{"as_of":"2026-08-09T14:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:04ddc4a4ebed383c88290548aa5af99478f56b94acc7490cd8abc433c8b62b70","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T22:28:11.667139Z","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":"2026-05-11T20:51:09.230185Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.02507","last_updated":"2024-10-03T14:15:00Z","snapshot_observed_at":"2026-07-06T19:27:03.209340Z","submitted_at":"2024-10-03T14:15:00Z","title":"Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02507","snapshot_observed_at":"2026-08-05T22:28:11.667139Z","title":"arXiv preprint arXiv:2410.02507 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.07010","last_updated":"2025-08-09T15:16:45Z","snapshot_observed_at":"2026-08-07T22:50:06.294600Z","submitted_at":"2025-08-09T15:16:45Z","title":"Narrative Memory in Machines: Multi-Agent Arc Extraction in Serialized TV","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T22:28:11.667139Z"},"links":{"cited_paper":"/paper/2410.02507","citing_paper":"/paper/2508.07010"},"observation_digest":"sha256:a5991299d1849c9e954168cf4dcf6793c9eb95195d21d6c59e962e1f9b1a7117","observation_id":"8ec8f5ff-5c58-4b2b-bad5-234f8c96c2a9","resolution":{"observed_at":"2026-08-05T22:28:11.667139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02507","last_updated":"2024-10-03T14:15:00Z","snapshot_observed_at":"2026-07-06T19:27:03.209340Z","submitted_at":"2024-10-03T14:15:00Z","title":"Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration","version":1},"cited_work":{"arxiv_id":"2410.02507","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02507","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"AgentSecBench: Benchmarking Agent Security Against Prompt Injection and Tool Misuse","venue":null,"work_id":"92fab32c-e30a-4555-82fa-b59424d82f46","year":2024},"citing_paper":{"arxiv_id":"2512.13564","last_updated":"2026-01-13T09:33:57Z","snapshot_observed_at":"2026-08-06T08:27:07.254588Z","submitted_at":"2025-12-15T17:22:34Z","title":"Memory in the Age of AI Agents","version":2},"reference_index":179,"source":"arxiv_source","source_observed_at":"2026-05-11T18:18:19.911342Z"},"links":{"cited_paper":"/paper/2410.02507","citing_paper":"/paper/2512.13564"},"observation_digest":"sha256:c5c4234734997fee6f65505290c65c4213276e395e3dce3e655796d4f6b454fd","observation_id":"ea363d11-bd5e-432e-b086-b5b34f3912f1","resolution":{"observed_at":"2026-05-11T18:18:20.571765Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2410.02507","last_updated":"2024-10-03T14:15:00Z","snapshot_observed_at":"2026-07-06T19:27:03.209340Z","submitted_at":"2024-10-03T14:15:00Z","title":"Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration","version":1},"cited_work":{"arxiv_id":"2410.02507","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02507","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"AgentSecBench: Benchmarking Agent Security Against Prompt Injection and Tool Misuse","venue":null,"work_id":"92fab32c-e30a-4555-82fa-b59424d82f46","year":2024},"citing_paper":{"arxiv_id":"2604.23338","last_updated":"2026-05-06T17:17:02Z","snapshot_observed_at":"2026-08-06T19:46:39.219000Z","submitted_at":"2026-04-25T14:57:15Z","title":"A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework","version":2},"reference_index":142,"source":"pdf_text","source_observed_at":"2026-05-08T07:53:13.746141Z"},"links":{"cited_paper":"/paper/2410.02507","citing_paper":"/paper/2604.23338"},"observation_digest":"sha256:57d121f9d675b4a52691a2c7d1251975e939b563b1921f32d1ad951e154d681b","observation_id":"205f962a-e3e0-43cd-a4ee-52dd66b5a171","resolution":{"observed_at":"2026-05-11T20:51:09.234878Z","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/2410.02507/citation-record","integrity":"/paper/2410.02507/integrity","json":"/paper/2410.02507/citation-record.json","paper":"/paper/2410.02507"},"outbound":[],"paper":{"arxiv_id":"2410.02507","last_updated":"2024-10-03T14:15:00Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T19:27:03.209340Z","submitted_at":"2024-10-03T14:15:00Z","title":"Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration"},"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 3 inbound Pith citation observations for arXiv:2410.02507."}