{"as_of":"2026-08-20T21:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e2d95579fe3a843cf688412e984fe92fba881d45ba0fb65c55405798b23eb9ef","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:56:00.593707Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T19:43:18.349015Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-10T22:35:49.573675Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"cited_work":{"arxiv_id":"2509.03594","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.03594","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c4f44ccc-647d-4ab3-862a-fd7424ec96f0","year":2025},"citing_paper":{"arxiv_id":"2604.05627","last_updated":"2026-04-07T09:28:55Z","snapshot_observed_at":"2026-08-12T14:05:56.095942Z","submitted_at":"2026-04-07T09:28:55Z","title":"Loss-aware state space geometry for quantum variational algorithms","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-10T19:43:18.349015Z"},"links":{"cited_paper":"/paper/2509.03594","citing_paper":"/paper/2604.05627"},"observation_digest":"sha256:e7ab219b18e04baee2266c9e22d1d414d2651122522ef1c6a7d893c3e9d5a67a","observation_id":"6908a2c0-63f3-4e95-9a0b-5df2d266a48b","resolution":{"observed_at":"2026-05-10T22:35:49.576720Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2509.03594/citation-record","integrity":"/paper/2509.03594/integrity","json":"/paper/2509.03594/citation-record.json","paper":"/paper/2509.03594"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:56:04.334671Z","title":"Visualizing the loss landscape of neural nets,","venue":null,"work_id":"3c618c82-ef1a-42c0-9a75-e53332ed3c65","year":2018},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.269437Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:70aceca7c7b4a197b47aaa8f7e01da6b7d89dcf2af7631c33831a7ee5bb2c261","observation_id":"f061259c-ee3b-47de-9fff-f6f35cc6f1f2","resolution":{"observed_at":"2026-08-05T10:56:04.440314Z","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":"2026-08-05T10:56:04.195834Z","title":"On the difficulty of training recurrent neural networks,","venue":null,"work_id":"d1e3262e-8d89-4b41-8f75-10de403d57c3","year":2013},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.330870Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:f6da245b9a21f983e7e403b396e7f8e5c2bd45da20883b02192ef596b7eafd7f","observation_id":"1777220b-2060-4ef4-b773-6d61d139956c","resolution":{"observed_at":"2026-08-05T10:56:04.256017Z","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":"1706.02677","last_updated":"2018-04-30T21:53:41Z","snapshot_observed_at":"2026-08-09T05:23:26.365677Z","submitted_at":"2017-06-08T16:51:53Z","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02677","snapshot_observed_at":"2026-08-05T10:55:58.388021Z","title":"Accurate, large minibatch sgd: Training imagenet in 1 hour,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.388021Z"},"links":{"cited_paper":"/paper/1706.02677","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:b8097b00ff5f142709efabacf18ccbe99456a8e647ce1f331aa07991f09cd777","observation_id":"52a71689-9fdc-4a56-90af-6e901bde2fc7","resolution":{"observed_at":"2026-08-05T10:55:58.388021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1608.03983","last_updated":"2017-05-03T16:28:09Z","snapshot_observed_at":"2026-07-06T05:06:55.589962Z","submitted_at":"2016-08-13T13:46:05Z","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.03983","snapshot_observed_at":"2026-08-05T10:55:58.476462Z","title":"Sgdr: Stochastic gradient descent with warm restarts,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.476462Z"},"links":{"cited_paper":"/paper/1608.03983","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:b6a091f4ce8d7b3ce04b51016caefe8bc180d16e92b815016e6fd4a760d70226","observation_id":"068c7a07-49dd-4e02-ad8a-14ee753feb3e","resolution":{"observed_at":"2026-08-05T10:55:58.476462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-05T10:55:58.520760Z","title":"Decoupled weight decay regularization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.520760Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:5645c0c64714c0e4a61b1a543e5edbb349a71d2d9121edff77c0e7069d78f7f6","observation_id":"817b0f69-58ad-431a-8fc3-39c738cd15d8","resolution":{"observed_at":"2026-08-05T10:55:58.520760Z","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":"2026-08-05T10:56:04.002987Z","title":"Muon: An optimizer for hidden layers in neural networks,","venue":null,"work_id":"d9e9bc79-d4d6-4845-a8d4-b9750b6805a1","year":2024},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.577916Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:e7aaceb8bee08b031f09108922ee8c55ee7c57b0860c9c33fa289f898030d218","observation_id":"e235e47e-98b0-4eb2-b56a-7da0eb299cb5","resolution":{"observed_at":"2026-08-05T10:56:04.105022Z","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":"2502.16982","last_updated":"2025-02-24T09:12:29Z","snapshot_observed_at":"2026-08-16T22:48:15.725816Z","submitted_at":"2025-02-24T09:12:29Z","title":"Muon is Scalable for LLM Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.16982","snapshot_observed_at":"2026-08-05T10:55:58.642054Z","title":"Muon is scalable for llm training,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.642054Z"},"links":{"cited_paper":"/paper/2502.16982","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:eec2c5a471e94cdb7b16d64b3d98ac1e79841db851917e2d43bd585c2bf3e8f2","observation_id":"97c8d094-e8ae-4dc9-9bef-5743b34b6c15","resolution":{"observed_at":"2026-08-05T10:55:58.642054Z","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":"2026-08-05T10:56:03.810073Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":"2fbbd4fb-f83b-4217-8c17-854b9baf68e2","year":1998},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.705877Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:dee3583e8ec7d13175f27d0acf1e6103b23e8a24bf7f80d409096bf4ef460cca","observation_id":"aa6921de-99a6-4d0f-8156-9fff6e17b0e0","resolution":{"observed_at":"2026-08-05T10:56:03.919227Z","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":"2026-08-05T10:56:03.656674Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"431e2bd2-4823-44a4-ac56-d271f1fa7a37","year":2016},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.794263Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:84e88b9a3276aba37d1d0d20e9cd377171dcb5bb9e5bac973ad9c9e9cf111d01","observation_id":"e1d9f955-e462-4d10-8238-388b08c0aa4d","resolution":{"observed_at":"2026-08-05T10:56:03.719745Z","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":"2026-08-05T10:55:58.861762Z","title":"Learning multiple layers of features from tiny images.(2009),","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.861762Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:175cd60f91e7370a4f694c11d1ace9ba1005a8f98ac3bd487c130e263b5c8da9","observation_id":"3d88dc33-5a7f-45b4-9c56-dc21137bee6e","resolution":{"observed_at":"2026-08-05T10:55:58.861762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-08-19T04:42:40.974785Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-05T10:55:58.932457Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.932457Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:0fc0fae0ab49d5a9eb825a49dddcac97eff5029ad07143092c49e6c6e10cd4c5","observation_id":"d2cfdfa2-5326-4cc9-82f1-43e76a9bbd79","resolution":{"observed_at":"2026-08-05T10:55:58.932457Z","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":"2026-08-05T10:55:58.996851Z","title":"Optimization by simulated annealing,","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:58.996851Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:79a3acdbbdeb30f8417428bd1977fc016c62a0921d4c2f2d85bfbb4b3eb52cb7","observation_id":"7ee3ccc1-0ab4-4026-b706-85256ba91849","resolution":{"observed_at":"2026-08-05T10:55:58.996851Z","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":"2026-08-05T10:56:03.498865Z","title":"Stochastic gradient hamiltonian monte carlo,","venue":null,"work_id":"cfaf2b3e-ba29-43fb-b52a-5e48a52c9822","year":2014},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.076632Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:2817c1cf76377e6ba0da7b13477396af1d3ca7706ef0f6d8ad7fb97ef93b4baf","observation_id":"28b39020-45a7-4cba-b807-5b5959616f9d","resolution":{"observed_at":"2026-08-05T10:56:03.590626Z","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":"2201.11137","last_updated":"2022-06-14T21:55:42Z","snapshot_observed_at":"2026-08-16T17:25:43.962912Z","submitted_at":"2022-01-26T19:00:05Z","title":"Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11137","snapshot_observed_at":"2026-08-05T10:55:59.130274Z","title":"Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.130274Z"},"links":{"cited_paper":"/paper/2201.11137","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:2afe8176fbd1a8914d2d8ff5eec82adeee41c66c175836c5459cf0da8f732847","observation_id":"f4d23ffa-d4f6-41be-935e-581b10c2a09e","resolution":{"observed_at":"2026-08-05T10:55:59.130274Z","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":"2026-08-05T10:56:03.231452Z","title":"Bayesian learning via stochastic gradient langevin dynamics,","venue":null,"work_id":"b5a234f6-403c-435b-ae5a-2b50f18bf862","year":2011},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.190702Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:1fb06d4d9887ef546c5ea7c6491230e45f0d2143ec7d1fee259ff26268e28058","observation_id":"b5083e43-3444-4eb0-8697-338f763d5c99","resolution":{"observed_at":"2026-08-05T10:56:03.362275Z","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":"2026-08-05T10:56:03.004516Z","title":"Absil, R","venue":null,"work_id":"f528567e-4545-4b0a-991b-07dcfd2b812e","year":2009},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.278695Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:2f7796bba415ddd036d39bc02926b701467e6b70939cdb90be08a87a0df462a6","observation_id":"15434504-4dbf-4e6f-9878-508aee379c22","resolution":{"observed_at":"2026-08-05T10:56:03.109355Z","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":"2026-08-05T10:56:02.865775Z","title":"A survey of geometric optimization for deep learning: from euclidean space to riemannian manifold,","venue":null,"work_id":"a19888a1-1434-4782-9202-5b2b54cd160d","year":2025},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.348000Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:807e821cb39683df22746e29944afa3d5da127755aa9ef2a8cee28211f81d6dd","observation_id":"be0c59bb-dac4-4611-bd5b-1ed79199392f","resolution":{"observed_at":"2026-08-05T10:56:02.935210Z","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":"2026-08-05T10:56:02.645184Z","title":"The unreasonable effectiveness of recurrent neural networks","venue":null,"work_id":"c7e175b0-e058-42dd-9c05-17aa8bd613c5","year":2015},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.422544Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:4b6124169b4873cb0326757cd9fa4194381fbca1920d6d4f9131034fad58dfbb","observation_id":"c7574e02-5237-488f-bcce-b7480a14f108","resolution":{"observed_at":"2026-08-05T10:56:02.734837Z","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":"2026-08-05T10:56:02.495578Z","title":"Induced metric repository","venue":null,"work_id":"5e53e0c7-c875-4e41-8806-3b8573c5c28b","year":null},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.507787Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:1cf7e1d34596a0b05f086f2d8f074af90b28d9a7a8a38c8a5fad1c200baf5e4d","observation_id":"721d0aac-b2f6-4baa-9ed1-3c7670134ad0","resolution":{"observed_at":"2026-08-05T10:56:02.581699Z","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":"2026-08-05T10:56:02.321520Z","title":"JAX: composable transformations of Python+NumPy programs,","venue":null,"work_id":"f45c8038-fad3-4f63-aeca-05e2c97e6555","year":2018},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.569009Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:96044137b572131b132c97ea76855b29a0df3ee9aebb68beb2948813a146cf9c","observation_id":"eadbb9f6-a804-4743-8fcd-8a55f2f4bd63","resolution":{"observed_at":"2026-08-05T10:56:02.412839Z","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":"2026-08-05T10:56:02.144347Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":"8fa118d9-3d64-4af9-8c28-189bc37e973c","year":2019},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.622404Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:7c0c44591c30b243400de348256b6fb61df0baff4e3cd4c449767c59c4842d13","observation_id":"04359872-bead-41a2-a75f-8f3ed9ff5620","resolution":{"observed_at":"2026-08-05T10:56:02.222963Z","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":"2026-08-05T10:56:01.859578Z","title":"Natural gradient works efficiently in learning,","venue":null,"work_id":"badf22b6-c4a4-4fa8-ad72-2d2c4bc17594","year":1998},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.687759Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:2fa97cf4581fdfbbd6e2072bb3a6a4743a866ade2335740671559338db993477","observation_id":"a7e22d77-8b98-4b1c-9f13-5e488890f826","resolution":{"observed_at":"2026-08-05T10:56:02.027742Z","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":"2303.05473","last_updated":"2023-03-06T04:03:56Z","snapshot_observed_at":"2026-08-16T15:50:36.126316Z","submitted_at":"2023-03-06T04:03:56Z","title":"Natural Gradient Methods: Perspectives, Efficient-Scalable Approximations, and Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.05473","snapshot_observed_at":"2026-08-05T10:55:59.753442Z","title":"Natural gradient methods: Perspectives, efficient-scalable approximations, and analysis,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.753442Z"},"links":{"cited_paper":"/paper/2303.05473","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:56427cf72d5b4be960ec43341fe6a65f7a80e661fa25ac1adc5ae8e67264158c","observation_id":"4a7cb814-b52a-43d5-b09e-7d447b75148d","resolution":{"observed_at":"2026-08-05T10:55:59.753442Z","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":"2026-08-05T10:56:01.691227Z","title":"Neural networks for machine learning","venue":null,"work_id":"45e78243-ce88-4287-be65-eef179397e93","year":2012},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.839756Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:726a671ab432387fa2f051b308eb06ecaf8ead42a47755ce0451b7179a6f6f27","observation_id":"c053faa7-3ac6-4cf3-8273-49bb553124ab","resolution":{"observed_at":"2026-08-05T10:56:01.765014Z","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":"2026-08-05T10:56:01.538701Z","title":"Adjustment of an inverse matrix corresponding to a change in one element of a given matrix,","venue":null,"work_id":"434d16f4-e31e-4379-ab21-bbae9c5d3b20","year":1949},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.915285Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:295740ae9c310d51fe8eae516163820329241a02e0be0af7fcb9cbfbbb7d794e","observation_id":"a6e6c778-4550-4027-97d4-513d5ce435cc","resolution":{"observed_at":"2026-08-05T10:56:01.609982Z","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":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-05T10:55:59.998979Z","title":"Scaling laws for neural language models,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:59.998979Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:3afb076fe488e8857778e9722ff0a59f41ddc60cf52475a8610c45755276278a","observation_id":"cdc46e4a-70d2-4ce8-967a-b71fecb59fd6","resolution":{"observed_at":"2026-08-05T10:55:59.998979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00409","last_updated":"2017-12-01T17:13:14Z","snapshot_observed_at":"2026-08-14T02:46:56.838057Z","submitted_at":"2017-12-01T17:13:14Z","title":"Deep Learning Scaling is Predictable, Empirically","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00409","snapshot_observed_at":"2026-08-05T10:56:00.077527Z","title":"Deep learning scaling is predictable, empirically,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T10:56:00.077527Z"},"links":{"cited_paper":"/paper/1712.00409","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:3b9b62c442624123cc463f31ee559bd1ca1f513df04d3a2362ada6875c1e40ab","observation_id":"7fe162c2-7476-4f3e-9672-1feb2ddda2e2","resolution":{"observed_at":"2026-08-05T10:56:00.077527Z","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":"2026-08-05T10:56:01.311211Z","title":"Some methods of speeding up the convergence of iteration methods,","venue":null,"work_id":"514ddf78-1f47-44c3-99f5-025ea0c26d79","year":1964},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T10:56:00.216609Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:9d30399201043cbe0065dba34b167b398b6c2bcb20c5c575e4bc568b0b86f9d0","observation_id":"f7557292-6285-4434-aa3e-936fbbd5cf75","resolution":{"observed_at":"2026-08-05T10:56:01.387271Z","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":"2026-08-05T10:56:01.151272Z","title":"A literature survey of benchmark functions for global optimisation problems,","venue":null,"work_id":"568c2b06-7754-44e7-8c17-e78bcf0142d7","year":2013},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T10:56:00.329362Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:2bb8427517f501c587a3546ab1a57a6cab605d6a8f54d203e4e3499f344e30bc","observation_id":"3efe0be9-91d3-41c9-8ed3-9e6a32652b50","resolution":{"observed_at":"2026-08-05T10:56:01.226726Z","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":"2026-08-05T10:56:01.001417Z","title":"An automatic method for finding the great- est or least value of a function,","venue":null,"work_id":"6c596d1e-f198-44f2-83ab-a576f9e918f0","year":1960},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T10:56:00.384449Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:c6462066e9c627650320a5095c85bd1e2e6614c5d812ef721825735d346d27c7","observation_id":"f61b9bad-10a1-488a-aaff-4d943e0fd694","resolution":{"observed_at":"2026-08-05T10:56:01.056733Z","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":"1606.08415","last_updated":"2023-06-06T01:53:32Z","snapshot_observed_at":"2026-08-13T19:48:28.322536Z","submitted_at":"2016-06-27T19:20:40Z","title":"Gaussian Error Linear Units (GELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.08415","snapshot_observed_at":"2026-08-05T10:56:00.440528Z","title":"Gaussian error linear units (gelus),","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T10:56:00.440528Z"},"links":{"cited_paper":"/paper/1606.08415","citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:98aee5089a486b7dda38751ca0e2096a4dcdbb22931e26c4afcb5a1ed46056f5","observation_id":"9b065e5b-89b6-4dd2-bcda-b0e1d92ea893","resolution":{"observed_at":"2026-08-05T10:56:00.440528Z","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":"2026-08-05T10:56:00.820230Z","title":"Taking the human out of the loop: A review of bayesian optimization,","venue":null,"work_id":"1008b256-e385-4727-88f4-c9f731a68408","year":2016},"citing_paper":{"arxiv_id":"2509.03594","last_updated":"2025-09-03T18:00:33Z","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T10:56:00.593707Z"},"links":{"citing_paper":"/paper/2509.03594"},"observation_digest":"sha256:127753bbed9701a037c1e05e06e0033b5fe9c794c260629862913de9edb7412b","observation_id":"602e2ac2-460a-4524-946f-d1d9cd0be655","resolution":{"observed_at":"2026-08-05T10:56:00.915060Z","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":"2509.03594","last_updated":"2025-09-03T18:00:33Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T08:07:10.614621Z","submitted_at":"2025-09-03T18:00:33Z","title":"The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":20},"total_outbound_references":32},"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 20 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2509.03594."}