{"as_of":"2026-08-10T10:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:baac8c9d06f39a84a515f67b7133e169c993abdfe66f2612303fceae759d7240","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T16:26:03.536313Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2502.01171/citation-record","integrity":"/paper/2502.01171/integrity","json":"/paper/2502.01171/citation-record.json","paper":"/paper/2502.01171"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T16:26:03.747498Z","title":null,"venue":null,"work_id":"7c78b57a-40f4-43f5-97c1-b72d3495ab1c","year":2020},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.526440Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:b2157593833d594ca5f8e0d1fdf65217ae59d508c6fa45f8aa42ffd93042276b","observation_id":"78164b19-dbfe-4288-89c8-a1b7bc12541f","resolution":{"observed_at":"2026-08-09T16:26:03.752218Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.12059","last_updated":"2024-03-06T21:45:16Z","snapshot_observed_at":"2026-08-06T01:50:38.513001Z","submitted_at":"2023-06-21T07:01:38Z","title":"EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.12059","snapshot_observed_at":"2026-08-09T16:26:03.492337Z","title":"Equiformerv2: Improved equivariant transformer for scaling to higher- degree representations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.492337Z"},"links":{"cited_paper":"/paper/2306.12059","citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:c237831d8b6bc544a4e0f6fbf7004de19954c3db852db50a76d224a033011aec","observation_id":"14217666-6a8b-4bed-bece-16d0d0d579da","resolution":{"observed_at":"2026-08-09T16:26:03.492337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.02541","last_updated":"2022-04-23T16:31:44Z","snapshot_observed_at":"2026-08-09T21:04:20.459714Z","submitted_at":"2022-02-05T12:53:40Z","title":"TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.02541","snapshot_observed_at":"2026-08-09T16:26:03.497705Z","title":"and De Fabritiis, G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.497705Z"},"links":{"cited_paper":"/paper/2202.02541","citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:7373c202aadc7cfb0e2ef7cb11ade127fc95779d5ef8ef66c758895098eaf862","observation_id":"b04e8d1f-8638-45d7-a848-66a324de1747","resolution":{"observed_at":"2026-08-09T16:26:03.497705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T16:26:03.792543Z","title":"Towards flexible, efficient, and effective tensor product networks","venue":null,"work_id":"657cf58d-f6c6-421b-882a-1cd54401afe2","year":2023},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.502300Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:44d634976b6474ac2bc8e6c4aab9380685a156241b73ed3d284a791e12d77672","observation_id":"eb7f05f7-0d4c-4290-80ec-bc4828e3f203","resolution":{"observed_at":"2026-08-09T16:26:03.797721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03794","last_updated":"2024-10-09T04:51:36Z","snapshot_observed_at":"2026-08-09T21:05:44.832004Z","submitted_at":"2024-06-06T07:05:58Z","title":"Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models","version":2},"cited_work":{"arxiv_id":"2406.03794","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.03794","snapshot_observed_at":"2026-08-09T16:26:03.601058Z","title":"Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models","venue":"cs.LG","work_id":"9d9a4df8-cf21-46b5-a65d-98e89003f1f4","year":2024},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.511522Z"},"links":{"cited_paper":"/paper/2406.03794","citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:80d5be8103ba65a1f65edd8fa31b719d0144cd8beac16ec4565e59e932ea766f","observation_id":"37f9d3fb-e4d5-445a-8abc-6c5a95802a43","resolution":{"observed_at":"2026-08-09T16:26:03.607308Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T16:26:03.776918Z","title":"(A) The Vectorial Node Interaction Block, which uses a long-short range message-passing mechanism","venue":null,"work_id":"d6846737-161e-4e60-944c-42bf11303e86","year":2022},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.516692Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:c82abc3e1ad058d3c4297bca0d230e68426286f740b5e0211378aeda21aa5dda","observation_id":"8e1c300e-ce46-473f-a06c-faf69bdf5b2a","resolution":{"observed_at":"2026-08-09T16:26:03.781841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T16:26:03.762259Z","title":null,"venue":null,"work_id":"fdb06a53-d01e-4f80-ada8-d5d47121e80c","year":2023},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.521425Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:a697fd0be9edc8ca73c323e4430cf99bdb3681f0a18a49a21918fa3484ea86ed","observation_id":"d691dc50-68dd-4401-9efc-7d5cd838e701","resolution":{"observed_at":"2026-08-09T16:26:03.767020Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T16:26:03.732036Z","title":null,"venue":null,"work_id":"8462f528-e7d1-43b4-87e3-727abc4ae59c","year":2023},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.531160Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:901a8618eb2c369d29eb99b80298b5737d6ec9585a9bb624642781f88008f332","observation_id":"c0d540c8-368b-43bb-a2e0-0e4c6e93bcb6","resolution":{"observed_at":"2026-08-09T16:26:03.736909Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T16:26:03.717013Z","title":null,"venue":null,"work_id":"00dd2f11-178d-44a7-8f74-f78e51c486d4","year":2021},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.536313Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:f27a0c9553f750a7a670acecc40185dce3c818a13f8ba5bcc36a858ccc8d3984","observation_id":"a45f950c-9288-46e2-a320-cfa823649472","resolution":{"observed_at":"2026-08-09T16:26:03.721533Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16853","last_updated":"2024-06-24T17:58:13Z","snapshot_observed_at":"2026-08-09T13:36:27.381661Z","submitted_at":"2024-06-24T17:58:13Z","title":"GeoMFormer: A General Architecture for Geometric Molecular Representation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16853","snapshot_observed_at":"2026-08-09T16:26:03.466879Z","title":"Geomformer: A general architecture for geomet- ric molecular representation learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":2000,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.466879Z"},"links":{"cited_paper":"/paper/2406.16853","citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:0b0e14757ffbd9924d307bf0d9d39582a222ff8d21a6db2c01930e34adb8aa8f","observation_id":"30145f5d-6635-44c4-b797-5efec871be62","resolution":{"observed_at":"2026-08-09T16:26:03.466879Z","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":"2026-08-09T16:26:03.461587Z","title":"URL https://doi.org/ 10.1021/ct200412r","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.461587Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:ddd364014138dc6af6267c0fb4f9c909b326862ffb3ed7e29928eb26927ec222","observation_id":"82024dda-406e-40a0-b338-de21098ea1dd","resolution":{"observed_at":"2026-08-09T16:26:03.461587Z","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":"2026-08-09T16:26:03.471853Z","title":"Se (3)-transformers: 3d roto-translation equivariant attention networks","venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.471853Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:0ac3be5a09d377af3a261eb3b915710d07b060ab1d59eff79dba2cfbe3ce9c33","observation_id":"fbcaa4fb-1244-4bdb-b3c8-8e0875a6f6c1","resolution":{"observed_at":"2026-08-09T16:26:03.471853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.02782","last_updated":"2022-09-30T15:21:29Z","snapshot_observed_at":"2026-08-10T02:45:11.062565Z","submitted_at":"2022-04-06T12:52:34Z","title":"GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.02782","snapshot_observed_at":"2026-08-09T16:26:03.476632Z","title":"L., and Das, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.476632Z"},"links":{"cited_paper":"/paper/2204.02782","citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:89fb6029879ec05e04c1907224f306e06758f949a02e2572cba2c5c0af3071ab","observation_id":"03db4cda-0e18-46a9-ad7e-0d7605a9af06","resolution":{"observed_at":"2026-08-09T16:26:03.476632Z","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":"10.1021/acs.jctc.2c00509","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"URL https://doi.org/ 10.1021/acs.jctc.2c00509","venue":"Journal of Chemical Theory and Computation","work_id":"49f5e6cb-1ea7-466d-b741-420970b58391","year":null},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.481757Z"},"links":{"citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:38a4dc8d2d92b0c02702ec57ce262fdcde1550a821f7c6adbbbe37d312e920d4","observation_id":"d16f8162-4707-4efa-a462-640eff6f28c5","resolution":{"observed_at":"2026-08-09T16:26:03.573854Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.16518","last_updated":"2023-08-16T08:21:22Z","snapshot_observed_at":"2026-07-06T14:11:59.014234Z","submitted_at":"2022-10-29T07:12:46Z","title":"ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules","version":3},"cited_work":{"arxiv_id":"2210.16518","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.16518","snapshot_observed_at":"2026-08-09T16:26:03.623900Z","title":"ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules","venue":"physics.chem-ph","work_id":"43cfcd6d-d204-446e-aee8-e52f19070b26","year":2022},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.506840Z"},"links":{"cited_paper":"/paper/2210.16518","citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:fad9af5034bf2e011c50a9e508873a7a9112e7ee697f9445111db618eae2908e","observation_id":"2d24938c-ed4c-4518-bdc4-b5aeff49ffd3","resolution":{"observed_at":"2026-08-09T16:26:03.629037Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.11990","last_updated":"2023-02-28T02:07:30Z","snapshot_observed_at":"2026-08-04T16:29:41.851417Z","submitted_at":"2022-06-23T21:40:37Z","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.11990","snapshot_observed_at":"2026-08-09T16:26:03.487255Z","title":"E2former: A linear-time efficient and equivariant transformer for scal- able molecular modeling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-09T16:26:03.487255Z"},"links":{"cited_paper":"/paper/2206.11990","citing_paper":"/paper/2502.01171"},"observation_digest":"sha256:23f63ca1bb357acfc3986da270f43bc83da82c601eceff04615dbd0bac89892d","observation_id":"e127e224-6adb-47b5-a804-e9bd601722e1","resolution":{"observed_at":"2026-08-09T16:26:03.487255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.01171","last_updated":"2025-05-22T12:37:26Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T21:05:20.566906Z","submitted_at":"2025-02-03T09:04:47Z","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":3,"verified_fuzzy":2},"total_outbound_references":16},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2502.01171."}