{"as_of":"2026-08-16T11:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e7adf0fc006ea9ac1c7ac9b8e2b2807bbec423d7ef6b00723cade94b9ed1578","coverage":[{"denominator":88,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":88,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:16:06.532164Z","state":"measured"},{"denominator":93,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":93,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T07:22:12.446051Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T20:47:22.987838Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13791","snapshot_observed_at":"2026-08-04T07:22:12.446051Z","title":"Cheng, Chong Sun, and Alán Aspuru-Guzik","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.27497","last_updated":"2026-07-18T14:25:55Z","snapshot_observed_at":"2026-08-15T08:35:26.012416Z","submitted_at":"2025-10-31T14:19:50Z","title":"InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-04T07:22:12.446051Z"},"links":{"cited_paper":"/paper/2505.13791","citing_paper":"/paper/2510.27497"},"observation_digest":"sha256:432a0ea7db8b2a7026232ab211cad2a47634b6082e157d883c7fea865a986050","observation_id":"cf3130ce-338b-41f9-8855-e2f7143766be","resolution":{"observed_at":"2026-08-04T07:22:12.446051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"cited_work":{"arxiv_id":"2505.13791","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13791","snapshot_observed_at":"2026-07-02T20:47:22.987838Z","title":"Daigavane, A., Kim, S","venue":null,"work_id":"a3a22ad1-015a-406f-826d-85956bed153e","year":2025},"citing_paper":{"arxiv_id":"2512.05844","last_updated":"2026-05-05T18:38:58Z","snapshot_observed_at":"2026-08-13T12:11:00.719020Z","submitted_at":"2025-12-05T16:18:07Z","title":"NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-17T00:22:18.828991Z"},"links":{"cited_paper":"/paper/2505.13791","citing_paper":"/paper/2512.05844"},"observation_digest":"sha256:0042d218a478410d94b795cfab49fc8f3f00efc73a01732fc00a094119f3596f","observation_id":"e448c6c4-6868-463b-9078-648370a702d6","resolution":{"observed_at":"2026-05-17T00:23:44.747044Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"cited_work":{"arxiv_id":"2505.13791","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13791","snapshot_observed_at":"2026-07-02T20:47:22.987838Z","title":"Daigavane, A., Kim, S","venue":null,"work_id":"a3a22ad1-015a-406f-826d-85956bed153e","year":2025},"citing_paper":{"arxiv_id":"2606.07239","last_updated":"2026-06-05T13:07:56Z","snapshot_observed_at":"2026-08-14T14:32:48.606060Z","submitted_at":"2026-06-05T13:07:56Z","title":"Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-27T22:52:42.933237Z"},"links":{"cited_paper":"/paper/2505.13791","citing_paper":"/paper/2606.07239"},"observation_digest":"sha256:41decd15f211eb065157ad0dfe8120b23625ae59444c17c72f3b6bf65f4b550a","observation_id":"77ab49d5-71c1-463c-a6ff-92dd8d254902","resolution":{"observed_at":"2026-07-02T16:17:09.035949Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"cited_work":{"arxiv_id":"2505.13791","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13791","snapshot_observed_at":"2026-07-02T20:47:22.987838Z","title":"Daigavane, A., Kim, S","venue":null,"work_id":"a3a22ad1-015a-406f-826d-85956bed153e","year":2025},"citing_paper":{"arxiv_id":"2606.08221","last_updated":"2026-06-06T15:16:40Z","snapshot_observed_at":"2026-08-15T19:14:12.060867Z","submitted_at":"2026-06-06T15:16:40Z","title":"De novo molecular generation with optical property preconditioning at the token level","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T20:09:18.084061Z"},"links":{"cited_paper":"/paper/2505.13791","citing_paper":"/paper/2606.08221"},"observation_digest":"sha256:f3772ce2df06ff9f13d8dffc247870a8021c84235b0327359805fad2a744c99c","observation_id":"ede9622c-c29f-4425-b614-3cb2be15a05a","resolution":{"observed_at":"2026-07-02T20:47:22.989309Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13791","snapshot_observed_at":"2026-08-01T22:02:28.332204Z","title":"Scalable Autoregressive 3D Molecule Gen- eration.arXiv:2505.13791, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15918","last_updated":"2026-07-17T12:48:57Z","snapshot_observed_at":"2026-08-15T02:56:08.147875Z","submitted_at":"2026-07-17T12:48:57Z","title":"Atomic Design Transformer: Scaffold-Conditioned 3D Molecule Generation via xTB-Reward Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T22:02:28.332204Z"},"links":{"cited_paper":"/paper/2505.13791","citing_paper":"/paper/2607.15918"},"observation_digest":"sha256:e513d7405cb6e6a48d8de03f2123d0ac1e3742619f11654f7709e1fad8e8467a","observation_id":"94cf721c-6b87-4d2e-afde-b273d4cb69d7","resolution":{"observed_at":"2026-08-01T22:02:28.332204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.13791/citation-record","integrity":"/paper/2505.13791/integrity","json":"/paper/2505.13791/citation-record.json","paper":"/paper/2505.13791"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:16:06.231549Z","title":"Accurate structure prediction of biomolecular interactions with alphafold 3","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.231549Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:e119b0d89db689d23ef3f2615f2f4fec58d22331dca1fb4316a712166aa95b51","observation_id":"1d0f07df-b07a-4de9-9e86-84b0131318a7","resolution":{"observed_at":"2026-08-15T20:16:06.231549Z","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":"2026-08-15T20:16:06.235346Z","title":"De novo design of protein structure and function with rfdiffusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.235346Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:0c8e417e64b5304fc1e2a8d56450ade3b992e482bc8da449bc4a83a3113f79fa","observation_id":"a9586fac-b4c4-4acd-b288-a3b62f46f05f","resolution":{"observed_at":"2026-08-15T20:16:06.235346Z","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":"2026-08-15T20:16:06.238528Z","title":"A generative model for inorganic materials design","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.238528Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:f91f9dd4a1a4c43ea417bcda2c5048adee759b8d0b20934c1af1a2cbdba9b8d3","observation_id":"1f5ae58f-ee3c-4e8b-bcc7-28a6d16c898d","resolution":{"observed_at":"2026-08-15T20:16:06.238528Z","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":"2026-08-15T20:16:06.241812Z","title":"Equivariant diffusion for molecule generation in 3d","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.241812Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:d16bc6c42921f2c7d4da2fd108d84dbd0622176e3bb34dcb49493ed7c28c9bc6","observation_id":"1a1c5109-770b-4e48-acbd-4dd942152091","resolution":{"observed_at":"2026-08-15T20:16:06.241812Z","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":"2026-08-15T20:16:06.244604Z","title":"Equivariant flow matching with hybrid probability transport for 3d molecule generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.244604Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:fd7d9c78de2a92f14ea4c3365a37bbb5aba78d29bf062ec1b42e7d07b3bb2299","observation_id":"c6353a9b-5154-4537-a01e-18afb5b6aaee","resolution":{"observed_at":"2026-08-15T20:16:06.244604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06262","last_updated":"2025-03-02T17:20:26Z","snapshot_observed_at":"2026-08-16T07:01:54.354235Z","submitted_at":"2024-10-08T18:02:29Z","title":"SymDiff: Equivariant Diffusion via Stochastic Symmetrisation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06262","snapshot_observed_at":"2026-08-15T20:16:06.247601Z","title":"Symdiff: Equivariant diffusion via stochastic symmetrisation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.247601Z"},"links":{"cited_paper":"/paper/2410.06262","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:4fb7c2badc8635da4214d9e7ab4df3ce5cb40431b3d3788531f2144e1fa216e9","observation_id":"260e47b1-1aca-497e-84d7-a9af27dc72e2","resolution":{"observed_at":"2026-08-15T20:16:06.247601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.03965","last_updated":"2025-05-22T08:08:43Z","snapshot_observed_at":"2026-08-16T07:04:03.925864Z","submitted_at":"2025-03-05T23:35:44Z","title":"All-atom Diffusion Transformers: Unified generative modelling of molecules and materials","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.03965","snapshot_observed_at":"2026-08-15T20:16:06.251020Z","title":"All-atom diffusion transformers: Unified generative modelling of molecules and materials","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.251020Z"},"links":{"cited_paper":"/paper/2503.03965","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:eee79861e258adbb19078a1adf1eb34d4ed9e2fe00578f41a7c81533b20ab3b9","observation_id":"a75aa89f-bb44-4f6b-a0c7-f7a1c6cd0d6b","resolution":{"observed_at":"2026-08-15T20:16:06.251020Z","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":"2026-08-15T20:16:06.254555Z","title":"Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.254555Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:3ecee20606a62a96ec7ebc8a470982d600866e96214ef619b48f7fd3fae2113a","observation_id":"3304b399-7ab0-4802-bc82-de22831f7b4e","resolution":{"observed_at":"2026-08-15T20:16:06.254555Z","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":"2026-08-15T20:16:06.257917Z","title":"An autoregressive flow model for 3d molecular geometry generation from scratch","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.257917Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:b7659e1082521664689f907dd355952e4b1dbbc8340c7cbfe22b1fa19bafd081","observation_id":"bb4190d6-219e-4716-bfe4-7c2c371bc2d0","resolution":{"observed_at":"2026-08-15T20:16:06.257917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16199","last_updated":"2024-09-21T00:28:52Z","snapshot_observed_at":"2026-08-16T07:04:06.671733Z","submitted_at":"2023-11-27T05:32:21Z","title":"Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16199","snapshot_observed_at":"2026-08-15T20:16:06.261483Z","title":"Symphony: Symmetry-equivariant point-centered spherical harmonics for molecule generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.261483Z"},"links":{"cited_paper":"/paper/2311.16199","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:f5e39fd166b85069371fefc39297ce59040712e2ec16b08fc18da178b6306a26","observation_id":"9821842a-559f-49cb-ba65-27752a002e3b","resolution":{"observed_at":"2026-08-15T20:16:06.261483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.05708","last_updated":"2023-05-09T18:35:38Z","snapshot_observed_at":"2026-08-16T07:04:05.739986Z","submitted_at":"2023-05-09T18:35:38Z","title":"Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.05708","snapshot_observed_at":"2026-08-15T20:16:06.265206Z","title":"Language models can generate molecules, materials, and protein binding sites directly in three dimensions as xyz, cif, and pdb files","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.265206Z"},"links":{"cited_paper":"/paper/2305.05708","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:7825037e13bcd465ee84015bc1b1436a9f20c545c35f2e8669986ce8b09494a4","observation_id":"0935c2db-6f10-4df8-a9ee-cdb90675f9ca","resolution":{"observed_at":"2026-08-15T20:16:06.265206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01564","last_updated":"2024-12-02T14:50:44Z","snapshot_observed_at":"2026-08-16T06:37:40.076395Z","submitted_at":"2024-12-02T14:50:44Z","title":"Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates","version":1},"cited_work":{"arxiv_id":"2412.01564","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.01564","snapshot_observed_at":"2026-08-15T20:16:06.979604Z","title":"Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates","venue":"cs.LG","work_id":"8016f62a-5bf3-463b-91fd-5bb8eaeff10d","year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.268764Z"},"links":{"cited_paper":"/paper/2412.01564","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:05342ec5cf558df07870f80d7e8790b83661a0e62da5ca0dd8161ab1970cf53f","observation_id":"7c594657-0017-49f1-bb2f-a46ff780d984","resolution":{"observed_at":"2026-08-15T20:16:06.984155Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11838","last_updated":"2024-11-01T14:45:36Z","snapshot_observed_at":"2026-08-16T04:54:46.407071Z","submitted_at":"2024-06-17T17:59:58Z","title":"Autoregressive Image Generation without Vector Quantization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11838","snapshot_observed_at":"2026-08-15T20:16:06.272362Z","title":"Autoregressive image generation without vector quantization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.272362Z"},"links":{"cited_paper":"/paper/2406.11838","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:4b334e2d61376a346e344bea82326fd3c4a76c5ae84a7e0b01ead72c79929d19","observation_id":"20d3fb56-0549-4b7d-b752-5cdd4ecdf6b8","resolution":{"observed_at":"2026-08-15T20:16:06.272362Z","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":"2026-08-15T20:16:06.275889Z","title":"E (n) equivariant normalizing flows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.275889Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:312750e45e4981b1d6f974b5734f14b6b7f717031353841fd34847d12ac09f14","observation_id":"6b0f0ec7-4c31-4eb0-b598-7724c6a15006","resolution":{"observed_at":"2026-08-15T20:16:06.275889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.15441","last_updated":"2024-03-17T08:40:06Z","snapshot_observed_at":"2026-08-15T04:05:43.643136Z","submitted_at":"2024-03-17T08:40:06Z","title":"Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.15441","snapshot_observed_at":"2026-08-15T20:16:06.279088Z","title":"Unified generative modeling of 3d molecules via bayesian flow networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.279088Z"},"links":{"cited_paper":"/paper/2403.15441","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:55d8052cd38a98fe224975ba97185433b8a83c561fc7b090278f01f7f7f4f2f8","observation_id":"db24c4d1-6394-4c96-ae3d-21fc9868e992","resolution":{"observed_at":"2026-08-15T20:16:06.279088Z","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":"2026-08-15T20:16:06.282529Z","title":"Geometric latent diffusion models for 3d molecule generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.282529Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:23e21ca0ba4c03c4758a67f799cc0ec90a93812f3a451086e7ac7bdcc310b535","observation_id":"b6f77226-b518-416a-83b7-6d65eb4d92cb","resolution":{"observed_at":"2026-08-15T20:16:06.282529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03655","last_updated":"2025-07-28T05:05:47Z","snapshot_observed_at":"2026-08-16T07:04:04.419128Z","submitted_at":"2024-10-04T17:57:35Z","title":"Geometric Representation Condition Improves Equivariant Molecule Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.03655","snapshot_observed_at":"2026-08-15T20:16:06.285927Z","title":"Geometric representation condition improves equivariant molecule generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.285927Z"},"links":{"cited_paper":"/paper/2410.03655","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:9e9597302081037393afb21deab041fdcd3f3808f3106aefbc89d6556a9365f0","observation_id":"072c6149-b57e-4c59-9130-25a66b151368","resolution":{"observed_at":"2026-08-15T20:16:06.285927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15252","last_updated":"2025-03-02T14:10:09Z","snapshot_observed_at":"2026-08-15T23:17:38.954078Z","submitted_at":"2024-05-24T06:22:01Z","title":"Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15252","snapshot_observed_at":"2026-08-15T20:16:06.289647Z","title":"Fast 3d molecule generation via unified geometric optimal transport","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.289647Z"},"links":{"cited_paper":"/paper/2405.15252","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:539f50966a40e940d042e70b602e5ff56252c98208286de81adf49563e53edd4","observation_id":"387c7ecd-7da5-48b5-ad7e-6bea4ebbfaa7","resolution":{"observed_at":"2026-08-15T20:16:06.289647Z","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":"2026-08-15T20:16:06.293301Z","title":"3d molecule generation by denoising voxel grids","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.293301Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:48572142ba110b161a438a23c1a2f413c4f61cc0b156f42ff8416a0b75ec043c","observation_id":"de895c9c-895a-449f-a36e-9e02b67fe910","resolution":{"observed_at":"2026-08-15T20:16:06.293301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.08508","last_updated":"2025-01-15T01:10:59Z","snapshot_observed_at":"2026-08-16T07:03:54.380745Z","submitted_at":"2025-01-15T01:10:59Z","title":"Score-based 3D molecule generation with neural fields","version":1},"cited_work":{"arxiv_id":"2501.08508","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.08508","snapshot_observed_at":"2026-08-15T20:16:06.919518Z","title":"Score-based 3D molecule generation with neural fields","venue":"cs.LG","work_id":"5e3f484a-1039-405b-847d-a7418c8af3ff","year":2025},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.296492Z"},"links":{"cited_paper":"/paper/2501.08508","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:929c78e4f5ea68abcdbf97316700d6750bed672a24deee21f99b596d211a039d","observation_id":"cd71ed92-3994-4e2e-8459-231cd30fa10c","resolution":{"observed_at":"2026-08-15T20:16:06.925067Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:16:07.405723Z","title":"Generating 3 D molecules for target protein binding","venue":null,"work_id":"5c2f7d0a-c0f4-4c97-a849-452cc2c0a721","year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.299980Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:ff310167e6f01ae3a0fcda45f91a4057c7a8c6c82005eb669f4517ec3529ebfb","observation_id":"13b0f162-b607-41ac-ace4-19b38c7ee2cd","resolution":{"observed_at":"2026-08-15T20:16:07.410487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04379","last_updated":"2025-07-24T04:36:04Z","snapshot_observed_at":"2026-08-13T04:24:48.856415Z","submitted_at":"2024-02-06T20:35:28Z","title":"Fine-Tuned Language Models Generate Stable Inorganic Materials as Text","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04379","snapshot_observed_at":"2026-08-15T20:16:06.302943Z","title":"Fine-tuned language models generate stable inorganic materials as text","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.302943Z"},"links":{"cited_paper":"/paper/2402.04379","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:4587b5e7c35f5634baf5ab2020bf63dadd85e72feb56ebbe2767d7fefffa4430","observation_id":"9b1c1963-7c5c-4d3f-8e39-a69cb69268cf","resolution":{"observed_at":"2026-08-15T20:16:06.302943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03686","last_updated":"2024-06-06T02:10:50Z","snapshot_observed_at":"2026-08-13T00:00:24.801536Z","submitted_at":"2024-06-06T02:10:50Z","title":"BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03686","snapshot_observed_at":"2026-08-15T20:16:06.306319Z","title":"Bindgpt: A scalable framework for 3d molecular design via language modeling and reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.306319Z"},"links":{"cited_paper":"/paper/2406.03686","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:46f0dbc921e0e35431d0aa373aefcf0d07da717d89ccc044b7a5aedb3f886f78","observation_id":"4b803682-ff31-47a1-88a0-2d39b189dcd6","resolution":{"observed_at":"2026-08-15T20:16:06.306319Z","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":"2026-08-15T20:16:06.310160Z","title":"Large language models are innate crystal structure generators","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.310160Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:883c1b763fefba4de9ad80624d02144bb9da5c076ecd5dcb4ea997243e0a2a50","observation_id":"a84c1ed4-03ab-469c-a699-b7e125e990ff","resolution":{"observed_at":"2026-08-15T20:16:06.310160Z","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":"2026-08-15T20:16:07.393121Z","title":"3dsmiles-gpt: 3d molecular pocket-based generation with token-only large language model","venue":null,"work_id":"6e27b74f-76b6-463b-8d16-b4ed8d52a6c2","year":2025},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.314186Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:cef1e3f4acbfcd3280c131415eb7f62405177be5773f1b21e02668a47e0e45ca","observation_id":"d4844c98-5d6e-4c9a-87f1-52e3018c2d8e","resolution":{"observed_at":"2026-08-15T20:16:07.397694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:16:06.317924Z","title":"Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.317924Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:d7397032851ac89fc78911ea1b7f70820fb7494d1e80bf822d0d46f3491884bd","observation_id":"d4e27062-8345-4d8a-ac86-e1fb2a66a046","resolution":{"observed_at":"2026-08-15T20:16:06.317924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01392","last_updated":"2024-12-10T01:32:23Z","snapshot_observed_at":"2026-08-12T23:31:10.623373Z","submitted_at":"2024-07-01T15:43:25Z","title":"Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01392","snapshot_observed_at":"2026-08-15T20:16:06.320502Z","title":"Diffusion forcing: Next-token prediction meets full-sequence diffusion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.320502Z"},"links":{"cited_paper":"/paper/2407.01392","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:5e7049b738b6c0b3abd2a0a43e95d1345b8403729516690f7ff1528617b2f53f","observation_id":"f11cde99-722d-417c-9a63-7ce98ec565ab","resolution":{"observed_at":"2026-08-15T20:16:06.320502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19722","last_updated":"2025-05-19T15:26:21Z","snapshot_observed_at":"2026-08-15T23:39:56.515456Z","submitted_at":"2024-11-29T14:14:59Z","title":"JetFormer: An Autoregressive Generative Model of Raw Images and Text","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.19722","snapshot_observed_at":"2026-08-15T20:16:06.323307Z","title":"Jetformer: An autoregressive generative model of raw images and text","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.323307Z"},"links":{"cited_paper":"/paper/2411.19722","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:1429a4961373d309749f2dd35b68c7a3c18cd7b2cf0fc24f4e3950bb760d5e75","observation_id":"076ae48b-189d-497e-b1cf-5042f4fdd4de","resolution":{"observed_at":"2026-08-15T20:16:06.323307Z","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":"2026-08-15T20:16:07.373266Z","title":"Trans-dimensional generative modeling via jump diffusion models","venue":null,"work_id":"f02a9686-1c65-41bf-a2d0-b4a86ba94a8d","year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.326846Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:e9c6e420f753859d7ece4c99ca214291a97f5cd072df6af442896e5ffbe4b37c","observation_id":"cb1e4f2e-83ec-4e40-9cfa-94c4927e7a1b","resolution":{"observed_at":"2026-08-15T20:16:07.377292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:16:06.329950Z","title":"Fourier features let networks learn high frequency functions in low dimensional domains","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.329950Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:05bb097e1ff00ea0582e03353c0b7628c48f34123dca4e5f0cbaba1bdd8ef961","observation_id":"4edd2aa5-1702-48be-892c-1d3776430dcd","resolution":{"observed_at":"2026-08-15T20:16:06.329950Z","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":"2026-08-15T20:16:06.333223Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.333223Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:a5bfce437a728efc233b29a55e9fba3ad04a44a817bf2ee5a7f730f76bfcb4ef","observation_id":"6204eff9-7625-45cb-ad45-e3bae6bbdd16","resolution":{"observed_at":"2026-08-15T20:16:06.333223Z","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":"2026-08-15T20:16:06.336314Z","title":"Elucidating the design space of diffusion-based generative models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.336314Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:8ab467efb1afebe61d2dbb28ed7a11dba73cad13e72d907964112cfe635a17ce","observation_id":"fd83db96-8341-45a0-ac18-abd9b7bf455e","resolution":{"observed_at":"2026-08-15T20:16:06.336314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-15T20:16:06.339392Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.339392Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:6de52a47a1964541a0ea7824e2e3dbba1da460fced4d65f33ce7380f392c9bb5","observation_id":"4ff76959-2bb6-4dc7-9c73-6ce459a3e536","resolution":{"observed_at":"2026-08-15T20:16:06.339392Z","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":"2026-08-15T20:16:06.342802Z","title":"A connection between score matching and denoising autoencoders","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.342802Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:4712d0bff839b8bedb5a714d626ff97a4b0db73347d9cdc0ca7b6345575f9c6e","observation_id":"04e0c366-c9ca-4806-ad09-cd3cdecc3e88","resolution":{"observed_at":"2026-08-15T20:16:06.342802Z","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":"2026-08-15T20:16:06.346000Z","title":"Tweedie’s formula and selection bias","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.346000Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:f5b82d43c7c421d5c7f9272b09f2b34dcc51b74b8c98328683406a713a746c88","observation_id":"6b4c277d-98d6-4231-9882-c2cee703f5d6","resolution":{"observed_at":"2026-08-15T20:16:06.346000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1709.07871","last_updated":"2017-12-18T21:25:53Z","snapshot_observed_at":"2026-08-14T21:55:45.403341Z","submitted_at":"2017-09-22T17:54:12Z","title":"FiLM: Visual Reasoning with a General Conditioning Layer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.07871","snapshot_observed_at":"2026-08-15T20:16:06.349211Z","title":"Film: Visual reasoning with a general conditioning layer","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.349211Z"},"links":{"cited_paper":"/paper/1709.07871","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:4057fdf26917820becf5c78df8911c9ac67ee8bbe56d05cac1ce6bbc054fab05","observation_id":"86cdc231-c5c8-422f-ba4f-3b23ffa60860","resolution":{"observed_at":"2026-08-15T20:16:06.349211Z","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":"2026-08-15T20:16:06.352838Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.352838Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:da32d81abf4029df1839e0edd7200625ac621efd285d01503b37b1da1e53edf8","observation_id":"5c8b1a7f-8e4f-4ee4-ae09-716003f7adfb","resolution":{"observed_at":"2026-08-15T20:16:06.352838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03385","last_updated":"2015-12-10T19:51:55Z","snapshot_observed_at":"2026-07-06T04:39:28.429064Z","submitted_at":"2015-12-10T19:51:55Z","title":"Deep Residual Learning for Image Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03385","snapshot_observed_at":"2026-08-15T20:16:06.356054Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.356054Z"},"links":{"cited_paper":"/paper/1512.03385","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:1aacbe77c56a73b520f5cee387362696c926a432068c4b5454e5193569f50f01","observation_id":"b0ba7d92-211b-4650-8b36-0ef67f9ff279","resolution":{"observed_at":"2026-08-15T20:16:06.356054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08691","last_updated":"2023-07-17T17:50:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-17T17:50:36Z","title":"FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08691","snapshot_observed_at":"2026-08-15T20:16:06.359470Z","title":"Flashattention-2: Faster attention with better parallelism and work partitioning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.359470Z"},"links":{"cited_paper":"/paper/2307.08691","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:c7aba5963bf0106f8474bd48051e015679b19f729c43f750d68c8e36bb951243","observation_id":"14683bcd-0eb6-4ad3-b7fe-19c2bdcdf307","resolution":{"observed_at":"2026-08-15T20:16:06.359470Z","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":"2026-08-15T20:16:06.362808Z","title":"Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.362808Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:0a8553905b16ad16011efcfb3e561b9fde1a4bf9c0b08c0f9939d9e0d7b7dca0","observation_id":"decbcab2-4f44-4e57-ab98-a81d610073ef","resolution":{"observed_at":"2026-08-15T20:16:06.362808Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.19110","last_updated":"2025-04-08T23:59:56Z","snapshot_observed_at":"2026-08-16T07:04:03.397393Z","submitted_at":"2024-10-24T19:23:09Z","title":"Bio2Token: All-atom tokenization of any biomolecular structure with Mamba","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.19110","snapshot_observed_at":"2026-08-15T20:16:06.366077Z","title":"Bio2token: All-atom tokenization of any biomolecular structure with mamba","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.366077Z"},"links":{"cited_paper":"/paper/2410.19110","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:922b7489a476ef226eeaea9248d61983eebc1189158d9c3e40445c760a0793fc","observation_id":"d1e426c1-7f64-4afa-a0ed-402c559f0cb1","resolution":{"observed_at":"2026-08-15T20:16:06.366077Z","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":"2026-08-15T20:16:06.369890Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.369890Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:5d9cf8dfbc3ab2d96cd27625dcaa5f676b85ee8c1e5cb13efa8e2e5c330b66ff","observation_id":"49c4c1d6-7992-4079-9270-b35f5d035edb","resolution":{"observed_at":"2026-08-15T20:16:06.369890Z","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":"2026-08-15T20:16:07.308679Z","title":"karpathy/ nanoGPT , January 2025","venue":null,"work_id":"6ae96543-ebdc-4101-b091-e4a94bd80eca","year":2025},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.373132Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:6ff4ccae6889d6607dfe314e8b6cfcaae1ade165bcf7ac75a6db09c3931b3a97","observation_id":"2ba6547c-0445-4938-8cff-aab413082129","resolution":{"observed_at":"2026-08-15T20:16:07.312762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:16:06.376476Z","title":"Palm: Scaling language modeling with pathways","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.376476Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:b63ec768b6417171354367895844c1d095535d38dc43215a2264c47821a24d11","observation_id":"021fde27-e295-424a-aa4b-78f8aeb76110","resolution":{"observed_at":"2026-08-15T20:16:06.376476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.14322","last_updated":"2023-10-16T18:43:25Z","snapshot_observed_at":"2026-08-13T10:05:19.453505Z","submitted_at":"2023-09-25T17:48:51Z","title":"Small-scale proxies for large-scale Transformer training instabilities","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.14322","snapshot_observed_at":"2026-08-15T20:16:06.379756Z","title":"Small-scale proxies for large-scale transformer training instabilities","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.379756Z"},"links":{"cited_paper":"/paper/2309.14322","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:98a5599e351202fc1519ce52d8cc8cefac45115128206a10ec3ee6634493df5c","observation_id":"530834bc-a8ab-44e6-8bf7-ad8bc82df586","resolution":{"observed_at":"2026-08-15T20:16:06.379756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.02027","last_updated":"2022-10-05T20:14:52Z","snapshot_observed_at":"2026-08-13T18:55:15.925966Z","submitted_at":"2021-06-29T04:37:23Z","title":"Efficient Sequence Packing without Cross-contamination: Accelerating Large Language Models without Impacting Performance","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.02027","snapshot_observed_at":"2026-08-15T20:16:06.383092Z","title":"Efficient sequence packing without cross-contamination: Accelerating large language models without impacting performance","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.383092Z"},"links":{"cited_paper":"/paper/2107.02027","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:fcc6034bce1d90cf2a1b9b6891c12d6b631079ec45a18845fc3939ff467ebffa","observation_id":"caf5b11a-3ae6-4cbb-b282-6ace4d03240d","resolution":{"observed_at":"2026-08-15T20:16:06.383092Z","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":"2026-08-15T20:16:06.386606Z","title":"Neural ordinary differential equations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.386606Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:3a2815c8fc7acef0515846fedd577f8ce12673d1ce03f34e6c1ac45c2825c621","observation_id":"760d0eb6-dcf7-4c19-8298-af75e8b5ea04","resolution":{"observed_at":"2026-08-15T20:16:06.386606Z","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":"2026-08-15T20:16:06.389990Z","title":"A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.389990Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:42b9d21660a855a3aa0448935af688061e7cb8a0711c74287b7db3f7da5f2a4d","observation_id":"06fc45bf-c99f-46bc-a1f6-fff3c8848e16","resolution":{"observed_at":"2026-08-15T20:16:06.389990Z","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":"2026-08-15T20:16:06.393203Z","title":"Quantum chemistry structures and properties of 134 kilo molecules","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.393203Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:7f552d493a1396dfd1474284ad5e81fcdae5e6172b19a51b3e260054f9f38f8d","observation_id":"7e79f3f7-8e8c-4a46-b4f0-7b0ef6026886","resolution":{"observed_at":"2026-08-15T20:16:06.393203Z","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":"2026-08-15T20:16:07.272094Z","title":"GEOM , energy-annotated molecular conformations for property prediction and molecular generation","venue":null,"work_id":"8a1d198c-b918-4717-9afd-d93395866119","year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.396614Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:04942d28e2ec229390dc37ddd3a7dd7a292da3632940d58d5fb1746d086bdb98","observation_id":"69096701-e909-47e7-8934-aca4d76324f8","resolution":{"observed_at":"2026-08-15T20:16:07.276858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:16:07.260706Z","title":"E (n) equivariant graph neural networks","venue":null,"work_id":"b1fc9b31-830b-4cd6-8b68-2f655b47f729","year":2021},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.399544Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:0f17a5941b059553fc6207a43b1e09e8a0a567072a9df1324e8b0474085dba25","observation_id":"f8564b81-7ba6-47f1-bff5-eb5d87b86ec8","resolution":{"observed_at":"2026-08-15T20:16:07.265308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-15T20:16:06.402654Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.402654Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:a08a6ccfb1a9df9f8939784347fd7b7c5d1c5fa567ec9cbebab4ff6adcf3a8f3","observation_id":"5f65786d-9a5c-4063-8e6a-6eec6bb9b24e","resolution":{"observed_at":"2026-08-15T20:16:06.402654Z","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":"2026-08-15T20:16:06.406914Z","title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.406914Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:53629850ff1284e7e859e91bbc5ef281bae5c52d5707211b2389b84c7f4a6527","observation_id":"af63a694-048a-42c0-8322-48f207ccfd16","resolution":{"observed_at":"2026-08-15T20:16:06.406914Z","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":"2026-08-15T20:16:06.410562Z","title":"Geometry-complete diffusion for 3d molecule generation and optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.410562Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:ecc6a7434c27f2f15bc5698bb75a83a2c72ecc229637ac632d97b8c253afc96f","observation_id":"3cf104c8-8fdc-47d9-9645-7008cd501944","resolution":{"observed_at":"2026-08-15T20:16:06.410562Z","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":"2026-08-15T20:16:07.239565Z","title":"Practical suggestions for better crystal structures","venue":null,"work_id":"ede0d609-483f-4fd5-b9f7-0c09867d2d0a","year":2009},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.413257Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:07bf4d0762e4f5fef8c3e910c446d0d28df73b3dfae3a7219a1bf18a696f275c","observation_id":"41b31eac-9498-4b11-be15-0e726f3cb43b","resolution":{"observed_at":"2026-08-15T20:16:07.243035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:16:07.228215Z","title":"Olex2: a complete structure solution, refinement and analysis program","venue":null,"work_id":"75db0114-35bd-49b9-aa2d-050d245606c6","year":2009},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.415840Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:7eed3ece25a2c67a46bfcf5b35fa0d3287f3f61658320c53e09ff47c32b7df82","observation_id":"c816603d-06ec-4368-ab70-175faed1915c","resolution":{"observed_at":"2026-08-15T20:16:07.232042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:16:06.418773Z","title":"Open babel: An open chemical toolbox","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.418773Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:1ecffe00f1ec5df06d63f3204ae62b86402b590e575a08cd66b7329aaa4b9853","observation_id":"d2e8c08d-95e5-44b6-b570-d92a27f7f545","resolution":{"observed_at":"2026-08-15T20:16:06.418773Z","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":"2026-08-15T20:16:07.210775Z","title":"Adding hydrogen atoms to molecular models via fragment superimposition","venue":null,"work_id":"435bdbc6-31ca-455a-bdf9-fed2aa2dc4a0","year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.424366Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:d51b0ab9a4bb7765958346ff08d88a515034c6a3a84ed4561185d751134455a1","observation_id":"48f2d414-32db-4985-82cb-ae268f329d64","resolution":{"observed_at":"2026-08-15T20:16:07.214549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:16:07.198860Z","title":"Diffdec: structure-aware scaffold decoration with an end-to-end diffusion model","venue":null,"work_id":"7dddf523-6bbf-462c-9bb6-cb89ea4172de","year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.427256Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:a98ecaf706a497248b2a7d1a3dac4dfe178bdcea8d5a234bf560767407dcdd1d","observation_id":"fb4e4dfa-ef86-4a31-831a-4a29e77fdc11","resolution":{"observed_at":"2026-08-15T20:16:07.203331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.16634","last_updated":"2022-12-05T22:10:52Z","snapshot_observed_at":"2026-08-13T16:12:12.278715Z","submitted_at":"2022-03-30T19:37:07Z","title":"Transformer Language Models without Positional Encodings Still Learn Positional Information","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.16634","snapshot_observed_at":"2026-08-15T20:16:06.430239Z","title":"Transformer language models without positional encodings still learn positional information","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.430239Z"},"links":{"cited_paper":"/paper/2203.16634","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:94cc4b9062c438e332608a7783cfaaad4b481ab65a32c27e4418f19591f026c8","observation_id":"5bd493a4-4dfd-41f1-a01e-147c4dcc1b14","resolution":{"observed_at":"2026-08-15T20:16:06.430239Z","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":"2026-08-15T20:16:06.434031Z","title":"The impact of positional encoding on length generalization in transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.434031Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:9b527cc5308264898d4cbc186c9e97fd455417aa2766abb5d72e2a426c0c6df3","observation_id":"5ef7f18c-42ae-41a3-bf82-a014efc87b2e","resolution":{"observed_at":"2026-08-15T20:16:06.434031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10502","last_updated":"2024-09-16T17:42:15Z","snapshot_observed_at":"2026-08-15T12:00:22.238750Z","submitted_at":"2024-09-16T17:42:15Z","title":"Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.10502","snapshot_observed_at":"2026-08-15T20:16:06.437468Z","title":"Causal language modeling can elicit search and reasoning capabilities on logic puzzles","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.437468Z"},"links":{"cited_paper":"/paper/2409.10502","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:563db661b30e907c911d36569c91bb3f4d61dca4df3449c69ac1a9d65a353586","observation_id":"2b2074f2-5b5c-4fb4-b2b3-8a405af4caa8","resolution":{"observed_at":"2026-08-15T20:16:06.437468Z","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":"2026-08-15T20:16:07.180076Z","title":"Do large language models need sensory grounding for meaning and understanding","venue":null,"work_id":"036c9735-0c96-484c-b35b-1548bab4f9b0","year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.440918Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:4025cf6d3d10969a52cd0680276853b34196a278dafb028f9107226819e4e10d","observation_id":"d2ae7eab-f970-41e3-9e91-d3f830dcac9d","resolution":{"observed_at":"2026-08-15T20:16:07.184481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06963","last_updated":"2025-07-29T03:57:01Z","snapshot_observed_at":"2026-08-14T19:31:04.007868Z","submitted_at":"2024-03-11T17:47:30Z","title":"The pitfalls of next-token prediction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.06963","snapshot_observed_at":"2026-08-15T20:16:06.444145Z","title":"The pitfalls of next-token prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.444145Z"},"links":{"cited_paper":"/paper/2403.06963","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:fd389f09a63a067d902aea6282c549e47ac0200c25d95520cdcd3c4aefded6ce","observation_id":"fdccb27a-928a-4e41-91e0-96d7dfeb58fa","resolution":{"observed_at":"2026-08-15T20:16:06.444145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06768","last_updated":"2025-08-19T23:35:57Z","snapshot_observed_at":"2026-08-13T15:59:25.224905Z","submitted_at":"2025-02-10T18:47:21Z","title":"Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06768","snapshot_observed_at":"2026-08-15T20:16:06.447678Z","title":"Train for the worst, plan for the best: Understanding token ordering in masked diffusions","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.447678Z"},"links":{"cited_paper":"/paper/2502.06768","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:063dcc4cedfbf971fd58519c4c73c673c891aabb9c016a917d4c5c0a3e378819","observation_id":"30837235-cb0d-42ca-9ade-88520a05ef68","resolution":{"observed_at":"2026-08-15T20:16:06.447678Z","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":"2026-08-15T20:16:06.451228Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.451228Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:bd8be7927da7e86810d99cea54ed69a99672429b53146fc38533e53091afc9fe","observation_id":"9ccc8d39-4054-47a5-98af-68e1cc2730cd","resolution":{"observed_at":"2026-08-15T20:16:06.451228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06769","last_updated":"2025-11-03T00:53:34Z","snapshot_observed_at":"2026-08-15T20:23:25.637230Z","submitted_at":"2024-12-09T18:55:56Z","title":"Training Large Language Models to Reason in a Continuous Latent Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06769","snapshot_observed_at":"2026-08-15T20:16:06.454678Z","title":"Training large language models to reason in a continuous latent space","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.454678Z"},"links":{"cited_paper":"/paper/2412.06769","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:6dbc882cd8e1de826a57ecabf0818003377244f6b57915fdecc02ab63af5040d","observation_id":"595ffd92-e240-4a00-8183-a012c1727843","resolution":{"observed_at":"2026-08-15T20:16:06.454678Z","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":"2026-08-15T20:16:06.458233Z","title":"Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.458233Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:9c9473ff5eccbc0828d84097ccaf803ebc27a4b8febe153466854d5f0aaeb139","observation_id":"f3cec5c8-0801-421f-8742-61a93f61c1af","resolution":{"observed_at":"2026-08-15T20:16:06.458233Z","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":"2026-08-15T20:16:07.158037Z","title":"Equivariant flow matching","venue":null,"work_id":"15b01847-0329-4147-bd20-99050f8bae4b","year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.461508Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:a26b8a3325627be9569a17f4a5de6626f36daa18ab3709ae75856142598cae31","observation_id":"c172ee21-733c-49a9-bf13-1b6b8f0d778a","resolution":{"observed_at":"2026-08-15T20:16:07.161472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14426","last_updated":"2025-02-01T17:02:32Z","snapshot_observed_at":"2026-08-16T07:04:06.012708Z","submitted_at":"2024-06-20T15:50:12Z","title":"Transferable Boltzmann Generators","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14426","snapshot_observed_at":"2026-08-15T20:16:06.464784Z","title":"Transferable boltzmann generators","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.464784Z"},"links":{"cited_paper":"/paper/2406.14426","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:26f7989b46fdeac4bd89174947874c4bea83c745793c96ec3716dc331c23bdb4","observation_id":"952233bb-4085-4d66-8f25-d297c30fe2bc","resolution":{"observed_at":"2026-08-15T20:16:06.464784Z","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":"2026-08-15T20:16:06.468370Z","title":"Scalable equilibrium sampling with sequential boltzmann generators","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.468370Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:a8e503c72fa7dc8ea533e32c01530b6a58d366d4de082c9e9926fca7a626dba6","observation_id":"1d5bf2d8-95ab-49a4-b3e4-6a22e6a99f55","resolution":{"observed_at":"2026-08-15T20:16:06.468370Z","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":"2026-08-15T20:16:07.146823Z","title":"Dual use of artificial-intelligence-powered drug discovery","venue":null,"work_id":"31081601-3dbb-4ac9-adbd-5cc4a483f0eb","year":2022},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.471699Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:fb29a4357d89fa947700f8f8fa9868e405ac51f58f2aa718c71306c169aff134","observation_id":"714b591f-d18d-41b9-9018-34806296abba","resolution":{"observed_at":"2026-08-15T20:16:07.150492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:16:06.475064Z","title":"Python reference manual, volume 111","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.475064Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:fe8c3d9e270899f8175eec9d1431db860c0f9187f13028de514d7d473af4d072","observation_id":"66cac4c0-bf0d-405c-a69b-c46e031fb806","resolution":{"observed_at":"2026-08-15T20:16:06.475064Z","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":"2026-08-15T20:16:06.478751Z","title":"Python for scientific computing","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.478751Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:6ff974bad70fef85537ca92a54174ca3251ad86f283c39113ddb8f39650221a2","observation_id":"9b236d7e-0f49-400b-95a1-b9ff4744ffd2","resolution":{"observed_at":"2026-08-15T20:16:06.478751Z","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":"2026-08-15T20:16:06.481997Z","title":"Py T orch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.481997Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:d1b7b5289c2c9bd380c0651b087fcac9bde52a9edd310e5d71ec1fdccc4093f8","observation_id":"d06c662b-7694-4096-a36d-b501f07bd064","resolution":{"observed_at":"2026-08-15T20:16:06.481997Z","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":"2026-08-15T20:16:06.485136Z","title":"PyTorch Lightning , March 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.485136Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:d7a1856586cda216655b39514da9b4e5cd1d53a018ea70989eb521807bd4cc70","observation_id":"8ab7c67e-4a6e-4858-916d-a52fce839413","resolution":{"observed_at":"2026-08-15T20:16:06.485136Z","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":"10.5281/zenodo.8254217","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:16:07.108023Z","title":"Scalfani, guillaume godin, Juuso Lehtivarjo, Rachel Walker, Axel Pahl, Francois Berenger, jasondbiggs, and strets123","venue":null,"work_id":"8f8b89cb-354e-4f3e-bb62-2ff081c1b346","year":2023},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.488738Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:a41b0d11eac6a64b80b6a8bd2e9a8b9cec2d96761aabfc2bb966df3803701cbd","observation_id":"4967524a-3e25-44c5-bd23-f9ce8889bbda","resolution":{"observed_at":"2026-08-15T20:16:07.113303Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:16:06.492903Z","title":"3Dmol .js: Molecular visualization with WebGL","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.492903Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:dbe2bcbde3507cb835714ffa74746100eed01994d2c67052b8ad38c75e2cd67d","observation_id":"04c2c7ca-69c8-49c9-a1e5-0ec9e5bbf500","resolution":{"observed_at":"2026-08-15T20:16:06.492903Z","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":"2026-08-15T20:16:06.497203Z","title":"Jupyter notebooks-a publishing format for reproducible computational workflows","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.497203Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:55efa96e4faf4231c9273f0660de7bea06a63cbbfcab7ebad2248f34f5b8db0f","observation_id":"acb68703-75fc-4a2c-9e27-3c7eb9174c0c","resolution":{"observed_at":"2026-08-15T20:16:06.497203Z","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":"2026-08-15T20:16:06.501257Z","title":"Matplotlib: A 2d graphics environment","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.501257Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:8e9f9f48581480adf6600c16cf7cb32871f4fd65d94df3120453378ccc8a514f","observation_id":"cc7ff275-7afc-475d-8c71-e63a8afe8170","resolution":{"observed_at":"2026-08-15T20:16:06.501257Z","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":"2026-08-15T20:16:06.505644Z","title":"Seaborn: statistical data visualization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.505644Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:89c12bdc3de8730fe9e91bdc96c11e377b2e754c81103d141468555f1276b5f1","observation_id":"e2f6c824-9da2-4128-b3b9-f03cb804f9d4","resolution":{"observed_at":"2026-08-15T20:16:06.505644Z","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":"2026-08-15T20:16:06.510362Z","title":"Harris, K","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.510362Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:facb2cd9cfe3b62cbbf3a0905b7b2eb7fceca9c476a3729bf446ef3a85209cf0","observation_id":"35c08b96-6e85-46b3-b6bd-77e429ddbeb6","resolution":{"observed_at":"2026-08-15T20:16:06.510362Z","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":"2026-08-15T20:16:06.514302Z","title":"Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \\'e fan J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.514302Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:3d2383c65d9203bfa4032e608aa40008b0a2b21e8f838226b301d6290a05957b","observation_id":"c418fb0f-4cca-41c4-9f18-ea22e1b72c00","resolution":{"observed_at":"2026-08-15T20:16:06.514302Z","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":"2026-08-15T20:16:06.517946Z","title":"pandas-dev/pandas: Pandas","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.517946Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:3e312e019e4dcdc468f2f08c5d3f5306e75f5cbbf1fd9be66a7749caeaa5c0cd","observation_id":"1d4e3f3a-dd0a-4b78-b6d3-c89d9f255b8c","resolution":{"observed_at":"2026-08-15T20:16:06.517946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05496","last_updated":"2024-12-07T01:46:38Z","snapshot_observed_at":"2026-08-14T20:35:19.337789Z","submitted_at":"2024-12-07T01:46:38Z","title":"Flex Attention: A Programming Model for Generating Optimized Attention Kernels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.05496","snapshot_observed_at":"2026-08-15T20:16:06.521729Z","title":"Flex attention: A programming model for generating optimized attention kernels","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.521729Z"},"links":{"cited_paper":"/paper/2412.05496","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:f07b2ad9f7272298c3bd57362e21e03464c13db0b3e5bdc3b747e2930e237e4b","observation_id":"523b1b14-0a05-415d-8097-9c1eb7484bef","resolution":{"observed_at":"2026-08-15T20:16:06.521729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-15T20:16:06.525752Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.525752Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:60e764658b360b62e18fb53bafe03233849b30deb48dec66926a6a1425cfbd8d","observation_id":"a7cf3856-7b3a-43dd-b342-07085e7dd17e","resolution":{"observed_at":"2026-08-15T20:16:06.525752Z","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":"2026-08-15T20:16:06.528942Z","title":"Analyzing and improving the training dynamics of diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.528942Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:1e63e91b7b09df9fc4be4ec555a52504e173d8d641859719e2fabc9024b7b6e8","observation_id":"c0ee522c-1ae0-420d-9df1-ea9e5df2c81a","resolution":{"observed_at":"2026-08-15T20:16:06.528942Z","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":"2026-08-15T20:16:06.532164Z","title":"Universal structure conversion method for organic molecules: from atomic connectivity to three-dimensional geometry","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation","version":2},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-15T20:16:06.532164Z"},"links":{"citing_paper":"/paper/2505.13791"},"observation_digest":"sha256:e9ef896966686427909026af4e87c419d6094514e6b1785c0a55bc159d03efaa","observation_id":"7ca4d52f-2697-4643-a690-1be18028a8d3","resolution":{"observed_at":"2026-08-15T20:16:06.532164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T07:01:35.099097Z","submitted_at":"2025-05-20T00:47:48Z","title":"Scalable Autoregressive 3D Molecule Generation"},"reference_resolution":{"displayed":88,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":72,"verified_exact":3,"verified_fuzzy":13},"total_outbound_references":88},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 5 inbound Pith citation observations for arXiv:2505.13791."}