{"as_of":"2026-08-19T00:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8f7441b7d8ceb45e65c0011bdf261adbe860d1f3d665be2840bdd64509fe5fe6","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":"2026-08-18T06:34:40.430872+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":"2026-08-15T20:53:22.504077Z","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":"2026-05-25T04:26:37.988620Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.09127","last_updated":"2024-05-10T16:38:23Z","snapshot_observed_at":"2026-08-18T23:12:27.574679Z","submitted_at":"2024-04-14T02:40:43Z","title":"Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09127","snapshot_observed_at":"2026-08-15T20:53:22.504077Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11811","last_updated":"2025-05-17T03:43:30Z","snapshot_observed_at":"2026-08-17T23:19:39.037583Z","submitted_at":"2025-05-17T03:43:30Z","title":"BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-15T20:53:22.504077Z"},"links":{"cited_paper":"/paper/2404.09127","citing_paper":"/paper/2505.11811"},"observation_digest":"sha256:a415554255cd0dfa6db72885da8dea2527a8aa4aa135e07b52a36b0f60aa4309","observation_id":"d0808c87-d07d-4f0f-9142-98e69f39b10a","resolution":{"observed_at":"2026-08-15T20:53:22.504077Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09127","last_updated":"2024-05-10T16:38:23Z","snapshot_observed_at":"2026-08-18T23:12:27.574679Z","submitted_at":"2024-04-14T02:40:43Z","title":"Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09127","snapshot_observed_at":"2026-08-05T22:54:25.726179Z","title":"A.; Hu, B.; and Kang, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.06225","last_updated":"2025-08-18T12:00:32Z","snapshot_observed_at":"2026-08-18T14:07:18.712141Z","submitted_at":"2025-08-08T11:11:22Z","title":"Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-05T22:54:25.726179Z"},"links":{"cited_paper":"/paper/2404.09127","citing_paper":"/paper/2508.06225"},"observation_digest":"sha256:51869fcdd8293f980036f2f3bc670a6ac30671a762b5ac08ff3703f804d9c140","observation_id":"0f50858b-323f-47ea-8b40-e029e9e1c88c","resolution":{"observed_at":"2026-08-05T22:54:25.726179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09127","last_updated":"2024-05-10T16:38:23Z","snapshot_observed_at":"2026-08-18T23:12:27.574679Z","submitted_at":"2024-04-14T02:40:43Z","title":"Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation","version":3},"cited_work":{"arxiv_id":"2404.09127","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.09127","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2404.09127 , year=","venue":null,"work_id":"5ef467d7-02b9-4e1c-bab9-96b1feb3389a","year":null},"citing_paper":{"arxiv_id":"2605.23414","last_updated":"2026-05-22T09:24:12Z","snapshot_observed_at":"2026-07-06T23:33:39.204744Z","submitted_at":"2026-05-22T09:24:12Z","title":"When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-25T04:25:26.710488Z"},"links":{"cited_paper":"/paper/2404.09127","citing_paper":"/paper/2605.23414"},"observation_digest":"sha256:16dda6f8d0de61c4ab78e36f4c3c78589646166ef101f80281580afd358faa2d","observation_id":"c4ed9f44-b613-4b65-b764-7fa261fe99d2","resolution":{"observed_at":"2026-05-25T04:26:37.993684Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09127","last_updated":"2024-05-10T16:38:23Z","snapshot_observed_at":"2026-08-18T23:12:27.574679Z","submitted_at":"2024-04-14T02:40:43Z","title":"Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09127","snapshot_observed_at":"2026-08-15T14:51:00.783072Z","title":"Confidence calibration and rationalization for llms via multi-agent deliberation.arXiv preprint arXiv:2404.09127, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.03648","last_updated":"2026-08-04T13:31:24Z","snapshot_observed_at":"2026-08-17T18:27:05.302744Z","submitted_at":"2026-08-04T13:31:24Z","title":"Group Perspective Matters: Regulating Debate Relationships Can Mitigate Blind Conformity in Multi-Agent Debate","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T14:51:00.783072Z"},"links":{"cited_paper":"/paper/2404.09127","citing_paper":"/paper/2608.03648"},"observation_digest":"sha256:00173c7e898f82a27a8f04f06c7c9272c77e15947181f5731a7104c2ac46756d","observation_id":"5a3172f0-1795-4586-a235-c2d510f96cb1","resolution":{"observed_at":"2026-08-15T14:51:00.783072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2404.09127/citation-record","integrity":"/paper/2404.09127/integrity","json":"/paper/2404.09127/citation-record.json","paper":"/paper/2404.09127"},"outbound":[],"paper":{"arxiv_id":"2404.09127","last_updated":"2024-05-10T16:38:23Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-18T23:12:27.574679Z","submitted_at":"2024-04-14T02:40:43Z","title":"Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.09127."}