{"as_of":"2026-08-08T17:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3acd3cca5f996590ec4be15394c2ddf2e4035be1ffeb9292ebbbc3ce8e9a0811","coverage":[{"denominator":67,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":67,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T04:47:29.640202Z","state":"measured"},{"denominator":67,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":67,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2602.04139/citation-record","integrity":"/paper/2602.04139/integrity","json":"/paper/2602.04139/citation-record.json","paper":"/paper/2602.04139"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:47:29.246903Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.246903Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:5644285bc0adb2c3611ecccd965c89b48a0a63c0caeb2b4abd34282294ce30e0","observation_id":"116a58af-02d0-45a5-9262-54fb512752ef","resolution":{"observed_at":"2026-08-03T04:47:29.246903Z","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":"2026-08-03T04:47:29.309711Z","title":"Gradient flows: in metric spaces and in the space of probability measures","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.309711Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:9731f359ca6218719e651dfbaf2cef3a6f883ffe54b8090d1f6fbca2c134e8cc","observation_id":"29d8b7f4-9a4e-4582-89b7-027f5b750f20","resolution":{"observed_at":"2026-08-03T04:47:29.309711Z","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":"2026-08-03T04:47:29.337582Z","title":"Neural operators for accelerating scientific simulations and design","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.337582Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:4481c3207b62ae7e1c66eaa9a3d9015a9fab3cbeac6ce96870c7cd5352492f2a","observation_id":"571a3e58-cdcc-44cf-a977-5b6603c38081","resolution":{"observed_at":"2026-08-03T04:47:29.337582Z","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":"2026-08-03T04:47:29.394086Z","title":"Weight uncertainty in neural network","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.394086Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:8b586c1efe432a5b40801bdc71bc688c3ee0d97bb475e3d2daa36a8f1046be79","observation_id":"01d3000e-67f5-4cfa-9409-f68ef7499cb3","resolution":{"observed_at":"2026-08-03T04:47:29.394086Z","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":"2026-08-03T04:47:29.422219Z","title":"Spherical fourier neural operators: Learning stable dynamics on the sphere","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.422219Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:452c1a3096997d835cf1a08ff99038ce27b53ea742b67ea517d2f4ff2b0bc96d","observation_id":"0e12c0c9-4a30-4639-9c69-eaf310d8de27","resolution":{"observed_at":"2026-08-03T04:47:29.422219Z","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":"2026-08-03T04:47:29.426090Z","title":"Why diffusion models don t memorize: The role of implicit dynamical regularization in training","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.426090Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:d702d3822b0cd00c165a3130865aec9122fda6c23d50fc3e1bae1d68160377f3","observation_id":"62fe5ed1-f19f-45b2-b325-0a38ddf439ca","resolution":{"observed_at":"2026-08-03T04:47:29.426090Z","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":"2026-08-03T04:47:29.429956Z","title":"Probabilistic neural operators for functional uncertainty quantification","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.429956Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:4f1153c1f7aeb5f631d5964295bea330fff3f67e1edc839d5f84abe715cd2ea2","observation_id":"47644e5c-3f80-4ac0-ae91-9fb5b2c4dcfd","resolution":{"observed_at":"2026-08-03T04:47:29.429956Z","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":"2026-08-03T04:47:29.433826Z","title":"S., Boffi, N","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.433826Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:ed5bc875cfe778dc62fd8475fd8fbc5e20c027dff7fbf2bb6195d29cf9f74c3c","observation_id":"5be6788c-2659-4d8c-b922-ba7e04903faa","resolution":{"observed_at":"2026-08-03T04:47:29.433826Z","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":"2026-08-03T04:47:29.437601Z","title":"Hyperdiffusion: Generating implicit neural fields with weight-space diffusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.437601Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:537c5a45957beac8a40d80cc9495b0c8212fcd4856882ce57bb6e48ad65ad1d0","observation_id":"2d302e84-cd5a-4307-bfc2-0379de313762","resolution":{"observed_at":"2026-08-03T04:47:29.437601Z","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":"2026-08-03T04:47:29.441141Z","title":"and Ghahramani, Z","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.441141Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:50622bb069841b36daf99a387664115d59106ee2b3f5a693b02bf03c8780c994","observation_id":"0f1a7686-e4f5-40f6-9084-cd258c578125","resolution":{"observed_at":"2026-08-03T04:47:29.441141Z","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":"2026-08-03T04:47:29.444446Z","title":"P., and Salimans, T","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.444446Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:79e702d7fde824eb1341d0316aa8659aff05941711d5a5d89f75bb8b8e1c2a6a","observation_id":"b9242a43-4ec6-42a0-9b5d-eb9b2641ea79","resolution":{"observed_at":"2026-08-03T04:47:29.444446Z","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":"2026-08-03T04:47:29.447871Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.447871Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:e16fb428581d2f623c3b0c4fa93a01ce3d23e199e6a46c5e77878bd14cfaae55","observation_id":"3da9188b-ef0a-417c-b04e-0bfaac9f33ae","resolution":{"observed_at":"2026-08-03T04:47:29.447871Z","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":"2026-08-03T04:47:29.451157Z","title":"Gnot: A general neural operator transformer for operator learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.451157Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:b964654c42297d7a2e331cc1b08d504a0a7d179e0ff79a11ef0bfadf1da4e80b","observation_id":"a88627b2-8145-460b-bb76-516480e4de93","resolution":{"observed_at":"2026-08-03T04:47:29.451157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03542","last_updated":"2024-05-07T01:57:00Z","snapshot_observed_at":"2026-07-06T17:40:20.922598Z","submitted_at":"2024-03-06T08:38:34Z","title":"DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03542","snapshot_observed_at":"2026-08-03T04:47:29.454528Z","title":"Dpot: Auto-regressive denoising operator transformer for large-scale pde pre-training","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.454528Z"},"links":{"cited_paper":"/paper/2403.03542","citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:e58c76cfbea7fabe319ce8122ee46ed223ec986ffa5c721342274504de984475","observation_id":"ba409a62-8b96-4b64-9b99-449960ccec68","resolution":{"observed_at":"2026-08-03T04:47:29.454528Z","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":"2026-08-03T04:47:29.458395Z","title":"Variational bayesian last layers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.458395Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:0e4c1e816c1ff7654df5ae06f303d4f74491543d6ece65ab563d476ce9f1fe07","observation_id":"75b2ba52-8cbe-4e55-871d-bf5fd53cb16d","resolution":{"observed_at":"2026-08-03T04:47:29.458395Z","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":"2026-08-03T04:47:29.461816Z","title":"Poseidon: Efficient foundation models for pdes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.461816Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:48a1e9cfdb6c21e11b67dc3637b026d93ae47b05a2ffb67d02243f2641bd2886","observation_id":"e3cff3f1-16c6-4e51-95ef-366470f7c1e8","resolution":{"observed_at":"2026-08-03T04:47:29.461816Z","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":"2026-08-03T04:47:29.465570Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.465570Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:5d8a91b5e33ed05ff93cd506e68a1ae0d625ed2b5790343431134a25ebea6897","observation_id":"dba77d51-86fe-4e6c-ba9d-4dd1e15ebaa1","resolution":{"observed_at":"2026-08-03T04:47:29.465570Z","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":"2026-08-03T04:47:29.469126Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.469126Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:03c77668bd6bc963d5ac28605abd26d07843c21a893b469eaa58e826a29ec279","observation_id":"d3fcc678-29bb-46e6-9808-96eadc2e8599","resolution":{"observed_at":"2026-08-03T04:47:29.469126Z","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":"2026-08-03T04:47:29.472430Z","title":"P., and Mallat, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.472430Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:b9c71c077959575d5e6101d56b2872d63a72303c3433720865a9dc7e873f08b0","observation_id":"56cbdbfd-d92a-4bbf-b4c0-1252245c2c18","resolution":{"observed_at":"2026-08-03T04:47:29.472430Z","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":"2026-08-03T04:47:29.475887Z","title":"Diffusion generative models in infinite dimensions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.475887Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:78897923e884d2d9abaf78242de1c1c39fb380d41409e0d0868d74d1e27e9153","observation_id":"8b5c1167-42c1-4dca-9602-bbb4c99c8d57","resolution":{"observed_at":"2026-08-03T04:47:29.475887Z","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":"2026-08-03T04:47:29.479131Z","title":"Functional flow matching","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.479131Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:4ccb37c4e95f674ab3910708c98eef38f78730a8c25f4fba3665e5e5ce800863","observation_id":"a00a3215-85dd-4a36-981a-c23d4e0ed281","resolution":{"observed_at":"2026-08-03T04:47:29.479131Z","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":"2026-08-03T04:47:29.482615Z","title":"Apebench: A benchmark for autoregressive neural emulators of pdes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.482615Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:7585cf9af922a3e970c7416a834ee4e18e2c32309991c5a2c1bece909fde8110","observation_id":"1cf41078-f2cf-4a20-aa16-7ffc2dee970e","resolution":{"observed_at":"2026-08-03T04:47:29.482615Z","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":"2026-08-03T04:47:29.485830Z","title":"Benchmarking autoregressive conditional diffusion models for turbulent flow simulation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.485830Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:593c41179a52e5869b7a3790220eabadb9b59cea5cd273b05b0c847e6706c95c","observation_id":"e35de0a5-a51c-43b5-a324-a58b3443659f","resolution":{"observed_at":"2026-08-03T04:47:29.485830Z","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":"2026-08-03T04:47:29.489320Z","title":"Tabddpm: Modelling tabular data with diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.489320Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:a966655a797d03e69d86888d4b84ad433dd08915c61c7804d4bbf7251c1cbbbb","observation_id":"d384f476-7868-42dc-a1a4-b6c7e49ca72c","resolution":{"observed_at":"2026-08-03T04:47:29.489320Z","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":"2026-08-03T04:47:29.492848Z","title":"Neural operator: Learning maps between function spaces with applications to pdes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.492848Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:d02e8fd0fccf201fc8fdfbb324523c47ea697274edf8616cd132ce4d55884649","observation_id":"80ec0964-5e8a-4227-96df-3101db79e2e9","resolution":{"observed_at":"2026-08-03T04:47:29.492848Z","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":"2026-08-03T04:47:29.496200Z","title":"Being bayesian, even just a bit, fixes overconfidence in relu networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.496200Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:2f339d7d391de27e7d45a82852b62f6c21d6328c6fc2917208ec0319bc0ec1bd","observation_id":"f6e499ad-778b-46c4-947c-b6210a98bc9e","resolution":{"observed_at":"2026-08-03T04:47:29.496200Z","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":"2026-08-03T04:47:29.499318Z","title":"Accurate uncertainties for deep learning using calibrated regression","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.499318Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:334c7de84e4f8356e7aede5e884536668197e3a2b804c5027754f891b02c1ad9","observation_id":"3f7bcd0e-ae50-439a-962d-1fa33263ae23","resolution":{"observed_at":"2026-08-03T04:47:29.499318Z","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":"2026-08-03T04:47:29.503061Z","title":"Score-based generative modeling secretly minimizes the wasserstein distance","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.503061Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:a1358ae06326893f5dabf87ba31a8c74b9ad33d0b874435131275b07012a545d","observation_id":"41b8df26-d55f-420f-b888-822df82dbe1c","resolution":{"observed_at":"2026-08-03T04:47:29.503061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.21890","last_updated":"2026-05-27T08:48:11Z","snapshot_observed_at":"2026-08-04T08:26:21.874634Z","submitted_at":"2025-10-24T02:29:02Z","title":"The Principles of Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.21890","snapshot_observed_at":"2026-08-03T04:47:29.506470Z","title":"The principles of diffusion models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.506470Z"},"links":{"cited_paper":"/paper/2510.21890","citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:8fac4a3e99486c444e04a5a09a8d30e31298649f0076b0e646c69088b6b5558f","observation_id":"8debd186-2706-44c1-872d-308da9256eb8","resolution":{"observed_at":"2026-08-03T04:47:29.506470Z","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":"2026-08-03T04:47:29.510174Z","title":"Simple and scalable predictive uncertainty estimation using deep ensembles","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.510174Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:f15d74a70e664561acd6139a03a4c9b5e416ee52dfc07f04ce7a2e7faedda676","observation_id":"cdd34fa0-f69d-4692-a1fa-59dcc736aeb3","resolution":{"observed_at":"2026-08-03T04:47:29.510174Z","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":"2026-08-03T04:47:29.513456Z","title":"Autoregressive image generation without vector quantization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.513456Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:9979ffe7b8dbb7807b5236ba8bb5cca72eaf65dc5c3c180658363733ae6bb72d","observation_id":"b1b55169-7139-4284-8cbe-db04f3a31e25","resolution":{"observed_at":"2026-08-03T04:47:29.513456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-07-06T10:05:26.653366Z","submitted_at":"2020-10-18T00:34:21Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08895","snapshot_observed_at":"2026-08-03T04:47:29.517043Z","title":"Fourier neural operator for parametric partial differential equations","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.517043Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:f5d28080289bf6b6b051518aa14564907ab6f5dbb33caa9910b6f20d55bb1c1f","observation_id":"9688eaca-2ac0-496c-8dbc-c56f9393ac5e","resolution":{"observed_at":"2026-08-03T04:47:29.517043Z","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":"2026-08-03T04:47:29.520920Z","title":"Z., Liu, B., and Anandkumar, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.520920Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:41f6e5ae73e380b5f290d38ee789dfec0438cf1c02860d88b162b624ddcf2dbf","observation_id":"30a976f4-d1d8-4ffa-bc67-4ceac5abf299","resolution":{"observed_at":"2026-08-03T04:47:29.520920Z","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":"2026-08-03T04:47:29.524242Z","title":"A., Stadler, M., Hundt, C., Azizzadenesheli, K., et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.524242Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:7fbaa7810faa80b2083bad22211ba8bcea7d756612ea0e19758d79160dfc5d3e","observation_id":"45bdac7e-d001-4bdf-8c4e-b49f81587fed","resolution":{"observed_at":"2026-08-03T04:47:29.524242Z","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":"2026-08-03T04:47:29.527740Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.527740Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:606dc544295ac25285a49ca99781011f79b414c3f437afa0e892fd4d14726835","observation_id":"0875b884-6847-41b2-8d0e-86da10de8be9","resolution":{"observed_at":"2026-08-03T04:47:29.527740Z","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":"2026-08-03T04:47:29.531592Z","title":"H., Kovachki, N","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.531592Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:a1741ef41c0698ca114665151d1646b0858e1c17cfaeab73d46915267bed0fc8","observation_id":"905a31a2-f7e1-42a9-8355-c1ee7789adda","resolution":{"observed_at":"2026-08-03T04:47:29.531592Z","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":"2026-08-03T04:47:29.534987Z","title":"B., Byun, T., Kang, T., Kim, S., Lee, K., and Choi, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.534987Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:9a32d776f8eff5f47119e3a2e7b5e627ed83cab9439c594a04234025afd98a85","observation_id":"5bb9e27b-104d-4785-b796-5977cbea329a","resolution":{"observed_at":"2026-08-03T04:47:29.534987Z","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":"2026-08-03T04:47:29.538580Z","title":"B-deeponet: An enhanced bayesian deeponet for solving noisy parametric pdes using accelerated replica exchange sgld","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.538580Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:fef6f193883f5a68fa28dd15905e36e5260f45d0fe2d0586cdc08d39413ecb55","observation_id":"64f0d0c0-7a5c-43b7-95df-cbd12968fb54","resolution":{"observed_at":"2026-08-03T04:47:29.538580Z","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":"2026-08-03T04:47:29.541954Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.541954Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:436924012310a0a900944af6707dc263f42ad6724744c0814a95876c87ce1d65","observation_id":"87db6e4a-98c7-4117-9e6a-4ef5ae44e675","resolution":{"observed_at":"2026-08-03T04:47:29.541954Z","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":"2026-08-03T04:47:29.545417Z","title":"Pde-refiner: Achieving accurate long rollouts with neural pde solvers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.545417Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:0215efd0f4fae71a4a097c54e5ef7c7a9148032c6f28a95a9feb75f3ed97cee0","observation_id":"1d3ad3d7-8191-491e-9ea6-e91ea23d36de","resolution":{"observed_at":"2026-08-03T04:47:29.545417Z","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":"2026-08-03T04:47:29.549186Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.549186Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:59496ffaa5eb182fe711644c6f5107c77425556768595e949d2b90b8af507df0","observation_id":"752cd8cd-4c7e-4b57-be90-3af9237b1135","resolution":{"observed_at":"2026-08-03T04:47:29.549186Z","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":"2026-08-03T04:47:29.552648Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.552648Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:75df5e0c6900692e8c86117df8eaf193112064695db1c1299aeec3a356afbf44","observation_id":"3dbd8bf4-d922-47e2-aff0-f763b6f9b8e6","resolution":{"observed_at":"2026-08-03T04:47:29.552648Z","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":"2026-08-03T04:47:29.556012Z","title":"Calibrated uncertainty quantification for operator learning via conformal prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.556012Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:9e97fc16824c61fe3f9ef3c160d4510f3a608ac55b6a446e4f2f978eaba27f81","observation_id":"67e456ef-426f-4530-aebb-b609b828d30e","resolution":{"observed_at":"2026-08-03T04:47:29.556012Z","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":"2026-08-03T04:47:29.559478Z","title":"Approximate bayesian neural operators: Uncertainty quantification for parametric PDE s","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.559478Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:c5720771190d2d088d02a609ab0752b1ffc30f78c51c6fd66dc5b44ec23b87e7","observation_id":"4863dd6a-0b56-4c62-9a9d-3ce2b249aff2","resolution":{"observed_at":"2026-08-03T04:47:29.559478Z","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":"2026-08-03T04:47:29.562835Z","title":"Linearization turns neural operators into function-valued gaussian processes","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.562835Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:1488a8eeb75d6931955659b32d0ba77eb7601d0bbbffa9f5e581a6cc8758d962","observation_id":"5671bcd6-2a5c-44ff-974c-599a1658a2e1","resolution":{"observed_at":"2026-08-03T04:47:29.562835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.11214","last_updated":"2022-02-22T22:19:35Z","snapshot_observed_at":"2026-08-02T21:09:07.860947Z","submitted_at":"2022-02-22T22:19:35Z","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.11214","snapshot_observed_at":"2026-08-03T04:47:29.566251Z","title":"Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.566251Z"},"links":{"cited_paper":"/paper/2202.11214","citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:dcd459dd23c72db3bd016d71a29fc8570337902772eaa19ea4379e9e371abbe9","observation_id":"453ab5ee-c0f8-4a85-91e7-26782b76cfc1","resolution":{"observed_at":"2026-08-03T04:47:29.566251Z","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":"2026-08-03T04:47:29.570006Z","title":"R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.570006Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:c2f8f0edc98074068bb5420b4bed10a7f290a97ba24022ce19fd63379575b921","observation_id":"933daf7c-8ca8-4ebe-82f7-ff159d4198a6","resolution":{"observed_at":"2026-08-03T04:47:29.570006Z","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":"2026-08-03T04:47:29.573508Z","title":"F., Meng, X., Zou, Z., Guo, L., and Karniadakis, G","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.573508Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:3aab161c290bc1bf58c655aadf07024ed335c2319d4f8c5c5f59e41d076252c1","observation_id":"959c9a88-73e9-474d-be7f-1e7ec4dc01b6","resolution":{"observed_at":"2026-08-03T04:47:29.573508Z","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":"2026-08-03T04:47:29.577125Z","title":"A., Florez, M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.577125Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:7f4852eeea3e5709a3d8aba81dcc63618b920eade77c3d3ce6ed8277a7914c31","observation_id":"2c17f553-f157-4d94-838c-d6003710823d","resolution":{"observed_at":"2026-08-03T04:47:29.577125Z","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":"2026-08-03T04:47:29.580677Z","title":"A., Ross, Z","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.580677Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:ea0b0ff5a4a9e06b4b21efa32c61f0f1145a9a7bbb3ce56f9ed39a5864db0784","observation_id":"48c4e5e0-3af1-4bcb-a91e-aad4049ff78d","resolution":{"observed_at":"2026-08-03T04:47:29.580677Z","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":"2026-08-03T04:47:29.584122Z","title":"and Louppe, G","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.584122Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:94ccdee3afc6db6e67daf75c8ff65303835bad257c30d9e3e45591a9a2c7639d","observation_id":"45594b8a-9130-410f-aeed-13dfcff4ea4b","resolution":{"observed_at":"2026-08-03T04:47:29.584122Z","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":"2026-08-03T04:47:29.591047Z","title":"Lost in latent space: An empirical study of latent diffusion models for physics emulation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.591047Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:19479ce4b7238118f7500ab375ea54a0f3fe87547fa2a759f5cd3f48a95e7217","observation_id":"58fa616f-edfc-4796-a24e-e234bab381f3","resolution":{"observed_at":"2026-08-03T04:47:29.591047Z","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":"2026-08-03T04:47:29.594646Z","title":"Neural stochastic pdes: Resolution-invariant learning of continuous spatiotemporal dynamics","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.594646Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:e62776675841d09b99143be07b07c16b4320fc439e1de90536401d7bcb7c0340","observation_id":"a68bc394-6de9-4e95-9bf6-a7b8494bc53d","resolution":{"observed_at":"2026-08-03T04:47:29.594646Z","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":"2026-08-03T04:47:29.598300Z","title":"E., Asimaki, D., and Azizzadenesheli, K","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.598300Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:3479ec116ce856df1a8bd891332b539c5e1485e796b3809586a9e5fc06dc6dea","observation_id":"5f764c68-5996-42c9-971d-d88e9ff0553a","resolution":{"observed_at":"2026-08-03T04:47:29.598300Z","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":"2026-08-03T04:47:29.601662Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.601662Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:3d4c0261c2c1a8e45f3dbb1036fa2645bbacb5b642b2f8adb734a55759e14c50","observation_id":"3f20e40f-7006-4803-8756-4ac468b4a01e","resolution":{"observed_at":"2026-08-03T04:47:29.601662Z","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":"2026-08-03T04:47:29.604838Z","title":"M., Turner, R., and Mathieu, E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.604838Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:baca2bbc0a2e3d673d06e32907dea2c14f622169230af1c564b632636a27ff6a","observation_id":"368a2c76-332d-41dc-9e0a-3e1a5ceaa023","resolution":{"observed_at":"2026-08-03T04:47:29.604838Z","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":"2026-08-03T04:47:29.607945Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.607945Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:4d64dc354c791975d223adb2fbb2b6a942c40c3a7b2d657b519e8e889a6b1d0c","observation_id":"117752ba-e1a8-463e-b12f-8cab4c250981","resolution":{"observed_at":"2026-08-03T04:47:29.607945Z","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":"2026-08-03T04:47:29.610987Z","title":"Selective underfitting in diffusion models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.610987Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:88bc29ecc4b51d21f7986f0946f5d7777ce749a7abe69bc598d97561277fddb7","observation_id":"9ecdc11f-5ace-4e2c-9403-28d812c63423","resolution":{"observed_at":"2026-08-03T04:47:29.610987Z","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":"2026-08-03T04:47:29.614072Z","title":"P., Kumar, A., Ermon, S., and Poole, B","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.614072Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:e60c6e194a60d3329b6846b78a538871c1ebfc7859d6c5caf4a263e3cf4e2edb","observation_id":"1c5b793d-ff67-44b5-9e6e-ab6a709ec467","resolution":{"observed_at":"2026-08-03T04:47:29.614072Z","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":"2026-08-03T04:47:29.617613Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.617613Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:d2e6fea6b35f9c6ae7a5ddec3999235bc974e80914afc4267a120f514eef2317","observation_id":"7127a565-3e85-46cb-a97b-9372dec92ece","resolution":{"observed_at":"2026-08-03T04:47:29.617613Z","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":"2026-08-03T04:47:29.620886Z","title":"Geofunflow: Geometric function flow matching for inverse operator learning over complex geometries","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.620886Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:927ecc4c2491645f7726991288c919e2139740b2b875bd59f8ae66817b853fa1","observation_id":"52f7e26f-3e19-4fa0-ac85-c9d23ab30b71","resolution":{"observed_at":"2026-08-03T04:47:29.620886Z","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":"2026-08-03T04:47:29.623873Z","title":"A., Klink, P., Pajarinen, J., and Peters, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.623873Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:a6a487afc354637576a623b3f151ebac8adbf9336b352044350034815afcc3ea","observation_id":"ca61a571-15d7-4860-adce-0f8c438e4fe9","resolution":{"observed_at":"2026-08-03T04:47:29.623873Z","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":"2026-08-03T04:47:29.627029Z","title":"Uncertainty quantification for fourier neural operators","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.627029Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:d42e4872be0373481c822ae894e1b021fad9cea14cac7f07815dbc36a2283c14","observation_id":"9b5a92f2-f438-48e1-9ee9-189ab99ccbae","resolution":{"observed_at":"2026-08-03T04:47:29.627029Z","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":"2026-08-03T04:47:29.630400Z","title":"Weight diffusion for future: Learn to generalize in non-stationary environments","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.630400Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:99e86cdcb33033ec4c38fff7e3cf36e85006e700cf23924aac97119d5efebb79","observation_id":"03f43841-da49-418c-8667-be997eee4fc3","resolution":{"observed_at":"2026-08-03T04:47:29.630400Z","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":"2026-08-03T04:47:29.633872Z","title":"Numerical methods for stochastic computations: a spectral method approach","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.633872Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:68ac3259bc297be2cdb6ad4c54c6053266a358d97f0cdf68e93855cd25454cee","observation_id":"930b5897-4dfe-4fca-b9e9-d54ed7c53fff","resolution":{"observed_at":"2026-08-03T04:47:29.633872Z","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":"2026-08-03T04:47:29.636987Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.636987Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:c5c4140e86c823408cd7aaf3083a01aa2fd24275dc5b7f0be3bc509f021fc6e7","observation_id":"c4a3bee9-7355-405a-8f1e-0a31ba37791c","resolution":{"observed_at":"2026-08-03T04:47:29.636987Z","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":"2026-08-03T04:47:29.640202Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-03T04:47:29.640202Z"},"links":{"citing_paper":"/paper/2602.04139"},"observation_digest":"sha256:62fa7fb204149f242567c56e01f2972e202b5f3518c70a332ee5c05c48e7178c","observation_id":"259aede2-33ea-4a48-8f25-7689029a844b","resolution":{"observed_at":"2026-08-03T04:47:29.640202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.04139","last_updated":"2026-05-25T06:18:08Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T10:59:39.735152Z","submitted_at":"2026-02-04T02:10:53Z","title":"Generative Neural Operators through Diffusion Last Layer"},"reference_resolution":{"displayed":67,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":67,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":67},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2602.04139."}