper":"/paper/2502.05497"},"observation_digest":"sha256:122d061489f00e993466459978d81590578a11dc9440a2545ec0e431fddc2ed1","observation_id":"1ada69b1-80bf-4472-a7eb-d6e712f7677b","resolution":{"observed_at":"2026-08-08T19:08:57.496779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17461","last_updated":"2024-01-30T21:49:30Z","snapshot_observed_at":"2026-08-09T02:49:29.351509Z","submitted_at":"2024-01-30T21:49:30Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17461","snapshot_observed_at":"2026-08-08T04:54:50.557083Z","title":"Synthetic dialogue dataset generation using llm agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08512","last_updated":"2025-08-14T07:15:48Z","snapshot_observed_at":"2026-08-09T13:56:26.887562Z","submitted_at":"2025-02-12T15:46:34Z","title":"Measuring Diversity in Synthetic Datasets","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T04:54:50.557083Z"},"links":{"cited_paper":"/paper/2401.17461","citing_paper":"/paper/2502.08512"},"observation_digest":"sha256:432e671faaaacabbc2c21ad8c2bda3f047d949f921b1809a353953e4097ecaef","observation_id":"220944f6-2a3a-4268-b3af-10d298f7bd51","resolution":{"observed_at":"2026-08-08T04:54:50.557083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17461","last_updated":"2024-01-30T21:49:30Z","snapshot_observed_at":"2026-08-09T02:49:29.351509Z","submitted_at":"2024-01-30T21:49:30Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17461","snapshot_observed_at":"2026-08-07T05:33:52.790878Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07647","last_updated":"2025-06-09T11:12:02Z","snapshot_observed_at":"2026-08-09T11:40:16.573138Z","submitted_at":"2025-06-09T11:12:02Z","title":"Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:33:52.790878Z"},"links":{"cited_paper":"/paper/2401.17461","citing_paper":"/paper/2506.07647"},"observation_digest":"sha256:b51da4e4665df8c961990cc3b63074b1ae479f270325f9d585dd438bbd9b8f0a","observation_id":"779e10bb-b056-4eaa-afea-d535442dcd85","resolution":{"observed_at":"2026-08-07T05:33:52.790878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17461","last_updated":"2024-01-30T21:49:30Z","snapshot_observed_at":"2026-08-09T02:49:29.351509Z","submitted_at":"2024-01-30T21:49:30Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17461","snapshot_observed_at":"2026-08-06T18:19:58.827943Z","title":"Abdullin, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08648","last_updated":"2025-07-11T14:51:33Z","snapshot_observed_at":"2026-08-07T22:32:35.843142Z","submitted_at":"2025-07-11T14:51:33Z","title":"DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T18:19:58.827943Z"},"links":{"cited_paper":"/paper/2401.17461","citing_paper":"/paper/2507.08648"},"observation_digest":"sha256:f8185cecef7f7f45c92f24d65a3f61e6cc923fffcf99806563eadab9413a7d05","observation_id":"ab493dd5-e709-427f-b346-f5071158885a","resolution":{"observed_at":"2026-08-06T18:19:58.827943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17461","last_updated":"2024-01-30T21:49:30Z","snapshot_observed_at":"2026-08-09T02:49:29.351509Z","submitted_at":"2024-01-30T21:49:30Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17461","snapshot_observed_at":"2026-08-06T05:22:28.669764Z","title":"arXiv preprint arXiv:2401.17461 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.01866","last_updated":"2025-08-03T17:42:00Z","snapshot_observed_at":"2026-08-06T23:06:11.846316Z","submitted_at":"2025-08-03T17:42:00Z","title":"Separation Logic of Generic Resources via Sheafeology","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T05:22:28.669764Z"},"links":{"cited_paper":"/paper/2401.17461","citing_paper":"/paper/2508.01866"},"observation_digest":"sha256:9bd303d6180ca3f48e00c8e400cbedd2047336c458548756720943c5977dc741","observation_id":"9f3562d8-d76f-428c-b6fc-862e55cf20a0","resolution":{"observed_at":"2026-08-06T05:22:28.669764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17461","last_updated":"2024-01-30T21:49:30Z","snapshot_observed_at":"2026-08-09T02:49:29.351509Z","submitted_at":"2024-01-30T21:49:30Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents","version":1},"cited_work":{"arxiv_id":"2401.17461","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.17461","snapshot_observed_at":"2026-06-28T17:02:24.240613Z","title":"Synthetic dialogue dataset generation using llm agents","venue":null,"work_id":"55c3136b-5f35-4190-b297-5ef311fcbf19","year":2024},"citing_paper":{"arxiv_id":"2508.18167","last_updated":"2026-05-14T18:20:00Z","snapshot_observed_at":"2026-08-04T20:09:49.064679Z","submitted_at":"2025-08-25T16:16:42Z","title":"DiscussLLM: Teaching Large Language Models When to Speak","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-21T22:09:03.740109Z"},"links":{"cited_paper":"/paper/2401.17461","citing_paper":"/paper/2508.18167"},"observation_digest":"sha256:03f34c049203127a3564bdb5bd5dec09e6317fe68606bd8e35d4c35fb4dbefb3","observation_id":"927e7aa8-7667-4a07-b965-6b988d96385a","resolution":{"observed_at":"2026-05-21T22:10:42.323259Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17461","last_updated":"2024-01-30T21:49:30Z","snapshot_observed_at":"2026-08-09T02:49:29.351509Z","submitted_at":"2024-01-30T21:49:30Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents","version":1},"cited_work":{"arxiv_id":"2401.17461","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.17461","snapshot_observed_at":"2026-06-28T17:02:24.240613Z","title":"Synthetic dialogue dataset generation using llm agents","venue":null,"work_id":"55c3136b-5f35-4190-b297-5ef311fcbf19","year":2024},"citing_paper":{"arxiv_id":"2606.01394","last_updated":"2026-05-31T18:36:41Z","snapshot_observed_at":"2026-08-03T04:21:58.267099Z","submitted_at":"2026-05-31T18:36:41Z","title":"UniD$^3$: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T16:59:11.402443Z"},"links":{"cited_paper":"/paper/2401.17461","citing_paper":"/paper/2606.01394"},"observation_digest":"sha256:93412c614043cd3e95e1ddebea63e9daf9d4a656e2bd3abf285916c8b0ef750f","observation_id":"0078284d-ed96-4856-91de-475d2c58f8f0","resolution":{"observed_at":"2026-06-28T17:02:24.241995Z","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/2401.17461/citation-record","integrity":"/paper/2401.17461/integrity","json":"/paper/2401.17461/citation-record.json","paper":"/paper/2401.17461"},"outbound":[],"paper":{"arxiv_id":"2401.17461","last_updated":"2024-01-30T21:49:30Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T02:49:29.351509Z","submitted_at":"2024-01-30T21:49:30Z","title":"Synthetic Dialogue Dataset Generation using LLM Agents"},"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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2401.17461."}