{"as_of":"2026-08-09T10:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6fc856b579b0aa996800c010e531d6a3ab6a908e0bd1c2a35d04c39118eb60a6","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T17:38:43.020803Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T08:14:36.233302Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.23101","snapshot_observed_at":"2026-08-02T08:14:36.233302Z","title":"arXiv preprint arXiv:2603.23101 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19406","last_updated":"2026-07-07T22:05:25Z","snapshot_observed_at":"2026-08-08T08:19:00.694686Z","submitted_at":"2026-07-07T22:05:25Z","title":"NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-02T08:14:36.233302Z"},"links":{"cited_paper":"/paper/2603.23101","citing_paper":"/paper/2607.19406"},"observation_digest":"sha256:6f0043d28ce4b5ed68522e9f20fc62e366b98702229343465f1fe58d196e6541","observation_id":"af5a8698-ebd3-4c58-84b0-3fc75a3e8c64","resolution":{"observed_at":"2026-08-02T08:14:36.233302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2603.23101/citation-record","integrity":"/paper/2603.23101/integrity","json":"/paper/2603.23101/citation-record.json","paper":"/paper/2603.23101"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T17:38:35.491394Z","title":"gold standard","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:35.491394Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:1a663be168f549949a993079d957e9a59e6490504eed1776d72ac632c87ceb92","observation_id":"96843300-20cb-4c31-a88b-3f74745c263f","resolution":{"observed_at":"2026-08-02T17:38:35.491394Z","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":"2026-08-02T17:38:35.613109Z","title":"Schmid, Sterling G","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:35.613109Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:9fa12c4cf4ea02b4e8d6834a157830d0f90f25c07a4d71c58eea2f77123609ad","observation_id":"aed83219-38de-4154-af1c-9f323da464fd","resolution":{"observed_at":"2026-08-02T17:38:35.613109Z","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":"2026-08-02T17:38:35.733992Z","title":"Granda, Liva Donina, Vincenza Dragone, De-Liang Long, and Leroy Cronin","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:35.733992Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:08d27cdec7d3e38e53a964c64ca816ae4a2a036fd9ebba0f12fd8693abc9108e","observation_id":"e18204e2-8594-4b0c-9a4a-b56b86580f63","resolution":{"observed_at":"2026-08-02T17:38:35.733992Z","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":"2026-08-02T17:38:35.856131Z","title":"Merz, Kenneth M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:35.856131Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:0b29e261effe716bf275ae5fe3a0c2315e5fa53cafe4074c851d9c24dd746fbc","observation_id":"bd12a2ce-61eb-4c20-91a3-7f6231463427","resolution":{"observed_at":"2026-08-02T17:38:35.856131Z","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":"2026-08-02T17:38:36.056110Z","title":"Artificial intelligence in spectroscopy: Advancing chemistry from prediction to generation and beyond","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:36.056110Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:33acf6e13742ae64d58e80ec4b307995252d34322801902040fc189df49d8736","observation_id":"7a6314ff-9b19-42bd-a29e-4f846bdbbfd5","resolution":{"observed_at":"2026-08-02T17:38:36.056110Z","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":"2026-08-02T17:38:36.149947Z","title":"Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:36.149947Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:3ee342eb8c2118dbe2b0cd944e8eba9bec94e588fe4493306285f7979ab1345c","observation_id":"d3d2b7b8-3da6-4708-b11f-7046a08db95b","resolution":{"observed_at":"2026-08-02T17:38:36.149947Z","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":"2026-08-02T17:38:36.305537Z","title":"From human labels to literature: Semi-supervised learning of nmr chemical shifts at scale","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:36.305537Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:5fbc0626b9da14bf68c3221a978c139e3dd9de766be0c007f9d342c14edc79fc","observation_id":"4aa41a22-7637-4638-aded-adbdbc143131","resolution":{"observed_at":"2026-08-02T17:38:36.305537Z","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":"2026-08-02T17:38:36.475775Z","title":"Accurate and efficient structure elucidation from routine one-dimensional nmr spectra using multitask machine learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:36.475775Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:cda8ed152a7bbef9f805d04e2f7c8aba55863af6a2747607479f9f4d4c54b8b1","observation_id":"8e1765fa-f65e-4399-addf-aaa0379f5886","resolution":{"observed_at":"2026-08-02T17:38:36.475775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.08441","last_updated":"2026-05-09T03:36:43Z","snapshot_observed_at":"2026-07-29T22:43:06.145336Z","submitted_at":"2025-08-04T13:33:38Z","title":"SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.08441","snapshot_observed_at":"2026-08-02T17:38:36.620379Z","title":"Language models can understand spectra: A multimodal model for molecular structure elucidation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:36.620379Z"},"links":{"cited_paper":"/paper/2508.08441","citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:6c19e69c1574c9c06cd65f4a22b368819977861e46ff8f88e9ccb33ff76ddacc","observation_id":"bd876173-ce94-4498-8854-ef5be76ef0a2","resolution":{"observed_at":"2026-08-02T17:38:36.620379Z","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":"2026-08-02T17:38:36.736050Z","title":"Molspectllm: A molecular foundation model bridging spectroscopy, molecule elucidation, and 3d structure generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:36.736050Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:a15ab6b8c6edd92adfa03be4aaecd25303f4f1db8d5c04ec1877c95152f2eddb","observation_id":"96c28266-9f84-4368-888c-32305c3d8ea6","resolution":{"observed_at":"2026-08-02T17:38:36.736050Z","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":"2026-08-02T17:38:36.906601Z","title":"Spectro: A multi-modal approach for molecule elucidation using ir and nmr data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:36.906601Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:c27df22cd76bebd42d868bea2b00945ac4674e29aff69a3276697a148cf14946","observation_id":"d0c8322c-546e-4de9-8aa8-5e80b66fe68c","resolution":{"observed_at":"2026-08-02T17:38:36.906601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.00640","last_updated":"2025-08-30T23:59:12Z","snapshot_observed_at":"2026-08-09T08:16:19.395880Z","submitted_at":"2025-08-30T23:59:12Z","title":"NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.00640","snapshot_observed_at":"2026-08-02T17:38:36.997442Z","title":"NMR-Solver : Automated structure elucidation via large-scale spectral matching and physics-guided fragment optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:36.997442Z"},"links":{"cited_paper":"/paper/2509.00640","citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:1f2c14715af120fb88355024dc84d730d2f70fb7c7c28baccfad8cb1b751771b","observation_id":"772e190f-4c7f-4452-a039-9e0224e152fe","resolution":{"observed_at":"2026-08-02T17:38:36.997442Z","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":"2026-08-02T17:38:37.142921Z","title":"Deepspinn--deep reinforcement learning for molecular structure prediction from infrared and 13 c nmr spectra","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:37.142921Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:211fe8fe05004812aaf41cbde507e43afaca83b4254953776cb442ae789e0b20","observation_id":"e28d56d8-c146-4e11-a2a5-ccefc2a273cf","resolution":{"observed_at":"2026-08-02T17:38:37.142921Z","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":"2026-08-02T17:38:37.307162Z","title":"Nmrexp: A database of 3.3 million experimental nmr spectra","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:37.307162Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:bb7b2e4c533d887068ef6738dbe02903a9a1afb7e347defa0a3df98b3ec07456","observation_id":"4a1e983d-932c-4a77-abcc-c5094f52ca7e","resolution":{"observed_at":"2026-08-02T17:38:37.307162Z","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":"2026-08-02T17:38:37.426612Z","title":"Helmus and Christopher P","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:37.426612Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:2d8683d6543628fa820501258097125e1439bd5029c7044714a19581ecd6ffc3","observation_id":"f74a76e9-fadc-439d-a4bc-147c60a81a04","resolution":{"observed_at":"2026-08-02T17:38:37.426612Z","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":"2026-08-02T17:38:37.558934Z","title":"Ernst, Geoffrey Bodenhausen, and Alexander Wokaun","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:37.558934Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:959442e2c85e2290cd4dc7fae0717de9dc21f7276d62ec89b6949ad3c2a0d1bf","observation_id":"08b204da-6e83-4a25-8594-0725e51af9df","resolution":{"observed_at":"2026-08-02T17:38:37.558934Z","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":"2026-08-02T17:38:37.738340Z","title":"Bartholdi and R","venue":null,"work_id":null,"year":1973},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:37.738340Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:1a8727351df9a92001dfab405cc39ab45acee799c312ba43fee7c08d69cfce38","observation_id":"e6b87557-18e1-4163-884a-da9ad3660831","resolution":{"observed_at":"2026-08-02T17:38:37.738340Z","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":"2026-08-02T17:38:37.905338Z","title":"Cooley and John W","venue":null,"work_id":null,"year":1965},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:37.905338Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:4087d2a46fb3d92cc1fe975e53c646f37ce0163a592d247fbafa9027eb674e01","observation_id":"7d35ab26-1c55-42b8-b10e-fda8ff6a056c","resolution":{"observed_at":"2026-08-02T17:38:37.905338Z","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":"2026-08-02T17:38:38.011927Z","title":"An efficient algorithm for automatic phase correction of NMR spectra based on entropy minimization","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:38.011927Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:ed8c23d04d56a90cda0bd596da7bf399eb14a58ecd5dceab771a6e1707466674","observation_id":"bc7a91d8-4ef9-4bd3-9611-10b387951368","resolution":{"observed_at":"2026-08-02T17:38:38.011927Z","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":"2026-08-02T17:38:38.136012Z","title":"pybaselines : A Python library of algorithms for the baseline correction of experimental data","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:38.136012Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:330653c3178e92361e974b35c97d81b7003b71273f541d5a17a58df3d5a12a17","observation_id":"04fad684-2d7a-4fcf-92c5-9b79f2217470","resolution":{"observed_at":"2026-08-02T17:38:38.136012Z","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":"2026-08-02T17:38:38.335806Z","title":"u ntert, and Kurt W \\","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:38.335806Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:b28f0da2420dab8503e5a7281f8f7df9591ba66e88e8b9b54fd6ea47dc703c5c","observation_id":"3f03dfd8-377d-418e-92d1-7f9403ee6912","resolution":{"observed_at":"2026-08-02T17:38:38.335806Z","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":"2026-08-02T17:38:38.482879Z","title":"NMR signal processing, prediction, and structure verification with machine learning techniques","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:38.482879Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:bcd906bfa0413df67287e04a984aef62982f4cb6a6befa46143f92d3ab0e442b","observation_id":"f8173958-01bd-4e54-a5cb-47071607a862","resolution":{"observed_at":"2026-08-02T17:38:38.482879Z","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":"2026-08-02T17:38:38.633158Z","title":"Rapid prediction of NMR spectral properties with quantified uncertainty","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:38.633158Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:e91c9be42027f6268feea3731044d06979ecfd47f0f14635a6ec5fa1557e58ec","observation_id":"c8af1b60-76aa-4f9d-973b-4cd06b50bac7","resolution":{"observed_at":"2026-08-02T17:38:38.633158Z","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":"2026-08-02T17:38:38.754020Z","title":"Computer-assisted structure elucidation ( CASE ): Current and future perspectives","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:38.754020Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:62c498a9cb5c999c48bdddb638e92a58deb86733287d51b019e0860743c831e4","observation_id":"0c4b9f97-7c2a-48fe-96e7-0f5a32a34d3c","resolution":{"observed_at":"2026-08-02T17:38:38.754020Z","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":"2026-08-02T17:38:38.867838Z","title":"Computationally-assisted discovery and structure elucidation of natural products","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:38.867838Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:afb5791705bac6b756a63023214df6f3db9e01ea409476099fb3b48ffff87e23","observation_id":"bad88b81-2c6b-412c-bf22-2467dff29980","resolution":{"observed_at":"2026-08-02T17:38:38.867838Z","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":"2026-08-02T17:38:39.042589Z","title":"Toolformer: Language models can teach themselves to use tools","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:39.042589Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:0b4cef4c9c1d72500cfd70f4855a52d86315dcd90b75fab175f943ff1344325f","observation_id":"0d0fedc2-ee8c-41ac-a2c6-232f27ec902f","resolution":{"observed_at":"2026-08-02T17:38:39.042589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.00445","last_updated":"2022-05-01T11:01:28Z","snapshot_observed_at":"2026-08-07T07:47:07.886786Z","submitted_at":"2022-05-01T11:01:28Z","title":"MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.00445","snapshot_observed_at":"2026-08-02T17:38:39.165698Z","title":"Mrkl systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:39.165698Z"},"links":{"cited_paper":"/paper/2205.00445","citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:30716c8a6a7be360195ed2c29cb8058b528e47c73a3bc0a785de019348e491f2","observation_id":"340597ea-6633-490b-9a6b-d4d5cd86537d","resolution":{"observed_at":"2026-08-02T17:38:39.165698Z","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":"2026-08-02T17:38:39.294676Z","title":"React: Synergizing reasoning and acting in language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:39.294676Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:8808095647f50b3369b9ba6060a2fbdab8462c481450c8dee9241ebdfd73d5ce","observation_id":"d35e1273-5c59-4702-a87b-cbaca578ba84","resolution":{"observed_at":"2026-08-02T17:38:39.294676Z","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":"2026-08-02T17:38:39.469623Z","title":"Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:39.469623Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:fadd5b700649d050082f01e0690ace9527f1d1b38f3e1eb94a2e8bdff4a43f65","observation_id":"147db6f1-9619-432a-9bd2-4513c55e52ae","resolution":{"observed_at":"2026-08-02T17:38:39.469623Z","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":"2026-08-02T17:38:39.620124Z","title":"Synergistic cross-modal learning for experimental nmr-based structure elucidation","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:39.620124Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:a172a41092a35ade0b4c9cd266310d25c649159a67a7c86e4dca5ffea40a3920","observation_id":"9f0224be-f37a-4cd8-9a63-04235aca6d53","resolution":{"observed_at":"2026-08-02T17:38:39.620124Z","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":"2026-08-02T17:38:39.766264Z","title":"Pubchem 2025 update","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:39.766264Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:89c6e49d79b91f00b219236df6a8f468f5ab43d22b622581f5cfcc40189d5b4e","observation_id":"2b340ffa-e286-4ffd-9cfc-ae9b37f0d859","resolution":{"observed_at":"2026-08-02T17:38:39.766264Z","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":"2026-08-02T17:38:39.895161Z","title":"Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:39.895161Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:3bca152f00207158945f21d8bb7211d09eca75f3409a3e674c21b8d65bc34e99","observation_id":"c4c94612-3627-4258-a0fd-9a54eaac6a3d","resolution":{"observed_at":"2026-08-02T17:38:39.895161Z","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":"2026-08-02T17:38:40.047614Z","title":"Loeffler, Jiazhen He, Alessandro Tibo, Jon Paul Janet, Alexey Voronov, Lewis H","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:40.047614Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:3592f7f40234288d30aeaa457554b32d1bc3b6a132c8829d5e9adc597462364c","observation_id":"1b6fd4ac-1265-4e40-bb29-43ccab7e58e5","resolution":{"observed_at":"2026-08-02T17:38:40.047614Z","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":"2026-08-02T17:38:40.229037Z","title":"Zare, and Patrick Riley","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:40.229037Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:f5fd58c7238b11407cb201dd7cde447056782e523d41cf0e393f53f58f4b092b","observation_id":"6f7f1065-4f8b-4bb1-b247-aec9c90ad2b1","resolution":{"observed_at":"2026-08-02T17:38:40.229037Z","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":"2026-08-02T17:38:40.380234Z","title":"A review of reinforcement learning in chemistry","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:40.380234Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:86ac3b28f7b9affe923550fb95ca56a29a628dd66f50493f1c62afeffc24cd19","observation_id":"be7e1701-eee2-4920-916a-05d3dcf36d87","resolution":{"observed_at":"2026-08-02T17:38:40.380234Z","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":"2026-08-02T17:38:40.521081Z","title":"Evaluation of reinforcement learning in transformer-based molecular design","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:40.521081Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:0da30de814489fd3d666becfc027e95a36946800a8b0161308f07e9cfde934bb","observation_id":"412d8001-9ac0-41e7-8717-8e4c52d25e22","resolution":{"observed_at":"2026-08-02T17:38:40.521081Z","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":"2026-08-02T17:38:40.687335Z","title":"Contact electron-spin coupling of nuclear magnetic moments","venue":null,"work_id":null,"year":1959},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:40.687335Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:fd4e7c80d598aa707b102c720731a08dd356bc9fbd6fdaff88ea0c8e53de8fe7","observation_id":"3bb17fde-ca08-46b2-8a5f-5d580a5d9efa","resolution":{"observed_at":"2026-08-02T17:38:40.687335Z","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":"2026-08-02T17:38:40.797918Z","title":"Poincar \\'e embeddings for learning hierarchical representations","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:40.797918Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:71c85da63e601f1e6f73ad1490763e50831efa0b1138b9b388a25ac41f020784","observation_id":"a614adca-79fa-4881-8efe-1e9d1db34e62","resolution":{"observed_at":"2026-08-02T17:38:40.797918Z","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":"2026-08-02T17:38:40.977329Z","title":"Learning continuous hierarchies in the lorentz model of hyperbolic geometry","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:40.977329Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:6c7be4f2b906a5b1113eca33fc0d16d3a42166d11c1c1a18b6a3b6198377fe20","observation_id":"45b1cf85-764d-4f25-990c-c51d44e9cb90","resolution":{"observed_at":"2026-08-02T17:38:40.977329Z","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":"2026-08-02T17:38:41.113471Z","title":"Hyperbolic neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:41.113471Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:027e2c2ec5a9145f40bbc53a22e00ce4510183f8aaeed19885f2a98a3b6113dc","observation_id":"3a75cbbb-8025-49ba-9517-1e3240957c75","resolution":{"observed_at":"2026-08-02T17:38:41.113471Z","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":"2026-08-02T17:38:41.295382Z","title":"Beyond lipschitz: Ranking binding affinity in hyperbolic space","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:41.295382Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:6cd46bf31b77819539154a2f1144187b1654ffbe38ce2bdc6566e55e10badb35","observation_id":"5e5067fe-a49f-4bc8-af9c-4d383cb06ea8","resolution":{"observed_at":"2026-08-02T17:38:41.295382Z","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":"2026-08-02T17:38:41.463953Z","title":"Uni-mol: A universal 3d molecular representation learning framework","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:41.463953Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:90f2744166881141cc20a143ff48283927070d011fc6e0cf455e317e036ce503","observation_id":"260b3174-d07f-4c31-9db4-a1851a199b9a","resolution":{"observed_at":"2026-08-02T17:38:41.463953Z","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":"2026-08-02T17:38:41.604135Z","title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:41.604135Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:a573418d3b9103596bd0e751bd0cd1595fe3c7a75be4267e8f355f593774d4e7","observation_id":"eb11fd15-a6ad-4e99-a8af-49e82176406d","resolution":{"observed_at":"2026-08-02T17:38:41.604135Z","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":"2026-08-02T17:38:41.725598Z","title":"Sutton and Andrew G","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:41.725598Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:b8f83c125f2e22bbb97def276404d1eb55850709fc6059631ab003c1080a76f0","observation_id":"07c054a2-013f-4cf7-a1e8-a3a5038e890d","resolution":{"observed_at":"2026-08-02T17:38:41.725598Z","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":"2026-08-02T17:38:41.859629Z","title":"Api-bank: A comprehensive benchmark for tool-augmented llms","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:41.859629Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:82dab827276a4d5e99c9e23d43a723edd3666f58e9732c57947fc2f97ed7cc51","observation_id":"87bff4ec-5d1e-4a71-af47-1b44e9118289","resolution":{"observed_at":"2026-08-02T17:38:41.859629Z","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":"2026-08-02T17:38:41.958846Z","title":"Patil, Tianjun Zhang, Xin Wang, and Joseph E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:41.958846Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:132f11bcad74d48752bcafcc5a0f6ec3b3b35920c86eebaba74baff0340f58f6","observation_id":"516d4148-3d5b-4dbe-9791-c863f700abf3","resolution":{"observed_at":"2026-08-02T17:38:41.958846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-02T17:38:42.130837Z","title":"Wu Gao, Yu Qiao, and Ping Luo","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:42.130837Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:93495b6ca99cc6dde69108dfbfdafa162d4d9face19292500e72496e09c860b4","observation_id":"0b82460a-3bd3-445a-a428-c722d5c1772c","resolution":{"observed_at":"2026-08-02T17:38:42.130837Z","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":"2026-08-02T17:38:42.318636Z","title":"Molreasoner: Toward effective and interpretable reasoning for molecular llms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:42.318636Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:5f2781918cd1764ba2c03104e4594581160df7442991c840d0c450021cd99af5","observation_id":"eafbda45-4ea0-4857-9a3e-b393c9b45c4d","resolution":{"observed_at":"2026-08-02T17:38:42.318636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.19598","last_updated":"2025-08-27T06:19:50Z","snapshot_observed_at":"2026-08-05T15:44:13.449330Z","submitted_at":"2025-08-27T06:19:50Z","title":"Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.19598","snapshot_observed_at":"2026-08-02T17:38:42.425254Z","title":"Encouraging good processes without the need for good answers: Reinforcement learning for llm agent planning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:42.425254Z"},"links":{"cited_paper":"/paper/2508.19598","citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:19b4c9a1ac366d355fc7b94100bcb576a635cd575dee442a48f6c8c148dd0185","observation_id":"227bb680-7c3d-424d-8699-9ccf6eaf1534","resolution":{"observed_at":"2026-08-02T17:38:42.425254Z","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":"2026-08-02T17:38:42.575757Z","title":"Introducing gpt-5.2","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:42.575757Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:b246a61d1754c6695e40a933228c53523b6dddf6b989f9dac404933ab0c74402","observation_id":"e8e1db1b-0be8-4b42-a76d-d1e7b20748cd","resolution":{"observed_at":"2026-08-02T17:38:42.575757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-02T17:38:42.721435Z","title":"Qwen2.5 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:42.721435Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:f82beb9bb0515452591ffa54d320f4f00b889ac352c917cceb380b75818f401a","observation_id":"5e06c61d-c235-4f94-9c65-4ade07a20804","resolution":{"observed_at":"2026-08-02T17:38:42.721435Z","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":"2026-08-02T17:38:42.864410Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:42.864410Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:0bdb119206d1dbb731146102ca7d9682eb1b0b67d5f9be8530f76a9618c4b676","observation_id":"4292dc65-934e-4c15-b9ca-2cebcd005f4d","resolution":{"observed_at":"2026-08-02T17:38:42.864410Z","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":"2026-08-02T17:38:43.020803Z","title":"Unilabos: An ai-native operating system for autonomous laboratories","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-02T17:38:43.020803Z"},"links":{"citing_paper":"/paper/2603.23101"},"observation_digest":"sha256:23aa8aeb38466e348f702ff1a84938c0fec9abf425c058f507f6daa5947ca95e","observation_id":"c06dff46-8535-46ce-b6af-dd8105e26f26","resolution":{"observed_at":"2026-08-02T17:38:43.020803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.23101","last_updated":"2026-07-20T03:26:10Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T07:36:31.611294Z","submitted_at":"2026-03-24T11:49:41Z","title":"SpecXMaster Technical Report"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":53,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":53},"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 9 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2603.23101."}