{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NOMOZ4M5DWFBN62HIE3VCTZVV6","short_pith_number":"pith:NOMOZ4M5","canonical_record":{"source":{"id":"2508.18922","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-26T10:55:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"76a2dfcd5b60248e01e154eddace48418c28e047611795062be2ae746808d9ba","abstract_canon_sha256":"99b385d5020fbac957a8aa0fcfc10209f66bf06b9ca1da105017915eb9212fb5"},"schema_version":"1.0"},"canonical_sha256":"6b98ecf19d1d8a16fb474137514f35afb960a81ac84adc5bc84cb3b34420ef4b","source":{"kind":"arxiv","id":"2508.18922","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.18922","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"arxiv_version","alias_value":"2508.18922v1","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18922","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"pith_short_12","alias_value":"NOMOZ4M5DWFB","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"pith_short_16","alias_value":"NOMOZ4M5DWFBN62H","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"pith_short_8","alias_value":"NOMOZ4M5","created_at":"2026-07-05T11:59:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NOMOZ4M5DWFBN62HIE3VCTZVV6","target":"record","payload":{"canonical_record":{"source":{"id":"2508.18922","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-26T10:55:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"76a2dfcd5b60248e01e154eddace48418c28e047611795062be2ae746808d9ba","abstract_canon_sha256":"99b385d5020fbac957a8aa0fcfc10209f66bf06b9ca1da105017915eb9212fb5"},"schema_version":"1.0"},"canonical_sha256":"6b98ecf19d1d8a16fb474137514f35afb960a81ac84adc5bc84cb3b34420ef4b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:33.767904Z","signature_b64":"VHIoBC+P6p6UR61/Rukdf883xsOv7RVu2ETRhIMEtA20fHKyPAbSRq6S2g4hkhVgLt/dEqpDgme2dBdqEXKvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b98ecf19d1d8a16fb474137514f35afb960a81ac84adc5bc84cb3b34420ef4b","last_reissued_at":"2026-07-05T11:59:33.767441Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:33.767441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.18922","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:59:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hb1d49fbATtkyxGxLqMFFqg2cAnvy0KEVMq+zbQEFUmsZhzmWBoUjSQy42K43otAx9oRQ15tv5DGaL2lxNOOBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T02:44:01.956406Z"},"content_sha256":"07019f838d84b2e7281c5a32237a7dcd364f5b79b85744b27d84d2396b449326","schema_version":"1.0","event_id":"sha256:07019f838d84b2e7281c5a32237a7dcd364f5b79b85744b27d84d2396b449326"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NOMOZ4M5DWFBN62HIE3VCTZVV6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Yao Wu","submitted_at":"2025-08-26T10:55:35Z","abstract_excerpt":"Temporal modeling in complex systems requires capturing dependencies across multiple time scales while managing inherent uncertainties. We propose HierCVAE, a novel architecture that integrates hierarchical attention mechanisms with conditional variational autoencoders to address these challenges. HierCVAE employs a three-tier attention structure (local, global, cross-temporal) combined with multi-modal condition encoding to capture temporal, statistical, and trend information. The approach incorporates ResFormer blocks in the latent space and provides explicit uncertainty quantification via p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18922","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/2508.18922/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:59:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8EvMuzNxceFDao99EUryyZcqU8WT4E4Qp0I+oY+OOwBfwhM5rSQy8TZ5nXHqHtt2eLB/PtyvVF2ifE3w4LSJBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T02:44:01.956900Z"},"content_sha256":"799ebf7649933e4ae5ce645ce0177feed88fcc4e8672c608d93de054f7601454","schema_version":"1.0","event_id":"sha256:799ebf7649933e4ae5ce645ce0177feed88fcc4e8672c608d93de054f7601454"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NOMOZ4M5DWFBN62HIE3VCTZVV6/bundle.json","state_url":"https://pith.science/pith/NOMOZ4M5DWFBN62HIE3VCTZVV6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NOMOZ4M5DWFBN62HIE3VCTZVV6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T02:44:01Z","links":{"resolver":"https://pith.science/pith/NOMOZ4M5DWFBN62HIE3VCTZVV6","bundle":"https://pith.science/pith/NOMOZ4M5DWFBN62HIE3VCTZVV6/bundle.json","state":"https://pith.science/pith/NOMOZ4M5DWFBN62HIE3VCTZVV6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NOMOZ4M5DWFBN62HIE3VCTZVV6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NOMOZ4M5DWFBN62HIE3VCTZVV6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"99b385d5020fbac957a8aa0fcfc10209f66bf06b9ca1da105017915eb9212fb5","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-26T10:55:35Z","title_canon_sha256":"76a2dfcd5b60248e01e154eddace48418c28e047611795062be2ae746808d9ba"},"schema_version":"1.0","source":{"id":"2508.18922","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.18922","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"arxiv_version","alias_value":"2508.18922v1","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18922","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"pith_short_12","alias_value":"NOMOZ4M5DWFB","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"pith_short_16","alias_value":"NOMOZ4M5DWFBN62H","created_at":"2026-07-05T11:59:33Z"},{"alias_kind":"pith_short_8","alias_value":"NOMOZ4M5","created_at":"2026-07-05T11:59:33Z"}],"graph_snapshots":[{"event_id":"sha256:799ebf7649933e4ae5ce645ce0177feed88fcc4e8672c608d93de054f7601454","target":"graph","created_at":"2026-07-05T11:59:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.18922/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Temporal modeling in complex systems requires capturing dependencies across multiple time scales while managing inherent uncertainties. We propose HierCVAE, a novel architecture that integrates hierarchical attention mechanisms with conditional variational autoencoders to address these challenges. HierCVAE employs a three-tier attention structure (local, global, cross-temporal) combined with multi-modal condition encoding to capture temporal, statistical, and trend information. The approach incorporates ResFormer blocks in the latent space and provides explicit uncertainty quantification via p","authors_text":"Yao Wu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-26T10:55:35Z","title":"HierCVAE: Hierarchical Attention-Driven Conditional Variational Autoencoders for Multi-Scale Temporal Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18922","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:07019f838d84b2e7281c5a32237a7dcd364f5b79b85744b27d84d2396b449326","target":"record","created_at":"2026-07-05T11:59:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"99b385d5020fbac957a8aa0fcfc10209f66bf06b9ca1da105017915eb9212fb5","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-26T10:55:35Z","title_canon_sha256":"76a2dfcd5b60248e01e154eddace48418c28e047611795062be2ae746808d9ba"},"schema_version":"1.0","source":{"id":"2508.18922","kind":"arxiv","version":1}},"canonical_sha256":"6b98ecf19d1d8a16fb474137514f35afb960a81ac84adc5bc84cb3b34420ef4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6b98ecf19d1d8a16fb474137514f35afb960a81ac84adc5bc84cb3b34420ef4b","first_computed_at":"2026-07-05T11:59:33.767441Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:59:33.767441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VHIoBC+P6p6UR61/Rukdf883xsOv7RVu2ETRhIMEtA20fHKyPAbSRq6S2g4hkhVgLt/dEqpDgme2dBdqEXKvAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:59:33.767904Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.18922","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07019f838d84b2e7281c5a32237a7dcd364f5b79b85744b27d84d2396b449326","sha256:799ebf7649933e4ae5ce645ce0177feed88fcc4e8672c608d93de054f7601454"],"state_sha256":"c7885d3d2373909e2b05685a469fd947f21190b1b05a7d6d65e01888231ce290"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C1uUabc6QX77JDB11FQFZBCtZ8VlhkQ3G1F+u0ESLB1c7bG6ItY1doF9JONbYt6aItou/knheEgMjJ9INjEDCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T02:44:01.960510Z","bundle_sha256":"0f64bd7f6da624c15079dcde1a600828db86d72bd0af1bf8ede6dd36008d7fd9"}}