{"as_of":"2026-08-06T18:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d080e82ba469fca38d15f9be30072c5a1d8513125542c12befca04eef761d2e8","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T01:10:01.044650Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.21515/citation-record","integrity":"/paper/2605.21515/integrity","json":"/paper/2605.21515/citation-record.json","paper":"/paper/2605.21515"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.19457","last_updated":"2026-02-14T11:42:30Z","snapshot_observed_at":"2026-08-06T03:52:41.836315Z","submitted_at":"2025-07-25T17:42:32Z","title":"GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2507.19457","doi":"10.48550/arxiv.2507.19457","metadata_source":"pith","pith_arxiv_id":"2507.19457","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning","venue":"cs.CL","work_id":"40b60d06-dc1c-4799-b75d-ff1eca653049","year":2025},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/2507.19457","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:c62a5419927b6546f4d378a7860537324fad5b5b9c1cd1e85280a907f37a8d61","observation_id":"6840fdb3-d9fd-4ddc-ba1f-daee2e2d7def","resolution":{"observed_at":"2026-05-22T01:10:51.499617Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-14T18:20:19.13226+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T18:20:19.13226+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":"2107.03374","doi":"10.48550/arxiv.2107.03374","metadata_source":"pith","pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Evaluating Large Language Models Trained on Code","venue":"cs.LG","work_id":"042493e9-b26f-4b4e-bbde-382072ca9b08","year":2021},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:0365ea9c226eaf5bf4e29c2d59df4bd499f072b11c8bd9513f2fc83759bae932","observation_id":"d5d4677f-57f6-4859-95a4-41a89a793366","resolution":{"observed_at":"2026-05-22T01:10:51.480849Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.01547","last_updated":"2019-11-25T13:02:04Z","snapshot_observed_at":"2026-07-06T08:34:41.399203Z","submitted_at":"2019-11-05T00:31:38Z","title":"On the Measure of Intelligence","version":2},"cited_work":{"arxiv_id":"1911.01547","doi":"10.1007/s11432-024-4231-5","metadata_source":"pith","pith_arxiv_id":"1911.01547","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"On the Measure of Intelligence","venue":"cs.AI","work_id":"d8980a59-aa48-447b-8852-b7aca2b41b2c","year":2019},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/1911.01547","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:36f1db3f4cb8f6e974faf1fe803defa1cd1a6850f012b24b17b24365d5ee5f13","observation_id":"3ab4194b-123c-4cca-95c3-54e53a8d43ec","resolution":{"observed_at":"2026-05-22T01:10:51.465651Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09110","last_updated":"2023-10-01T21:44:23Z","snapshot_observed_at":"2026-08-01T19:14:56.803459Z","submitted_at":"2022-11-16T18:51:34Z","title":"Holistic Evaluation of Language Models","version":2},"cited_work":{"arxiv_id":"2211.09110","doi":"10.1007/bf01194075","metadata_source":"pith","pith_arxiv_id":"2211.09110","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Holistic Evaluation of Language Models","venue":"cs.CL","work_id":"cc02a01e-7218-47dc-8e66-3333e7e4adec","year":2022},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/2211.09110","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:883e12ff2f2604c42b19dde5e9f0accd27dcd08fe5a3abdcd1eb14b240c120a3","observation_id":"ca6b8249-2f58-407c-bcf5-96ffa3433fb2","resolution":{"observed_at":"2026-05-22T01:10:51.509602Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00595","last_updated":"2024-05-06T10:20:26Z","snapshot_observed_at":"2026-08-06T10:08:45.100381Z","submitted_at":"2023-12-31T22:21:36Z","title":"State of What Art? A Call for Multi-Prompt LLM Evaluation","version":3},"cited_work":{"arxiv_id":"2401.00595","doi":"10.48550/arxiv.2401.00595","metadata_source":"arxiv_reference","pith_arxiv_id":"2401.00595","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"CoRR, abs/2401.00595","venue":"arXiv (Cornell University)","work_id":"9571587f-84df-407b-baa8-6c9557faa0c7","year":2024},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/2401.00595","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:163a5e8258a1d49204f8b868bd420b4e2238cb2ab5577f3e8ea06a27cd826daa","observation_id":"7d7e74f0-daba-4236-9275-bd8ee43cafac","resolution":{"observed_at":"2026-05-22T01:10:51.491532Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14445","last_updated":"2025-06-17T14:34:13Z","snapshot_observed_at":"2026-07-06T20:39:43.403470Z","submitted_at":"2025-02-20T10:52:38Z","title":"PredictaBoard: Benchmarking LLM Score Predictability","version":2},"cited_work":{"arxiv_id":"2502.14445","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.14445","snapshot_observed_at":"2026-07-02T06:56:44.742211Z","title":"Predictaboard: Benchmarking llm score predictability.arXiv preprint arXiv:2502.14445","venue":null,"work_id":"b3a5401f-6f01-405e-963e-bc00c6d158f4","year":null},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/2502.14445","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:b1c9202e000853410df9440d7fbd52deabe570f7d7aefc64bf5f457d13fc9ba5","observation_id":"e21eef0d-5f96-4312-87b6-5a3c2a2e59e8","resolution":{"observed_at":"2026-05-22T01:10:51.505079Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.10457","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T15:49:58.253902Z","title":"Rethinking llm evaluation: Can we evaluate llms with 200x less data?arXiv preprint arXiv:2510.10457","venue":null,"work_id":"0f5cbf2d-976b-4944-8bbf-b0a824c9e0a1","year":null},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:cff8311db8353ba749e2eeaf07eadaa0c7029148473c171feab714b2d3d14735","observation_id":"bbd273dc-c414-4879-9436-6775be60b9bd","resolution":{"observed_at":"2026-05-22T01:10:51.486505Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.11171","last_updated":"2023-03-07T17:57:37Z","snapshot_observed_at":"2026-07-06T12:50:22.773056Z","submitted_at":"2022-03-21T17:48:52Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","version":4},"cited_work":{"arxiv_id":"2203.11171","doi":"10.1101/2025.04.03.646459","metadata_source":"pith","pith_arxiv_id":"2203.11171","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","venue":"cs.CL","work_id":"8c6d5a6b-b5cc-4105-9c84-9c34bb9375bb","year":2022},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/2203.11171","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:9a71971b9cc0129f7870de56df4a913b11e22c87645604de9512d988b70d093b","observation_id":"3cf58e6c-70ee-4307-bc91-615bdf42c734","resolution":{"observed_at":"2026-05-22T01:10:51.515085Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-21T18:52:42.88633+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T18:52:42.88633+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"How predictable are large language model capabilities? a case study on big-bench","venue":null,"work_id":"469f414d-3cdc-4142-95f3-cd0bb0c0ee2e","year":2023},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:10f2ab5c205ed19931fc991fbfefafb5204414115979f1305add2d06df8b47b0","observation_id":"216bf09e-04a0-405f-b176-dcab6716d076","resolution":{"observed_at":"2026-05-22T01:10:52.297960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00663","last_updated":"2020-05-02T00:16:09Z","snapshot_observed_at":"2026-07-06T09:16:59.519524Z","submitted_at":"2020-05-02T00:16:09Z","title":"Benchmarking Multimodal Regex Synthesis with Complex Structures","version":1},"cited_work":{"arxiv_id":"2005.00663","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.00663","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhou, L., Schellaert, W., Mart´ınez-Plumed, F., Moros-Daval, Y ., Ferri, C., and Hern´andez-Orallo, J","venue":null,"work_id":"43dc7f14-d158-40f2-9a26-63f24c0a7453","year":2005},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/2005.00663","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:cd721cdc01631420e50bf7904b4f99319f98d3cddd11e55ef35523045c5b723e","observation_id":"cde12683-5273-462d-aedc-452a077f7ad6","resolution":{"observed_at":"2026-05-22T01:10:51.471541Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41586-024-07930-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL https://www.nature.com/articles/ s41586-024-07930-y","venue":"Nature","work_id":"73e4508a-0123-4a2b-ad7d-ee8cddda5079","year":2024},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:2883987724210ce579464056bbc59fc3e34ede2d714d10b3fe1e8e1d4b59abbd","observation_id":"e1a61f1e-f36a-42f6-a825-9c303e334c6a","resolution":{"observed_at":"2026-05-22T01:10:51.139835Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-22T22:52:59.377502+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T22:52:59.377502+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06167","last_updated":"2025-01-07T03:59:37Z","snapshot_observed_at":"2026-08-03T19:47:14.971777Z","submitted_at":"2023-10-09T21:36:21Z","title":"Predictable Artificial Intelligence","version":3},"cited_work":{"arxiv_id":"2310.06167","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.06167","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6ea22da8-640d-4b7f-b11a-fc094baad939","year":null},"citing_paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-22T01:10:01.044650Z"},"links":{"cited_paper":"/paper/2310.06167","citing_paper":"/paper/2605.21515"},"observation_digest":"sha256:b5ddcfc995a1f6a6087c00e04914159c3ac1e0b9902437b183ce1d5fbd8f9b5d","observation_id":"cc955960-89bf-4a9c-9806-74e1f0a7db4f","resolution":{"observed_at":"2026-05-22T01:10:51.476097Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.21515","last_updated":"2026-05-15T10:58:01Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T15:42:03.964184Z","submitted_at":"2026-05-15T10:58:01Z","title":"Predicting Performance of Symbolic and Prompt Programs with Examples"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":0,"verified_exact":9,"verified_fuzzy":1},"total_outbound_references":12},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2605.21515."}