{"as_of":"2026-08-05T08:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ac5e7bae5be643a73109b5ca468983f8742af29791477d0e8a2f21383386e752","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T22:34:06.906427Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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":"2026-06-27T21:56:39.900301Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-02T17:37:14.707396Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"cited_work":{"arxiv_id":"2509.23986","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.23986","snapshot_observed_at":"2026-07-02T17:37:14.707396Z","title":"Tusoai: Agentic optimization for scientific methods","venue":"cs.AI","work_id":"c9918cc1-94a9-4312-adcf-e4c2a48f45c9","year":2025},"citing_paper":{"arxiv_id":"2605.06530","last_updated":"2026-05-07T16:31:43Z","snapshot_observed_at":"2026-07-06T23:19:00.794334Z","submitted_at":"2026-05-07T16:31:43Z","title":"SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-08T09:39:46.973876Z"},"links":{"cited_paper":"/paper/2509.23986","citing_paper":"/paper/2605.06530"},"observation_digest":"sha256:eec06ae93eceececa9b859417433aafdcd765a7f132b4d9868342ffa7cdf2f57","observation_id":"d1e84cd2-d048-4198-aec9-bc7ea687f35f","resolution":{"observed_at":"2026-05-20T00:02:55.804523Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"cited_work":{"arxiv_id":"2509.23986","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.23986","snapshot_observed_at":"2026-07-02T17:37:14.707396Z","title":"Tusoai: Agentic optimization for scientific methods","venue":"cs.AI","work_id":"c9918cc1-94a9-4312-adcf-e4c2a48f45c9","year":2025},"citing_paper":{"arxiv_id":"2605.07335","last_updated":"2026-05-08T06:40:24Z","snapshot_observed_at":"2026-08-02T20:01:29.592210Z","submitted_at":"2026-05-08T06:40:24Z","title":"CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-11T01:07:26.231896Z"},"links":{"cited_paper":"/paper/2509.23986","citing_paper":"/paper/2605.07335"},"observation_digest":"sha256:f9dff12d16bd281cecb4352961507c43062aa618519289d291e9f1f62b9a4047","observation_id":"121288c7-b6c1-411a-b19c-169802764a39","resolution":{"observed_at":"2026-05-20T00:02:55.804523Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"cited_work":{"arxiv_id":"2509.23986","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.23986","snapshot_observed_at":"2026-07-02T17:37:14.707396Z","title":"Tusoai: Agentic optimization for scientific methods","venue":"cs.AI","work_id":"c9918cc1-94a9-4312-adcf-e4c2a48f45c9","year":2025},"citing_paper":{"arxiv_id":"2606.07718","last_updated":"2026-06-05T15:38:18Z","snapshot_observed_at":"2026-07-06T23:47:19.773490Z","submitted_at":"2026-06-05T15:38:18Z","title":"A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T21:56:39.900301Z"},"links":{"cited_paper":"/paper/2509.23986","citing_paper":"/paper/2606.07718"},"observation_digest":"sha256:a476308d9decdfbf96958add423a140a04c805e7bb63c48c5fbaca2b0d015a16","observation_id":"5f263f6b-bff8-464f-9495-18f06cf42cc5","resolution":{"observed_at":"2026-07-02T17:37:14.708758Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.23986/citation-record","integrity":"/paper/2509.23986/integrity","json":"/paper/2509.23986/citation-record.json","paper":"/paper/2509.23986"},"outbound":[{"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":"Semantic scholar","venue":null,"work_id":"a4be2d5e-1056-4b28-8c4b-912a91e55613","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:bf5b0e6c6fce1b4e7ed8d3f8ef5962c389ec1b0d2c99329560a4f218f7c264c7","observation_id":"5eeeefd5-1972-49c4-b240-23c1346220c6","resolution":{"observed_at":"2026-05-21T22:34:24.630572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.14199","last_updated":"2024-11-21T15:07:42Z","snapshot_observed_at":"2026-07-06T19:53:47.605701Z","submitted_at":"2024-11-21T15:07:42Z","title":"OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs","version":1},"cited_work":{"arxiv_id":"2411.14199","doi":"10.48550/arxiv.2411.14199","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.14199","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Weld and Doug Downey and Wen","venue":"arXiv (Cornell University)","work_id":"7b90f8a6-0a1c-48f8-94fc-7f16671e9bae","year":2024},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2411.14199","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:cec87b1c5a7f8808f0a18618e07eb67299a3d8eb9563aba0d461e241626a7a3c","observation_id":"29338dad-9689-4615-9e81-b444d86fc608","resolution":{"observed_at":"2026-05-21T22:34:23.817614Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-25T19:23:21.463275+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T19:23:21.463275+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.06503","last_updated":"2026-05-21T01:47:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-09-08T10:08:36Z","title":"An AI system to help scientists write expert-level empirical software","version":3},"cited_work":{"arxiv_id":"2509.06503","doi":"10.48550/arxiv.2509.06503","metadata_source":"pith","pith_arxiv_id":"2509.06503","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"An AI system to help scientists write expert-level empirical software","venue":"cs.AI","work_id":"9082297a-69f2-49a6-826b-4d0476eebeb7","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2509.06503","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:499695abf79cb25d99aca014275ac794b03f6113d93ce704c78b3ba524a0b325","observation_id":"66bced95-6ae6-4c54-9ade-6c8aafd3c66a","resolution":{"observed_at":"2026-05-21T22:34:23.807513Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41586-025-09221-6","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Bower, E","venue":"Nature","work_id":"c5ef183e-f27f-4da3-af19-c3f31c733aac","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:8b2655f411c68fd26947ab5b0b861ead1570e938afe5dc336eec437b3c40e329","observation_id":"c0795c81-ec9f-49da-b5aa-f0ad7ce8f31f","resolution":{"observed_at":"2026-05-21T22:34:23.494056Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41467-020-15543-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Roumeliotis, et al","venue":"Nature Communications","work_id":"46f0eeb3-3e34-48a6-94dd-75e4dd97e92f","year":2020},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:83d796701ec8cedee723f5a4701e8c065831b65c533323817d43c68bfcde87fb","observation_id":"3596d654-e197-4cd1-8753-7a2aa8ebe8ca","resolution":{"observed_at":"2026-05-21T22:34:23.497132Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2337/dc14-2769","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Chakera, Anna M","venue":"Diabetes Care","work_id":"3c7b8041-2042-4761-abce-cadcb82496e0","year":2015},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:c101f397e29e53fffc39065c6b2ef3ab64f26ac216919d594342bd0ec21be119","observation_id":"1adb897b-5e97-4b9e-aab8-6840958c4868","resolution":{"observed_at":"2026-05-21T22:34:23.490769Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41590-018-0120-4","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Nature Immunology","work_id":"602ee089-33b1-46a6-b1af-b9b536736f78","year":2018},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:417cf5ef8096f0177f08d500e813b24ec4a24b2cd31922141100fa6b9d28648d","observation_id":"f8960f2d-87f4-4f4b-b903-a8654070d102","resolution":{"observed_at":"2026-05-21T22:34:23.487603Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance","venue":null,"work_id":"305f32f4-d637-457b-8edb-b4c330f8306f","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:03aab53ce0f9ac204a41c09c694c4cdde0fcd757688e77a4fa26787bc84b2f15","observation_id":"f009be91-7579-4a70-bf30-1baa6f10e72e","resolution":{"observed_at":"2026-05-21T22:34:24.587669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.06505","last_updated":"2020-03-13T23:10:39Z","snapshot_observed_at":"2026-07-06T09:04:37.917150Z","submitted_at":"2020-03-13T23:10:39Z","title":"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data","version":1},"cited_work":{"arxiv_id":"2003.06505","doi":"10.48550/arxiv.2003.06505","metadata_source":"pith","pith_arxiv_id":"2003.06505","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data","venue":"stat.ML","work_id":"32ca4e6c-bd72-4586-8594-40eb6bcb6582","year":2020},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2003.06505","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:f0bc0d14cd23dcc422716829817f282e77b65cbf732a848548b5dc647d3449aa","observation_id":"32d8b357-4674-4403-b2c9-3bf4dc0ae97b","resolution":{"observed_at":"2026-05-21T22:34:23.810638Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-23T01:54:03.658463+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T01:54:03.658463+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08155","last_updated":"2020-09-18T15:38:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-02-19T13:09:07Z","title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages","version":4},"cited_work":{"arxiv_id":"2002.08155","doi":"10.48550/arxiv.2002.08155","metadata_source":"pith","pith_arxiv_id":"2002.08155","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages","venue":"cs.CL","work_id":"abd850e7-0a69-416d-b504-be33a55a8399","year":2020},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2002.08155","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:4bddaa1e88051b696735149c8aee2decd5aecd5285856dfedb4c488a9fff9ec4","observation_id":"a4bceed7-2c12-4bfc-8d6a-316b0b84330f","resolution":{"observed_at":"2026-05-21T22:34:23.814355Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Efficient and robust automated machine learning","venue":null,"work_id":"4643b706-5169-4ee8-ad05-1dba9f4a66da","year":2015},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:d90d28bd50dff5a3c77fd92ffd9b348d1ec96ffad5433b7b270b493dd5b771e8","observation_id":"03fa1458-05c7-4f79-a97b-0f8fc2ba3396","resolution":{"observed_at":"2026-05-21T22:34:24.607118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1056/nejm199303113281005","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Alan Permutt, Jacques S","venue":"New England Journal of Medicine","work_id":"75e80783-2f31-46a3-8a18-2454be126511","year":1993},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:077ccfcbfbdd42aff05b8b185918e36adaa20a7c1bb055adb6d483944727ddc4","observation_id":"10f045d5-43f4-41d5-886c-c669f3c9970b","resolution":{"observed_at":"2026-05-21T22:34:23.509230Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Empowering biomedical discovery with ai agents","venue":null,"work_id":"1caa23d3-bc4f-48b5-a3a8-819f55204ad0","year":2024},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:1f76d51c60c00f751b831c15a2561a047996f94320b7dc621a0e50249198d381","observation_id":"5eedb0ce-db15-40d1-9e69-7be4d70ab44a","resolution":{"observed_at":"2026-05-21T22:34:24.621170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41588-022-01087-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dey, Joseph Nasser, Kumar A","venue":"Nature Genetics","work_id":"81404b4a-1818-41e3-bddf-2db097c2baa7","year":2022},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:4198fc6c519e17aef31dd1c66e59fc5105a14ac22f9dda00addc12ec7351a275","observation_id":"9e65d72f-3b98-41c0-af95-e07588259148","resolution":{"observed_at":"2026-05-21T22:34:23.503658Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17453","last_updated":"2024-05-28T06:50:38Z","snapshot_observed_at":"2026-07-06T17:36:11.854303Z","submitted_at":"2024-02-27T12:26:07Z","title":"DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning","version":5},"cited_work":{"arxiv_id":"2402.17453","doi":"10.48550/arxiv.2402.17453","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.17453","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ds-agent: Automated data science by empowering large language models with case-based reasoning","venue":"arXiv (Cornell University)","work_id":"9b3a7eb1-abdb-4160-bd11-0b8bd93384f9","year":2024},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2402.17453","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:157b5f0cdcd3b0835a9dcbe65838b46c88654cbbc8c1ee92495163c34cdfecad","observation_id":"44f2056f-ab41-4a1b-b5ce-6602ea9b8569","resolution":{"observed_at":"2026-05-21T22:34:23.772365Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Biomni: A general-purpose biomedical ai agent","venue":null,"work_id":"d1b417be-05bc-488d-b202-20e7ace1f3a2","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:2e308644049c5f3612d41dda8dfe3178b9718ad3fa0af518572e2f53aa1d02fc","observation_id":"57efa7fb-b734-417a-b6a0-2288ae8372e7","resolution":{"observed_at":"2026-05-21T22:34:24.626035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13138","last_updated":"2025-02-18T18:57:21Z","snapshot_observed_at":"2026-08-05T07:03:00.655237Z","submitted_at":"2025-02-18T18:57:21Z","title":"AIDE: AI-Driven Exploration in the Space of Code","version":1},"cited_work":{"arxiv_id":"2502.13138","doi":"10.48550/arxiv.2502.13138","metadata_source":"pith","pith_arxiv_id":"2502.13138","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"AIDE: AI-Driven Exploration in the Space of Code","venue":"cs.AI","work_id":"22aa3d2a-9edd-44c4-b8b8-1442ea805e01","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2502.13138","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:b5a239568be01c9e9520aab899fca8947a55b1fde250064592c05b07aeafa9de","observation_id":"c5f4a6bf-4586-4297-90f2-0c79aa5eb2d0","resolution":{"observed_at":"2026-05-21T22:34:23.775102Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-25T19:23:24.934132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T19:23:24.934132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.02004","last_updated":"2025-07-01T20:52:01Z","snapshot_observed_at":"2026-08-04T21:25:19.124859Z","submitted_at":"2025-07-01T20:52:01Z","title":"STELLA: Self-Evolving LLM Agent for Biomedical Research","version":1},"cited_work":{"arxiv_id":"2507.02004","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.02004","snapshot_observed_at":"2026-07-03T05:27:40.411201Z","title":"arXiv preprint arXiv:2507.02004 , year=","venue":null,"work_id":"6e678603-492c-4b16-814d-a844c1707248","year":2024},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2507.02004","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:34ab6579e3fa6ff7d1abf8ce5ce10beac69c33b757dc89ddd258ab06960887e5","observation_id":"43a2d21d-58ea-476a-98b4-6e2f1be95849","resolution":{"observed_at":"2026-05-21T22:34:23.784285Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"From variant to function in human disease genetics","venue":null,"work_id":"0a5cadc0-878d-4b00-9b54-9d672e82ff42","year":2021},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:3fa08606b629497eb321d9f92f357a9ee7cae4d3cd52e0e4633045224a9d4af9","observation_id":"de97890f-ed0b-4506-a8a8-7f9b36502426","resolution":{"observed_at":"2026-05-21T22:34:24.615932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"H2o automl: Scalable automatic machine learning","venue":null,"work_id":"beaadb18-e9a6-458b-8331-8c1bdf9db874","year":2020},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:17e3b3302a1f32833bc94b94901f3ca60782a75722171f4593444078c11f3a15","observation_id":"46c5e5a0-9aaa-4b80-807e-030b3d502c20","resolution":{"observed_at":"2026-05-21T22:34:24.623735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2025.05.24.25328275","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Sullivan, Jens Hjerling-Leffler, Naomi R","venue":"medRxiv","work_id":"260a3f81-0902-42a9-a569-fe589a8e5c20","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:784380e039ff4769ba85e61ff7c0af52e87f34cb936e685e3ac81d4d000a604a","observation_id":"cc006ca2-2997-4522-9515-2c83347bb477","resolution":{"observed_at":"2026-05-21T22:34:23.506784Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41467-021-27729-z","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Linderman, Jiajun Zhao, Maria Roulis, et al","venue":"Nature Communications","work_id":"a751aca7-7a9d-4935-bc3e-bb5c3c6cf7a7","year":2022},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:a9d82f9f47843904c42e025ffe4d079837285ccd3f826ee7bfc5ffee5559cd30","observation_id":"71bc5a78-e849-4d1f-b27c-8321d72f7ac4","resolution":{"observed_at":"2026-05-21T22:34:23.517601Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.09055","last_updated":"2019-04-23T06:29:32Z","snapshot_observed_at":"2026-08-01T17:05:53.501765Z","submitted_at":"2018-06-24T00:06:13Z","title":"DARTS: Differentiable Architecture Search","version":2},"cited_work":{"arxiv_id":"1806.09055","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.09055","snapshot_observed_at":"2026-07-10T21:27:35.708872Z","title":"DARTS: Differentiable Architecture Search","venue":"cs.LG","work_id":"2f7f6e44-42e7-498e-a68f-a01e092a8903","year":2018},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/1806.09055","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:ec10e6082dbd867527618f9072031f114d2b095f4aaa03a862f39b2cde6167cf","observation_id":"be8277de-cbf2-4973-bf07-00e0eee7b94c","resolution":{"observed_at":"2026-05-21T22:34:23.778271Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41587-025-02694-w","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Nature Biotechnology","work_id":"f6b62cb7-0506-4d9d-95b8-bbc6447f36a4","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:23d77bfb8834f523b28ce48c758a9b1f38ec11b6458d973ca1a1264979b5624b","observation_id":"8218f1b4-6446-47a4-9a8f-eef3751d3103","resolution":{"observed_at":"2026-05-21T22:34:23.500586Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Large language models surpass human experts in predicting neuroscience results","venue":null,"work_id":"6fedf494-4e98-4217-9d7f-4e6d23ba254b","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:56a2a119d0074bd72d590df73be16c5676efa8fd47a038ec0b4fe13d0e9637bd","observation_id":"98b62349-f1d4-4723-91b1-50fe5229c7ef","resolution":{"observed_at":"2026-05-21T22:34:24.618802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller","venue":null,"work_id":"0fa86f61-8987-4e6a-aa06-a57d0a32cf65","year":2024},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:2a41851b62b882a1e5fca2bdae0f0bacf957c92ef7d169fbe355819e8ffd02b1","observation_id":"b8cd0f1b-cb64-4e12-9338-0ae64dfdee8c","resolution":{"observed_at":"2026-05-21T22:34:24.613142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2025.09.01.673319","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Miller, Matthew Greenig, Benjamin Tenmann, and Bo Wang","venue":"bioRxiv (Cold Spring Harbor Laboratory)","work_id":"737510be-c6e0-457b-8867-732dba504d85","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:148c73830facb3d4fac25931841e1979eb00eec9d6e34bdf3079c7db0fd26727","observation_id":"421181b9-dfe7-492e-8485-7a9976db908a","resolution":{"observed_at":"2026-05-21T22:34:23.515095Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/nar/gkae1078","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ajay Nadig, Joseph M Replogle, Angela N Pogson, et al","venue":"Nucleic Acids Research","work_id":"548d576d-9d39-4538-850b-0d3cff401739","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:064009fe00e00e6123b93542c7a2fc7ad061ff9cea016848c2bb2beab3510fb8","observation_id":"bdab9276-ba74-4f58-aab8-067ec66b0ca6","resolution":{"observed_at":"2026-05-21T22:34:23.512248Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15692","last_updated":"2025-08-28T04:12:45Z","snapshot_observed_at":"2026-07-06T21:44:22.901650Z","submitted_at":"2025-05-27T18:11:25Z","title":"MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement","version":3},"cited_work":{"arxiv_id":"2506.15692","doi":"10.48550/arxiv.2506.15692","metadata_source":"pith","pith_arxiv_id":"2506.15692","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mle-star: Machine learning engineering agent via search and targeted refinement","venue":"cs.LG","work_id":"18961cf8-9dc4-402c-8a09-dee6cdb17563","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2506.15692","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:937de3870719d8115ec1f6bb9894855c4fc8bd9797813d149beac6a81532e9cc","observation_id":"3b6f9146-c113-4ca5-b456-545766ad8586","resolution":{"observed_at":"2026-05-21T22:34:23.769493Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Tpot: A tree-based pipeline optimization tool for automating machine learning","venue":null,"work_id":"99d6685f-bc90-4c70-b58e-17f3a40dd948","year":2016},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:4f9899e83d8edf14861005201f3a693fcb6b317d51e77f73402fb2761f87f000","observation_id":"a240e81f-fc7f-4770-8267-04e5a0c28c96","resolution":{"observed_at":"2026-05-21T22:34:24.628365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Introducing chatgpt agent: Bridging research and action","venue":null,"work_id":"e4e4df97-4dc7-481c-8f12-95b3693cba4d","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:cbdfd722a1f2eba511ce5b1b00cd5c2a56881e517e927afcf4368b229dca4284","observation_id":"e9f0be5a-9349-466f-9279-2eb8eda11776","resolution":{"observed_at":"2026-05-21T22:34:24.584945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16998","last_updated":"2025-01-28T14:52:16Z","snapshot_observed_at":"2026-08-04T14:22:12.879849Z","submitted_at":"2025-01-28T14:52:16Z","title":"Large Language Models for Code Generation: The Practitioners Perspective","version":1},"cited_work":{"arxiv_id":"2501.16998","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.16998","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Large language models for code generation: The practitioners perspective","venue":null,"work_id":"954e7e20-6a8a-46ee-bb6f-e50169a30aa6","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2501.16998","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:fe1156463e1a18ad0a1f59dac5cf92408c3e83a6835dca3d2510a4778eb98610","observation_id":"a4118927-bdd8-4479-8bf5-4373e4ecbd38","resolution":{"observed_at":"2026-05-21T22:34:23.791229Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Mathematical discoveries from program search with large language models","venue":null,"work_id":"b68c699b-6f8a-42e6-9fec-8c0000a58b2a","year":2024},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:31de865d9999b51cf2c14d3d2d4402f0c575e6d8591bd510b54ad9d7d0806ecb","observation_id":"6522bb75-e442-4e14-8610-6fa5be46a737","resolution":{"observed_at":"2026-05-21T22:34:24.582862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00262-025-04035-x","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Seo, Sang K","venue":"Cancer Immunology Immunotherapy","work_id":"daead6b4-be27-41c9-bc74-fa85272ced95","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:047c5a6f1bf3128e6b97fa65ed5fb38e81591f7feef5ffed308a58d656f87036","observation_id":"cf66c00d-7588-4ef2-8121-4a237c0fe5f5","resolution":{"observed_at":"2026-05-21T22:34:23.481102Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Boxlm: Unifying structures and semantics of medical concepts for diagnosis prediction in healthcare","venue":null,"work_id":"162fe88d-4fa4-442c-8d9f-a2ebd881bf53","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:f94d956e5c5d025f42bc11f23d8010385b043850b9657dc2981df152bfdc65e6","observation_id":"91a3d5a9-40fe-4c18-a285-7db4896df33b","resolution":{"observed_at":"2026-05-21T22:34:24.590456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2508.02276","doi":"10.48550/arxiv.2508.02276","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Cellforge: Agentic design of virtual cell models","venue":"ArXiv.org","work_id":"cb9ecdad-2855-4b87-92c1-a9d3dcc9a20c","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:b0f2e0ac9a350cc516efcc3eb591fb2a668569315960cfa14a155a1746fe8f53","observation_id":"5f585861-18d0-4624-a768-109689b502b1","resolution":{"observed_at":"2026-05-21T22:34:23.797816Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.16938","last_updated":"2025-07-22T15:05:22Z","snapshot_observed_at":"2026-07-06T21:28:43.610849Z","submitted_at":"2025-05-22T17:27:43Z","title":"InternAgent: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification","version":3},"cited_work":{"arxiv_id":"2505.16938","doi":"10.48550/arxiv.2505.16938","metadata_source":"arxiv_reference","pith_arxiv_id":"2505.16938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"CoRR , volume =","venue":"ArXiv.org","work_id":"edf06200-2610-4531-94a8-2b9a80fd108c","year":2026},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2505.16938","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:d74773d17061d63a6ceadf63944846aed6a977f00343e13edce5c271c41568ca","observation_id":"dab6946c-126b-4301-b8bf-295d040083f8","resolution":{"observed_at":"2026-05-21T22:34:23.804595Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-25T19:23:28.034454+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T19:23:28.034454+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02958","last_updated":"2025-06-06T10:13:12Z","snapshot_observed_at":"2026-07-06T19:27:20.458797Z","submitted_at":"2024-10-03T20:01:09Z","title":"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML","version":2},"cited_work":{"arxiv_id":"2410.02958","doi":"10.48550/arxiv.2410.02958","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02958","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automl-agent: A multi-agent llm framework for full-pipeline automl","venue":"arXiv (Cornell University)","work_id":"8cffffc2-7c6c-4825-a674-7fcb3cc10c5e","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2410.02958","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:a5ab937ea93bda343fe9570e79b6c60c3fe751dfc7fc83c7a74e79c1562fec74","observation_id":"60bc4674-b765-4019-b04a-1f5d1fc0f955","resolution":{"observed_at":"2026-05-21T22:34:23.801120Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"NAS -bench-360: Benchmarking neural architecture search on diverse tasks","venue":null,"work_id":"bc01a175-8e97-4306-bc37-f32e7e8a47e1","year":2022},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:6fd9a6cd7e390183aea9f9c454ba78f757ca1c2c8ec1f9f104f6b6aa78478e8e","observation_id":"5a515e29-178f-4324-a079-dcfb129c70d9","resolution":{"observed_at":"2026-05-21T22:34:24.587892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.14738","doi":"10.48550/arxiv.2505.14738","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"R&d-agent: Automating data-driven ai solution building through llm-powered automated research, development, and evolution","venue":"ArXiv.org","work_id":"e2b911a1-ed00-46c4-bb7c-4a2a6ba115ae","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:453b4a6f8fa0fc2229eeecf87bedcb6771cd4c24e51cda96db9e876ffe70e5d7","observation_id":"2d0a8644-415e-48f1-97aa-a0e5d2dbdf84","resolution":{"observed_at":"2026-05-21T22:34:23.787609Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.03916","last_updated":"2025-04-09T16:27:02Z","snapshot_observed_at":"2026-07-06T20:17:43.203959Z","submitted_at":"2025-01-07T16:31:10Z","title":"Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback","version":3},"cited_work":{"arxiv_id":"2501.03916","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.03916","snapshot_observed_at":"2026-07-04T17:50:00.385641Z","title":"arXiv preprint arXiv:2501.03916 (2025)","venue":null,"work_id":"a2eda710-2bab-45f3-a6dd-4d974c63d91f","year":2025},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"cited_paper":"/paper/2501.03916","citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:8262a75fedf2bec180ff467ff2f287e506f7bc2aa47e310e8b2a13a50403f177","observation_id":"aee9e069-ae01-4c67-b85f-9977dbaef5c7","resolution":{"observed_at":"2026-05-21T22:34:23.794279Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"Polygenic enrichment distinguishes disease associations of individual cells in single-cell rna-seq data","venue":null,"work_id":"ddb46e82-ff68-4b00-8984-7750345f83b5","year":2022},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:daa2f674f1a45ea24b8d44c863d5ad0ce780119b3a1304f5dce25d6dac6f2c23","observation_id":"4da86e16-a958-4ae1-b702-d27b1bb4ebc5","resolution":{"observed_at":"2026-05-21T22:34:24.590902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"An automated framework for efficiently designing deep convolutional neural networks in genomics","venue":null,"work_id":"5ae03024-53e5-4dc6-9032-69cc3575d915","year":2021},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:5b6e74ea7cbfe14cc65265d22cc37defca55207ba7a616fe1e1e2d630863f7fe","observation_id":"50dd02af-d625-4ee4-ab44-fb8b3a5450b6","resolution":{"observed_at":"2026-05-21T22:34:24.595514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-09T00:05:48.311803Z","title":"write newline","venue":null,"work_id":"8e5fda61-e601-4df4-8204-015bee341570","year":null},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:0a020919ba6b451ef71b053215c1b29bc2e4a8ad3ed61e84fff9799a928e6cca","observation_id":"b4052157-f527-4299-9235-bf92a5e76314","resolution":{"observed_at":"2026-05-21T22:34:24.598482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-08T23:55:44.012295Z","title":"@esa (Ref","venue":null,"work_id":"b058608d-98d0-4821-a4ae-403d2b7cd411","year":null},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:1ba44e3fb890742b895f3b99f05bff1f3a2b947731e83d5e417fbd84123f6654","observation_id":"64bdd3ef-b083-439f-8385-7f145bff794c","resolution":{"observed_at":"2026-05-21T22:34:24.601430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-09T00:05:48.337191Z","title":null,"venue":null,"work_id":"ea79bfb8-d434-45e9-8607-416d3839ec5c","year":null},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:39af41c18b5a435341b4b582d8d08a59a37230bd3ed6ac99b35c9aba1d3afc34","observation_id":"9a646be7-758b-4fc3-8c74-0a1388137d77","resolution":{"observed_at":"2026-05-21T22:34:24.604432Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":null,"venue":null,"work_id":"d051d6aa-efca-49eb-a66d-8ea01bf21294","year":null},"citing_paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-21T22:34:06.906427Z"},"links":{"citing_paper":"/paper/2509.23986"},"observation_digest":"sha256:27f3a8928290aae838a2b4c0919f8ef9e6767b6c5e26f87e88d65d0359d60fd8","observation_id":"6d1e10a0-3c06-4e97-b1fc-ef119bdc80a3","resolution":{"observed_at":"2026-05-21T22:34:24.610388Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.23986","last_updated":"2026-05-16T22:09:55Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T22:31:00.843452Z","submitted_at":"2025-09-28T17:30:44Z","title":"TusoAI: Agentic Optimization for Scientific Methods"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":2,"verified_exact":23,"verified_fuzzy":18},"total_outbound_references":47},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 3 inbound Pith citation observations for arXiv:2509.23986."}