{"as_of":"2026-08-06T12:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8141f229f9795d008ea92d0a8944e7d6fd8180b0e868f653126aea5f38425ec2","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T05:32:47.288482Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.25006/citation-record","integrity":"/paper/2606.25006/integrity","json":"/paper/2606.25006/citation-record.json","paper":"/paper/2606.25006"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T05:32:47.288482Z","title":"Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences.Chemical Science, 15(9):3130–3139, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:c5862c9f363626c88b7a45922a06bb9efa6658b04122e06a779727ba5ad3c031","observation_id":"55da3f65-5ccc-4cbc-b286-0a4b8cfcb1e4","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta.Bioinformatics, 26(5):689–691, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:7b9d53446643a24045c827366d21f2d72b816028b42eca2245ee00e00d0f4666","observation_id":"c30b1441-5a72-4579-9051-bc3d172ec610","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in neural information processing systems, 35:16344–16359, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:99b314399c50a14631aa2ecc8fd0eeb55cd3648e90e6c4229e3ca3f9320d8803","observation_id":"2349ea2d-f466-4804-8ddb-82adaff6e0c8","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Vector neurons: A general framework for so (3)-equivariant networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:0093686f6ed0941d6088cc8c30e50e67a840ca8c9f2887519f8b1f1b1b7811ea","observation_id":"4d8d1a03-52e3-4474-9310-397b38213ab0","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:e4d664e4159f0f3b9173bebd5b6d5605d9fe7e663785829cde97b35710ba03e9","observation_id":"76014e12-c3f7-4bab-8df6-3b67f1dc8ad8","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"An equivariant pretrained transformer for unified 3d molecular representation learning.Nature Communications, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:eb6049acd1ed417e47133175262c8650e4014e4b1c874d12992322d2ab7b8335","observation_id":"b3f78075-3b16-4cda-b288-b9c696ec98b4","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Full-atom peptide design with geometric latent diffusion.Advances in Neural Information Processing Systems, 37:74808–74839, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:c7f7a502497fae6cdb15b9f23c0a6219ba8ced64d23b4a5f81a42c4d6103eaa4","observation_id":"79c5114d-820d-4aab-8394-91ff1eaed616","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Unimomo: Unified generative modeling of 3d molecules for de novo binder design","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:b516c873df6e5df521208b64fc1016d75a0deea90dc225d46ee773549472bc73","observation_id":"7e93fc8c-936f-4376-b969-db00bad2239e","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Full-atom peptide design based on multi-modal flow matching","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:f56c6b5a9138d4e43479c77a4ade68befe95f8a2a784f492cc957aba3a8a0d98","observation_id":"027c5bac-41fb-4fee-b6c3-57aca9c29979","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Equiformer: Equivariant graph attention transformer for 3d atomistic graphs","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:e131d8cecbfcc211ee4189a6b1b3538b841af634b485380df516d56b268e1e88","observation_id":"54cef8c8-35f6-4b00-a92f-036d6744ac25","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:cd91005dd7ec6896d0fa732f696218ba4b2320c5d224dcd8fd86d5f8762f4e1e","observation_id":"93e171ea-efb3-4505-917f-7021a5be2e56","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:3824feb6362a5cac5fe5eef8638ff4c1ba4fdef8092e25af512369fe9c75f841","observation_id":"730ed2d1-01b7-4db5-8719-0091b589239f","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:1a4ed033003e2305026d8689f8d41f38746ed48940a739f3dc8a62ad52d64756","observation_id":"228d5951-ed84-402e-9811-e8517eee918c","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:a18150cdc1f305fcbdf762d2b1e57dcba5b04bccf8a986d4afa82a51acf26e75","observation_id":"27807728-4bbc-48be-81ac-57edc7bbc608","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"E (n) equivariant graph neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:2eb3c844e0c3304c13ff928aabf6db0651172841a5c072609fcd503d7ec4d0f7","observation_id":"61ed4796-8476-4b69-a344-f49185558db7","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:ffaed0efe7b34d860221ad79bff554674fd90bdf1e01485a7a2529125d2b32d1","observation_id":"993863bb-b8d7-409e-b097-97e49311657b","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Schnet–a deep learning architecture for molecules and materials.The Journal of chemical physics, 148(24), 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:4c5b58ee4db3178ca090c6e4750be7aef0173ebfcaf3c3536b58b7881e661d50","observation_id":"a1b30ffc-1dcb-43ec-8146-b53f1d892292","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05202","last_updated":"2020-02-12T19:57:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-02-12T19:57:13Z","title":"GLU Variants Improve Transformer","version":1},"cited_work":{"arxiv_id":"2002.05202","doi":"10.48550/arxiv.2002.05202","metadata_source":"pith","pith_arxiv_id":"2002.05202","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GLU Variants Improve Transformer","venue":"cs.LG","work_id":"17d0763c-1016-41ab-a478-478e890765eb","year":2020},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"cited_paper":"/paper/2002.05202","citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:5ae46be7f124c6572bb88c9252cfb69f58dba91b862caad2b61a4d5c7f469468","observation_id":"b864b925-5631-44fc-b2a5-0484a775ed7d","resolution":{"observed_at":"2026-07-04T13:09:50.165779Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-13T15:50:07.002485+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T15:50:07.002485+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-26T05:32:47.288482Z","title":"Score- based generative modeling through stochastic differential equations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:cfc77908e142cb69e1b6069c2f0255cbbc71efe8411003614f4c2a33c62eb7ae","observation_id":"438d3b2f-6766-42f9-b7e2-2bfc4dd5e6cb","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Equivariant transformers for neural network based molecular poten- tials","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:72c574033f5995677d64df422f2cdc21015c7fd7997ebd92493e8cd081886dea","observation_id":"c9ceafc5-ad36-4c3e-a704-5961d3dee49d","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.08219","last_updated":"2018-05-18T20:09:34Z","snapshot_observed_at":"2026-07-06T06:24:51.822169Z","submitted_at":"2018-02-22T18:17:31Z","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","version":3},"cited_work":{"arxiv_id":"1802.08219","doi":"10.48550/arxiv.1802.08219","metadata_source":"pith","pith_arxiv_id":"1802.08219","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","venue":"cs.LG","work_id":"f3524378-9b19-478d-b727-18606df1f7ac","year":2018},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"cited_paper":"/paper/1802.08219","citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:918729c72294095476104da8b3822463722d39589e9c31ea37d22663e266760d","observation_id":"75603d4e-491e-4953-9e81-fc7d21d45394","resolution":{"observed_at":"2026-07-04T13:09:50.169491Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-26T05:32:47.288482Z","title":"Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.Nucleic acids research, 50(D1):D439–D444, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:28fb82c2c855691d1350fe5e8408646b45a313fd76317f0f391f9a5ab610493e","observation_id":"f599ca7b-6975-44e8-ac5b-950140f35fed","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Target-specific de novo peptide binder design with diffpepbuilder.Journal of Chemical Information and Modeling, 64(24):9135–9149, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:6d388844c57e77dcb8994e74b1da50b9d7f197f80964112a720377a5aa7ffae6","observation_id":"1c9729aa-f0de-429f-a1ed-fd0b388ce7eb","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Therapeutic peptides: current applications and future directions.Signal transduction and targeted therapy, 7(1):48, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:e0177190cdb24e60a6e15c2a6a366c60ee13c0c08c4f2259b02b650d668a60e4","observation_id":"4fdb5edc-6a85-4935-abc6-22fdfb4b8390","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Flashbias: Fast computation of attention with bias","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:c3f5e4bbd1770c5b24d7a1a883597c3a2094d709eb41536e0cf91bbf8e61e7e8","observation_id":"c2fab3f4-a8b0-4c1e-89e5-244dc98d4f2a","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","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":"2026-06-26T05:32:47.288482Z","title":"Root mean square layer normalization.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T05:32:47.288482Z"},"links":{"citing_paper":"/paper/2606.25006"},"observation_digest":"sha256:6bb415736451a69f07acf8a22ff3984d4f2940aedafeedfc4efead5f1a6dcf24","observation_id":"0a3f0f06-baec-4727-b2e3-2ba89de23fb8","resolution":{"observed_at":"2026-06-26T05:32:47.288482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.25006","last_updated":"2026-06-25T02:08:23Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T03:34:25.855241Z","submitted_at":"2026-06-23T17:25:04Z","title":"Scalable Peptide Design via Memory-Efficient Equivariant Transformer"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":26},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2606.25006."}