{"as_of":"2026-08-06T12:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f9107502141413ee8969774fc34f289f5ca91c7fcabda79b53a606851f2e5d69","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T10:30:58.506286Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"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/2605.20190/citation-record","integrity":"/paper/2605.20190/integrity","json":"/paper/2605.20190/citation-record.json","paper":"/paper/2605.20190"},"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":"Do as I can, not as I say: Grounding language in robotic affor- dances","venue":null,"work_id":"81a96295-c82c-4b6c-9d51-2efb4541411d","year":2022},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:8909e4c3d9fe4761c8a9e5a2efe57f7e51e5163e7a4207ffa00ccec9c6adbfdb","observation_id":"2ca15554-2021-4458-960e-e380ac5a7072","resolution":{"observed_at":"2026-05-21T10:34:08.338418Z","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":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":"Claude Sonnet 4.5: System Card","venue":null,"work_id":"e6c959f8-60bb-4acc-9f4b-e3f0db69d6cd","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:8b7eb05682fa58ba92736cdcc835b20e7c073f15ab4249d5b6630c96c85c2aa4","observation_id":"97e021e9-7935-46b6-98aa-9153cf277af0","resolution":{"observed_at":"2026-05-21T10:34:08.340826Z","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":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":"Dennis, Jr","venue":null,"work_id":"074f9461-69ba-4b6d-b6ab-1f7af2f6adcf","year":2006},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:bc99ade02e315512f144ac3f42ecaff487818a199d9c3d047c0af1b9f48d7e8b","observation_id":"c4338f3e-946d-4706-9fa5-e061092d0c73","resolution":{"observed_at":"2026-05-21T10:34:08.342908Z","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":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":"Intern- s1: A scientific multimodal foundation model","venue":null,"work_id":"282803c0-9508-4067-bcd6-e28ea52be114","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:451a7d14f9def977a5602f6a39e922c9079dd6b918e402246e424cdbce71fd82","observation_id":"28f00372-0b1a-479e-8303-bd01da0e494d","resolution":{"observed_at":"2026-05-21T10:34:08.335673Z","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":"2407.21320","last_updated":"2024-08-07T04:34:11Z","snapshot_observed_at":"2026-07-06T18:54:49.923926Z","submitted_at":"2024-07-31T04:01:08Z","title":"MetaOpenFOAM: an LLM-based multi-agent framework for CFD","version":2},"cited_work":{"arxiv_id":"2407.21320","doi":"10.48550/arxiv.2407.21320","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.21320","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Metaopenfoam: an llm-based multi-agent framework for cfd","venue":"arXiv (Cornell University)","work_id":"4c822567-2aab-4d69-bfdc-eb603096e828","year":2024},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"cited_paper":"/paper/2407.21320","citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:a70b3b82af8a4de7b62d40ff90a5149c14205521559edcb810c201449594d460","observation_id":"480cff6f-c98d-4621-b9a1-79a8cffb913b","resolution":{"observed_at":"2026-05-21T10:34:07.587090Z","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":"2026-05-20T23:23:20.782466+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T23:23:20.782466+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":"Christiano, Jan Leike, Tom B","venue":null,"work_id":"9fee639f-963d-4d32-8c1e-c4686cebe75a","year":2017},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:c71b9e872263e513e600d2812c05127c495537eb8ec4c423fe52285e6f8674fe","observation_id":"53bbbdd8-7c73-41c5-a93c-d137bc87c791","resolution":{"observed_at":"2026-05-21T10:34:08.333331Z","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":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":"Cadquery","venue":null,"work_id":"d807340c-947f-4bfe-b2a7-563d7c7c9893","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:828422792c48e45cfe7dd1ebb7895ea04b21b58e4f27255212ebabe42a4f6c35","observation_id":"dccf2e6c-b40a-4673-9d34-bb9d6f37adb1","resolution":{"observed_at":"2026-05-21T10:34:08.330852Z","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":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":"Fine-tuning a large language model for automating computational fluid dynam- ics simulations.Theoretical and Applied Mechanics Letters, page 100594","venue":null,"work_id":"5d388896-75e8-412d-aff9-b6049eab0414","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:6c4bec5c073e01d9473310eb73eb9a385ad5c1a85c258d3ab18333f767ba2b03","observation_id":"c8ab1b2a-0a5b-4db4-927e-72986267e441","resolution":{"observed_at":"2026-05-21T10:34:08.328613Z","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":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":"Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities.International journal for numer- ical methods in engineering, 79(11):1309–1331","venue":null,"work_id":"fd0de25a-5c22-4515-9363-3e773e4afce4","year":2009},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:6cbe52e8a2c10b5a954a74f59818abe0bcc6fd8acdced8a454327f45e2433fd9","observation_id":"2460cbf4-dbd8-494c-b5b4-840bf25542f0","resolution":{"observed_at":"2026-05-21T10:34:08.320433Z","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":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":"Gemini 3 flash model card","venue":null,"work_id":"24b55f19-f133-49c2-b2e1-2e22b8445419","year":null},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:066b37ba79881d578b2f52ab7590fff86ea2545e7de358c7467f70d06d9c4399","observation_id":"066f5f43-0b76-4cec-8f6c-a538bc47b455","resolution":{"observed_at":"2026-05-21T10:34:08.323188Z","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":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":"ded67a07-7acb-4969-8259-e7825a3f192a","year":null},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:ae8a6b37ce53eac776acbf2be4244955e0cd70b95994273d6f0a53931d5f781a","observation_id":"ceb8e0ff-1c24-4043-ac8c-bdc3889f1354","resolution":{"observed_at":"2026-05-21T10:34:08.326013Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Completely de- randomized self-adaptation in evolution strategies.Evolu- tionary Computation, 9(2):159–195","venue":null,"work_id":"65699921-71ad-4ae8-a6bd-3fd30601c45c","year":2001},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:29ade1d9eea09e672ee48ba62d8114b6dd27792b8940b284befaf94da74171be","observation_id":"070f80aa-6a65-4373-ace7-28dbb812f0f5","resolution":{"observed_at":"2026-05-21T10:34:08.310469Z","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":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":"Difftaichi: Differentiable programming for physical simulation","venue":null,"work_id":"f5f04e46-56f1-4201-8d15-d2b631aec568","year":2020},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:88ef95f73e7a7864651191237e968eb8e0e97d6afa16ad9a9aabfeaa1c00305e","observation_id":"5c4a4b1b-984c-4710-9971-d6b060921e2c","resolution":{"observed_at":"2026-05-21T10:34:08.312736Z","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":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":"Jones, Matthias Schonlau, and William J","venue":null,"work_id":"0c1031f6-5a32-437c-b092-6ee78a689777","year":1998},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:f68f39115381fdddd45585b4d342a5871f5fe788f8f0077f2fb8399b0023849c","observation_id":"2f8dd9c7-f33e-4367-941d-146f45a922c0","resolution":{"observed_at":"2026-05-21T10:34:08.314879Z","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":"2205.00445","last_updated":"2022-05-01T11:01:28Z","snapshot_observed_at":"2026-07-06T13:05:30.057038Z","submitted_at":"2022-05-01T11:01:28Z","title":"MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning","version":1},"cited_work":{"arxiv_id":"2205.00445","doi":"10.48550/arxiv.2205.00445","metadata_source":"pith","pith_arxiv_id":"2205.00445","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning","venue":"cs.CL","work_id":"e393ecfc-aa97-4014-bb7a-90ebd9f00535","year":2022},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"cited_paper":"/paper/2205.00445","citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:6f6d1c760dbfbbebdb816dee8ee5e51e488b4a22d6166fcae4f934a5b2ae2754","observation_id":"bdd5181f-0c8c-47d4-b4a8-8a3fd2cfb8e5","resolution":{"observed_at":"2026-05-21T10:34:07.583485Z","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":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":"Internbootcamp technical report: Boosting llm reasoning with verifiable task scaling","venue":null,"work_id":"11fd4da9-6d78-4856-ac55-aaa3fca34daa","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:3753e84e8ad0530d5b19378604e32588ce6a0eb5473127c0a1422eed4cab155a","observation_id":"6a5746a6-2b12-4afd-920d-bdf772549da1","resolution":{"observed_at":"2026-05-21T10:34:08.305247Z","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":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":"Llm4cad: Multi-modal large language models for 3d computer-aided design generation","venue":null,"work_id":"a1c9f917-5cd0-4d68-9252-7f3426ed130d","year":2024},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:af5225b2708418a8944cec5926f6e29166d59ec5146e546271218a90c201fdf4","observation_id":"a87586cb-203f-4b0e-b811-c8ecdf25f67b","resolution":{"observed_at":"2026-05-21T10:34:08.302739Z","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":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":"Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar","venue":null,"work_id":"7643a642-e6bc-4585-b0bc-4d90827dc449","year":2021},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:94224c710b4bf6299981d903da4355d2a0a55d52c697fe9bb073378ad597ab37","observation_id":"36254669-39de-4309-bcaf-e2362a5259e0","resolution":{"observed_at":"2026-05-21T10:34:08.297563Z","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":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":"Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators.Nature Machine Intelligence, 3:218–229","venue":null,"work_id":"63a7e6cf-14fd-42c3-881a-affe9acc2b17","year":2021},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:229cb65080d62840f9f243dbb84b660c710bc07d38eadd1e90b24e4a80e74631","observation_id":"9f2e7119-796e-4461-b43d-bfc3e8eb4fa7","resolution":{"observed_at":"2026-05-21T10:34:08.300214Z","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":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":"Cad- assistant: tool-augmented vllms as generic cad task solvers","venue":null,"work_id":"d2ddc37b-c805-4293-bc86-de74911fff27","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:a64a527692cdc6e606bbf761b302b13ab0d2449e87612acbf79850c77c24d0f3","observation_id":"3a94cb0a-6bfa-4ddd-8ace-b3e01e206e5f","resolution":{"observed_at":"2026-05-21T10:34:08.307607Z","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":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":"Llama 4 Scout (17B×16E) Instruct: Model Card","venue":null,"work_id":"0db0c4c2-d23d-426f-8005-f778fbff66e9","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:e53c9f3ed9f6a8638491336261a11dd3b6102f38bdc69cb8807b469c971574ed","observation_id":"b72afdd2-667d-450a-bc44-e8eb62b675c6","resolution":{"observed_at":"2026-05-21T10:34:08.317527Z","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":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":"Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agar- wal, Katarina Slama, Alex Ray, et al","venue":null,"work_id":"051d7fe3-975c-4ee0-9b3b-e1392ab76fb5","year":2022},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:d5206daaf925eeee108106b4ab4ac5405cb14548f6c646e6c2aadcf3860dc98a","observation_id":"107da7ec-78ab-4454-a3f3-ef3476d3b410","resolution":{"observed_at":"2026-05-21T10:34:08.284985Z","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":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":"Toolllm: Facilitating large language models to mas- ter 16000+ real-world apis","venue":null,"work_id":"e54389ac-06c9-48c1-a6a9-d6ea891f6e22","year":2024},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:158616c6a302dfe75eaa560b669fd779afd239608b96c587b18c165b50bfbc8f","observation_id":"b95f8279-023f-46fa-846f-682943170cab","resolution":{"observed_at":"2026-05-21T10:34:08.283644Z","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":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":"Karniadakis","venue":null,"work_id":"ced8bf46-5d5a-4a18-840d-28002ccfeb71","year":2019},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:ad07a332c8c60226563988a6352ccd9fef3fffdbc2441179f34f695ecc71d9ba","observation_id":"0164dec8-b49a-4fd8-a681-754d19985cac","resolution":{"observed_at":"2026-05-21T10:34:08.292137Z","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":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":"Freecad, 2001–2017","venue":null,"work_id":"044a2be4-e605-4903-8c29-3703a472e973","year":2001},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:2154d60f90bdb8d170df91c6c3a2742ded251eaa995ebf54a3e5d29472494529","observation_id":"81880080-0588-4cf7-8cf9-d5a05bd8e344","resolution":{"observed_at":"2026-05-21T10:34:08.290990Z","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":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":"Toolformer: Language models can teach themselves to use tools","venue":null,"work_id":"fd8f4d3f-95c8-4865-ab47-e1ecb4c5f1f0","year":2023},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:c886f47fc3d01c8ea43d85e0d7cc3c3a6e5ea4421c6bf353240c083cae2fb46e","observation_id":"974946ce-b310-4fc8-a1c8-2a43940d13f9","resolution":{"observed_at":"2026-05-21T10:34:08.274876Z","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":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":"ea152f19-79b2-43e2-936a-1e3c7f008ccc","year":2020},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:f99cfea578f360d4ff4cd0f05269bb436c6d0d9435a9498abe5737785469d799","observation_id":"24433164-0261-4447-b7cf-f4eb77a81d3b","resolution":{"observed_at":"2026-05-21T10:34:08.276909Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"8951d7a5-9e1b-4951-a48f-4a087d375a50","year":2024},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:d38384ced99f7b435ddecdcdfd44fc39ade5104a1b4b5969b3cfacf50833fd73","observation_id":"0df8e90c-9b02-4b31-8428-53deb83a71fc","resolution":{"observed_at":"2026-05-21T10:34:08.289725Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2409.19256","last_updated":"2024-10-02T04:01:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-28T06:20:03Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","version":2},"cited_work":{"arxiv_id":"2409.19256","doi":"10.1145/3689031.3696075.url:","metadata_source":"pith","pith_arxiv_id":"2409.19256","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","venue":"cs.LG","work_id":"7eb9c9f4-b322-4bba-8011-09ff8d6ad801","year":2024},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"cited_paper":"/paper/2409.19256","citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:16fc26822f37dc855529751928d745b45dedaa4f4bc4d428dc8129d3c89826be","observation_id":"0762058a-4906-402e-955a-db14be521256","resolution":{"observed_at":"2026-05-21T10:34:07.582866Z","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":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":"0fbefb25-d580-4deb-a1fd-82172979d7c9","year":2012},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:c1d03986c09a9e8fe6035e6159a8cf3c5bbf5d919c2cc55df92b04bb3f7aa34a","observation_id":"1815f181-29d1-49db-bf1e-bf3c1af868cf","resolution":{"observed_at":"2026-05-21T10:34:08.274577Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Qwen3 technical report","venue":null,"work_id":"97618b1a-5de4-4162-ae97-37b91cefc1ec","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:3df704247e64be4ef2cd25c733a4c9b8f55186362db6143a6a71e71b9f903f4c","observation_id":"b1778acd-6047-4aaa-83c3-0d98ad469fe2","resolution":{"observed_at":"2026-05-21T10:34:08.286071Z","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":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":"Smith, Daniel Khashabi, and Hannaneh Hajishirzi","venue":null,"work_id":"642ef86a-9c81-4fd0-b552-4be10761653d","year":2023},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:d2651abb5fac571c52afa365992f85f31b3ac8ccfcb3d89db2fd8e9b4053e9ad","observation_id":"6ef16fc4-3e5c-4db5-9637-3bf563f0aaa0","resolution":{"observed_at":"2026-05-21T10:34:08.293562Z","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":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":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":"bccd6fae-c34f-4018-b623-7438776dfa39","year":2022},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:71a16537c1a4e6d8a818284e2da0d0e07418d5d062831a7d88cd573ffc3a594f","observation_id":"ef217be9-edf7-44cf-970d-4da09fffa3a0","resolution":{"observed_at":"2026-05-21T10:34:08.261195Z","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":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":"4c149274-6b18-45fc-8a5b-12e6ebf53d74","year":null},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:574d16b3f62e2800bb380ce68ac981fc2be9f2db4e7637c44972c51b78b3db88","observation_id":"de67afe9-290c-4148-b2e1-ed61ab5f3868","resolution":{"observed_at":"2026-05-21T10:34:08.258492Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"55257108-0ebe-4113-a1d5-e97dba234425","year":2022},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:029d6ec339f6c58324a8e1ebd371b8fc06bb60e24b30b8250d506a8ab6121577","observation_id":"8a04c487-046a-401f-83f3-d4369c757166","resolution":{"observed_at":"2026-05-21T10:34:08.260935Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2505.06507","last_updated":"2025-05-10T04:47:08Z","snapshot_observed_at":"2026-08-04T10:02:58.928426Z","submitted_at":"2025-05-10T04:47:08Z","title":"Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilities","version":1},"cited_work":{"arxiv_id":"2505.06507","doi":"10.48550/arxiv.2505.06507","metadata_source":"pith","pith_arxiv_id":"2505.06507","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Text-to-CadQuery: A New Paradigm for CADgenerationwithscalablelargemodelcapabilities","venue":"cs.AI","work_id":"0ad704e8-21d5-4a90-84f9-f52547c17b80","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"cited_paper":"/paper/2505.06507","citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:f07796d9021d48184d48b7085484f1d783ca33fde4a0e284e8ab1116b2133c8f","observation_id":"f1e3cf45-aeff-4eb5-9d0b-3e3b4cb6c80c","resolution":{"observed_at":"2026-05-21T10:34:07.571099Z","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":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cfdagent: A language-guided, zero-shot multi-agent system for complex flow simulation.Physics of Fluids, 37 (11)","venue":null,"work_id":"f58d56be-7dc2-4fd9-9508-f3c47d5d91b8","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:e3f2b084d3b704275d1a645e47400cd0204032f6a9eeae8d90a341a1f200d312","observation_id":"e3a065bd-6c7a-4e6a-bfa3-0238ea4529a7","resolution":{"observed_at":"2026-05-21T10:34:08.263900Z","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":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":"Qwen3 technical report","venue":null,"work_id":"18369c94-ec1f-45c0-9c81-641d2049c98b","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:1f1bfe5ac12a4a59f7dfab955c20beb8281690880f2b20f5f2cef46e186983a8","observation_id":"cea5e878-b9c7-4f6a-b753-cb7858461130","resolution":{"observed_at":"2026-05-21T10:34:08.245961Z","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":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":"Narasimhan, and Yuan Cao","venue":null,"work_id":"049613ee-efd2-45c2-b4a7-48afa9126b32","year":2023},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:ce1ab7e34fc13e90ee240663c96083f6eb6852fe4f01c9cf71fc6c98bf16d89a","observation_id":"b1907724-59b9-4d7e-8490-cc94243a9012","resolution":{"observed_at":"2026-05-21T10:34:08.265449Z","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":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":"Openecad: An efficient visual language model for editable 3d-cad design","venue":null,"work_id":"166a2e05-f376-43ee-b350-04b0b0f6036c","year":2024},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:24c0cc1a6d1a2c5db2d32f90ecb8e8c66679710d98fceb20025921756fb75e7e","observation_id":"14638207-4c18-4160-adeb-df2cdab166fb","resolution":{"observed_at":"2026-05-21T10:34:08.268158Z","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":null,"cited_work":{"arxiv_id":"2505.04997","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T07:57:45.411746Z","title":"Foam-agent: Towards automated intelligent cfd workflows","venue":null,"work_id":"9aacc6a3-2935-4205-a9f1-58a0286dc1b0","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:d3ff99f263b06496244c50fe83aac325db98cf00833da86916a84df6cd7787f8","observation_id":"e7e0061b-9d02-4881-a042-3baed155e4a4","resolution":{"observed_at":"2026-05-21T10:34:07.579747Z","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":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Marti: A framework for multi-agent llm systems reinforced training and inference","venue":null,"work_id":"e449b984-79ea-4612-a7ac-1a9c7e670819","year":null},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:95be0a0ca77c03733eb76ab066f1e6b13548803ed13dcb856ea7acf358c0b9d6","observation_id":"f8c34ae0-55fc-4938-a987-92dbc9e35a4d","resolution":{"observed_at":"2026-05-21T10:34:08.270427Z","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":null,"cited_work":{"arxiv_id":"2504.08621","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T01:47:32.114873Z","title":"Zhang, Z","venue":null,"work_id":"80867bd7-88ba-4858-b982-8ecba3e71acb","year":2025},"citing_paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-21T10:30:58.506286Z"},"links":{"citing_paper":"/paper/2605.20190"},"observation_digest":"sha256:62e03d9ea0a673ce4ec775756460390ce0f8d09b4d417a16658dccf899a10988","observation_id":"7f7570e9-eac0-4457-94d3-49d2b896f18c","resolution":{"observed_at":"2026-05-21T10:34:07.574755Z","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"}}],"paper":{"arxiv_id":"2605.20190","last_updated":"2026-04-01T14:14:09Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-01T14:14:09Z","title":"Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":6,"verified_exact":5,"verified_fuzzy":31},"total_outbound_references":43},"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 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2605.20190."}