{"as_of":"2026-08-08T10:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:95088f6e6e71a4bdbea450c42edd19ad09d24ab2394efa9088543ec90b2e553f","coverage":[{"denominator":81,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":81,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T23:10:47.968889Z","state":"measured"},{"denominator":81,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":81,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2602.14885/citation-record","integrity":"/paper/2602.14885/integrity","json":"/paper/2602.14885/citation-record.json","paper":"/paper/2602.14885"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:10:40.321017Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:40.321017Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:263325127ae53e8fc73117b669b5699ef7e6090c98426fbf3b5dff3860f79715","observation_id":"ccc21f5e-6244-488a-813c-0aaa19b2a4aa","resolution":{"observed_at":"2026-08-02T23:10:40.321017Z","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":"2026-08-02T23:10:40.429452Z","title":"III A, we propose an approach for designing an RNN that switches between attractors based on an input","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:40.429452Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:9d3227557396ab15b608314248a2674381f05d6efb9022a7baea5588dad6b625","observation_id":"3c0ec2cc-7537-4d6c-aaad-07a70d0cd855","resolution":{"observed_at":"2026-08-02T23:10:40.429452Z","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":"2026-08-02T23:10:40.543739Z","title":"More specifically we use CubicSplinefromscipy.interpolate[68]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:40.543739Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:000f7fff411f2a2938bd6bd7f7c60d5ae2c859e418068262c4aa358a88c00aef","observation_id":"7f569d97-b567-4074-9fb3-ab910341c89f","resolution":{"observed_at":"2026-08-02T23:10:40.543739Z","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":"2026-08-02T23:10:40.669805Z","title":"Panela)of Fig","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:40.669805Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:722d8c4ab05d99568965c666f8dd82554571fddf710ec3871ce87b4275113c48","observation_id":"8951c797-f57a-45bd-a709-984765f85db0","resolution":{"observed_at":"2026-08-02T23:10:40.669805Z","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":"2026-08-02T23:10:40.799008Z","title":"As before, we define the attractor dynamics,−∇V, with Gaussian wells","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:40.799008Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:6f5b5914173870fb938238c476d81c7f9f59860527e1af14ee5187599e7332b0","observation_id":"62adcd40-c40b-4379-8fe3-8d2b760cf169","resolution":{"observed_at":"2026-08-02T23:10:40.799008Z","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":"2026-08-02T23:10:40.870497Z","title":"II, we use the following parameters","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:40.870497Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:6534c42d95fda8e21600ed385832441392f16ee5130ba379228772d3200e5f5d","observation_id":"e762c76c-4bd1-4e41-a805-9ad3b3b84869","resolution":{"observed_at":"2026-08-02T23:10:40.870497Z","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":"2026-08-02T23:10:41.013799Z","title":"III, we use the following parameters","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.013799Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:d3bec24afc6f185926818fcc8feb4a69c07bec48d2f4a90eda670567304b6f18","observation_id":"9bee6343-ce23-4ae1-b50a-2560f4dd668c","resolution":{"observed_at":"2026-08-02T23:10:41.013799Z","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":"2026-08-02T23:10:41.106570Z","title":"D, we use the following parameters","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.106570Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:90bd3e153b4d24e078a392bafafe7c2d76a8d2f19a8f12c5d4a50643407776e7","observation_id":"daeee060-632a-49e1-9489-dab1065d5073","resolution":{"observed_at":"2026-08-02T23:10:41.106570Z","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":"2026-08-02T23:10:41.180687Z","title":"F, we train RNNs withN=64 with 25,000 samples in[−4,4] 2 for 30,000 epochs and setσ=0.25","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.180687Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:a24eae2d628d1c40c76d0cc35ec9a56167ab232dd26046ba13a45e8d6c7c2400","observation_id":"97c5e1b7-d2d1-4d79-9841-165dbb6d899c","resolution":{"observed_at":"2026-08-02T23:10:41.180687Z","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":"2026-08-02T23:10:41.287687Z","title":"G, we train RNNs withN=1024 with 125,000 samples for 2000 epochs using batches of size 1024","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.287687Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:84d5a6f733949c96428ff7dc742db0b2e143b6920cc14634d379a7d775e001dc","observation_id":"7fc75e1f-9201-4f2a-8b88-6e7af685df3b","resolution":{"observed_at":"2026-08-02T23:10:41.287687Z","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":"2026-08-02T23:10:41.391720Z","title":"Decoding the brain: From neural representations to mechanistic models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.391720Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:1c23839d32676aaf6804af48d2d7e4121cab2031dc60f6858658fbcea3202e4e","observation_id":"892b259d-c3e9-4ac1-8b1d-fb3eefa3fe82","resolution":{"observed_at":"2026-08-02T23:10:41.391720Z","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":"2026-08-02T23:10:41.454777Z","title":"Compu- tation through neural population dynamics,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.454777Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:18f78fc7d01c7eead12c74755318cd3d445bdf492e5ac52f01ab6e4e29c04a53","observation_id":"c0cafac2-cc68-4fc2-b696-f849e2250759","resolution":{"observed_at":"2026-08-02T23:10:41.454777Z","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":"2026-08-02T23:10:41.555112Z","title":"Inferring single-trial neural popula- tion dynamics using sequential auto-encoders,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.555112Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:03c3900d1294f1b48b7696d1ed22618e84dfff9412a4e029d95d6d8117f26106","observation_id":"72e5ddd7-edb7-4bf4-9cb0-a2e6072bd141","resolution":{"observed_at":"2026-08-02T23:10:41.555112Z","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":"2026-08-02T23:10:41.616950Z","title":"Reconstructing computational system dynamics from neural data with recur- rent neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.616950Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:9bd94a51ad9dd800c125dab65b4b1a4759ac1a95052de3de5bbd27d199f321ae","observation_id":"c13aedb9-31a6-4ef9-a8bb-d124e7253a6d","resolution":{"observed_at":"2026-08-02T23:10:41.616950Z","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":"2026-08-02T23:10:41.719910Z","title":"Generating coherent patterns of activity from chaotic neural networks,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.719910Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:1787e25eb79aa9a2b8b037d51ad674a81e01d57ac0fb08703d2db2f0811e388c","observation_id":"53d0558a-1a1b-4dd2-9f19-a30836400de6","resolution":{"observed_at":"2026-08-02T23:10:41.719910Z","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":"2026-08-02T23:10:41.816618Z","title":"Optimal control of transient dynamics in balanced networks supports generation of complex movements,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.816618Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:91d2c6f2aec5109eda4305b2b929b1dd4db9cec155c8ab4595e03e9fabfbdd62","observation_id":"7fc78375-07e4-423c-9de2-c0dd8d7f4e74","resolution":{"observed_at":"2026-08-02T23:10:41.816618Z","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":"2026-08-02T23:10:41.891622Z","title":"Next generation reservoir computing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.891622Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:397b5d0dcad56cdf30072cce0d40b7133d292d828451464e1d2eb2621f60c93f","observation_id":"8032bae9-adc7-494e-8949-cb869c4bc856","resolution":{"observed_at":"2026-08-02T23:10:41.891622Z","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":"2026-08-02T23:10:41.961098Z","title":"A neural machine code and pro- gramming framework for the reservoir computer,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:41.961098Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:9b5a5db7eac057126e9a4325883f14ff38cc942cd772bdd9c2981a011255761b","observation_id":"7014c050-b398-48c5-af8b-7e17147de2ab","resolution":{"observed_at":"2026-08-02T23:10:41.961098Z","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":"2026-08-02T23:10:42.026691Z","title":"Reservoir- computing based associative memory and itinerancy for com- plex dynamical attractors,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.026691Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:10a1d1b24504157bc658625f1f6f9cc9d71dc6136ad8016e433b8f61014e9713","observation_id":"b553eefc-aee3-4ae6-8ceb-489b26480782","resolution":{"observed_at":"2026-08-02T23:10:42.026691Z","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":"2026-08-02T23:10:42.096480Z","title":"Neural networks and physical systems with emer- gent collective computational abilities,","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.096480Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:7b295912dc344340414b6608e2db75265b511d18ce9a271d1cdd0ea15f22deaf","observation_id":"58c01a68-39d1-43db-a2e8-b0f79d557deb","resolution":{"observed_at":"2026-08-02T23:10:42.096480Z","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":"2026-08-02T23:10:42.170823Z","title":"Neurons with graded response have collective computational properties like those of two-state neurons,","venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.170823Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:05ee43d9ec90ec367f79f7506aa2c5ca89b2e80d113f35b7fc55919946ce75de","observation_id":"39c62e4b-da9c-41fb-b392-42395233b76f","resolution":{"observed_at":"2026-08-02T23:10:42.170823Z","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":"2026-08-02T23:10:42.238928Z","title":"Pavliotis,Stochastic Processes and Applications: Diffu- sion Processes, the Fokker-Planck and Langevin Equations","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.238928Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:0df84079c664eb8ea0adf2195e0a1d123b3660d0479dcd2685f286a8b80fe4ad","observation_id":"f6aeaf7f-2754-44e6-b410-c4a5db73d667","resolution":{"observed_at":"2026-08-02T23:10:42.238928Z","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":"2026-08-02T23:10:42.324201Z","title":null,"venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.324201Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:c86e97c40678317d510192e366b0c11d2a3ea59f2cfc232257f8b01d9bc32d94","observation_id":"82b70e74-012e-4965-86b9-38a62f6b6337","resolution":{"observed_at":"2026-08-02T23:10:42.324201Z","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":"2026-08-02T23:10:42.398414Z","title":"Attractor and integrator networks in the brain,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.398414Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:31b0a9ca4c06c45d7272f07de1ef3d9d166fd62ba237f8824ac971e9c6eb7767","observation_id":"1dce1f3a-7f19-4847-82aa-cf6dc72cdcc4","resolution":{"observed_at":"2026-08-02T23:10:42.398414Z","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":"2026-08-02T23:10:42.488100Z","title":"Temporal association in asym- metric neural networks,","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.488100Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:3d97c91494d02044d45213f9118a028f1002d6736c52dac6196350ab84626823","observation_id":"57d56093-a2a3-4dc7-8fb6-d7f05df76453","resolution":{"observed_at":"2026-08-02T23:10:42.488100Z","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":"2026-08-02T23:10:42.581476Z","title":"Irreversible spin glasses and neural networks,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.581476Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:aedd17be350f64c10a4a7cf01358afb07585fedafe49c7404f7c98df596ebe27","observation_id":"aed1680d-97d3-4357-8076-39d6703ecf82","resolution":{"observed_at":"2026-08-02T23:10:42.581476Z","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":"2026-08-02T23:10:42.674892Z","title":"Dynamics of spin systems with randomly asymmetric bonds: Langevin dynamics and a spherical model,","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.674892Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:17fb671488e73531f4d698d34cef74cfebd165fbf8547e28d3d6c80ed71554c1","observation_id":"68421fc9-04df-4adf-a704-943486b801a4","resolution":{"observed_at":"2026-08-02T23:10:42.674892Z","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":"2026-08-02T23:10:42.838093Z","title":"Nonequilibrium landscape theory of neural networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.838093Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:f2c7b95bd45c2f2a457d6844ea85f9b5a3a8afe72497b561bd67ca62409848ae","observation_id":"7783dcb6-d1f7-4d58-ad3e-53afde885924","resolution":{"observed_at":"2026-08-02T23:10:42.838093Z","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":"2026-08-02T23:10:42.955683Z","title":"Nonequilibrium physics of brain dy- namics,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:42.955683Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:c4bd129dc6576b21bfa818086048a7c8bd79116f85e56af4b854f7431a5a1957","observation_id":"b15a34fc-afeb-4bad-91c2-c07c6b34fa5f","resolution":{"observed_at":"2026-08-02T23:10:42.955683Z","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":"2026-08-02T23:10:43.061148Z","title":"En- hanced associative memory, classification, and learning with active dynamics,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.061148Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:ed324613af078fad2e84bac218fb8386040199678db7c01a506a78e55f98d6a3","observation_id":"3dc7607a-94d4-49cf-a107-1ca7688b1e10","resolution":{"observed_at":"2026-08-02T23:10:43.061148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22749","last_updated":"2026-05-21T10:17:37Z","snapshot_observed_at":"2026-07-06T21:32:28.358564Z","submitted_at":"2025-05-28T18:10:03Z","title":"Self-orthogonalizing attractor neural networks emerging from the free energy principle","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22749","snapshot_observed_at":"2026-08-02T23:10:43.137976Z","title":"Self-orthogonalizing attractor neu- ral networks emerging from the free energy principle,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.137976Z"},"links":{"cited_paper":"/paper/2505.22749","citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:49270a8f799687a33acbccd7c046e8942bac72a4bf06edd09e4006f2f194e820","observation_id":"ce05411d-48b9-4094-a3d5-339c328c19e1","resolution":{"observed_at":"2026-08-02T23:10:43.137976Z","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":"2026-08-02T23:10:43.184218Z","title":"Nonequilibrium thermodynamics of associative memory continuous-time recurrent neural net- works,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.184218Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:30874f2e435251ffe116b42b59b7c3f98ad4076159243248253501c3780fa81e","observation_id":"b03ff5ce-a2b5-45fd-b56d-e0d034dcd298","resolution":{"observed_at":"2026-08-02T23:10:43.184218Z","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":"2026-08-02T23:10:43.379066Z","title":"Statistical mechanics of recurrent neural networks I—Statics,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.379066Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:1dc079aa646af85e44227c8f0480147c9b4ebe09396ff7ff63aee261c4b8c1cc","observation_id":"6de2ff39-2b7c-492b-afab-320005dcdb3e","resolution":{"observed_at":"2026-08-02T23:10:43.379066Z","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":"2026-08-02T23:10:43.533313Z","title":"Statistical mechanics of recurrent neural networks II — Dynamics,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.533313Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:7457dff26579e1fef6c8cf63251a43d363dc6e5685144387091867eb953ec86e","observation_id":"089f1742-3bf5-4ecf-a49c-1fb7b189c9f3","resolution":{"observed_at":"2026-08-02T23:10:43.533313Z","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":"2026-08-02T23:10:43.658099Z","title":"A neural manifold view of the brain,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.658099Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:f84cc3fd4532461aed64efb480efd44fd7534640bf9c043548f19b15d8b9392f","observation_id":"4b5ec8da-594b-415c-9f87-44745c712f84","resolution":{"observed_at":"2026-08-02T23:10:43.658099Z","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":"2026-08-02T23:10:43.742202Z","title":"Neu- ral manifolds for the control of movement,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.742202Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:962756a50da17522918a0fe8f190dd2124bf55e95d1121a8fcd68e4869ce056c","observation_id":"dc986dd6-971e-4f4c-a3ec-756f45169717","resolution":{"observed_at":"2026-08-02T23:10:43.742202Z","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":"2026-08-02T23:10:43.796634Z","title":"Opening the black box: low- dimensional dynamics in high-dimensional recurrent neural networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.796634Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:e7727fa1009158df92c0c9f5470343900085ef1307afe457db846eb4ce5dd3a7","observation_id":"4c08f8a7-ac70-4764-b74f-3df1b038f78f","resolution":{"observed_at":"2026-08-02T23:10:43.796634Z","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":"2026-08-02T23:10:43.877470Z","title":"Linking connectivity, dy- namics, and computations in low-rank recurrent neural net- works,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.877470Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:d201c2d0700cbf46ab360838ce71eb66afcf0c1cd8b9124eb8f11f87e7389a15","observation_id":"6ff65916-ed4e-4e0e-aa4a-e5fa07d3ccf2","resolution":{"observed_at":"2026-08-02T23:10:43.877470Z","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":"2026-08-02T23:10:43.959556Z","title":"High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:43.959556Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:a903621ba7fa7945ec6f7dc3bf569e321c725003a4df8259bf38ea8eb501ccee","observation_id":"2a38ced3-0952-4f26-aba2-60d3e99ac831","resolution":{"observed_at":"2026-08-02T23:10:43.959556Z","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":"2026-08-02T23:10:44.013677Z","title":"The low-rank hypothesis of complex systems,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.013677Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:74ba0483b60b28fb378f01f7fcde699efb3e85ba20f00185138c90b474c4f2cc","observation_id":"0c02833a-0b99-4764-90f7-b19f4cc670f1","resolution":{"observed_at":"2026-08-02T23:10:44.013677Z","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":"2026-08-02T23:10:44.067362Z","title":"Predicting network dynamics without requiring the knowledge of the interaction graph,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.067362Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:9f2bc478d20ece7a1998e3e94196e7703cf5ad3f28ea8169c6e91b26a4b4ccb3","observation_id":"7bce9104-e811-4bef-98b4-4faee0f7d3f1","resolution":{"observed_at":"2026-08-02T23:10:44.067362Z","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":"2026-08-02T23:10:44.121121Z","title":"Testing the mani- fold hypothesis,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.121121Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:067a6713d63db06d99526f33cb9849f9ca59ff49382db10ab77411a9f0d7b1cb","observation_id":"62a15461-825c-4096-8b5a-70f9f724afca","resolution":{"observed_at":"2026-08-02T23:10:44.121121Z","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":"2026-08-02T23:10:44.176149Z","title":"Neural manifold analysis of brain circuit dynamics in health and disease,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.176149Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:8e7e46714e3a92797d566055419983fbf3bc5efe6082e94a70a25be04d0fc30f","observation_id":"6f2695f1-fd6b-46dd-a56d-cd6cf6b03425","resolution":{"observed_at":"2026-08-02T23:10:44.176149Z","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":"2026-08-02T23:10:44.218539Z","title":"Time for memories,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.218539Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:00f0ac16d4b831069670379e6699bf45a200f658bde775536893dbc4fcf6e7e1","observation_id":"2bf2a9cc-99bf-4996-ae35-fe465b7f1e29","resolution":{"observed_at":"2026-08-02T23:10:44.218539Z","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":"2026-08-02T23:10:44.273804Z","title":"The ”echo state","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.273804Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:b84f5e297eb61dd5fab6f26af621ab1b59d5b9149402a109d982ad7e7310ba78","observation_id":"9639e46f-8f93-4857-a725-1839afcd6f6e","resolution":{"observed_at":"2026-08-02T23:10:44.273804Z","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":"2026-08-02T23:10:44.382576Z","title":"Eliasmith and C","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.382576Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:a25326e89a30ab9727963372991bb31ffa560695c044b9f2f981094ddf414743","observation_id":"ea2136bd-010e-4327-bcf2-786a6dddf245","resolution":{"observed_at":"2026-08-02T23:10:44.382576Z","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":"2026-08-02T23:10:44.440641Z","title":"Backpropagation through time: what it does and how to do it,","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.440641Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:9c5000afa3ae532cb01832e3d3599044f81198409c8d2e752da204ce39a76bbe","observation_id":"ddabfff2-07f5-495e-8eaa-baadc9bbac68","resolution":{"observed_at":"2026-08-02T23:10:44.440641Z","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":"2026-08-02T23:10:44.489169Z","title":"Learning long-term de- pendencies with gradient descent is difficult,","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.489169Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:5c87deb4d866667d5484a623eea263b7a08a01e69b646b435c3e27037c74a8ad","observation_id":"a7aeac56-e4a9-409a-bfcd-efe8d4bf8503","resolution":{"observed_at":"2026-08-02T23:10:44.489169Z","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":"2026-08-02T23:10:44.544875Z","title":"Multilayer feedfor- ward networks are universal approximators,","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.544875Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:13b828385e6e86ff9f0aad2b1eaa4af7082641bbabf753877ebffceb3bf56916","observation_id":"8b10d989-34be-4cf7-ab33-2031949290c8","resolution":{"observed_at":"2026-08-02T23:10:44.544875Z","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":"2026-08-02T23:10:44.597626Z","title":"Goodfellow, Y","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.597626Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:ec314cd85a6dce77b166189a3bdbc581be658c6b11e6765fb104d0be990d667e","observation_id":"b4ca47b4-caa4-4af1-8240-39c1c7f08a0c","resolution":{"observed_at":"2026-08-02T23:10:44.597626Z","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":"2026-08-02T23:10:44.642983Z","title":"Helmholtz decomposition and po- tential functions for n-dimensional analytic vector fields,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.642983Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:cfbe1199bf21f3032e997acc42307bdc8b2f6523a62a79eda6b1b43fe0cb82c7","observation_id":"dbb8a2cb-935f-4b2e-aee0-7d3ba543318a","resolution":{"observed_at":"2026-08-02T23:10:44.642983Z","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":"2026-08-02T23:10:44.655617Z","title":"The entropy production of stationary diffusions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.655617Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:d3f8fbd6c3ec002260c3aa89a5a370686c68b1b9be1c0dab435390e143abacef","observation_id":"9121e054-45d5-4c88-8a22-a1f5cf89ddae","resolution":{"observed_at":"2026-08-02T23:10:44.655617Z","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":"2026-08-02T23:10:44.718145Z","title":"Non-reversible processes: GENERIC, hypocoercivity and fluctuations,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.718145Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:936e614a44750e6009372de5d85d0bddd0fc5f8f6b9677c3f7b9d73083f618f8","observation_id":"720314ea-da81-4807-8d3f-46e15c012ae6","resolution":{"observed_at":"2026-08-02T23:10:44.718145Z","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":"2026-08-02T23:10:44.864845Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.864845Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:55b935af611dae24fe84f7b0e0664ba54c0be990741739bc998acf763032916f","observation_id":"e96df0bf-1d34-432a-a9d4-461849e17f55","resolution":{"observed_at":"2026-08-02T23:10:44.864845Z","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":"2026-08-02T23:10:44.981060Z","title":"A novel three-dimensional au- tonomous chaotic system generating two, three and four-scroll attractors,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:44.981060Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:2a0af94eab8548d490e0ed3b8beddc06acedae07b58fc233c72608f7b844e953","observation_id":"8face924-3a35-4308-ba69-ea9d7f1fed8d","resolution":{"observed_at":"2026-08-02T23:10:44.981060Z","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":"2026-08-02T23:10:45.087595Z","title":"LIII. On lines and planes of closest fit to sys- tems of points in space,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.087595Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:41a82d1968539cc6c877be246938ea420ed3f31a15f8f9cac6e41b953b304bfb","observation_id":"19a914cf-0538-46fd-903f-c05c3e19500b","resolution":{"observed_at":"2026-08-02T23:10:45.087595Z","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":"2026-08-02T23:10:45.154442Z","title":"Neu- ral population dynamics during reaching,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.154442Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:f4ad2c04d4232407dd7ea03bfaa7cb3fed4a0489926d13f274d6092e0eb9903a","observation_id":"57414585-4a84-4b39-a4f1-ca05b0acad60","resolution":{"observed_at":"2026-08-02T23:10:45.154442Z","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":"2026-08-02T23:10:45.243087Z","title":"Dimensionality reduction for large-scale neural recordings,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.243087Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:eb03595a502acfd2d41ec6e6b3bc6b638df95f045b5b33e082169d015194a9b9","observation_id":"ab819e3a-6bb9-4d82-b1d8-cf3d2529da03","resolution":{"observed_at":"2026-08-02T23:10:45.243087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.01275","last_updated":"2021-12-18T07:52:16Z","snapshot_observed_at":"2026-08-06T11:49:52.490227Z","submitted_at":"2021-11-01T21:52:48Z","title":"Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.01275","snapshot_observed_at":"2026-08-02T23:10:45.327157Z","title":"Recurrent neural network models for working memory of continuous vari- ables: activity manifolds, connectivity patterns, and dynamic codes,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.327157Z"},"links":{"cited_paper":"/paper/2111.01275","citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:cffdc2dbf5d1536230b4a238134f981e2997f5c88f852313f3fb3236da28f01c","observation_id":"8de3d8eb-a6c2-4fbc-813e-88aed25a328f","resolution":{"observed_at":"2026-08-02T23:10:45.327157Z","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":"2026-08-02T23:10:45.395400Z","title":"Vidal, Y","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.395400Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:8d6b72abda728dc17b1588a83cf5141ec05c3fb9a226af9397a4e0129ecc43aa","observation_id":"79c9a4e5-ba05-477f-a090-dc072c30e612","resolution":{"observed_at":"2026-08-02T23:10:45.395400Z","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":"2026-08-02T23:10:45.509301Z","title":"Free energy, value, and attractors,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.509301Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:7d61ea040c389ac81fe3fb6f80eb7cd758be1848b573c7e02466f600f05e40f0","observation_id":"9d377c63-405d-4e79-8468-e6770c4792cb","resolution":{"observed_at":"2026-08-02T23:10:45.509301Z","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":"2026-08-02T23:10:45.587173Z","title":"Energy-based models,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.587173Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:051ebed779fc9471ef7747cf5628f69b751aeb84590e3f94cd91101d4dbf653a","observation_id":"ef428c53-ca22-41f1-8bd2-66120605d376","resolution":{"observed_at":"2026-08-02T23:10:45.587173Z","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":"2026-08-02T23:10:45.700661Z","title":"Broken detailed balance and entropy production in directed networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.700661Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:0527dc8d95b86e11ff56a0b7e918e20193135cbb7ce547b098923052b6fc33b9","observation_id":"faa5f04f-6a8b-41ad-8ca1-af0f1bc707d8","resolution":{"observed_at":"2026-08-02T23:10:45.700661Z","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":"2026-08-02T23:10:45.814594Z","title":"Non- reciprocal phase transitions,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:45.814594Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:f0a2e105876a884e5eb484e5c23d76a5d0e584528a75918ec70e5fa80a141d88","observation_id":"c3f50c83-6ec7-4c10-a437-97ae1ee2091b","resolution":{"observed_at":"2026-08-02T23:10:45.814594Z","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":"2026-08-02T23:10:46.022041Z","title":"Curl descent: Non-gradient learning dynamics with sign-diverse plasticity,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:46.022041Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:cfa40ee656037fca97f001b32b34da3ae08630508db323b73b9b42f68214c1fd","observation_id":"c83b86e7-4ded-4c44-9fcf-15c02d9d05b9","resolution":{"observed_at":"2026-08-02T23:10:46.022041Z","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":"2026-08-02T23:10:46.208421Z","title":"Jiang, M","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:46.208421Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:7e6529fe3a455fe9b65fec71892c174ee86ee96c61f3ac24d2ea6ce82e53d217","observation_id":"d92fdfc6-f90f-49ad-b4c5-44e39d815a26","resolution":{"observed_at":"2026-08-02T23:10:46.208421Z","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":"2026-08-02T23:10:46.342210Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:46.342210Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:20e40738380de7bc413dc01e8ee6ce2f423a0fe9975f62d5d66053e7dd3ea47d","observation_id":"3067f927-0cff-46e4-9dc2-394421523bea","resolution":{"observed_at":"2026-08-02T23:10:46.342210Z","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":"2026-08-02T23:10:46.498652Z","title":"Engineering recurrent neural networks from task-relevant manifolds and dynamics,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:46.498652Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:36a9034f6f22ba2480edd3f465c347bd6583cb3d63e17350c96414f942e7a0b6","observation_id":"4fa468a6-4862-439e-9731-265353e09235","resolution":{"observed_at":"2026-08-02T23:10:46.498652Z","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":"2026-08-02T23:10:46.580395Z","title":"Theory of orientation tuning in visual cortex,","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:46.580395Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:573fb0b9f598c179cfa43f4bbfabf3097229b31e8035db26359ccefe46017930","observation_id":"96601484-8abf-4625-9850-3ea59e43b875","resolution":{"observed_at":"2026-08-02T23:10:46.580395Z","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":"2026-08-02T23:10:46.698052Z","title":"Shaping manifolds in equivariant recurrent neural networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:46.698052Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:7d7066731143a826fe84cb4e3ecc36c572fa20fa7d09e64a53f91d9f4dabcfc7","observation_id":"f11f73ae-4208-4ddc-9361-904821d5e3ab","resolution":{"observed_at":"2026-08-02T23:10:46.698052Z","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":"2026-08-02T23:10:46.789070Z","title":"Recurrent network models of sequence generation and memory,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:46.789070Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:6a8b8452a3cfc9d4d81de09f2ed95607ea2c1c6477557146bcf1a0339e8a076a","observation_id":"36b1e5b7-c20b-423d-8d56-24ad60acdbe7","resolution":{"observed_at":"2026-08-02T23:10:46.789070Z","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":"2026-08-02T23:10:47.017525Z","title":"Nonequilibrium thermodynamics of the asymmetric Sherrington-Kirkpatrick model,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.017525Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:940a29500425118eaf291360a92f17d65baca2d24ff94a7bbfec217b78818221","observation_id":"960a8070-5070-4701-af95-a38651f7ba23","resolution":{"observed_at":"2026-08-02T23:10:47.017525Z","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":"2026-08-02T23:10:47.128241Z","title":"A unifying framework for mean-field theories of asymmetric kinetic Ising systems,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.128241Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:e22dd5264a849e2536b19b232151fffb03658f269d2cb61ca84fe5c90764f2e1","observation_id":"5e371761-958c-45cf-bb2a-2de64b800e07","resolution":{"observed_at":"2026-08-02T23:10:47.128241Z","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":"2026-08-02T23:10:47.247637Z","title":"Decomposing the local arrow of time in interacting systems,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.247637Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:4ecb57550209327f999e273e42172b5480dbb835620cfbc3a881af53b5495d0c","observation_id":"ff2682dc-06a9-4da1-bdfe-bdf224bfb150","resolution":{"observed_at":"2026-08-02T23:10:47.247637Z","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":"2026-08-02T23:10:47.334207Z","title":"A complete recipe for stochastic gradient MCMC,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.334207Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:59c3d61aa83e8a11f753ffbd6a186718ecf02170de178520bdea198cf5a8ae0f","observation_id":"493f109a-8bc3-4985-b9b1-0f84b25c4b6c","resolution":{"observed_at":"2026-08-02T23:10:47.334207Z","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":"2026-08-02T23:10:47.445400Z","title":"Kloeden and E","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.445400Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:b1b3debda17f7729945a8b2af09398d070549a410aa8e3a203ebe7c2ab01427b","observation_id":"92d5989c-7f03-4492-92cb-ac7a5f4a5438","resolution":{"observed_at":"2026-08-02T23:10:47.445400Z","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":"2026-08-02T23:10:47.567458Z","title":"Hastie, R","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.567458Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:ab0c9c6cb10c9f7bb3a20c23cf56be472512cee10d41ab6a34698b999ea600c2","observation_id":"65ba3c8f-9691-4ea2-844d-aaa90165a33c","resolution":{"observed_at":"2026-08-02T23:10:47.567458Z","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":"2026-08-02T23:10:47.685842Z","title":"Scipy 1.0: fundamental algorithms for scientific comput- ing in Python,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.685842Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:d9e64813cc6c4cbfb8cd902aff805c40843e31f3e297bc9c77c2802dd3d3343a","observation_id":"89ebda79-3873-4fe5-b870-6aceb7ea7349","resolution":{"observed_at":"2026-08-02T23:10:47.685842Z","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":"2026-08-02T23:10:47.769468Z","title":null,"venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.769468Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:e20bda4a8a8ccdd703ead1adf53b1a32e77283912e64188a093cca1d19f8e22a","observation_id":"59d043e6-aeeb-490c-b9ee-2233054f2d3d","resolution":{"observed_at":"2026-08-02T23:10:47.769468Z","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":"2026-08-02T23:10:47.881818Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.881818Z"},"links":{"citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:f3658369b0023eadb3f87e869f1347735cfd2727336e398f6c13b1b74aeb6278","observation_id":"b77701fb-c7af-4b06-bc0f-95a38bfc59b9","resolution":{"observed_at":"2026-08-02T23:10:47.881818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.05366","last_updated":"2026-04-27T15:50:24Z","snapshot_observed_at":"2026-07-06T22:35:13.699594Z","submitted_at":"2025-11-07T15:53:10Z","title":"Coarse-graining nonequilibrium diffusions with Markov chains","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.05366","snapshot_observed_at":"2026-08-02T23:10:47.968889Z","title":"Coarse-graining nonequilibrium diffusions with Markov chains,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-02T23:10:47.968889Z"},"links":{"cited_paper":"/paper/2511.05366","citing_paper":"/paper/2602.14885"},"observation_digest":"sha256:883d165b3df7804c84911a664f6b4b9c4cc3db4563516ea48950890f0d08c231","observation_id":"b4a8b528-46f7-46bf-a97f-e25cf8d0467f","resolution":{"observed_at":"2026-08-02T23:10:47.968889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.14885","last_updated":"2026-06-03T11:34:12Z","latest_version":2,"primary_category":"cond-mat.dis-nn","snapshot_observed_at":"2026-08-02T23:09:03.126974Z","submitted_at":"2026-02-16T16:15:59Z","title":"Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks"},"reference_resolution":{"displayed":81,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":81,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":81},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 0 inbound Pith citation observations for arXiv:2602.14885."}