{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:NFEAXWS6JWXZJ62TVQEQT2AKSM","short_pith_number":"pith:NFEAXWS6","canonical_record":{"source":{"id":"2607.17783","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T10:15:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d81f738c12b970d7d3376e23dab07cd3dba93d8a18b6a9c77a8a5a3f3ea57e4e","abstract_canon_sha256":"061dc4f6133a5dd8e444e41027610705e7a80f3dca5d52f13de7bfdb639a36d2"},"schema_version":"1.0"},"canonical_sha256":"69480bda5e4daf94fb53ac0909e80a9329bc25fd56cb7b2e9e3c6aa823d851a7","source":{"kind":"arxiv","id":"2607.17783","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.17783","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"arxiv_version","alias_value":"2607.17783v1","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17783","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"pith_short_12","alias_value":"NFEAXWS6JWXZ","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"pith_short_16","alias_value":"NFEAXWS6JWXZJ62T","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"pith_short_8","alias_value":"NFEAXWS6","created_at":"2026-07-21T02:21:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:NFEAXWS6JWXZJ62TVQEQT2AKSM","target":"record","payload":{"canonical_record":{"source":{"id":"2607.17783","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T10:15:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d81f738c12b970d7d3376e23dab07cd3dba93d8a18b6a9c77a8a5a3f3ea57e4e","abstract_canon_sha256":"061dc4f6133a5dd8e444e41027610705e7a80f3dca5d52f13de7bfdb639a36d2"},"schema_version":"1.0"},"canonical_sha256":"69480bda5e4daf94fb53ac0909e80a9329bc25fd56cb7b2e9e3c6aa823d851a7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T02:21:59.978690Z","signature_b64":"ojgGT++Bvotgup3FIyck7QXZi6cZEKqqFArRaAXYNENyQ2vMwlZ1lKr2/o66Vq5XZFeICYULOp9MkQSoDQalCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69480bda5e4daf94fb53ac0909e80a9329bc25fd56cb7b2e9e3c6aa823d851a7","last_reissued_at":"2026-07-21T02:21:59.977805Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T02:21:59.977805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.17783","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":"2026-07-21T02:21:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hkvO9yTjUGAS1gNneZ5GskhDijNkGqrTNLgZGCF8QNok5psRIVnTTGjGxM9rLhziZL41gk4m8TJstW4tEqlhDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T03:20:23.995671Z"},"content_sha256":"a577d50f84f9c4de2a0e259a522fd7fe7b989bd513526c05d927dad67dbe608c","schema_version":"1.0","event_id":"sha256:a577d50f84f9c4de2a0e259a522fd7fe7b989bd513526c05d927dad67dbe608c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:NFEAXWS6JWXZJ62TVQEQT2AKSM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Johannes De Smedt, Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu","submitted_at":"2026-07-20T10:15:47Z","abstract_excerpt":"Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural networks have achieved strong performance for these tasks by modeling sequential dependencies in event logs, their black-box nature limits trust and practical adoption. Feature attribution methods are often used to address this, but applying them directly poses a dilemma: event-level attributions impose high computational complexity for long traces, while explanations based on aggregated trace representations often "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17783","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/2607.17783/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":"2026-07-21T02:21:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PS9jVry5QPKK1qX+n5BI2o5QRWBVqmoiX0s77MDnuNUrDyhE0LMJ52kn7YPqBTakzV17FzSVYEcSiDdveOKXBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T03:20:23.996219Z"},"content_sha256":"c2afb7a3fec7e38bca21a2fbb3b550d9d8fa5514efd5fa25f8b4c36318389df0","schema_version":"1.0","event_id":"sha256:c2afb7a3fec7e38bca21a2fbb3b550d9d8fa5514efd5fa25f8b4c36318389df0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NFEAXWS6JWXZJ62TVQEQT2AKSM/bundle.json","state_url":"https://pith.science/pith/NFEAXWS6JWXZJ62TVQEQT2AKSM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NFEAXWS6JWXZJ62TVQEQT2AKSM/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":"2026-08-19T03:20:24Z","links":{"resolver":"https://pith.science/pith/NFEAXWS6JWXZJ62TVQEQT2AKSM","bundle":"https://pith.science/pith/NFEAXWS6JWXZJ62TVQEQT2AKSM/bundle.json","state":"https://pith.science/pith/NFEAXWS6JWXZJ62TVQEQT2AKSM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NFEAXWS6JWXZJ62TVQEQT2AKSM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:NFEAXWS6JWXZJ62TVQEQT2AKSM","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":"061dc4f6133a5dd8e444e41027610705e7a80f3dca5d52f13de7bfdb639a36d2","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T10:15:47Z","title_canon_sha256":"d81f738c12b970d7d3376e23dab07cd3dba93d8a18b6a9c77a8a5a3f3ea57e4e"},"schema_version":"1.0","source":{"id":"2607.17783","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.17783","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"arxiv_version","alias_value":"2607.17783v1","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17783","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"pith_short_12","alias_value":"NFEAXWS6JWXZ","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"pith_short_16","alias_value":"NFEAXWS6JWXZJ62T","created_at":"2026-07-21T02:21:59Z"},{"alias_kind":"pith_short_8","alias_value":"NFEAXWS6","created_at":"2026-07-21T02:21:59Z"}],"graph_snapshots":[{"event_id":"sha256:c2afb7a3fec7e38bca21a2fbb3b550d9d8fa5514efd5fa25f8b4c36318389df0","target":"graph","created_at":"2026-07-21T02:21:59Z","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/2607.17783/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural networks have achieved strong performance for these tasks by modeling sequential dependencies in event logs, their black-box nature limits trust and practical adoption. Feature attribution methods are often used to address this, but applying them directly poses a dilemma: event-level attributions impose high computational complexity for long traces, while explanations based on aggregated trace representations often ","authors_text":"Johannes De Smedt, Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T10:15:47Z","title":"Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17783","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:a577d50f84f9c4de2a0e259a522fd7fe7b989bd513526c05d927dad67dbe608c","target":"record","created_at":"2026-07-21T02:21:59Z","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":"061dc4f6133a5dd8e444e41027610705e7a80f3dca5d52f13de7bfdb639a36d2","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T10:15:47Z","title_canon_sha256":"d81f738c12b970d7d3376e23dab07cd3dba93d8a18b6a9c77a8a5a3f3ea57e4e"},"schema_version":"1.0","source":{"id":"2607.17783","kind":"arxiv","version":1}},"canonical_sha256":"69480bda5e4daf94fb53ac0909e80a9329bc25fd56cb7b2e9e3c6aa823d851a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"69480bda5e4daf94fb53ac0909e80a9329bc25fd56cb7b2e9e3c6aa823d851a7","first_computed_at":"2026-07-21T02:21:59.977805Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-21T02:21:59.977805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ojgGT++Bvotgup3FIyck7QXZi6cZEKqqFArRaAXYNENyQ2vMwlZ1lKr2/o66Vq5XZFeICYULOp9MkQSoDQalCA==","signature_status":"signed_v1","signed_at":"2026-07-21T02:21:59.978690Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.17783","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a577d50f84f9c4de2a0e259a522fd7fe7b989bd513526c05d927dad67dbe608c","sha256:c2afb7a3fec7e38bca21a2fbb3b550d9d8fa5514efd5fa25f8b4c36318389df0"],"state_sha256":"ac8428c63b9ab1efc72816856ab3b1420ba9843ff51617b53a267a0d91407a32"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ELIl9lTkF4ROuQPgGN1Gu+joYOdkoGmU2osoJp6pbrvEFzpi/+a6XQe3Pp8KVyKRQ/OjNxnbQuE16XJOLNpwBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T03:20:24.003919Z","bundle_sha256":"b1d4fa9e2ffc30a776ae67dd1ed1a807f29716ac104327d062e152415c45d3ac"}}