{"as_of":"2026-08-17T05:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b7b9245e7077d386a53c18bdb81fa842b6dcbc502c0e8fb1a8471f3cdf1a268a","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:35:55.844205Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-20T19:48:17.049547Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-20T19:48:57.285015Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"cited_work":{"arxiv_id":"2505.21666","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.21666","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Efficient controllable dif- fusion via optimal classifier guidance","venue":null,"work_id":"f17948e8-f7a3-46c6-a726-758f6af58f3b","year":2025},"citing_paper":{"arxiv_id":"2605.15855","last_updated":"2026-05-15T11:14:13Z","snapshot_observed_at":"2026-08-12T15:32:26.557707Z","submitted_at":"2026-05-15T11:14:13Z","title":"Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models?","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-20T19:48:17.049547Z"},"links":{"cited_paper":"/paper/2505.21666","citing_paper":"/paper/2605.15855"},"observation_digest":"sha256:66cf6ab83a2131ccca9775a802c04e1254205de97079dc6d270c6aa82b85a62a","observation_id":"dbf1b86c-b20a-40c2-b057-910954ff97af","resolution":{"observed_at":"2026-05-20T19:48:57.286646Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.21666/citation-record","integrity":"/paper/2505.21666/integrity","json":"/paper/2505.21666/citation-record.json","paper":"/paper/2505.21666"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:59.461286Z","title":"Reinforcement learning: Theory and algorithms","venue":null,"work_id":"41240788-187f-4f0e-aae0-eb6731d9e322","year":2019},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:50.759894Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:fb40c7c2303690ea90b5395c99b279c93a5caeed5a78772d2a22f9d18f13b5cf","observation_id":"e320b79c-585b-44b9-940e-133aa30dfccd","resolution":{"observed_at":"2026-08-07T13:35:59.582619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:50.840386Z","title":"Reverse-time diffusion equation models","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:50.840386Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:f0824a55fc09e07d8ca2002a0bd49fcc41a56a32da048d04effa83a17414974c","observation_id":"e7d50b61-8f4d-401e-b59c-cf24be03b13f","resolution":{"observed_at":"2026-08-07T13:35:50.840386Z","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":"2026-08-07T13:35:50.981168Z","title":"Ledsam, Agnieszka Grabska-Barwinska, Kyle R","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:50.981168Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:9f398e265b38df2b0132408f5500ba0a5530f38142585b890024fc50a65bad78","observation_id":"4c98874e-a4ca-48c2-a2f3-1a8f85ea0ff1","resolution":{"observed_at":"2026-08-07T13:35:50.981168Z","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":"2026-08-07T13:35:51.082274Z","title":"Training diffusion models with reinforcement learning, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.082274Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:2195cebb00e441dd71d56e97f87226193f9c9009276d8a9b59f2e11cc33675e9","observation_id":"d4ec4a4c-4682-4607-b80f-de2f07892154","resolution":{"observed_at":"2026-08-07T13:35:51.082274Z","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":"2026-08-07T13:35:51.204188Z","title":"Prediction, learning, and games","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.204188Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:005a891af6606810a2d1173a8dc9cc8b9e45e968edb3086d0c8054692d09c045","observation_id":"0f3d4c93-fd4a-4f49-8b35-a35a3f09b8b8","resolution":{"observed_at":"2026-08-07T13:35:51.204188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:59.184015Z","title":"Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions","venue":null,"work_id":"ba24eec8-23ae-400f-94eb-9294e64c7c1e","year":2023},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.323814Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:603c72e703c5fcd6e2779166ebabb0af2aa90003f5b3010a03a9ba9f98a9bb5b","observation_id":"91d691da-8a0c-4b59-ac0d-ce601880796f","resolution":{"observed_at":"2026-08-07T13:35:59.277240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11215","last_updated":"2023-04-15T22:24:53Z","snapshot_observed_at":"2026-08-16T16:30:32.276132Z","submitted_at":"2022-09-22T17:55:01Z","title":"Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.11215","snapshot_observed_at":"2026-08-07T13:35:51.426915Z","title":"Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.426915Z"},"links":{"cited_paper":"/paper/2209.11215","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:825064c2e7524addcca433a84ff9ecc1ca08c0428ea1ad79105e38f10fd33433","observation_id":"1e86bae1-ebd7-44d9-9cbe-a962ca9ace3d","resolution":{"observed_at":"2026-08-07T13:35:51.426915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.03194","last_updated":"2025-05-06T05:31:10Z","snapshot_observed_at":"2026-08-16T23:56:57.992214Z","submitted_at":"2025-05-06T05:31:10Z","title":"Convergence Of Consistency Model With Multistep Sampling Under General Data Assumptions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.03194","snapshot_observed_at":"2026-08-07T13:35:51.525957Z","title":"Convergence of consistency model with multistep sampling under general data assumptions","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.525957Z"},"links":{"cited_paper":"/paper/2505.03194","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:541b780309fac93eb3e822e89a55c451b433476a3e04b354c852536a6ed95353","observation_id":"ea71e4fa-c31d-4f4c-b869-de15b8fee1fb","resolution":{"observed_at":"2026-08-07T13:35:51.525957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:59.026682Z","title":"Diffusion posterior sampling for general noisy inverse problems","venue":null,"work_id":"6135d31a-aea9-4863-ae5a-b90e84902d6e","year":2023},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.600039Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:4a38ad7126ace366a877d8dda30f3ea66e1c79c239ea0a1d22c724af82ff8f4f","observation_id":"111a84dc-c58d-4cf0-ac56-2f6a7607deaa","resolution":{"observed_at":"2026-08-07T13:35:59.110072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:58.839369Z","title":null,"venue":null,"work_id":"fd86395a-ddfc-444d-b3b1-702d80cd585a","year":2024},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.708454Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:41427b225a5b1a16cc7bcdc5d2fc8d8ca9683c5822c2457b33e353ea50d4ef9e","observation_id":"312e47ae-6559-4b7b-b0bb-49f44ad91bfd","resolution":{"observed_at":"2026-08-07T13:35:58.913945Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:58.735772Z","title":"Particle methods: An introduction with applications","venue":null,"work_id":"ab75ac0b-971d-4106-b52e-6482f9cd7872","year":2014},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.818470Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:c788e0d1ab36a1599db54104adaabfd71067eb9e8dae66f600085ee94a3b41af","observation_id":"8470793f-c27b-4b34-aa37-002351d6d697","resolution":{"observed_at":"2026-08-07T13:35:58.769288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:58.613951Z","title":null,"venue":null,"work_id":"f0c7e842-43a8-4752-b185-475dee4f91ca","year":2017},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:51.947081Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:0e100f3c07176b08535d11e0d8411d7a7f0a2b7290afa58fada6e4d6cf85e483","observation_id":"73fd9000-21f4-49c7-90cc-04ea9b7d24ba","resolution":{"observed_at":"2026-08-07T13:35:58.650703Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.05233","last_updated":"2021-06-01T17:49:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-05-11T17:50:24Z","title":"Diffusion Models Beat GANs on Image Synthesis","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.05233","snapshot_observed_at":"2026-08-07T13:35:52.105208Z","title":"Diffusion models beat gans on image synthesis, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.105208Z"},"links":{"cited_paper":"/paper/2105.05233","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:7d7103ff31f2c91d7ee08772996943f4ddcd65e9848cd605454de983cdcdb330","observation_id":"7f356624-82e1-4090-8548-e491ca06e924","resolution":{"observed_at":"2026-08-07T13:35:52.105208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:58.347443Z","title":null,"venue":null,"work_id":"7c880f0f-1e64-46a7-a33d-113c1de6c88a","year":2025},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.208627Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:ee1cd178bf7f6ba77c69b46beaefa8063b4b1bcfc2fbec6d508a2844b5f1ec82","observation_id":"d6965fab-6c88-428d-8f66-1975195fbee5","resolution":{"observed_at":"2026-08-07T13:35:58.539580Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16381","last_updated":"2023-11-01T04:48:26Z","snapshot_observed_at":"2026-08-16T15:29:37.331753Z","submitted_at":"2023-05-25T17:35:38Z","title":"DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16381","snapshot_observed_at":"2026-08-07T13:35:52.278640Z","title":"Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.278640Z"},"links":{"cited_paper":"/paper/2305.16381","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:45ac91e2f2d28308b69e8b48b589fe1161e64889f547dcf489d627e03d5ff6e9","observation_id":"e554284a-6d25-47a7-b933-c8e91ed80e86","resolution":{"observed_at":"2026-08-07T13:35:52.278640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"cs/0408007","last_updated":"2004-08-02T21:24:41Z","snapshot_observed_at":"2026-08-15T18:59:00.318508Z","submitted_at":"2004-08-02T21:24:41Z","title":"Online convex optimization in the bandit setting: gradient descent without a gradient","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"cs/0408007","snapshot_observed_at":"2026-08-07T13:35:52.404690Z","title":"Online convex optimization in the bandit setting: gradient descent without a gradient","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.404690Z"},"links":{"cited_paper":"/paper/cs/0408007","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:0a2e84504e2dc1c20b1f746709501dd9dcdf05e5de11e918a051834407bc112c","observation_id":"51750b43-8fb0-49ae-b43e-37b9d84f058d","resolution":{"observed_at":"2026-08-07T13:35:52.404690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.13487","last_updated":"2023-07-11T16:47:42Z","snapshot_observed_at":"2026-08-16T17:31:55.368873Z","submitted_at":"2021-12-27T02:53:44Z","title":"The Statistical Complexity of Interactive Decision Making","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.13487","snapshot_observed_at":"2026-08-07T13:35:52.527298Z","title":"The statistical complexity of interactive decision making","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.527298Z"},"links":{"cited_paper":"/paper/2112.13487","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:c66b430ea3ba42ef2c8e7c20e6eb9228be07b7bb175a1cbce01c1c4a0342e84c","observation_id":"a368d61a-bd03-4875-8d09-8fc017794761","resolution":{"observed_at":"2026-08-07T13:35:52.527298Z","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":"2026-08-07T13:35:52.599152Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.599152Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:8ffd50d167852d745382518b4c8049290916864f41a4edb1b700e01faf3d2797","observation_id":"aaa469f1-ab79-4fed-a381-eced19bea5db","resolution":{"observed_at":"2026-08-07T13:35:52.599152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12598","last_updated":"2022-07-26T01:42:07Z","snapshot_observed_at":"2026-08-14T06:37:15.299690Z","submitted_at":"2022-07-26T01:42:07Z","title":"Classifier-Free Diffusion Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12598","snapshot_observed_at":"2026-08-07T13:35:52.680634Z","title":"Classifier-free diffusion guidance","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.680634Z"},"links":{"cited_paper":"/paper/2207.12598","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:b07a88070f833a9de9fd0dd3c691a361c04d3210bb9427e3b78476c804e942ae","observation_id":"23d73fbc-d618-44cd-a739-a2acc3230673","resolution":{"observed_at":"2026-08-07T13:35:52.680634Z","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":"2026-08-07T13:35:52.839014Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.839014Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:49098d668c53d7224760e470ca04cda47c0070b1e7f4642547c78dc885da1e6a","observation_id":"d6525ceb-3ab7-467f-9e5b-1dc4f53c0be6","resolution":{"observed_at":"2026-08-07T13:35:52.839014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02303","last_updated":"2022-10-05T14:41:38Z","snapshot_observed_at":"2026-07-06T13:59:57.800591Z","submitted_at":"2022-10-05T14:41:38Z","title":"Imagen Video: High Definition Video Generation with Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02303","snapshot_observed_at":"2026-08-07T13:35:52.956830Z","title":"Imagen video: High definition video generation with diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:52.956830Z"},"links":{"cited_paper":"/paper/2210.02303","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:123110c64ab0325abe1182096bd4fa547b602bd0631a587d362191badc00ddd9","observation_id":"0a72e928-00ed-451c-aec8-0f26999b582e","resolution":{"observed_at":"2026-08-07T13:35:52.956830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:58.142909Z","title":"Equivariant diffusion for molecule generation in 3d","venue":null,"work_id":"8ab64cae-84d6-4c6c-9a29-47d1938500c8","year":2022},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.105459Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:4f984722a3e4da21f1b71eaf792d5b60a02771378e4312baff39ac15a8fb8cda","observation_id":"e08cf41e-8cbc-4fe4-a431-fb613755e2da","resolution":{"observed_at":"2026-08-07T13:35:58.227611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.08252","last_updated":"2024-10-25T02:50:44Z","snapshot_observed_at":"2026-08-16T13:26:12.987371Z","submitted_at":"2024-08-15T16:47:59Z","title":"Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.08252","snapshot_observed_at":"2026-08-07T13:35:53.244828Z","title":"Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.244828Z"},"links":{"cited_paper":"/paper/2408.08252","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:ec26b9c38c876cc8895efeb37cf3d079182abd1537f6f1326af0b059b645adf1","observation_id":"b300ba7d-577c-47d2-9a58-37c9d483bdc5","resolution":{"observed_at":"2026-08-07T13:35:53.244828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17022","last_updated":"2024-06-03T20:50:26Z","snapshot_observed_at":"2026-08-16T14:48:48.486166Z","submitted_at":"2023-10-25T22:00:05Z","title":"Controlled Decoding from Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17022","snapshot_observed_at":"2026-08-07T13:35:53.364878Z","title":"Controlled decoding from language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.364878Z"},"links":{"cited_paper":"/paper/2310.17022","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:1c2919230e8e664ffdb67e96026f86d61a74e9e12c844c9bb7db24c0c92ababc","observation_id":"9e6b5854-1167-44b5-8b3c-69da21fe4bd6","resolution":{"observed_at":"2026-08-07T13:35:53.364878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:57.939297Z","title":"Unlocking guidance for discrete state-space diffusion and flow models","venue":null,"work_id":"f00ba1aa-3742-45cf-9396-b0bd1df19b37","year":2025},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.437983Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:52c58ece1054a6b57507ee93f20b0e98014d2599ff417151ef343c3e6298ae86","observation_id":"38de6df6-0cdd-4e10-ab4c-44e8f768bf6b","resolution":{"observed_at":"2026-08-07T13:35:58.051307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.03673","last_updated":"2024-06-22T08:07:39Z","snapshot_observed_at":"2026-08-16T14:06:38.190280Z","submitted_at":"2024-03-25T15:40:22Z","title":"RL for Consistency Models: Faster Reward Guided Text-to-Image Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.03673","snapshot_observed_at":"2026-08-07T13:35:53.528157Z","title":"Chang, Yiyi Zhang, Kianté Brantley, and Wen Sun","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.528157Z"},"links":{"cited_paper":"/paper/2404.03673","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:9eb4d9a68e3403534e34cbfe955354151105d81b3c6644b575a7b9b8fcc49db8","observation_id":"91fdf053-4d34-4f38-b4fd-41e3e264bfb1","resolution":{"observed_at":"2026-08-07T13:35:53.528157Z","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":"2026-08-07T13:35:53.646225Z","title":"Aligning text-to-image diffusion models with reward backpropagation, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.646225Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:9a61c12337241dddd5144d5b282ab3acfe6735bf6349d4cfd17815cc967fe05b","observation_id":"9d73bf03-13a2-4e66-a313-ad1c216a62b9","resolution":{"observed_at":"2026-08-07T13:35:53.646225Z","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":"2026-08-07T13:35:53.728105Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.728105Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:c6e19cf9707b58fe662c688128f241a8321a5339756088db32b1a4a5e32b9b5f","observation_id":"7bf87574-11ac-42e8-940e-27b92e4d3710","resolution":{"observed_at":"2026-08-07T13:35:53.728105Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1406.5979","last_updated":"2014-06-23T17:00:28Z","snapshot_observed_at":"2026-08-14T23:29:22.914436Z","submitted_at":"2014-06-23T17:00:28Z","title":"Reinforcement and Imitation Learning via Interactive No-Regret Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1406.5979","snapshot_observed_at":"2026-08-07T13:35:53.861689Z","title":"Reinforcement and imitation learning via interactive no-regret learning","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.861689Z"},"links":{"cited_paper":"/paper/1406.5979","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:4474c350c3b2b7886fc1967fc55c809031b20fea3d6e01202ad642b4b50ff96f","observation_id":"77cdfb21-4813-4681-957e-74602518e24f","resolution":{"observed_at":"2026-08-07T13:35:53.861689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:57.696701Z","title":"A reduction of imitation learning and structured prediction to no-regret online learning","venue":null,"work_id":"bc4e6a4a-a177-4eff-84da-5a877e47e19c","year":2011},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:53.960860Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:dcf72623e8b914ef2f8f4d3bc1195125451687b48627db76046c69e99683384e","observation_id":"a7431e84-8d96-43e5-a2d8-8cfb4bf84ce0","resolution":{"observed_at":"2026-08-07T13:35:57.781828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:54.066827Z","title":"Photorealistic text-to-image diffusion models with deep language understanding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.066827Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:3666bcdca2d52649e4ec2630e346a9ea3e15c7d52a266c72338dd4b04c287586","observation_id":"7e51db02-dfd6-4d72-81b0-fabeec099c3a","resolution":{"observed_at":"2026-08-07T13:35:54.066827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:57.496802Z","title":"Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T","venue":null,"work_id":"da70f6c2-0a24-4cbb-ae86-b9221eed833e","year":2024},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.171750Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:2c1b78e6b2f818a30628e27d501969378ec19051f7dd8377a0dc28b26a55529a","observation_id":"521761c0-120f-4829-9102-640140820d80","resolution":{"observed_at":"2026-08-07T13:35:57.621308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41587-019-0164-5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Sample, Ban Wang, David W","venue":"Nature Biotechnology","work_id":"1ff8f656-bb5b-4034-8025-0d9c3a01593b","year":2019},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.276837Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:a4f63552a6048a5493ef4ae20deb3569fea846892378c2c0c40bee0826be73a0","observation_id":"c0e45e15-bcb3-45ef-85cd-0bbb873c6e95","resolution":{"observed_at":"2026-08-07T13:35:56.038585Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-07T13:35:54.351375Z","title":"Proximal policy optimization algorithms","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.351375Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:9525f6e2aa114222b49917ea01034b98490deed3c50fcf21afcc503fa98bc45f","observation_id":"4422cad5-ca07-4680-be63-b1818f92f079","resolution":{"observed_at":"2026-08-07T13:35:54.351375Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:57.184024Z","title":"Laion aesthetics","venue":null,"work_id":"57640ebf-cc31-430c-81c4-bc51c5b89e35","year":2022},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.443482Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:ef5b53e7a0edc95f7f0faacf5d5fe8b016a9cd316d710900babc124ab97dd1d8","observation_id":"de62b37f-f5b2-4222-a82c-231e8030d172","resolution":{"observed_at":"2026-08-07T13:35:57.335915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:54.550545Z","title":"Online learning and online convex optimization","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.550545Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:c8c7509492115420776fdc17c48942d6e4c30b277fa57d0956db13b8c929bf7d","observation_id":"9f824062-c01e-47dd-9f96-c55eb9b03ce2","resolution":{"observed_at":"2026-08-07T13:35:54.550545Z","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":"2026-08-07T13:35:54.660337Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.660337Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:5f2dad63b48c313c1640c0e16acf132fd7d4b3f5341102b7b6383c561c53c209","observation_id":"afe12a05-20b4-4804-aba4-47e3e14e99db","resolution":{"observed_at":"2026-08-07T13:35:54.660337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:56.903232Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":"7ff1a9bd-e068-41c6-9350-b9bebd5d3102","year":2021},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.847070Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:c047742a258dc07dc3dd52bcc1fbcf8f3fad7bf678a412fe363a16a304a2b873","observation_id":"a39ffa9b-7fb5-42b1-93ca-dd4c20a656ae","resolution":{"observed_at":"2026-08-07T13:35:57.017670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:54.952572Z","title":"Online non-convex learning: Following the perturbed leader is optimal","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:54.952572Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:287877c4dacb260490b68cf78191ccd23ea971e87fba5422f6c7069b508839cb","observation_id":"4faf8b09-8e49-4081-a0f1-4c9f7e74a7ce","resolution":{"observed_at":"2026-08-07T13:35:54.952572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:56.715346Z","title":"Deeply aggrevated: Differentiable imitation learning for sequential prediction","venue":null,"work_id":"0638e2fc-8170-47a6-b8e2-31f280c45a9b","year":2017},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:55.039468Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:10296a8e5f90e111a967adb7ef81f9ffdf29f9329ddf08350d4e06d1cbfa4b77","observation_id":"c95d834f-f392-4f10-845a-5665aee99bc9","resolution":{"observed_at":"2026-08-07T13:35:56.831713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04119","last_updated":"2023-03-20T00:22:03Z","snapshot_observed_at":"2026-08-16T16:54:21.634214Z","submitted_at":"2022-06-08T18:35:08Z","title":"Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04119","snapshot_observed_at":"2026-08-07T13:35:55.131666Z","title":"Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:55.131666Z"},"links":{"cited_paper":"/paper/2206.04119","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:39ae168aba7e690ffeff13caa56bbc4472def329ea62c997694b8d8682419634","observation_id":"f68814dd-b9b8-48bf-b305-3cb447500ae0","resolution":{"observed_at":"2026-08-07T13:35:55.131666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:56.546848Z","title":"Fine-tuning of continuous-time diffusion models as entropy-regularized control, 2024 a","venue":null,"work_id":"7950d56f-2821-416a-823b-6f740eb8b69e","year":2024},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:55.254403Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:f4470c1998df93fc27db915ae70487431e1712e0a120a99a58de6f403a38a6eb","observation_id":"c3e6b05c-46e4-4434-816b-5f15962cde06","resolution":{"observed_at":"2026-08-07T13:35:56.614339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16359","last_updated":"2024-07-18T08:21:54Z","snapshot_observed_at":"2026-08-16T14:15:25.383069Z","submitted_at":"2024-02-26T07:24:32Z","title":"Feedback Efficient Online Fine-Tuning of Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16359","snapshot_observed_at":"2026-08-07T13:35:55.408711Z","title":"Feedback efficient online fine-tuning of diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:55.408711Z"},"links":{"cited_paper":"/paper/2402.16359","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:ec4a7bb15e42ea53c67254ad595219f70825ef4758aa3e10e8e993f4d1a178b1","observation_id":"c6716728-b669-4148-9752-cb810132f004","resolution":{"observed_at":"2026-08-07T13:35:55.408711Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12799","last_updated":"2025-04-04T15:09:19Z","snapshot_observed_at":"2026-08-16T13:17:09.503412Z","submitted_at":"2024-09-19T14:10:38Z","title":"The Central Role of the Loss Function in Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2409.12799","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.12799","snapshot_observed_at":"2026-08-07T13:35:56.231248Z","title":"The Central Role of the Loss Function in Reinforcement Learning","venue":"stat.ML","work_id":"45f2c756-5621-44a1-bcf5-d93e987ca59a","year":2024},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:55.501010Z"},"links":{"cited_paper":"/paper/2409.12799","citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:d3cd7e7ff5a00fce7dafea6ad2c63c8a19adaddf2fc8ed7451f6a03388670b33","observation_id":"7db38395-0fb1-41fc-845e-d6c8e32f7c40","resolution":{"observed_at":"2026-08-07T13:35:56.301881Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:35:55.617283Z","title":"Practical and asymptotically exact conditional sampling in diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:55.617283Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:5ca7e7448ef7632f73edf1a418fbd74a7c5a8d2889b6fe1347ac188c3a14fcd4","observation_id":"1d242014-f72f-4238-9e5e-cff2378c7613","resolution":{"observed_at":"2026-08-07T13:35:55.617283Z","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":"2026-08-07T13:35:55.747176Z","title":"Q : Provably optimal distributional rl for llm post-training","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:55.747176Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:422abd141350ee1c87aff766ae652250747aa45175d731378a405726438e55e1","observation_id":"6d42253b-37d2-4b19-a7dc-bc70c06ec089","resolution":{"observed_at":"2026-08-07T13:35:55.747176Z","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":"2026-08-07T13:35:55.844205Z","title":"Maximum entropy inverse reinforcement learning","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:55.844205Z"},"links":{"citing_paper":"/paper/2505.21666"},"observation_digest":"sha256:7a04f2ed1838edf129e8c3e0c4f7d525bb92d5d4cc0dd7629cc9a099aaf5cc74","observation_id":"387490e6-3a5d-493b-bad8-ceea850e769e","resolution":{"observed_at":"2026-08-07T13:35:55.844205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.21666","last_updated":"2025-05-27T18:46:21Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T11:17:54.901511Z","submitted_at":"2025-05-27T18:46:21Z","title":"Efficient Controllable Diffusion via Optimal Classifier Guidance"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":33,"verified_exact":2,"verified_fuzzy":12},"total_outbound_references":47},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2505.21666."}