{"as_of":"2026-08-17T12:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e3769e2355cdc87353ba232786e1a11e03624e8efd0c5ced78d1009f6af67e38","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":"2026-08-17T06:30:58.91139+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":"2026-08-17T02:54:33.978396Z","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":"sha256:92bcb42410bf54b3970e70c118e5e46848f2bef6e7aa23a5cdcfbd0f4d6d04cd","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":"2026-08-17T02:54:33.978396Z","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":"sha256:9c2eba292e10be6287501c55e64c766e81bea494e4ae23e994cd6762b910f2a3","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-17T02:54:33.978396Z","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":"sha256:9bb1f0b12f61eaddee4508d58bb7620fd8d1aa203119100182e4e23d0fcce174","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-17T02:54:33.978396Z","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":"sha256:884db6afc609b133cb4c05cf1d0da97318aaaaac962bc8d5249d0d97142f3c9c","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13791","last_updated":"2025-09-08T22:33:04Z","snapshot_observed_at":"2026-08-17T02:54:33.978396Z","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:f640f691bf6c75bb3fa4a42f5cf0ddc1489a856e103a0ff2d1dc5a4979c7bdab","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":"2026-08-17T02:54:33.978396Z","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":"sha256:441d75379c0fceb321c9987faf3601d84ef7965b3a38dbef2d34cef1b0f06633","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":"2026-08-17T02:54:33.978396Z","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:ffb92dade589e2856a73b2ca627af442fbdebfcd1a05551c1d5ba36597bb7298","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":"2026-08-17T02:54:33.978396Z","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:e06b92612d7784f680545379c89f1924a0f0eec83eda60ae2028b2790e557a6a","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":"2026-08-17T02:54:33.978396Z","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":"sha256:862732f226b9503aaf59d735a9faf3cb5ba57081c1d0071112bfc734c9a5f787","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":"2026-08-17T02:54:33.978396Z","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":"sha256:2d45bf9efe51c7468b76246dfbd3757377d0054e8907b93b0f3388a448822bfa","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":"2026-08-17T10:58:04.303337Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:4aa80de8bdde95eb1b2917908f10d75e58ebb3346aeeef0e821ba3619ed1e214","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":"2026-08-16T12:52:42.104405Z","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":"2026-08-17T02:54:33.978396Z","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:ec85bca18ab2510ef2447b6d2cd1815da7c6cb50e720fa69be2b1ee58c756086","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":"2026-08-17T02:54:33.978396Z","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":"sha256:8f25af683196ec22faf98ba7efa525f9d3f7da6869b56f7317f153b31089b559","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":"2026-08-17T02:54:33.978396Z","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":"sha256:5562d7a3918d19c389a88ec3f9881f6ce0a11b9b60ea565cde8799e7f32b9c1c","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":"2026-08-16T14:40:12.466159Z","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":"2026-08-17T02:54:33.978396Z","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:d478a94b686ab59ed62cfdf5957bfa268cb3cee6f4718fe86a0aa6176746bff2","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":"2026-08-16T15:34:22.262858Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:8e26742ddb3fda9f64e0f66fedf42a1951944e1284f5174e438bf9dda097bc7b","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":"2026-08-17T02:54:33.978396Z","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:efa9de9956f77bd5a784d6591bd2286458e31300206513272dfe3d8e50be3da4","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11838","last_updated":"2024-11-01T14:45:36Z","snapshot_observed_at":"2026-08-16T13:42:07.461220Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:60f5f01a24f44d3f6f56d842c84ad8597678c693ea95e49e79726d0683963910","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":"2026-08-17T02:54:33.978396Z","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:c9a9bf3b7ec3525f578f89b89044951d30b450466be450f9c8b2cfbb4596a184","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":"2026-08-16T14:09:06.700403Z","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":"2026-08-17T02:54:33.978396Z","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:f2c2806b5d916d5bfb371029315cb63b8423c34706e5de7ef6f6a7f7b92c6e4f","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":"2026-08-17T02:54:33.978396Z","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":"sha256:86f1309e1d46db610d2c337a8897cb0462e8b5d45112c5ca110ca3b7a27ca251","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":"2026-08-16T13:12:29.593669Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:7fcae36f4367db4f44c44b4176a9b5d781015139885ed8d3fc5be6ee82a82fb7","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":"2026-08-17T07:14:17.264914Z","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":"2026-08-17T02:54:33.978396Z","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:d0e8f1696f99287cffc8bee997d7384cd2e0f62aa7109cb9de87d15cccd26473","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":"2026-08-17T02:54:33.978396Z","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":"sha256:358fc0feaae04a434deede46389befc2df7ceb23987e04bd99feb9f5d8b25f52","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":"2026-08-17T07:09:43.455337Z","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":"2026-08-17T02:54:33.978396Z","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:b8a90da0c064353094827a3ee77077e4f26d0b2c36483cda0a54d3752904ed49","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:64d91c899a21c77baaa6458640ab5ebf9a380f125152ff2a890a64d0f826d424","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04379","last_updated":"2025-07-24T04:36:04Z","snapshot_observed_at":"2026-08-16T14:20:51.403751Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:319b35a4ba0b69cc63b86c7b5e32766edbde78a1f4afa973a29fc3a9aded8ab4","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":"2026-08-16T13:45:42.757223Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:5c2fb80d735480eb85e85d8ee4a0466e32341ae327cc25c8dc378205db812d41","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":"2026-08-17T02:54:33.978396Z","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":"sha256:220f367bbfa4dae812e7aca6d19a7a3444f07529eb6bf8f72dc8f0b3b0eeec1c","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":"2026-08-17T02:54:33.978396Z","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":"sha256:9262052dc8e220ee4113d95fa10bf0ed0998d2881661a22c1cd60c0298415a96","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:9494d5b38b99385c65266079adcf47285041a52f285b1e48cf507ba7763c987b","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":"2026-08-16T13:38:04.174848Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:119a53eb1a76c62735b3b45b94562cd5ce76cccce58541a0dbfb6b362a377d34","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":"2026-08-17T02:54:33.978396Z","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:f2667716b7ed033f6552ca329b785fad30e11c5eef34f4e9c050fe7b07de49d0","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":"2026-08-17T02:54:33.978396Z","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:f2b3b0226e4d773b380ef074955aa15ccebfb20b43ce8dae7e11f4d96d24ea65","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:947567e6396a16e5410911c7f89dcf4f09a099d919063963841f73fb6a49997c","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":"2026-08-17T02:54:33.978396Z","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:da429bc6aed124fd7031943af6df277ebea461ab8851b7ce77d73c40316542b2","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":"2026-08-17T02:54:33.978396Z","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":"sha256:0ec1c027faa6e9b6920f1d3204d0587681790382a7215466c53839a62bf4c32a","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":"2026-08-17T02:54:33.978396Z","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:d55e64f2de9829cf7ee98e802dcc37fefbf0d1432418bcfcf51b094edffe0ed7","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":"2026-08-17T02:54:33.978396Z","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":"sha256:520a072f04eb1837e973840f189dc54ecfd92030a004ea3ce46504b508b16b44","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":"2026-08-17T02:54:33.978396Z","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:aebf9327a335950d384a7a117031f1cddcc2787443145fdff9bfa80f5bc8fc98","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":"2026-08-17T02:54:33.978396Z","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:dc3da0b601fb9b23cfcd7489e1961a52bc90680c476c09ff0d045054aad5fb85","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":"2026-08-17T02:54:33.978396Z","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:ea842288a3bdea2b3e3550267607909e783b5ed8221f0edfce9318d19a4cd52a","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":"2026-08-17T02:54:33.978396Z","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":"sha256:8f5366d18f652799144e1ab22560fbca37b0607d44740ebe5c20be76d1ce0618","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":"2026-08-17T02:54:33.978396Z","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:d7562273053386e1df0f0ad3f7e5437aad9b6a91a9f80d83acfc2a0fedf807ad","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":"2026-08-17T02:54:33.978396Z","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:dc0708dc65c177f6d5b92c58544c6d7905fcf19cf3b2510f0f332f49b350dcbb","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":"2026-08-16T15:58:43.754497Z","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":"2026-08-17T02:54:33.978396Z","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:e0b8d24ecb1af9046a229587586e97f3822874e2aea6551cd28a140e9bd953b2","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":"2026-08-17T02:54:33.978396Z","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":"sha256:2e81500206279c02740402d49c7cb888b5fb592d46fe86b45073320db56c244a","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":"2026-08-17T02:54:33.978396Z","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:ebbdc8b6486004b201d96fe85087c990ac94aa737d29c13a36cc55b4031ff532","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:644beaf455c4811589f5c714ffb364b9e878d1005c163934cb3105d87e02888f","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":"2026-08-16T14:57:46.398467Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:5cec248f601b644c4575f7890e22d4f16435c557b9e761197ad860b8fe152ea2","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":"2026-08-17T03:10:15.141603Z","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":"2026-08-17T02:54:33.978396Z","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:c01a45d6d6106ddc7e46f2bd6f1b6b3f1716d5a2f38c170301cb399ca95f72bc","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":"2026-08-17T02:54:33.978396Z","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":"sha256:834e34d16dfc291bb55591ca485af867d880cc0eb59b33a891b0ded0a1cacbcd","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":"2026-08-17T02:54:33.978396Z","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:ab7e4b11f9964a58ec73c246e54e87a76164d79f7e4ac6d58427d3f88a225d9b","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":"2026-08-17T02:54:33.978396Z","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:d44204457616eb0821610b0a87a01e350e9431577986660619b49bc4d0b86051","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":"2026-08-17T02:54:33.978396Z","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":"sha256:8f2747e8e9c597484a8c3250a7792ca9f650496d2576072556ad911dc524bb90","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:84707de8f10cdaac0fd77618a9c17a39374b48aec3c49447e6cff9ead7d98ea1","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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:ca3e093ee11549755cad4d0090585d1182ce001ce370f114127a30f0756d4a0e","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":"2026-08-17T02:54:33.978396Z","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:f279fdbaf3600126dd6ab0f95d45e956c3b6087984c4aed8bac45f323d9261e1","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":"2026-08-17T02:54:33.978396Z","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":"sha256:5f92ca8d91782afd911167f2b11a0626ebaae008d72f86d3c4ed2a635e4f9a25","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":"2026-08-17T02:54:33.978396Z","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":"sha256:4290f469aeee58e06060962b7d81037377d64305fea6523e283d5209154a999b","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:835aef367aa4e005bad70164d9f5db47fef1075bbbf30505a650a455acc571fc","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:783ca4bc46b7031b83636ec70c9abb30c617b50017caf827cd5dde653fb70a29","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":"2026-08-17T02:54:33.978396Z","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:de0b9a26c47834752f938af867d84680317bfd07f43f125268b504a086e22ae1","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:6fbfb0a77034d6d6672e4979d6722145f8dacec740bebceae59506c08261e12a","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.16634","last_updated":"2022-12-05T22:10:52Z","snapshot_observed_at":"2026-08-16T17:10:27.441937Z","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":"2026-08-17T02:54:33.978396Z","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:e4e12c42228f88df4d5050a36b43968658da24a06db2129a300f3dc92e3475d8","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":"2026-08-17T02:54:33.978396Z","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":"sha256:95fd533ee0d2741ae79d83d035679edbb0c212f278f56612eca6ab77004a3c71","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":"2026-08-16T13:18:05.144759Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:3b71e602b62040aa8841ceefade11707936a0945cacc44aefed0b385d1dc6299","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":"2026-08-17T02:54:33.978396Z","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":"sha256:8a7d451914db623227873e4e1b6aad1e159a67fa00ecec680324671b5018ab78","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06963","last_updated":"2025-07-29T03:57:01Z","snapshot_observed_at":"2026-08-16T14:10:51.973960Z","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":"2026-08-17T02:54:33.978396Z","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":"sha256:5f760b9bad098ae81fedd952c99f211cf978c96f3285d38517627baae096dee3","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":"2026-08-17T02:54:33.978396Z","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":"sha256:9ff691b9f8371bbe79970a95a9862fea3c3bd855fde5ed3858526936789f8cb8","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":"2026-08-17T02:54:33.978396Z","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":"sha256:6fff4cddfb2f0e87b3e29b220074d4c1144885e74d9164391b0f26512fe1e2de","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":"2026-08-17T06:12:37.525438Z","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":"2026-08-17T02:54:33.978396Z","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:a479b36dbc164a954e9a88589f3e9baae0dd1fa578233adff6e7e9491a6b8b3a","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":"2026-08-17T02:54:33.978396Z","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:f520c0a08f11f2c5e703935fb48b654cc6c054b6e1c5861fe5e669422c1d9c8b","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":"2026-08-17T02:54:33.978396Z","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:b134aaf97ddac32e425e4c4609406ef13ba9cbe3e3525b4474b9a51399abd1c0","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14426","last_updated":"2025-02-01T17:02:32Z","snapshot_observed_at":"2026-08-16T13:41:03.596206Z","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":"2026-08-17T02:54:33.978396Z","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:f369d7ab11fc756a3efa88d99acdd2fe1b5e127a2dc8d80f2f1e5e23166f18ec","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":"2026-08-17T02:54:33.978396Z","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":"sha256:78c6987e66b5a63dcb62467cb201c4e6f2974bf4c04bc237bd0cffabf11d513e","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":"2026-08-17T02:54:33.978396Z","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":"sha256:171f537d38f0f800be473498e963ba5810dc4288ddf077dbaf2529e7575a446e","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:694b75e9f8d2e8523ab99b32167495ecca6910bc1ffc7d9843a595157353013c","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":"2026-08-17T02:54:33.978396Z","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:fdb004538a256610ebaa032caa287f761d83141d1ea7cbf713bcb72410885218","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":"2026-08-17T02:54:33.978396Z","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":"sha256:32d4b0bb975338d75ad9e38c19965ed9ff4c94b0a06e9ee3712a99c9b85a3644","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":"2026-08-17T02:54:33.978396Z","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":"sha256:1ac1c5c8fed0684f931ccf53153056401e0fa7dcf5e72a4fb9c175ad4e6893f6","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":"2026-08-17T02:54:33.978396Z","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:c7ae35d0845616c98299c0a59e28191399cf6add14e74bb465e325b126160e30","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 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-17T02:54:33.978396Z","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":"sha256:307efcfc9d6e112d7e45f71d9288c36fe64504fac47da3c47dab61e7cc99246f","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":"2026-08-17T02:54:33.978396Z","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":"sha256:339126386798f38d16c9c91d88ed56eb3434477045d21ff425d330c96dccc3fb","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":"2026-08-17T02:54:33.978396Z","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:de3e9a264067663abf769e72ae500509bc1542d86f6e5482ae3608832f1c5249","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":"2026-08-17T02:54:33.978396Z","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":"sha256:895fee32f9abec3aa1d4c78481f1a086d081fb77dfeec44a7bcbae50fdd90678","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":"2026-08-17T02:54:33.978396Z","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":"sha256:5be680dd2960622bd655de306dc494d0a9307d74536d5fd984db7e94ce5ebf23","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":"2026-08-17T02:54:33.978396Z","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:d807c68a76a5900e28655834e7c8f17d96dc7c75138ec6a7dc007954672fdb87","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":"2026-08-17T02:54:33.978396Z","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:fdb30adcbd526f98300d8ebb147039abcabb9558c8c0e27c504a041996d54bf8","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":"2026-08-17T02:54:33.978396Z","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":"sha256:19fe03ca285d4fba5e5446bb5f49ab8cb88de8bc75b8bbcd8f223f60ddb99be8","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":"2026-08-17T02:54:33.978396Z","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":"sha256:234f775b2cca5ae2e3857e4ebf8d77fb684b4b754ded5fbac8bb86fe39f706d4","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":"2026-08-17T02:54:33.978396Z","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":"sha256:008002e21ac9f2d0dd5d16181cb36182b672bc484da216225744fa1d75f27314","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":"2026-08-17T02:54:33.978396Z","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":"sha256:2dcd5c1bc63ba02c3e647fcb11eb76c2e5ef852bc8ceb6eadcedb3baf5575e78","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":"2026-08-17T02:54:33.978396Z","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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 5 inbound Pith citation observations for arXiv:2505.13791."}