{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XH5GD72APPOJEJJZ64UMFNCO2Q","short_pith_number":"pith:XH5GD72A","schema_version":"1.0","canonical_sha256":"b9fa61ff407bdc922539f728c2b44ed4046c52b5ddfcaf2fbe10da75252c6337","source":{"kind":"arxiv","id":"2607.10700","version":1},"attestation_state":"computed","paper":{"title":"An Extreme Value Perspective on Learning Stress Laws","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"q-fin.RM","authors_text":"Anand Deo, Mantu Gupta","submitted_at":"2026-07-12T10:46:41Z","abstract_excerpt":"We introduce Self-Similar Generative Estimation (SS-GEN), a method for simulating multivariate tail events and estimating rare-event probabilities in both heavy and light-tailed settings. SS-GEN exploits asymptotic tail structure to decompose the tail distribution into an explicit radial component and a nonparametric angular component, reducing tail learning to a compact-domain problem that can be handled by off-the-shelf deep generative models. The resulting sampler generates representative extreme scenarios and supports probability estimation far beyond the observed data. Under mild nonparam"},"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":"2607.10700","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2026-07-12T10:46:41Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1343349e32a7262c04aeffcc8f884c5646bdd927e3ea7d52c68f01bd60a0e57e","abstract_canon_sha256":"856b70255a4b666e40388859321c66eca0249928270814629b379b8b33b3c724"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:21:35.346340Z","signature_b64":"ugSPC5yO10d7pn1iSBBWOvHZra+55rgpanfeUf3bilDFpopa8c1kgxdr1I99k9nlEP7OWbp5/Dm4emlrUpYnAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9fa61ff407bdc922539f728c2b44ed4046c52b5ddfcaf2fbe10da75252c6337","last_reissued_at":"2026-07-14T01:21:35.345518Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:21:35.345518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Extreme Value Perspective on Learning Stress Laws","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"q-fin.RM","authors_text":"Anand Deo, Mantu Gupta","submitted_at":"2026-07-12T10:46:41Z","abstract_excerpt":"We introduce Self-Similar Generative Estimation (SS-GEN), a method for simulating multivariate tail events and estimating rare-event probabilities in both heavy and light-tailed settings. SS-GEN exploits asymptotic tail structure to decompose the tail distribution into an explicit radial component and a nonparametric angular component, reducing tail learning to a compact-domain problem that can be handled by off-the-shelf deep generative models. The resulting sampler generates representative extreme scenarios and supports probability estimation far beyond the observed data. Under mild nonparam"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10700","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/2607.10700/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":"2607.10700","created_at":"2026-07-14T01:21:35.345940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.10700v1","created_at":"2026-07-14T01:21:35.345940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10700","created_at":"2026-07-14T01:21:35.345940+00:00"},{"alias_kind":"pith_short_12","alias_value":"XH5GD72APPOJ","created_at":"2026-07-14T01:21:35.345940+00:00"},{"alias_kind":"pith_short_16","alias_value":"XH5GD72APPOJEJJZ","created_at":"2026-07-14T01:21:35.345940+00:00"},{"alias_kind":"pith_short_8","alias_value":"XH5GD72A","created_at":"2026-07-14T01:21:35.345940+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/XH5GD72APPOJEJJZ64UMFNCO2Q","json":"https://pith.science/pith/XH5GD72APPOJEJJZ64UMFNCO2Q.json","graph_json":"https://pith.science/api/pith-number/XH5GD72APPOJEJJZ64UMFNCO2Q/graph.json","events_json":"https://pith.science/api/pith-number/XH5GD72APPOJEJJZ64UMFNCO2Q/events.json","paper":"https://pith.science/paper/XH5GD72A"},"agent_actions":{"view_html":"https://pith.science/pith/XH5GD72APPOJEJJZ64UMFNCO2Q","download_json":"https://pith.science/pith/XH5GD72APPOJEJJZ64UMFNCO2Q.json","view_paper":"https://pith.science/paper/XH5GD72A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.10700&json=true","fetch_graph":"https://pith.science/api/pith-number/XH5GD72APPOJEJJZ64UMFNCO2Q/graph.json","fetch_events":"https://pith.science/api/pith-number/XH5GD72APPOJEJJZ64UMFNCO2Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XH5GD72APPOJEJJZ64UMFNCO2Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XH5GD72APPOJEJJZ64UMFNCO2Q/action/storage_attestation","attest_author":"https://pith.science/pith/XH5GD72APPOJEJJZ64UMFNCO2Q/action/author_attestation","sign_citation":"https://pith.science/pith/XH5GD72APPOJEJJZ64UMFNCO2Q/action/citation_signature","submit_replication":"https://pith.science/pith/XH5GD72APPOJEJJZ64UMFNCO2Q/action/replication_record"}},"created_at":"2026-07-14T01:21:35.345940+00:00","updated_at":"2026-07-14T01:21:35.345940+00:00"}