{"as_of":"2026-08-21T03:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6b12eb960aa5515a5155b1bd9a4a87bfde5f74b121060aab68eb516e4e4cb541","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:58:16.185561Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2506.06815/citation-record","integrity":"/paper/2506.06815/integrity","json":"/paper/2506.06815/citation-record.json","paper":"/paper/2506.06815"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.727044Z","title":"Compound classification using the scikit-learn library.Tutorials in Chemoinformatics, pages 223–239, 2017","venue":null,"work_id":"47dc805e-708e-4a79-88cd-e5df49330154","year":2017},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.039516Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:068fdab87157e0576d06085da858957599a9bb4a8a0e208d49adefc917488469","observation_id":"a618ab1d-74b6-4f6a-aa46-1b8a9492abd3","resolution":{"observed_at":"2026-08-07T05:58:16.731512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T05:58:16.712308Z","title":"A consensus-based global optimization method for high dimensional machine learning problems.ESAIM: Control, Optimisation and Calculus of Variations, 27:S5, 2021","venue":null,"work_id":"570b3563-3499-4de9-8b60-34e4d4cdc807","year":2021},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.044519Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:2c196c566435f12303d2cefdcf544c475995c87b0fa3767c2b3a920ad3173fea","observation_id":"e4126470-b195-4e9e-a7d7-0c7c2018662a","resolution":{"observed_at":"2026-08-07T05:58:16.716671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05446","last_updated":"2020-06-16T00:58:12Z","snapshot_observed_at":"2026-08-18T17:26:08.268070Z","submitted_at":"2019-10-11T23:51:09Z","title":"On Empirical Comparisons of Optimizers for Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05446","snapshot_observed_at":"2026-08-07T05:58:16.049205Z","title":"On empirical comparisons of optimizers for deep learning.arXiv preprint arXiv:1910.05446, 2019","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.049205Z"},"links":{"cited_paper":"/paper/1910.05446","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:e4569cb3146e296da19cd5fb020ae2b3927e05af92f851db6004e6ade3bfbc3c","observation_id":"749f7995-b674-4411-8b2c-ef67e4a55150","resolution":{"observed_at":"2026-08-07T05:58:16.049205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00402","last_updated":"2022-08-17T07:59:00Z","snapshot_observed_at":"2026-08-16T17:45:10.083505Z","submitted_at":"2021-10-31T03:54:26Z","title":"Global Optimization via Schr{\\\"o}dinger-F{\\\"o}llmer Diffusion","version":6},"cited_work":{"arxiv_id":"2111.00402","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.00402","snapshot_observed_at":"2026-08-07T05:58:16.412971Z","title":"Global Optimization via Schr{\\\"o}dinger-F{\\\"o}llmer Diffusion","venue":"math.OC","work_id":"6213b16d-dcb4-49e5-bc80-534f95b5cd21","year":2021},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.054183Z"},"links":{"cited_paper":"/paper/2111.00402","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:5552f2ec8d14e9cd86f9fdfdbad441a437ad7a9d10deb3b0ebbb2d341278fb97","observation_id":"058fbd0c-0cc2-4de5-895d-0a7d88e0471b","resolution":{"observed_at":"2026-08-07T05:58:16.420040Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.06766","last_updated":"2018-11-20T21:57:15Z","snapshot_observed_at":"2026-08-14T18:50:40.504829Z","submitted_at":"2018-07-18T03:58:02Z","title":"Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.06766","snapshot_observed_at":"2026-08-07T05:58:16.058878Z","title":"Convergence guarantees for rmsprop and adam in non-convex optimization and an empirical comparison to nesterov acceleration.arXiv preprint arXiv:1807.06766, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.058878Z"},"links":{"cited_paper":"/paper/1807.06766","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:a1c9e6b8ab0bcb253db130adb8594c3c67d54933dd71d334b837835fb57de2d7","observation_id":"f3990ab4-01a3-49e5-aaaa-caf350b92913","resolution":{"observed_at":"2026-08-07T05:58:16.058878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.696250Z","title":"UCI machine learning repository, 2017","venue":null,"work_id":"7de619a1-ccfd-430e-9ccc-766bd9ab0fd5","year":2017},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.063600Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:582b1c8ec996e4ea9610f26391930d099c96551eca494246076476cea93475da","observation_id":"c173e111-0d29-4fc9-beed-6bb978e29e69","resolution":{"observed_at":"2026-08-07T05:58:16.701498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.069051Z","title":"Adaptive subgradient methods for online learning and stochastic optimization.Journal of machine learning research, 12(7), 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.069051Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:e22ea590f72b8821d5be71173e9090f1f708e46c2bd505cd842f2468ce713bfc","observation_id":"1e5dbbbb-e828-49c8-a1bd-6a13f0e3652b","resolution":{"observed_at":"2026-08-07T05:58:16.069051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.666434Z","title":"An entropy approach to the time reversal of diffusion processes","venue":null,"work_id":"2c06a01f-5dd0-4936-9db5-1941302834cf","year":1984},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.073099Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:b887a3bd3310641572793366d0b46419d734ca1de49d51b9aae83850b1d2dd98","observation_id":"23380587-d6ac-4373-bc13-41f85cdd9c76","resolution":{"observed_at":"2026-08-07T05:58:16.670758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10880","last_updated":"2021-09-04T03:38:49Z","snapshot_observed_at":"2026-08-16T18:15:44.892978Z","submitted_at":"2021-06-21T06:39:49Z","title":"Schr{\\\"o}dinger-F{\\\"o}llmer Sampler: Sampling without Ergodicity","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10880","snapshot_observed_at":"2026-08-07T05:58:16.078461Z","title":"Schr ¨odinger-f¨ollmer sampler: sampling without ergodicity.arXiv preprint arXiv:2106.10880, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.078461Z"},"links":{"cited_paper":"/paper/2106.10880","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:846a3602305cbdb46ad78b5ed1f3c5189c7ae85b8ef07e6b1f8fd3c4234b3e6d","observation_id":"b13557fc-4684-4fba-9d74-a97fa9b19af0","resolution":{"observed_at":"2026-08-07T05:58:16.078461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.07628","last_updated":"2017-12-20T18:34:08Z","snapshot_observed_at":"2026-08-16T07:35:00.867452Z","submitted_at":"2017-12-20T18:34:08Z","title":"Improving Generalization Performance by Switching from Adam to SGD","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.07628","snapshot_observed_at":"2026-08-07T05:58:16.083181Z","title":"Improving generalization performance by switching from adam to sgd.arXiv preprint arXiv:1712.07628, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.083181Z"},"links":{"cited_paper":"/paper/1712.07628","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:7a4f049c3c12f49c1da6023fa09f200b845214933a1b23e88faee20a90dc0067","observation_id":"a93e3d08-13c8-4146-b0f1-4991d84ef036","resolution":{"observed_at":"2026-08-07T05:58:16.083181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-07T05:58:16.087664Z","title":"Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.087664Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:639dc7e06ee2201025657e6fcebbddb2029adfeb33a218d09fee54cb21038c4b","observation_id":"7601cd32-2502-4cf8-b79f-a7a5c6006401","resolution":{"observed_at":"2026-08-07T05:58:16.087664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.094260Z","title":"Kirkpatrick, C","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.094260Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:ca37a92f80eec693430fdce2406dcb943040c4e7c5fb8751e5eac076c52a56dd","observation_id":"82fdb5ed-f5a4-4ed6-bb9e-1761c0e87777","resolution":{"observed_at":"2026-08-07T05:58:16.094260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.100379Z","title":"The mnist database of handwritten digits.http://yann","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.100379Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:dfe3036ebc671ba57a104607cd9bd9719dd4fca80576e0caf58c18bca9afb789","observation_id":"d70d8832-2728-4fed-abd6-9bba53a78151","resolution":{"observed_at":"2026-08-07T05:58:16.100379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.640611Z","title":"On the convergence of stochastic gradient descent with adaptive stepsizes","venue":null,"work_id":"baec88d8-6e3e-49cf-a162-f59d3bc27616","year":2019},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.105541Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:dd35af18240884087f0fae7b6f5aa2c984110604acddf646b5337e49229e48b9","observation_id":"b0951e77-a15c-4bc7-8644-c78f86645bb2","resolution":{"observed_at":"2026-08-07T05:58:16.645051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.111517Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.111517Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:15095945c1c132987c78c4177744f76d3144c52dad41b611a0bcb58046a4ae98","observation_id":"3bbe9e90-d849-4644-996c-1506db90e7be","resolution":{"observed_at":"2026-08-07T05:58:16.111517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06807","last_updated":"2015-11-21T01:11:29Z","snapshot_observed_at":"2026-08-20T07:39:13.176516Z","submitted_at":"2015-11-21T01:11:29Z","title":"Adding Gradient Noise Improves Learning for Very Deep Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06807","snapshot_observed_at":"2026-08-07T05:58:16.116602Z","title":"Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.116602Z"},"links":{"cited_paper":"/paper/1511.06807","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:e6622a5be7dfdbe246893506a00cee669a4e7a0555bbee3e8a1a2fa56f338b12","observation_id":"b03f8dc1-01f2-4372-b724-064cc0db3672","resolution":{"observed_at":"2026-08-07T05:58:16.116602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.121743Z","title":"A method of solving a convex programming problem with con- vergence rate o\\bigl(kˆ2\\bigr)","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.121743Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:456d2995acb70636a3918d3acb2aee4d8c840a5f3a68bd0afcac559e4f9a19fe","observation_id":"d447581b-dc7f-4ff2-953e-3881ad3736e2","resolution":{"observed_at":"2026-08-07T05:58:16.121743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.126905Z","title":"On the expressive power of deep neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.126905Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:3cdc3f11f9cb4d6fa900538e747cf31a0b3aa4971603e7e871188131fd49e299","observation_id":"10396d51-d429-435b-833b-301a13b988cd","resolution":{"observed_at":"2026-08-07T05:58:16.126905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-08-15T12:50:58.405488Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-07T05:58:16.131172Z","title":"Hierarchical text-conditional image generation with clip latents.arXiv preprint arXiv:2204.06125, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.131172Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:93bed95782f18a98db0b5e1f87064b6a5a3e6e1d7a539470e98c7b824e6e2f50","observation_id":"86d19016-c968-403e-a6c7-260e89011e89","resolution":{"observed_at":"2026-08-07T05:58:16.131172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.587722Z","title":"Rapin and O","venue":null,"work_id":"3f69b7f5-5093-490d-8794-9efc9d67d5b5","year":2018},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.136073Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:72a230efec5cf8703596051dedb9e6c27597eca0edfeac6f42cd37ecc7b07400","observation_id":"8f93e91e-24d6-46d0-ae69-37bf85764364","resolution":{"observed_at":"2026-08-07T05:58:16.596113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T05:58:16.567404Z","title":"To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models","venue":null,"work_id":"29fca6e2-1982-4bcd-9569-562acc7a02dc","year":null},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.140591Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:89c14131a4001153a4337d6da87fde55a2b7cba51dfb52bad79bb029d8d54e3e","observation_id":"6bc0304f-9fd3-4c39-9113-1bacb2ec4c2d","resolution":{"observed_at":"2026-08-07T05:58:16.573312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T05:58:16.550598Z","title":"A stochastic approximation method.The annals of math- ematical statistics, pages 400–407, 1951","venue":null,"work_id":"b034bdba-3602-49ed-b82c-18cf4c73cb63","year":1951},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.144927Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:b70003f76bd5f7c025061757e9f36d2c2b088d9681aaa1004ca6490ced5315b7","observation_id":"8b542cb1-ea05-4678-a423-52e148ad34e6","resolution":{"observed_at":"2026-08-07T05:58:16.555049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.149141Z","title":"Rubinstein","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.149141Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:12febee3511e1c302b9aa22d28826cfe0576ad26fa3b132069e2df725f4d40dd","observation_id":"48705083-0c92-46fc-b12b-4116a7578265","resolution":{"observed_at":"2026-08-07T05:58:16.149141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.531422Z","title":"A generalized path integral control approach to reinforcement learning.J","venue":null,"work_id":"5bdadb27-9f9a-4bc2-ada5-24843fe3d9b9","year":2010},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.153302Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:c2cf83e3c92d09109805f551af9eb48238e643cd29c3a6a956990f4cf0f8439f","observation_id":"28bc1a7d-a4eb-4e83-ba50-84a4c4306a03","resolution":{"observed_at":"2026-08-07T05:58:16.536929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T05:58:16.511669Z","title":"An incremental gradient (-projection) method with momentum term and adaptive stepsize rule.SIAM Journal on Optimization, 8(2):506–531, 1998","venue":null,"work_id":"82603a1d-5216-4449-adde-cfb6926fd606","year":1998},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.157212Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:a9210f637aa8c0f329f620cc37ed9cc66333e66f16fc1a2655017be1b8787685","observation_id":"dd0d1cf9-d6b5-4cb4-8015-cd079ac1946b","resolution":{"observed_at":"2026-08-07T05:58:16.516790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T05:58:16.491494Z","title":"Theoretical guarantees for sampling and inference in generative models with latent diffusions","venue":null,"work_id":"fab52a47-8e7c-453a-b973-3054598e8b23","year":null},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.161237Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:929e3838a830ed11076b52d16a9a49c06ae039fbaf8206226ee921c5447de195","observation_id":"cda913e3-6f20-4125-86af-b327f6cab971","resolution":{"observed_at":"2026-08-07T05:58:16.496861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.10510","last_updated":"2022-10-25T19:40:38Z","snapshot_observed_at":"2026-08-19T23:05:35.507330Z","submitted_at":"2021-11-20T03:51:18Z","title":"Bayesian Learning via Neural Schr\\\"odinger-F\\\"ollmer Flows","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.10510","snapshot_observed_at":"2026-08-07T05:58:16.165486Z","title":"Bayesian learning via neural schr ¨odinger-f¨ollmer flows.arXiv preprint arXiv:2111.10510, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.165486Z"},"links":{"cited_paper":"/paper/2111.10510","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:10212943ddb0453941bcbf7821f7dbc6b541b17fc0ac72965b933ee6d0a9973f","observation_id":"0d689ce5-5c43-4a33-ab78-edf3872d92f4","resolution":{"observed_at":"2026-08-07T05:58:16.165486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13834","last_updated":"2023-08-16T21:40:47Z","snapshot_observed_at":"2026-08-16T15:52:25.183021Z","submitted_at":"2023-02-27T14:37:16Z","title":"Denoising Diffusion Samplers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13834","snapshot_observed_at":"2026-08-07T05:58:16.170288Z","title":"Denoising diffusion samplers.arXiv preprint arXiv:2302.13834, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.170288Z"},"links":{"cited_paper":"/paper/2302.13834","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:4fd7e311cac6c09dec0c958731b81e9513531d7d2cfd33fed9732eb6950afafb","observation_id":"7deb5e6f-c3e6-4862-aa62-b2b58dd276e7","resolution":{"observed_at":"2026-08-07T05:58:16.170288Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.470647Z","title":"Bayesian learning via stochastic gradient langevin dy- namics","venue":null,"work_id":"7568f065-30e6-4e3d-823f-6a4c2bdbb29a","year":2011},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.174263Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:d76edb0ed05d9df17607ffa9391527b9923d47fc9f80eee0399c9231262b5748","observation_id":"29871469-cd3f-4650-b5c2-3c94f726d87f","resolution":{"observed_at":"2026-08-07T05:58:16.476355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-08-17T20:34:32.061171Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-08-07T05:58:16.180145Z","title":"Path integral sampler: a stochastic control approach for sampling.arXiv preprint arXiv:2111.15141, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.180145Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:aa7b6a94a3db25b1d652ffd8b83be421d47ec5a0c49d554e169696ccb3af11d0","observation_id":"dc2446fb-caaa-495c-ab9c-1db78d88089f","resolution":{"observed_at":"2026-08-07T05:58:16.180145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:58:16.452179Z","title":"10 PATHINTEGRALOPTIMISER A.3","venue":null,"work_id":"6cc8c4ce-d082-4859-965b-d38f5ee42404","year":2020},"citing_paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:58:16.185561Z"},"links":{"citing_paper":"/paper/2506.06815"},"observation_digest":"sha256:203f78e4755bdcace7609b1afc379253d5b003e1e75f49d899566d789390a94a","observation_id":"7afd49c4-525f-4f6c-b739-2837a233e0c2","resolution":{"observed_at":"2026-08-07T05:58:16.458186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.06815","last_updated":"2025-06-07T14:46:18Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T18:45:08.714033Z","submitted_at":"2025-06-07T14:46:18Z","title":"Path Integral Optimiser: Global Optimisation via Neural Schr\\\"odinger-F\\\"ollmer Diffusion"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":1,"verified_fuzzy":13},"total_outbound_references":31},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.06815."}