{"as_of":"2026-08-10T03:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:342714390970f7dd5a139407edbfdca50de93ef84589ce0c632c87b5df6a57ba","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":"2026-08-07T04:26:30.706921Z","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":"2026-05-22T00:40:51.175108Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.03092","last_updated":"2025-06-18T04:06:03Z","snapshot_observed_at":"2026-08-05T13:27:52.801293Z","submitted_at":"2024-12-04T07:44:35Z","title":"REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03092","snapshot_observed_at":"2026-08-07T04:26:30.706921Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10716","last_updated":"2025-06-12T14:05:09Z","snapshot_observed_at":"2026-08-10T00:41:38.907529Z","submitted_at":"2025-06-12T14:05:09Z","title":"PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T04:26:30.706921Z"},"links":{"cited_paper":"/paper/2412.03092","citing_paper":"/paper/2506.10716"},"observation_digest":"sha256:449e8031dc783977320bacd6734a0aeb4a14612c250a4a228e2974743898606e","observation_id":"8ceaadcd-ca06-4cbc-88d0-db7db19fd77a","resolution":{"observed_at":"2026-08-07T04:26:30.706921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03092","last_updated":"2025-06-18T04:06:03Z","snapshot_observed_at":"2026-08-05T13:27:52.801293Z","submitted_at":"2024-12-04T07:44:35Z","title":"REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03092","snapshot_observed_at":"2026-08-06T18:40:53.192851Z","title":"Zhang, H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.07735","last_updated":"2025-07-10T13:15:20Z","snapshot_observed_at":"2026-08-08T05:47:28.468443Z","submitted_at":"2025-07-10T13:15:20Z","title":"GuardVal: Dynamic Large Language Model Jailbreak Evaluation for Comprehensive Safety Testing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T18:40:53.192851Z"},"links":{"cited_paper":"/paper/2412.03092","citing_paper":"/paper/2507.07735"},"observation_digest":"sha256:9b6f66f300e90c8836a5efdfa5590764a45c572926b8d5242ff5e8861f9f8c9c","observation_id":"b39b6a7e-b402-4be7-b234-b3bd9e2faf58","resolution":{"observed_at":"2026-08-06T18:40:53.192851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03092","last_updated":"2025-06-18T04:06:03Z","snapshot_observed_at":"2026-08-05T13:27:52.801293Z","submitted_at":"2024-12-04T07:44:35Z","title":"REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization","version":2},"cited_work":{"arxiv_id":"2412.03092","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.03092","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhang, H","venue":null,"work_id":"cbdf306d-87ad-4c0b-9b5d-d0a6669ad3d5","year":2024},"citing_paper":{"arxiv_id":"2507.21035","last_updated":"2026-05-17T21:43:43Z","snapshot_observed_at":"2026-07-06T22:04:01.721229Z","submitted_at":"2025-07-28T17:55:08Z","title":"GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis","version":3},"reference_index":157,"source":"pdf_text","source_observed_at":"2026-05-22T00:37:11.945418Z"},"links":{"cited_paper":"/paper/2412.03092","citing_paper":"/paper/2507.21035"},"observation_digest":"sha256:1307833187c58648c5e2fec900f12aca673ad1b22e0171e0781801f4706e654e","observation_id":"fdc63d6d-d330-4b23-b978-1fc8837145db","resolution":{"observed_at":"2026-05-22T00:40:51.178041Z","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":"2412.03092","last_updated":"2025-06-18T04:06:03Z","snapshot_observed_at":"2026-08-05T13:27:52.801293Z","submitted_at":"2024-12-04T07:44:35Z","title":"REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization","version":2},"cited_work":{"arxiv_id":"2412.03092","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.03092","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhang, H","venue":null,"work_id":"cbdf306d-87ad-4c0b-9b5d-d0a6669ad3d5","year":2024},"citing_paper":{"arxiv_id":"2507.21046","last_updated":"2026-01-16T20:59:08Z","snapshot_observed_at":"2026-08-01T06:32:44.461162Z","submitted_at":"2025-07-28T17:59:05Z","title":"A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence","version":4},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-14T22:23:14.621091Z"},"links":{"cited_paper":"/paper/2412.03092","citing_paper":"/paper/2507.21046"},"observation_digest":"sha256:76680864cf022b62e3dc37eccb08c5642de660bb2a1a95006ca1fbcd1095b062","observation_id":"378ddfd8-d895-43ef-a9b2-004aab598aec","resolution":{"observed_at":"2026-05-14T22:23:15.634181Z","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":"2412.03092","last_updated":"2025-06-18T04:06:03Z","snapshot_observed_at":"2026-08-05T13:27:52.801293Z","submitted_at":"2024-12-04T07:44:35Z","title":"REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization","version":2},"cited_work":{"arxiv_id":"2412.03092","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.03092","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhang, H","venue":null,"work_id":"cbdf306d-87ad-4c0b-9b5d-d0a6669ad3d5","year":2024},"citing_paper":{"arxiv_id":"2605.00702","last_updated":"2026-05-01T14:45:20Z","snapshot_observed_at":"2026-07-06T23:14:06.327027Z","submitted_at":"2026-05-01T14:45:20Z","title":"Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory","version":1},"reference_index":180,"source":"arxiv_source","source_observed_at":"2026-05-09T19:10:30.849963Z"},"links":{"cited_paper":"/paper/2412.03092","citing_paper":"/paper/2605.00702"},"observation_digest":"sha256:fc91d1c75774693e0ba5ebf9d45eadd78f23fae6e4fa7034e76bea3a3c775d8c","observation_id":"62e9b0ea-2f16-438a-8366-dada30b93055","resolution":{"observed_at":"2026-05-11T15:51:34.375792Z","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":"2412.03092","last_updated":"2025-06-18T04:06:03Z","snapshot_observed_at":"2026-08-05T13:27:52.801293Z","submitted_at":"2024-12-04T07:44:35Z","title":"REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization","version":2},"cited_work":{"arxiv_id":"2412.03092","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.03092","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhang, H","venue":null,"work_id":"cbdf306d-87ad-4c0b-9b5d-d0a6669ad3d5","year":2024},"citing_paper":{"arxiv_id":"2605.21318","last_updated":"2026-05-20T15:47:26Z","snapshot_observed_at":"2026-07-06T23:31:46.877766Z","submitted_at":"2026-05-20T15:47:26Z","title":"TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-21T05:00:49.763040Z"},"links":{"cited_paper":"/paper/2412.03092","citing_paper":"/paper/2605.21318"},"observation_digest":"sha256:11c89dfd8561d2078d9ee77a6290d457441b26cbe970e1559f58ccde38d3ba0f","observation_id":"7d29d5ab-841c-4c71-a70a-1399db731b02","resolution":{"observed_at":"2026-05-21T05:03:57.988132Z","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":"2412.03092","last_updated":"2025-06-18T04:06:03Z","snapshot_observed_at":"2026-08-05T13:27:52.801293Z","submitted_at":"2024-12-04T07:44:35Z","title":"REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03092","snapshot_observed_at":"2026-08-03T16:45:51.527637Z","title":"arXiv preprint arXiv:2412.03092 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28947","last_updated":"2026-07-31T02:05:45Z","snapshot_observed_at":"2026-08-05T23:12:09.617852Z","submitted_at":"2026-07-31T02:05:45Z","title":"Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-03T16:45:51.527637Z"},"links":{"cited_paper":"/paper/2412.03092","citing_paper":"/paper/2607.28947"},"observation_digest":"sha256:420f7adcffc54823427b7da07a92abeeb86b2d6231ef9d3bdc2b9dda3b9a6e10","observation_id":"3708d41d-1e5a-4c28-b9b1-6d1f6d8a7209","resolution":{"observed_at":"2026-08-03T16:45:51.527637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.03092/citation-record","integrity":"/paper/2412.03092/integrity","json":"/paper/2412.03092/citation-record.json","paper":"/paper/2412.03092"},"outbound":[],"paper":{"arxiv_id":"2412.03092","last_updated":"2025-06-18T04:06:03Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-05T13:27:52.801293Z","submitted_at":"2024-12-04T07:44:35Z","title":"REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization"},"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 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2412.03092."}