{"as_of":"2026-08-16T05:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03562fbf3c7becb3db985ea6369f59faebda2fb9f3452e6639e5948d5490a8c2","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:06:04.885642Z","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":"2026-07-03T22:08:59.317437Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-08-15T23:06:04.885642Z","title":"Radova , author W","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.05652","last_updated":"2025-05-08T21:18:10Z","snapshot_observed_at":"2026-08-15T22:57:51.256815Z","submitted_at":"2025-05-08T21:18:10Z","title":"Fast and Fourier Features for Transfer Learning of Interatomic Potentials","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T23:06:04.885642Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2505.05652"},"observation_digest":"sha256:724c364041279894c5a7861e5dc1d560efc3ffc470c74531d717e691b2eb62db","observation_id":"ffaa67f0-a5d5-4896-ab72-ff0a4593c70d","resolution":{"observed_at":"2026-08-15T23:06:04.885642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-08-15T22:42:48.141483Z","title":"(36) Mausenberger, S.; Müller, C.; Tkatchenko, A.; Marquetand, P.; González, L.; Westermayr, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.06711","last_updated":"2025-06-07T14:29:50Z","snapshot_observed_at":"2026-08-15T22:33:02.838262Z","submitted_at":"2025-05-10T17:30:22Z","title":"Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics","version":3},"reference_index":2379,"source":"pdf_text","source_observed_at":"2026-08-15T22:42:48.141483Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2505.06711"},"observation_digest":"sha256:53aff8c0dd0337ba1850db63cc83f472dfc05be066ece08a917ac51b1d5e4ac9","observation_id":"e89a1a02-88ef-495f-bbb9-a0d4252d2dba","resolution":{"observed_at":"2026-08-15T22:42:48.141483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-08-07T04:17:22.235035Z","title":"G., Allen, C","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10956","last_updated":"2025-06-12T17:55:48Z","snapshot_observed_at":"2026-08-13T07:55:22.187993Z","submitted_at":"2025-06-12T17:55:48Z","title":"Distillation of atomistic foundation models across architectures and chemical domains","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:22.235035Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2506.10956"},"observation_digest":"sha256:508842278ac11640aa46542fe244a0785acf6265c4531fb0a42eef6526f37adf","observation_id":"7793a48a-2e9f-4d72-b1cb-37440fba5cb3","resolution":{"observed_at":"2026-08-07T04:17:22.235035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-08-15T19:44:50.513861Z","title":"Preprint at arXiv:2502.15582 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.15223","last_updated":"2025-06-18T08:08:07Z","snapshot_observed_at":"2026-08-15T19:38:08.974992Z","submitted_at":"2025-06-18T08:08:07Z","title":"An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T19:44:50.513861Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2506.15223"},"observation_digest":"sha256:65f5a8659a84e973ff8cbdc0fc5dfc1100d15a54ce7b7c0ab9441363987f7c9b","observation_id":"7419fe5f-78e9-4ff5-b4ff-c55fc26face8","resolution":{"observed_at":"2026-08-15T19:44:50.513861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":"2502.15582","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-07-03T22:08:59.317437Z","title":"https://arxiv.org/abs/2502.15582","venue":null,"work_id":"2ccd8fdc-3484-4fbb-b4dc-2d9306402c2b","year":2025},"citing_paper":{"arxiv_id":"2507.06929","last_updated":"2026-05-20T11:43:28Z","snapshot_observed_at":"2026-08-06T10:02:36.050054Z","submitted_at":"2025-07-09T15:11:55Z","title":"Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-21T23:24:44.688855Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2507.06929"},"observation_digest":"sha256:d41712d20fb2f3297c2b347f700f95300ebe34054ca3d314d4275ccbcbab8755","observation_id":"c2ac60e8-62ac-4328-a532-ae0371a0f4f3","resolution":{"observed_at":"2026-05-21T23:25:45.257726Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":"2502.15582","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-07-03T22:08:59.317437Z","title":"https://arxiv.org/abs/2502.15582","venue":null,"work_id":"2ccd8fdc-3484-4fbb-b4dc-2d9306402c2b","year":2025},"citing_paper":{"arxiv_id":"2604.27685","last_updated":"2026-04-30T10:20:58Z","snapshot_observed_at":"2026-08-11T16:41:00.974083Z","submitted_at":"2026-04-30T10:20:58Z","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-07T06:32:25.205347Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2604.27685"},"observation_digest":"sha256:9bfa34916def8a91b84d97b5000b929b44fc640a35aad94b6036b513276e4ce4","observation_id":"d71aa4b7-e999-4bee-9aa2-8ced49f11250","resolution":{"observed_at":"2026-05-12T10:21:29.049689Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":"2502.15582","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-07-03T22:08:59.317437Z","title":"https://arxiv.org/abs/2502.15582","venue":null,"work_id":"2ccd8fdc-3484-4fbb-b4dc-2d9306402c2b","year":2025},"citing_paper":{"arxiv_id":"2606.06848","last_updated":"2026-06-05T02:46:46Z","snapshot_observed_at":"2026-08-02T03:17:12.205390Z","submitted_at":"2026-06-05T02:46:46Z","title":"Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-27T20:50:41.513859Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2606.06848"},"observation_digest":"sha256:292570f3dea5abdec56d83f62f3945ea95e796bc7538c7b3bd32dbac6121e33a","observation_id":"06cef116-76ee-412e-a0c6-04981f674461","resolution":{"observed_at":"2026-07-02T20:07:22.554910Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":"2502.15582","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-07-03T22:08:59.317437Z","title":"https://arxiv.org/abs/2502.15582","venue":null,"work_id":"2ccd8fdc-3484-4fbb-b4dc-2d9306402c2b","year":2025},"citing_paper":{"arxiv_id":"2606.12704","last_updated":"2026-06-10T21:46:04Z","snapshot_observed_at":"2026-08-15T10:18:26.264729Z","submitted_at":"2026-06-10T21:46:04Z","title":"Fine-tuning MLIP foundation models: strategies for accuracy and transferability","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T07:38:30.547968Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2606.12704"},"observation_digest":"sha256:f27c0b3f41cf70cfb4ec24b63e2c748840f269e8d0da733bf36bdfec07e2da2d","observation_id":"3d6126be-94d9-46c7-bfc2-d48fbf717e2f","resolution":{"observed_at":"2026-07-03T13:38:19.662482Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":"2502.15582","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-07-03T22:08:59.317437Z","title":"https://arxiv.org/abs/2502.15582","venue":null,"work_id":"2ccd8fdc-3484-4fbb-b4dc-2d9306402c2b","year":2025},"citing_paper":{"arxiv_id":"2606.17954","last_updated":"2026-06-16T14:08:19Z","snapshot_observed_at":"2026-08-06T12:35:16.936993Z","submitted_at":"2026-06-16T14:08:19Z","title":"Revisiting quantum effects on dislocation glide in bcc metals from DFT calculations and machine-learning potentials","version":1},"reference_index":132,"source":"arxiv_source","source_observed_at":"2026-06-26T23:45:03.135167Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2606.17954"},"observation_digest":"sha256:0fe1ec371414ad63f1a367c09c867016042a4a6c3b5ea7b6949797960d7bcfa2","observation_id":"db827820-49c1-4541-ba2c-2804bce32c65","resolution":{"observed_at":"2026-07-03T22:08:59.319090Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15582","snapshot_observed_at":"2026-07-12T02:31:03.871783Z","title":"Radova , author W","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03433","last_updated":"2026-07-03T15:46:15Z","snapshot_observed_at":"2026-08-06T09:45:27.251520Z","submitted_at":"2026-07-03T15:46:15Z","title":"Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-07-12T02:31:03.871783Z"},"links":{"cited_paper":"/paper/2502.15582","citing_paper":"/paper/2607.03433"},"observation_digest":"sha256:951a38a7a01e5c35d7b4805365de28595d00c834407dbac97872b888926d3f7d","observation_id":"c78ec005-71a5-431e-a992-50b7774c72c2","resolution":{"observed_at":"2026-07-12T02:31:03.871783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.15582/citation-record","integrity":"/paper/2502.15582/integrity","json":"/paper/2502.15582/citation-record.json","paper":"/paper/2502.15582"},"outbound":[],"paper":{"arxiv_id":"2502.15582","last_updated":"2025-07-29T17:19:27Z","latest_version":3,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-10T01:53:30.659618Z","submitted_at":"2025-02-21T16:45:05Z","title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2502.15582."}