{"as_of":"2026-08-10T02:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e34803bcc33089f1c674dc6406c85548cb0e808b6a74cd7fd117f700d742f432","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":"2026-08-04T05:46:03.922150Z","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":"2026-08-09T06:31:02.800959+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/2603.25062/citation-record","integrity":"/paper/2603.25062/integrity","json":"/paper/2603.25062/citation-record.json","paper":"/paper/2603.25062"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T05:46:03.922150Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.922150Z"},"links":{"citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:696ce4fe2c6da6b1f51ff3dbc408d1cfa7706b348ba0dfcacfc911a0613416e4","observation_id":"110dc180-00d9-47d5-bffa-1382cc6ca402","resolution":{"observed_at":"2026-08-04T05:46:03.922150Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13230","last_updated":"2020-11-26T10:55:05Z","snapshot_observed_at":"2026-08-09T19:46:49.318806Z","submitted_at":"2020-11-26T10:55:05Z","title":"Molecular representation learning with language models and domain-relevant auxiliary tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13230","snapshot_observed_at":"2026-08-04T05:46:02.930024Z","title":"Molecular representation learning with language models and domain-relevant aux- iliary tasks.arXiv preprint arXiv:2011.13230,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:02.930024Z"},"links":{"cited_paper":"/paper/2011.13230","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:4fbddafab575ca7808e5269db3b3b7484edef357e5289d0c429c759a141fe939","observation_id":"f7d9aca3-5598-41f8-a41e-ae2c816cba16","resolution":{"observed_at":"2026-08-04T05:46:02.930024Z","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":"2026-08-04T05:46:03.056550Z","title":"Lost in translation: Chemical language models and the misunderstanding of molecule structures","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.056550Z"},"links":{"citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:7a1ae35bf06a2ccccec9056b65a41d49ebf34531bb3233ccad096610a5dec2c3","observation_id":"41ccb4f3-9e7e-476d-9067-c77af8587768","resolution":{"observed_at":"2026-08-04T05:46:03.056550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.04738","last_updated":"2019-11-12T08:44:49Z","snapshot_observed_at":"2026-07-06T08:36:19.641682Z","submitted_at":"2019-11-12T08:44:49Z","title":"SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.04738","snapshot_observed_at":"2026-08-04T05:46:03.162622Z","title":null,"venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.162622Z"},"links":{"cited_paper":"/paper/1911.04738","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:d99d198a866a8d9a46dd08932a71a6da2e7154956b69482d3d46251cf63db49e","observation_id":"bcd3785c-ad4a-4999-9f93-9f9edb8f4977","resolution":{"observed_at":"2026-08-04T05:46:03.162622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12265","last_updated":"2020-02-18T19:49:48Z","snapshot_observed_at":"2026-08-04T16:33:25.229105Z","submitted_at":"2019-05-29T08:11:52Z","title":"Strategies for Pre-training Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.12265","snapshot_observed_at":"2026-08-04T05:46:03.263683Z","title":"Strategies for pre-training graph neural networks.arXiv preprint arXiv:1905.12265,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.263683Z"},"links":{"cited_paper":"/paper/1905.12265","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:94facdb4eec9e2efba19036c79a8b4cb420338c1e8e1570f9abaa22666f226e5","observation_id":"b2533a4a-f4c6-4489-880f-1f6b19e3978f","resolution":{"observed_at":"2026-08-04T05:46:03.263683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-04T05:46:03.501872Z","title":"Roberta: A robustly optimized bert pretraining approach","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.501872Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:c0f4bb2446b0ad1f3bc664a85d2773e2d7a4eddf3cf733c704e7b582fede32ba","observation_id":"38fbc45c-cc0c-4363-9d2a-bb83613efe4c","resolution":{"observed_at":"2026-08-04T05:46:03.501872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.09382","last_updated":"2020-02-27T05:34:03Z","snapshot_observed_at":"2026-08-07T03:50:10.854073Z","submitted_at":"2020-01-26T01:12:40Z","title":"GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.09382","snapshot_observed_at":"2026-08-04T05:46:03.639361Z","title":"Graphaf: a flow-based autoregressive model for molec- ular graph generation.arXiv preprint arXiv:2001.09382,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.639361Z"},"links":{"cited_paper":"/paper/2001.09382","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:2bc8afdaa50cf79e9447dc13512b6a6abe1492a3a89ce1c73eccd89d5aaa5865","observation_id":"9bd67490-6724-4a47-863d-f15c47b6c041","resolution":{"observed_at":"2026-08-04T05:46:03.639361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-04T05:46:03.357724Z","title":"Mistral 7b.arXiv preprint arXiv:2310.06825,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.357724Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:4e9a11c0a783950712718a8ca9bd43da2b2491fb2e666c92a75c9195f038b52b","observation_id":"70d74fa0-3b90-45ba-85ca-a54952af6532","resolution":{"observed_at":"2026-08-04T05:46:03.357724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.04198","last_updated":"2022-04-28T15:46:08Z","snapshot_observed_at":"2026-07-06T12:06:18.160822Z","submitted_at":"2021-11-07T22:54:23Z","title":"TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.04198","snapshot_observed_at":"2026-08-04T05:46:03.727732Z","title":"Tacl: Improving bert pre-training with token-aware contrastive learning.arXiv preprint arXiv:2111.04198,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.727732Z"},"links":{"cited_paper":"/paper/2111.04198","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:fd7b5884d8233cc839b4af0425551f3526f73748ff8549093d1400bd4b91f4e6","observation_id":"48d0a345-cfac-4e11-b4e3-25756c89fabb","resolution":{"observed_at":"2026-08-04T05:46:03.727732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05376","last_updated":"2023-10-02T17:03:01Z","snapshot_observed_at":"2026-07-06T15:14:32.823996Z","submitted_at":"2023-04-11T17:41:13Z","title":"ChemCrow: Augmenting large-language models with chemistry tools","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.05376","snapshot_observed_at":"2026-08-04T05:46:02.660443Z","title":"M., Cox, S., Schilter, O., Baldassari, C., White, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:02.660443Z"},"links":{"cited_paper":"/paper/2304.05376","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:e373570ebeef2583dc1f8e2cce018c8ae15868fd6749b9af6f407edc2b31b1ee","observation_id":"fa80700f-7024-4c2f-8885-5bb1eb9ee4c6","resolution":{"observed_at":"2026-08-04T05:46:02.660443Z","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":"2026-08-04T05:46:02.424883Z","title":null,"venue":null,"work_id":null,"year":2064},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:02.424883Z"},"links":{"citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:adcc7f7cfe71c11421ba503a61b4e01a8136f0c6a4e27a908ea7b22f2bdbc06b","observation_id":"940d5b7e-9e10-47bf-819c-640d4b278823","resolution":{"observed_at":"2026-08-04T05:46:02.424883Z","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":"2026-08-04T05:46:02.807825Z","title":"Bert: Pre-training of deep bidirectional transformers for lan- guage understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:02.807825Z"},"links":{"citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:469a7cd77e38adeaa6f1b02881f7d30c7b0357f8dd90f717290bc8de208862ee","observation_id":"b288f1f6-a98c-4b09-b8b1-9c5f6c8ae38a","resolution":{"observed_at":"2026-08-04T05:46:02.807825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.07076","last_updated":"2017-05-17T11:24:43Z","snapshot_observed_at":"2026-08-01T16:28:43.806032Z","submitted_at":"2017-03-21T07:13:13Z","title":"SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.07076","snapshot_observed_at":"2026-08-04T05:46:02.573804Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:02.573804Z"},"links":{"cited_paper":"/paper/1703.07076","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:30239de1add3d4c59e46384531694cb04bc7c294e672cff61c268df6c3285524","observation_id":"2e9953b6-b232-4f70-a86a-10fb861a415a","resolution":{"observed_at":"2026-08-04T05:46:02.573804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.05979","last_updated":"2025-07-13T03:58:31Z","snapshot_observed_at":"2026-08-08T18:14:11.505888Z","submitted_at":"2025-03-07T23:24:24Z","title":"Learning-Order Autoregressive Models with Application to Molecular Graph Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.05979","snapshot_observed_at":"2026-08-04T05:46:03.844443Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.844443Z"},"links":{"cited_paper":"/paper/2503.05979","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:5927862dac08038fecdf090a61c1e0f5492e3dbfeeffa3371791035dc3827e5f","observation_id":"6e7cbfc6-179a-4ed1-a3a0-76c15df8e8cc","resolution":{"observed_at":"2026-08-04T05:46:03.844443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.09885","last_updated":"2020-10-23T04:22:37Z","snapshot_observed_at":"2026-08-08T06:02:13.053327Z","submitted_at":"2020-10-19T21:41:41Z","title":"ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.09885","snapshot_observed_at":"2026-08-04T05:46:02.737292Z","title":"Chemberta: large-scale self-supervised pretraining for molecular prop- erty prediction.arXiv preprint arXiv:2010.09885,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:02.737292Z"},"links":{"cited_paper":"/paper/2010.09885","citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:9768413c8c6cc84bfd71bec4c23f6e9757128a26a2bb2ac8738425c3367dcaa2","observation_id":"57cb0530-4723-493c-bfb6-66d53fa39a6d","resolution":{"observed_at":"2026-08-04T05:46:02.737292Z","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":"2026-08-04T05:46:03.443659Z","title":"Druggpt: a gpt-based strategy for designing potential ligands targeting specific proteins.bioRxiv, pp","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-04T05:46:03.443659Z"},"links":{"citing_paper":"/paper/2603.25062"},"observation_digest":"sha256:81bc549f822b43b8f94a01ae23beddaca3db8b74cf71505188d4207e46121fb3","observation_id":"b1fd868d-3c8a-44d9-8abf-f53c188cfae8","resolution":{"observed_at":"2026-08-04T05:46:03.443659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.25062","last_updated":"2026-08-01T10:34:48Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T03:17:24.536416Z","submitted_at":"2026-03-26T05:55:17Z","title":"SIGMA: Semantic Identifier Grouping for Molecular Autoregression"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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:2603.25062."}