{"as_of":"2026-08-08T01:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:48059bff4ff4751a19856528bfe57aa07f35ee93f9ce96546da1b473d6017027","coverage":[{"denominator":76,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":76,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:56:59.946357Z","state":"measured"},{"denominator":76,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":76,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2507.21367/citation-record","integrity":"/paper/2507.21367/integrity","json":"/paper/2507.21367/citation-record.json","paper":"/paper/2507.21367"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:57:00.567163Z","title":"Style blind domain generalized semantic segmentation via covariance alignment and semantic consis- tence contrastive learning","venue":null,"work_id":"b872718d-b5c5-42ed-885b-1be1d65e2e2a","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.754018Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:669bd3bfe9963a69fa81b4c40b800ecc8e2607c5b1b75cd3849455479155eba0","observation_id":"1853c321-2521-47ad-87b7-10f709a5ad4a","resolution":{"observed_at":"2026-08-06T12:57:00.570172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.558653Z","title":"Metareg: Towards domain generalization using meta- regularization","venue":null,"work_id":"a0828d0f-083c-435a-a5ba-cc3b7170376d","year":2018},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.757302Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:a1f0207842059d7f86c3192fcdd8387f37d9f5042735ccbe82563bda04b70505","observation_id":"026622a1-fbc7-419c-a6a1-43c0c0bffd61","resolution":{"observed_at":"2026-08-06T12:57:00.561395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.550730Z","title":"Lara: Latents and rays for multi-camera bird’s-eye-view semantic segmen- tation","venue":null,"work_id":"e3a6c578-8477-47db-8312-5a2ef751e04a","year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.760214Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:c5e7ea67af459671bd35f6da58e56579c54c71c9c9bfed59926780a971ec7769","observation_id":"8205e890-1503-472b-84f3-d233ab09ae34","resolution":{"observed_at":"2026-08-06T12:57:00.553474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.541945Z","title":"Collaborating foundation models for domain generalized semantic segmentation","venue":null,"work_id":"412170ba-9bc1-435b-aba4-560b5468d33c","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.763226Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:80e253d1adf720c4439f9ae02993645dfd853a5eb728feb269641ba2809730d2","observation_id":"bc8cd3e2-bb79-483f-99a7-8c64a4b6d2f2","resolution":{"observed_at":"2026-08-06T12:57:00.545202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.532891Z","title":"Learning content- enhanced mask transformer for domain generalized urban- scene segmentation","venue":null,"work_id":"e00a92f9-6dba-4667-ae1c-6d7fbbf437d4","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.766034Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:6f8791ef1963055f0b66f54611fb07d7b84369f3fa4b5b5615b705ca8f6c24f1","observation_id":"696bfd34-200f-4211-a913-70b172768fc4","resolution":{"observed_at":"2026-08-06T12:57:00.535817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.524016Z","title":"Unirestore: Unified perceptual and task-oriented image restoration model using diffusion prior","venue":null,"work_id":"920eccc8-229d-469c-8528-268f0ef2595f","year":2025},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.768726Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:1e93688566d074f7036e131eebcca65fb1ac640b1791e7566878a046de1c2376","observation_id":"6dee16c6-51a0-4409-9d65-4b424574b9cf","resolution":{"observed_at":"2026-08-06T12:57:00.526963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-07T13:44:53.690521Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-06T12:56:59.771448Z","title":"Rethinking atrous convolution for seman- tic image segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.771448Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:4a2b4335c2ab8b549c30ef7aaa35643b4b7246d91e16599af0ab515f438071d0","observation_id":"2fa1d4a3-4365-4672-b794-b1dc5168020a","resolution":{"observed_at":"2026-08-06T12:56:59.771448Z","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":"2026-08-06T12:57:00.515125Z","title":"Rvsl: Robust vehicle similarity learning in real hazy scenes based on semi-supervised learning","venue":null,"work_id":"ded098a2-158c-4bfa-a839-df95281dfe94","year":2022},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.774531Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:6f610c232a6fdbdd4addc899e732672713d5fda8ada16dfd6798dd2485a9d581","observation_id":"ad4c5377-a368-4ece-8482-e5ed267226ec","resolution":{"observed_at":"2026-08-06T12:57:00.518390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.506115Z","title":"Sjdl-vehicle: Semi-supervised joint defogging learning for foggy vehicle re-identification","venue":null,"work_id":"472661df-b4c4-4fd9-b233-e432b2c7d726","year":2022},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.776896Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:e75f8d5ff95e5824c9b890107f3d0c1ddd49b6966b1be4c4331d8398df67a37a","observation_id":"baff2005-87d4-4025-9b02-81365e1f1647","resolution":{"observed_at":"2026-08-06T12:57:00.509209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.497396Z","title":"Schwing, Alexan- der Kirillov, and Rohit Girdhar","venue":null,"work_id":"99ab46af-f259-40b4-ba72-efac4c0c1a43","year":null},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.779355Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:cc6c703f0fbd7636820e52783eba6beb0189e97586a808fd64b403a93cd56445","observation_id":"de8fefd7-c17b-4576-b718-715cf04ad088","resolution":{"observed_at":"2026-08-06T12:57:00.500289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.488623Z","title":"Robustnet: Improving domain generalization in urban-scene segmentation via in- stance selective whitening","venue":null,"work_id":"0020e1c8-3d4f-477b-baac-525916705cd4","year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.782102Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:d5e5f5f1ba252a2bdc249cf1777d254be958fee2996c12d08af48aea3b340166","observation_id":"e7d50ea0-a185-4789-a07f-d48ea4c4affb","resolution":{"observed_at":"2026-08-06T12:57:00.491551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:56:59.784793Z","title":"The cityscapes dataset for semantic urban scene understanding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.784793Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:305931820acac5c422d13e1ecb0aa6071c0eec26b49d363c3cb5989b486a8743","observation_id":"995086ac-7ef4-4565-9055-a3e83948b264","resolution":{"observed_at":"2026-08-06T12:56:59.784793Z","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":"2026-08-06T12:57:00.474580Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"f9b2bc41-1c48-42b8-ab27-d1f76d8d0a3d","year":2009},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.787477Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:7e04f029b26ff3119db74db879bcae9875fe47bd25e297d42262f0e5165f472e","observation_id":"0b03fec0-c69e-4f5a-81bb-7934234ebc2d","resolution":{"observed_at":"2026-08-06T12:57:00.477606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:56:59.789787Z","title":"Diffusion models beat gans on image synthesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.789787Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:833c9d243f7f74ca710798b92e1871b8be77b90e0b0f2ef9714f6411ae968c8e","observation_id":"a1bd0096-a461-4b17-babb-94eafb8bbdaf","resolution":{"observed_at":"2026-08-06T12:56:59.789787Z","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":"2026-08-06T12:57:00.460694Z","title":"Hgformer: Hierarchical grouping transformer for domain generalized semantic segmentation","venue":null,"work_id":"d86f97bf-79d2-4b41-b46f-db4da7e7956c","year":null},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.792336Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:29689f99c06da272bdbc0d869c7663cb6533423bf9cf105a9f4082ee13ee8c71","observation_id":"66bac002-184f-41ff-8f71-c03c51e41ae1","resolution":{"observed_at":"2026-08-06T12:57:00.463616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.452898Z","title":"Domain generalization via model-agnostic learning of semantic features","venue":null,"work_id":"280a08e9-7038-4d11-bac7-cd15d3919348","year":2019},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.795039Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:94356163156f71272e1061a3bc7db4bba01dd791ab5d122b274720650d69b6be","observation_id":"148d77af-2b83-4209-9996-580036946d8d","resolution":{"observed_at":"2026-08-06T12:57:00.455894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.445950Z","title":null,"venue":null,"work_id":"b34a8e45-9633-44a7-bbb1-333fc5907e02","year":2014},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.797459Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:b4ec890c47f297327ec45bb36170008516c612ae147592ab7ba323051f5dd854","observation_id":"77726f8c-7bb3-4bf1-8bd3-d87529c3bcd7","resolution":{"observed_at":"2026-08-06T12:57:00.448306Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.439051Z","title":"Unsupervised domain adaptation by backpropagation","venue":null,"work_id":"aba2af3f-1947-4282-9e5c-97951ecba67a","year":2015},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.799846Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:ffebc7b2b5d03d074baab6174f931e108eaf109f4712717297dfa7677c8f52c9","observation_id":"a12ba44f-34c8-45f9-9460-6ce619b98ae8","resolution":{"observed_at":"2026-08-06T12:57:00.441624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.431767Z","title":"Kleijn, Mengjie Zhang, and David Balduzzi","venue":null,"work_id":"79ed2b2e-e7ba-4a11-8027-d6883118bb0d","year":2015},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.802151Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:96070f52ec8d5c0ad6815db0bf7f55204c8e6909a22a657ede3c281d8c33ef4f","observation_id":"a12e57d2-e9d0-49dc-a2b1-408bb2b89f20","resolution":{"observed_at":"2026-08-06T12:57:00.434291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.02138","last_updated":"2023-07-05T09:28:25Z","snapshot_observed_at":"2026-07-06T15:50:29.812363Z","submitted_at":"2023-07-05T09:28:25Z","title":"Prompting Diffusion Representations for Cross-Domain Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.02138","snapshot_observed_at":"2026-08-06T12:56:59.804675Z","title":"Prompting diffusion representations for cross-domain semantic segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.804675Z"},"links":{"cited_paper":"/paper/2307.02138","citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:6f6d2c2b0ce550c1fd2564e453c2ac753a757d88ab0fed0161632aa7c96b706b","observation_id":"3c822dac-8f4c-47e9-a65b-9a5872fb6bc3","resolution":{"observed_at":"2026-08-06T12:56:59.804675Z","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":"2026-08-06T12:57:00.424682Z","title":"Zhang, Shaoqing Ren, and Jian Sun","venue":null,"work_id":"35781cfd-fdce-4801-a7f8-0c6f22f38fc3","year":2015},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.807416Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:d155dd0022c6a753c6bf7750c070d3169e16cd93a35449d3d66fae39a7115e77","observation_id":"c4389561-6aef-4286-89cc-77da7c009bc3","resolution":{"observed_at":"2026-08-06T12:57:00.427276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.417018Z","title":null,"venue":null,"work_id":"e72adcf1-b0de-4cda-aab3-67c5978dce33","year":2020},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.809659Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:36722937171df55e1ee0f2860e17c2623ed7169c9e0dad1f25d189537ed41cd2","observation_id":"f4959427-83b7-4c89-b180-72ac2f0bf6d7","resolution":{"observed_at":"2026-08-06T12:57:00.419637Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.408205Z","title":"Planning-oriented autonomous driving","venue":null,"work_id":"bd20eee3-a6a8-42eb-9dfd-b96c3b7db1a1","year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.811937Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:0e8ea539cec4c02b5695ff6e4072f8d11813c35f3aae7f92335c45e07607fb2d","observation_id":"7643948c-ce5f-47cd-85e9-c6007cb7412f","resolution":{"observed_at":"2026-08-06T12:57:00.410889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.399196Z","title":"Fsdr: Frequency space domain randomization for domain generalization","venue":null,"work_id":"364d33c0-2d1b-467f-95d0-f21394d76c14","year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.814457Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:60b2e2f67bac1e5427751a1179d0fead9cac95355c731220d5982bc1f044347b","observation_id":"a67f1dc8-e3d3-4610-a7a0-37f3657ad328","resolution":{"observed_at":"2026-08-06T12:57:00.402227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.390355Z","title":"Itera- tive normalization: Beyond standardization towards efficient whitening","venue":null,"work_id":"cefbb37f-da7a-43be-b0d9-67ffdce2e3e0","year":2019},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.816900Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:9a7df645fbe4209062c0e2133bdf53f76d6de1e24cf1345da3ddc56e8b1015c3","observation_id":"20819b53-706c-4840-ac8d-4a868fad8428","resolution":{"observed_at":"2026-08-06T12:57:00.393785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.382798Z","title":"Style projected clustering for domain generalized semantic seg- mentation","venue":null,"work_id":"956a03fd-9503-4df2-b370-a9e2dd65be44","year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.819339Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:298a6e035ea4cd3437690ac697bfd8758fcf40b96c54a6b9fd81a42800398042","observation_id":"ff2f8b35-171b-4eea-9f72-d7e8320bca97","resolution":{"observed_at":"2026-08-06T12:57:00.385476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.375264Z","title":"Batch normalization: Accelerating deep network training by reducing internal co- variate shift","venue":null,"work_id":"a0338b1e-050f-4592-a4c5-270e65fa8b8d","year":2015},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.821679Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:dbc5771af93b1c74152fe69bb579ac30016af94c2ccbd5e37abc012eb609cdd3","observation_id":"873f0125-9abc-4461-b300-f4c15dd18e9e","resolution":{"observed_at":"2026-08-06T12:57:00.377998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00777","last_updated":"2024-11-21T09:10:23Z","snapshot_observed_at":"2026-08-02T21:30:59.673995Z","submitted_at":"2024-06-02T15:33:46Z","title":"Diffusion Features to Bridge Domain Gap for Semantic Segmentation","version":2},"cited_work":{"arxiv_id":"2406.00777","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.00777","snapshot_observed_at":"2026-08-06T12:57:00.013389Z","title":"Diffusion Features to Bridge Domain Gap for Semantic Segmentation","venue":"cs.CV","work_id":"164d515a-cdfc-4079-9b21-a8a323d16073","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.824361Z"},"links":{"cited_paper":"/paper/2406.00777","citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:7ba1ec8232fa7e4e16b9e6a55ed28032b0f5c36fa1b787e12a2bc3743f82adda","observation_id":"13b550d0-e9b4-4e27-984f-e830050f5fa3","resolution":{"observed_at":"2026-08-06T12:57:00.016612Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.367439Z","title":"Dgin- style: Domain-generalizable semantic segmentation with image diffusion models and stylized semantic control","venue":null,"work_id":"01e0d171-ebc9-429d-b6d5-1692d6e9d9b4","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.827137Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:7669614f524f1d3469f19d730c5aa9773723eb74bf5a9f0e0166f598a2508388","observation_id":"06ae482b-a3e0-4c30-8476-307359e0797a","resolution":{"observed_at":"2026-08-06T12:57:00.370181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.359622Z","title":"Scedit: Efficient and controllable image diffusion generation via skip connection editing","venue":null,"work_id":"08bd169d-8eb8-4be9-8151-254a7b38af11","year":null},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.829533Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:c109959b452b0dfc27854ea84ca5708155c0c29a31fa9a8b9c1e175c07510961","observation_id":"66193430-fb3e-4730-bf2d-99c339f004f0","resolution":{"observed_at":"2026-08-06T12:57:00.362390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.352296Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":"e185e6e8-09f3-477f-ab07-161479ed7b19","year":2014},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.832127Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:a52e820d8e2600e568a8e7788f01bf497a9444bca9df6c66364d95cee977a603","observation_id":"392dac4d-c765-40d9-8c6b-7ae65bc2d13e","resolution":{"observed_at":"2026-08-06T12:57:00.354781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.344179Z","title":"Kingma and Max Welling","venue":null,"work_id":"5d0187bc-51a7-473b-9421-e9abb228a67b","year":2013},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.834601Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:2e71667c6e5f816dc02fcd24987731087796dc7590b110ac7cefb2a203017a6e","observation_id":"425e8a0c-c093-49e1-b25e-2cfec5091978","resolution":{"observed_at":"2026-08-06T12:57:00.347010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.00630","last_updated":"2023-04-14T00:20:30Z","snapshot_observed_at":"2026-08-03T22:15:47.891015Z","submitted_at":"2021-07-01T17:43:20Z","title":"Variational Diffusion Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.00630","snapshot_observed_at":"2026-08-06T12:56:59.837083Z","title":"Kingma, Tim Salimans, Ben Poole, and Jonathan Ho","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.837083Z"},"links":{"cited_paper":"/paper/2107.00630","citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:4b7efd35f8c67cf1446db47910f6e319bcf4276675fbb584170674d8bb031141","observation_id":"14ed586d-3a0c-4e78-81aa-6e568cb4e555","resolution":{"observed_at":"2026-08-06T12:56:59.837083Z","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":"2026-08-06T12:57:00.336065Z","title":"Wildnet: Learning domain generalized semantic seg- mentation from the wild","venue":null,"work_id":"86b890bb-61dd-4430-9959-37ebe9255f0a","year":2022},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.839919Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:8507d3db04e0b662f60b58d0564cac190ea30e400849fa702722bb80ad8ddede","observation_id":"7c3297fb-b9ad-421a-85dd-a23148863577","resolution":{"observed_at":"2026-08-06T12:57:00.338766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.327953Z","title":"Hospedales","venue":null,"work_id":"0896223e-f8a6-4406-a662-b26c6fe8d40c","year":2017},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.842411Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:1235d4fa2d460b2b07f8444bc9d1135d5f137048da835c4b33eea7af012e1058","observation_id":"91c97e1c-7ed0-4b2b-8c9c-a05ec5e75522","resolution":{"observed_at":"2026-08-06T12:57:00.330716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.320306Z","title":"Domain generalization with ad- versarial feature learning","venue":null,"work_id":"4aa211ca-4824-4910-b543-addcdb2711ac","year":2018},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.845222Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:574f586cd5c4293654c4008ec091c2da925a96a9db051c1340958d6816d54904","observation_id":"c3063ede-d039-4179-8f0b-27dcb55fa2c6","resolution":{"observed_at":"2026-08-06T12:57:00.322964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.312682Z","title":"Deep domain generaliza- tion via conditional invariant adversarial networks","venue":null,"work_id":"8d8d29d1-686c-4b80-8bbb-8533901762d1","year":null},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.847780Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:3c648f5e94091275b1628193499bfc37e118dba78b195187982892adb16cb9f1","observation_id":"9426fc25-bf35-492c-925a-2fc0acdc77d2","resolution":{"observed_at":"2026-08-06T12:57:00.315456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.304559Z","title":"Cdformer:when degradation prediction embraces diffusion model for blind image super-resolution","venue":null,"work_id":"1cf8cad3-285d-4850-b7eb-7e380b86c89c","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.850491Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:4d803d087eabcef39ba330cac1743549a5c486dc8c8e14ddbaee3fdd1337c1c2","observation_id":"056ff619-97a1-411a-8717-3d2f1da158c5","resolution":{"observed_at":"2026-08-06T12:57:00.307461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.296596Z","title":"Unbiased faster r-cnn for single- source domain generalized object detection","venue":null,"work_id":"af56e7ee-8076-4d52-a20e-3b878bc8d91e","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.852987Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:4231231ebc3960791b731e0c8cb48098f9bed720ccb44363f3ed96926454a7c6","observation_id":"4eaa2667-9a48-4393-89fa-3ad2c05b4ca8","resolution":{"observed_at":"2026-08-06T12:57:00.299582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:56:59.855499Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.855499Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:2b118efa320458a8eaee032b505b6e3a3e5a858ca1c60193d93ad7c9a45481bf","observation_id":"b0c636b0-97bb-45b2-b256-31427c3cd138","resolution":{"observed_at":"2026-08-06T12:56:59.855499Z","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":"2026-08-06T12:57:00.284491Z","title":"Adjeroh, and Gi- anfranco Doretto","venue":null,"work_id":"ad42db59-1316-4ec9-a78c-35319d06075a","year":2017},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.858019Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:27537e395bd391e1edd8bde1a335dbab1e5c2ed41c314d992639cdd13e718d25","observation_id":"b15e0865-de36-492a-851c-be3a7ecb0d44","resolution":{"observed_at":"2026-08-06T12:57:00.286890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.277357Z","title":"The mapillary vistas dataset for semantic understanding of street scenes","venue":null,"work_id":"ffdef35a-1ac5-4f0d-8459-36b3595bd01a","year":2017},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.860549Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:25afdbb3ff48fe90dc631e6cfaae71251bccca0370bdf3769977ca622f137085","observation_id":"a9fc0e92-445b-484c-818a-5a6b1233bbe6","resolution":{"observed_at":"2026-08-06T12:57:00.279945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.270410Z","title":"Embodied visual active learning for semantic segmentation","venue":null,"work_id":"23c22aa5-3197-41cc-b1cf-46bac6631de5","year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.862993Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:5c295717285fae5e36c47605dee9ef004bb501126b5fb4565e8127c1c28efe39","observation_id":"4dbeba87-79cb-400c-bfc8-ae2272d5e694","resolution":{"observed_at":"2026-08-06T12:57:00.273046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.263072Z","title":"Au- tonomous mobile robot navigation independent of road boundary using driving recommendation map","venue":null,"work_id":"3bad1e8f-7952-45d7-8698-9793acee199b","year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.865458Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:65b58222b2e04b84f589089d5ce7b6f3425de4bd2abd4b20c583169c8487840e","observation_id":"881659a8-45f9-4be2-8c6b-2f7369314f6c","resolution":{"observed_at":"2026-08-06T12:57:00.265808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.256139Z","title":"Two at once: Enhancing learning and generalization capacities via ibn-net","venue":null,"work_id":"1cfe0b90-cad5-495e-bee2-7bd70750c3fa","year":2018},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.867958Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:1c582736a95fd4340cfad5384a4f28f6d4e86d9e0bb5dc7f664e2a121021eea5","observation_id":"7324c391-a9a5-45dd-9f13-f39632833043","resolution":{"observed_at":"2026-08-06T12:57:00.258636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.248921Z","title":"Switchable whitening for deep representation learning","venue":null,"work_id":"a6b3828d-4030-4402-a55d-40365ef36249","year":2019},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.870312Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:eaa0955f7498f81326b96d6af82ccfde447d971c4bb943783f098ae10d8547c5","observation_id":"04b17a74-8c8e-434f-9b11-824909d15fd1","resolution":{"observed_at":"2026-08-06T12:57:00.251435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.241753Z","title":"Global and local texture randomization for synthetic-to-real semantic segmentation","venue":null,"work_id":"2d6f63de-97f4-4f5d-82c1-7f32db2587cb","year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.872769Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:b958073ac9e7b4642833e5396fad52f171fee6e25ea99a38daad5882ff4151e6","observation_id":"bc40c922-ab6f-4226-a922-aafc3864531c","resolution":{"observed_at":"2026-08-06T12:57:00.244288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.234413Z","title":"Semantic-aware domain generalized segmentation","venue":null,"work_id":"c3783eb0-d074-46c3-94d4-00a6eb1e3bc5","year":2022},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.875276Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:7ea9f52259761f21b17cb326e0d98891698e5b280d3685d6e9024b4e652bab74","observation_id":"0461f8ae-8f02-476c-becd-d298739c50e5","resolution":{"observed_at":"2026-08-06T12:57:00.237015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.227011Z","title":"Semantic-aware domain generalized segmentation","venue":null,"work_id":"03b6f091-d727-4485-8183-277eb389280a","year":2022},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.877853Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:4940c5d71dbcb72950b164a3f196b5dcd256f22d723c5b69c8851e9138db9d65","observation_id":"207d72f7-4fa4-4a55-be54-03d9045c5226","resolution":{"observed_at":"2026-08-06T12:57:00.229658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00637","last_updated":"2023-09-29T05:32:46Z","snapshot_observed_at":"2026-07-30T14:57:15.227452Z","submitted_at":"2023-06-01T13:00:53Z","title":"Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00637","snapshot_observed_at":"2026-08-06T12:56:59.880375Z","title":"Richter, Christo- pher J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.880375Z"},"links":{"cited_paper":"/paper/2306.00637","citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:184cb56754048c90c9ba94d5af75002b038f30ab64592b621445a35ad4cb15cb","observation_id":"22079b12-2811-4406-88b9-c9cb05e76bd7","resolution":{"observed_at":"2026-08-06T12:56:59.880375Z","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":"2026-08-06T12:57:00.219583Z","title":"Lead: Learn- ing decomposition for source-free universal domain adapta- tion","venue":null,"work_id":"10d2cece-2bfd-4bbf-98b7-92acf009d3b6","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.883309Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:b91acf7754e261e684c261e7437ada44d3077b06834b253fcd09cafecda94ac0","observation_id":"d7935f45-0a2f-4a7a-9a9b-0e65f7acb5a2","resolution":{"observed_at":"2026-08-06T12:57:00.222324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.212189Z","title":"Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun","venue":null,"work_id":"17da527d-20ca-4d90-a95b-ece000ea9b27","year":2016},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.886143Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:38e4ff0dc79005aa44fff63cfd2010d175bcd23863e81573c974ab12fd7f2ee3","observation_id":"102f8c27-92c7-4e15-8369-e1bfaca11ebe","resolution":{"observed_at":"2026-08-06T12:57:00.214920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.205184Z","title":"Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer","venue":null,"work_id":"fe20ca91-0c65-4b03-bbea-e927be7defd7","year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.888553Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:fa63e20079fd43df10ad97ea9a3ff828bc9ee58f6669e03d245c7d50307524a7","observation_id":"a83ce77e-e1ec-46f2-a214-58ac06724d5a","resolution":{"observed_at":"2026-08-06T12:57:00.207725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.198193Z","title":null,"venue":null,"work_id":"7a9a8653-fe7b-4193-8764-aa6eff03806b","year":2016},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.890999Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:c72fa97420637cd3c720005b11ca7abb2579e8de47193cda47f1f934e819c7d8","observation_id":"a9ada530-5333-4d0a-877b-01f52a7d8b7a","resolution":{"observed_at":"2026-08-06T12:57:00.200575Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.190417Z","title":"Acdc: The adverse conditions dataset with correspondences for se- mantic driving scene understanding","venue":null,"work_id":"654fdb59-1557-4fe6-b2f6-87feb6b0bbda","year":2021},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.893368Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:b91262db8309006f120ce2a3fe09db216557a3e72f095af3134c8c71c6118b43","observation_id":"e132d4fb-2113-404c-bca3-2d1b0de8cdd4","resolution":{"observed_at":"2026-08-06T12:57:00.193262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.183064Z","title":"Learning to optimize domain specific normalization for domain generalization","venue":null,"work_id":"bde3c67f-d67a-4117-93a6-904e35c0761b","year":2019},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.895740Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:47eb4c425aca059c756f3f9287d8f61583c9f61f28fb016c5a6afcffb7e955e2","observation_id":"437e93c7-8892-4853-8199-9472efdcbd22","resolution":{"observed_at":"2026-08-06T12:57:00.185769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-06T12:56:59.898182Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.898182Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:346382454643099ca46416cd5661324cf6e3af9571a98db8a1a155eaea2cf06b","observation_id":"4af74b30-7164-4857-8e5a-efde1ffed514","resolution":{"observed_at":"2026-08-06T12:56:59.898182Z","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":"2026-08-06T12:57:00.175666Z","title":"Your classifier can secretly suffice multi-source domain adaptation","venue":null,"work_id":"b3ef68df-29e3-4dfc-8c7e-50ec66a465b9","year":2020},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.900972Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:f237b0789f0b14c7656b64658e98fbc70af672843267ac445e4467214130e49d","observation_id":"f1748537-0e78-43c1-9612-68bf66d588c4","resolution":{"observed_at":"2026-08-06T12:57:00.178411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.168492Z","title":"Exploiting diffusion prior for real-world image super-resolution","venue":null,"work_id":"edca8361-c003-4e5c-ac48-eb052f7ad3a3","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.903378Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:49c9bd4f7b1924e2a0d5226c9ab07f8c6f38fea7ad8316690f1b3d1203604268","observation_id":"c4dd9dad-8c9d-410c-8ac7-87c9af32539c","resolution":{"observed_at":"2026-08-06T12:57:00.171086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.161153Z","title":"Recovering realistic texture in image super-resolution by deep spatial feature transform","venue":null,"work_id":"a47b2bfd-1b58-46f7-8959-13cdfd384eea","year":2018},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.906287Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:7ce0748859012f8de0aa283b4b988a077face95981fccce25819a951cfcabb3f","observation_id":"75f72a9c-2b26-48f2-9df4-8a8474b97285","resolution":{"observed_at":"2026-08-06T12:57:00.163732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05628","last_updated":"2024-06-09T03:32:32Z","snapshot_observed_at":"2026-07-06T18:27:41.409205Z","submitted_at":"2024-06-09T03:32:32Z","title":"Domain Generalization Guided by Large-Scale Pre-Trained Priors","version":1},"cited_work":{"arxiv_id":"2406.05628","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.05628","snapshot_observed_at":"2026-08-06T12:56:59.973265Z","title":"Domain Generalization Guided by Large-Scale Pre-Trained Priors","venue":"cs.LG","work_id":"326f4dbc-7fe1-452f-895c-c9a530d9cab5","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.908831Z"},"links":{"cited_paper":"/paper/2406.05628","citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:6317456212e8c4f933473961f2388d0a85f9e5bd34c90160f358b9dce6200f42","observation_id":"7c7b94d6-11e1-42e2-9635-6c17290309a3","resolution":{"observed_at":"2026-08-06T12:56:59.978068Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.153232Z","title":"Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation","venue":null,"work_id":"5622ef4e-04d7-476d-9929-e35111f6094f","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.911446Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:71a11cf7a335051c740bb46470d87c39a29d5bb28f421cb649b96722dd8e812d","observation_id":"41ca0db8-877a-430b-a2de-d8c0c717f014","resolution":{"observed_at":"2026-08-06T12:57:00.156493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.145319Z","title":"Datasetdm: Synthesizing data with perception anno- tations using diffusion models","venue":null,"work_id":"18210c85-c2e3-4f5e-a185-f3ade18d33ae","year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.913827Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:d08fa6e43f9f835376ee71aca73b57c1e73b753231b3ec2e85f09068df0f8e18","observation_id":"5a7953d4-cf22-44f5-900a-0193bd63f83e","resolution":{"observed_at":"2026-08-06T12:57:00.148048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.137852Z","title":"Diffir: Efficient diffusion model for image restoration","venue":null,"work_id":"8d9868d9-1ede-4ea1-942d-1547b9c285c0","year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.916178Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:e55597735d729113e5339a80fe45084548b156df72e06869d441505021032c83","observation_id":"fe1bb35e-a6b7-47c2-bf58-014ed51ceffe","resolution":{"observed_at":"2026-08-06T12:57:00.140756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.129540Z","title":"Dirl: Domain-invariant representation learning for gen- eralizable semantic segmentation","venue":null,"work_id":"aa3833b4-1542-45c9-9970-de5d52926567","year":2022},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.918652Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:8e5b32adfae303c52a6a35d416854a3a85445a33fb17979eb1427b58635c1c34","observation_id":"9254a145-6fe8-4b59-b70e-801197828add","resolution":{"observed_at":"2026-08-06T12:57:00.132491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.120775Z","title":"Generalized seman- tic segmentation by self-supervised source domain projec- tion and multi-level contrastive learning","venue":null,"work_id":"9bb18543-83e8-4271-b528-3fd099055410","year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.921090Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:cf26fc3fa2726d7a96d44b24da028625f1c0955ee2212fec52b518b1ece41a91","observation_id":"35ab0839-892f-40fd-a4d9-40827addab86","resolution":{"observed_at":"2026-08-06T12:57:00.123585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.112724Z","title":"Bdd100k: A diverse driving dataset for heterogeneous multitask learning","venue":null,"work_id":"6cbabddb-0901-4972-ba8b-42e36da3ad4c","year":2018},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.923566Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:5537c9424854cc64efc9c1b9d35dfeae67e6be9f1112ade796b9dcf0ee95725c","observation_id":"f79a00d5-8358-4597-9bb2-e0a69197df92","resolution":{"observed_at":"2026-08-06T12:57:00.115422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.104653Z","title":"Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong","venue":null,"work_id":"b76b89df-8251-483f-aac1-5c80230af68d","year":2019},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.926261Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:80a2c05aa6efdf962ba3e47d9c826730792ce8d93a965c692529f1a66e3929c0","observation_id":"e43a94aa-5654-41f4-8705-70105df05bce","resolution":{"observed_at":"2026-08-06T12:57:00.107513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.096701Z","title":"Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation","venue":null,"work_id":"24f98d99-673b-4655-b36b-4ed555c42a29","year":null},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.928857Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:7fcdd6ce181544c3876983ad78b8dc3a60dfcb89d0ae4e97d741daf3b4a15219","observation_id":"757ed4fc-1d43-4dd8-be25-a10c4f8cc114","resolution":{"observed_at":"2026-08-06T12:57:00.099489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.088590Z","title":"C3net: Compound conditioned controlnet for multi- modal content generation","venue":null,"work_id":"e3ade622-ca4b-4bad-9a08-7edf3301bc08","year":2024},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.931398Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:8c1049076d6cbad70cbb2b3521062fedd1ee35dd282ab1fd91c88e94254cebb4","observation_id":"bddc296c-e9da-44d8-952e-f7495995ab0c","resolution":{"observed_at":"2026-08-06T12:57:00.091638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:56:59.933912Z","title":"Adding conditional control to text-to-image diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.933912Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:f6da3ec720f08f11bd6a1e473aca9f870b1a8b9e05e6381dc79ea6c02002afe9","observation_id":"e60e0c11-6149-45ca-846f-01dfb1e5c342","resolution":{"observed_at":"2026-08-06T12:56:59.933912Z","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":"2026-08-06T12:57:00.074879Z","title":"Mamba as a bridge: Where vision foundation models meet vision language models for domain-generalized semantic segmentation","venue":null,"work_id":"cf4529e1-1148-471b-81f7-4b8976101a40","year":2025},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.936356Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:04e5b3734b8725fb4d09702562a3ebda1708f8dd2faea1e082875ff73bac9ef0","observation_id":"002c0c48-a8e2-4e73-9a4a-f77490159023","resolution":{"observed_at":"2026-08-06T12:57:00.077856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.066456Z","title":"Fishertune: Fisher- guided robust tuning of vision foundation models for domain generalized segmentation","venue":null,"work_id":"5848504b-9c84-4f68-9c67-e92be7c071d8","year":2025},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.938973Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:36c47f2c8e017547c2fba8f3333070993bf6af91f54e5992a4556ec95401853b","observation_id":"8f75dbfd-879e-4243-a383-4030d3d5cefc","resolution":{"observed_at":"2026-08-06T12:57:00.069297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.057882Z","title":"Uni-controlnet: All-in-one control to text-to-image diffusion models","venue":null,"work_id":"4893831a-c21e-40e4-a6bb-c2d7a6a431f2","year":2023},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.941332Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:b2e8262fa8c0b8ecf45af79c8ef8411c31ee05cbaf2578939854c4729b53b5cd","observation_id":"012e7206-01c4-433f-9ad5-0ccdd94072e9","resolution":{"observed_at":"2026-08-06T12:57:00.060758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.049441Z","title":"Sebe, and Gim Hee Lee","venue":null,"work_id":"0c28aa6c-f52e-4aaa-96f3-cb8f9a9583fe","year":2022},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.943749Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:edbab31b8ee1cb449efcb725913524421b189925545981d928299dcc6b157d14","observation_id":"9f6aa5cc-eca5-4d69-b08f-504a45fc5f21","resolution":{"observed_at":"2026-08-06T12:57:00.052548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:57:00.040878Z","title":null,"venue":null,"work_id":"fc9a31d0-84ff-449c-a92c-5893b6d9c2c2","year":2022},"citing_paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T12:56:59.946357Z"},"links":{"citing_paper":"/paper/2507.21367"},"observation_digest":"sha256:1a8946053f0fe388fee3337062a877c01ea81e2a80627d1c82f1706fe20cfeef","observation_id":"8c6f1c9e-5c11-4a0f-bf21-f35538e19744","resolution":{"observed_at":"2026-08-06T12:57:00.043722Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21367","last_updated":"2025-07-28T22:27:58Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T12:56:59.072845Z","submitted_at":"2025-07-28T22:27:58Z","title":"Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation"},"reference_resolution":{"displayed":76,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":2,"verified_fuzzy":61},"total_outbound_references":76},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 0 inbound Pith citation observations for arXiv:2507.21367."}