{"as_of":"2026-08-17T22:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1f7fca58e3ec8d1f2e85a4b1e6fd042ad65388cf997ff6bd60b53ee82a8c7716","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":"2026-08-17T06:30:58.91139+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":"2026-08-10T18:49:26.018697Z","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":"2026-06-30T20:05:04.477464Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.04123","last_updated":"2024-03-07T00:44:01Z","snapshot_observed_at":"2026-08-16T14:12:07.883399Z","submitted_at":"2024-03-07T00:44:01Z","title":"Exploring LLM-based Agents for Root Cause Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04123","snapshot_observed_at":"2026-08-10T18:49:26.018697Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.11031","last_updated":"2025-01-19T12:46:01Z","snapshot_observed_at":"2026-08-11T15:08:13.712273Z","submitted_at":"2025-01-19T12:46:01Z","title":"AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T18:49:26.018697Z"},"links":{"cited_paper":"/paper/2403.04123","citing_paper":"/paper/2501.11031"},"observation_digest":"sha256:24c50a1465376bdc0c80d368d7ec50b1e4dc07878a8b2e16b2f3c3f6f8ddf57b","observation_id":"c6353ef5-63a4-4be9-b381-a32e0d9e36c0","resolution":{"observed_at":"2026-08-10T18:49:26.018697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04123","last_updated":"2024-03-07T00:44:01Z","snapshot_observed_at":"2026-08-16T14:12:07.883399Z","submitted_at":"2024-03-07T00:44:01Z","title":"Exploring LLM-based Agents for Root Cause Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04123","snapshot_observed_at":"2026-08-08T19:46:28.986960Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05352","last_updated":"2025-02-07T21:46:52Z","snapshot_observed_at":"2026-08-17T00:13:07.515086Z","submitted_at":"2025-02-07T21:46:52Z","title":"ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks","version":1},"reference_index":365,"source":"pdf_text","source_observed_at":"2026-08-08T19:46:28.986960Z"},"links":{"cited_paper":"/paper/2403.04123","citing_paper":"/paper/2502.05352"},"observation_digest":"sha256:e4d617e27fdbc5162a0827381ad2c9a95679fca954a2982ae423dc2c34a4cbfa","observation_id":"b2537391-61d0-4d00-a9fc-cf0f6ec47733","resolution":{"observed_at":"2026-08-08T19:46:28.986960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04123","last_updated":"2024-03-07T00:44:01Z","snapshot_observed_at":"2026-08-16T14:12:07.883399Z","submitted_at":"2024-03-07T00:44:01Z","title":"Exploring LLM-based Agents for Root Cause Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04123","snapshot_observed_at":"2026-08-06T23:26:36.899570Z","title":"arXiv preprint arXiv:2403.04123 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12472","last_updated":"2025-06-23T02:40:16Z","snapshot_observed_at":"2026-08-15T01:11:16.764656Z","submitted_at":"2025-06-23T02:40:16Z","title":"A Survey of AIOps in the Era of Large Language Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T23:26:36.899570Z"},"links":{"cited_paper":"/paper/2403.04123","citing_paper":"/paper/2507.12472"},"observation_digest":"sha256:e552a359229faa85392a79df5c22092a0462dfe0c4d1e4119395197c54bc4763","observation_id":"0a0aba03-7532-4f8a-82e2-ddae9fb71592","resolution":{"observed_at":"2026-08-06T23:26:36.899570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04123","last_updated":"2024-03-07T00:44:01Z","snapshot_observed_at":"2026-08-16T14:12:07.883399Z","submitted_at":"2024-03-07T00:44:01Z","title":"Exploring LLM-based Agents for Root Cause Analysis","version":1},"cited_work":{"arxiv_id":"2403.04123","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.04123","snapshot_observed_at":"2026-06-30T20:05:04.477464Z","title":"Roy et al","venue":null,"work_id":"76fbe265-6fdb-4978-b2ad-dc3bc6f68ea3","year":2024},"citing_paper":{"arxiv_id":"2604.23366","last_updated":"2026-04-25T16:20:28Z","snapshot_observed_at":"2026-08-11T05:49:40.296086Z","submitted_at":"2026-04-25T16:20:28Z","title":"GSAR: Typed Grounding for Hallucination Detection and Recovery in Multi-Agent LLMs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-08T08:16:25.456532Z"},"links":{"cited_paper":"/paper/2403.04123","citing_paper":"/paper/2604.23366"},"observation_digest":"sha256:f09d001f83e70a5f27e85094d2f65a1ac17075da4c69404f7df84c3ed2932d40","observation_id":"8763703c-7141-45e5-99c8-f997aeeb7259","resolution":{"observed_at":"2026-05-11T20:41:13.630412Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04123","last_updated":"2024-03-07T00:44:01Z","snapshot_observed_at":"2026-08-16T14:12:07.883399Z","submitted_at":"2024-03-07T00:44:01Z","title":"Exploring LLM-based Agents for Root Cause Analysis","version":1},"cited_work":{"arxiv_id":"2403.04123","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.04123","snapshot_observed_at":"2026-06-30T20:05:04.477464Z","title":"Roy et al","venue":null,"work_id":"76fbe265-6fdb-4978-b2ad-dc3bc6f68ea3","year":2024},"citing_paper":{"arxiv_id":"2605.00936","last_updated":"2026-05-01T03:29:18Z","snapshot_observed_at":"2026-08-16T04:40:31.888657Z","submitted_at":"2026-05-01T03:29:18Z","title":"EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-09T19:33:41.176821Z"},"links":{"cited_paper":"/paper/2403.04123","citing_paper":"/paper/2605.00936"},"observation_digest":"sha256:cd2a556432c11cdbefd3b6f3d9de379cc1fb4b7d258cd64ac83e8a7ac4cca379","observation_id":"da0ebbf4-9178-4835-9159-836d4c270001","resolution":{"observed_at":"2026-05-11T15:36:09.625359Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04123","last_updated":"2024-03-07T00:44:01Z","snapshot_observed_at":"2026-08-16T14:12:07.883399Z","submitted_at":"2024-03-07T00:44:01Z","title":"Exploring LLM-based Agents for Root Cause Analysis","version":1},"cited_work":{"arxiv_id":"2403.04123","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.04123","snapshot_observed_at":"2026-06-30T20:05:04.477464Z","title":"Roy et al","venue":null,"work_id":"76fbe265-6fdb-4978-b2ad-dc3bc6f68ea3","year":2024},"citing_paper":{"arxiv_id":"2605.14866","last_updated":"2026-05-14T14:13:59Z","snapshot_observed_at":"2026-08-12T18:06:42.941200Z","submitted_at":"2026-05-14T14:13:59Z","title":"Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-30T20:04:34.841056Z"},"links":{"cited_paper":"/paper/2403.04123","citing_paper":"/paper/2605.14866"},"observation_digest":"sha256:002e8427fee0f4da6f3e449fbfd896c899edf28f2cf56fdfe5f09a35b1d9ecd4","observation_id":"0822e447-bbde-4879-8a9a-6ec7b04f6575","resolution":{"observed_at":"2026-06-30T20:05:04.479467Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.04123/citation-record","integrity":"/paper/2403.04123/integrity","json":"/paper/2403.04123/citation-record.json","paper":"/paper/2403.04123"},"outbound":[],"paper":{"arxiv_id":"2403.04123","last_updated":"2024-03-07T00:44:01Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-16T14:12:07.883399Z","submitted_at":"2024-03-07T00:44:01Z","title":"Exploring LLM-based Agents for Root Cause Analysis"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.04123."}