{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VYW4VYH45HMV3R4QCFM7V3IO63","short_pith_number":"pith:VYW4VYH4","schema_version":"1.0","canonical_sha256":"ae2dcae0fce9d95dc7901159faed0ef6f18187e45091809ee6d857d0e8b0e51f","source":{"kind":"arxiv","id":"2607.12833","version":1},"attestation_state":"computed","paper":{"title":"ANGLE: Angular Neural Generative Learning via Engression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Archi Roy, Rajdeep Pathak, Tanujit Chakraborty","submitted_at":"2026-07-14T14:51:48Z","abstract_excerpt":"Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regression targets the conditional mean, which is often geometrically misleading for circular responses under multimodal, skewed, or asymmetric data structures. To address these limitations, a lightweight deep generative framework, namely ANGLE, is introduced for non-parametric distributional regression on the circle. The full conditional distribution of an angular response, given Euclidean and circular covariates, is learned through a generative map o"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.12833","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-07-14T14:51:48Z","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"title_canon_sha256":"7459640b0ed3f80f36269024d9de8045d41899701559bcf93aba2713e7387b3b","abstract_canon_sha256":"b38c40c667089182d73826bee0de7d1c475af217630bab7381ca83a5bece0bab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-15T01:22:20.760168Z","signature_b64":"KKQVQLHvAG7Wf4CULrFdx81sQyZyA1AbG0U0ZtOC/mSflUH4QxVW4cr+NACSQeOKjlNA/qzgTdKq0vJ30zGYBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae2dcae0fce9d95dc7901159faed0ef6f18187e45091809ee6d857d0e8b0e51f","last_reissued_at":"2026-07-15T01:22:20.759381Z","signature_status":"signed_v1","first_computed_at":"2026-07-15T01:22:20.759381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ANGLE: Angular Neural Generative Learning via Engression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Archi Roy, Rajdeep Pathak, Tanujit Chakraborty","submitted_at":"2026-07-14T14:51:48Z","abstract_excerpt":"Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regression targets the conditional mean, which is often geometrically misleading for circular responses under multimodal, skewed, or asymmetric data structures. To address these limitations, a lightweight deep generative framework, namely ANGLE, is introduced for non-parametric distributional regression on the circle. The full conditional distribution of an angular response, given Euclidean and circular covariates, is learned through a generative map o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.12833","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.12833/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.12833","created_at":"2026-07-15T01:22:20.759781+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.12833v1","created_at":"2026-07-15T01:22:20.759781+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.12833","created_at":"2026-07-15T01:22:20.759781+00:00"},{"alias_kind":"pith_short_12","alias_value":"VYW4VYH45HMV","created_at":"2026-07-15T01:22:20.759781+00:00"},{"alias_kind":"pith_short_16","alias_value":"VYW4VYH45HMV3R4Q","created_at":"2026-07-15T01:22:20.759781+00:00"},{"alias_kind":"pith_short_8","alias_value":"VYW4VYH4","created_at":"2026-07-15T01:22:20.759781+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63","json":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63.json","graph_json":"https://pith.science/api/pith-number/VYW4VYH45HMV3R4QCFM7V3IO63/graph.json","events_json":"https://pith.science/api/pith-number/VYW4VYH45HMV3R4QCFM7V3IO63/events.json","paper":"https://pith.science/paper/VYW4VYH4"},"agent_actions":{"view_html":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63","download_json":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63.json","view_paper":"https://pith.science/paper/VYW4VYH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.12833&json=true","fetch_graph":"https://pith.science/api/pith-number/VYW4VYH45HMV3R4QCFM7V3IO63/graph.json","fetch_events":"https://pith.science/api/pith-number/VYW4VYH45HMV3R4QCFM7V3IO63/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63/action/storage_attestation","attest_author":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63/action/author_attestation","sign_citation":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63/action/citation_signature","submit_replication":"https://pith.science/pith/VYW4VYH45HMV3R4QCFM7V3IO63/action/replication_record"}},"created_at":"2026-07-15T01:22:20.759781+00:00","updated_at":"2026-07-15T01:22:20.759781+00:00"}