{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BL5SB5WBAGJRRFZ33FJO5QU5B4","short_pith_number":"pith:BL5SB5WB","canonical_record":{"source":{"id":"2503.12761","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-17T02:54:02Z","cross_cats_sorted":[],"title_canon_sha256":"5987f9c046a6648760725cb0fd194136201c0eb0e1a5a56bd014f7a1b8164a56","abstract_canon_sha256":"6c049ba3fef513e1d6aa7ac45d9ccf8246847cd6a476228f5b1551d633e635b6"},"schema_version":"1.0"},"canonical_sha256":"0afb20f6c1019318973bd952eec29d0f0cd3ac7efa877b5cc11eaf1c959b53d4","source":{"kind":"arxiv","id":"2503.12761","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.12761","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"arxiv_version","alias_value":"2503.12761v1","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.12761","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"pith_short_12","alias_value":"BL5SB5WBAGJR","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"pith_short_16","alias_value":"BL5SB5WBAGJRRFZ3","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"pith_short_8","alias_value":"BL5SB5WB","created_at":"2026-07-05T10:32:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BL5SB5WBAGJRRFZ33FJO5QU5B4","target":"record","payload":{"canonical_record":{"source":{"id":"2503.12761","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-17T02:54:02Z","cross_cats_sorted":[],"title_canon_sha256":"5987f9c046a6648760725cb0fd194136201c0eb0e1a5a56bd014f7a1b8164a56","abstract_canon_sha256":"6c049ba3fef513e1d6aa7ac45d9ccf8246847cd6a476228f5b1551d633e635b6"},"schema_version":"1.0"},"canonical_sha256":"0afb20f6c1019318973bd952eec29d0f0cd3ac7efa877b5cc11eaf1c959b53d4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:45.669064Z","signature_b64":"45ViHrnERGFO5M3xD+njvZHZAWMkmNE7CflTyeEXtHbmz2eSjLiK4RhaOO9O1VQSAX0XIcZlRNole3ENxX2EDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0afb20f6c1019318973bd952eec29d0f0cd3ac7efa877b5cc11eaf1c959b53d4","last_reissued_at":"2026-07-05T10:32:45.668395Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:45.668395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.12761","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":"2026-07-05T10:32:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EEQCyfqE99jvaFChKt9uicq8io2xZl6jWZXwk4ut383f1EbWnCGd0hCVfPsDoWuTKms4sBU52BBmBLQXakUDBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:09:48.964187Z"},"content_sha256":"a9f36ea6fd05b1ca3ccfdf0b00eb47477e9f73733dc2cc89926368b477e0647e","schema_version":"1.0","event_id":"sha256:a9f36ea6fd05b1ca3ccfdf0b00eb47477e9f73733dc2cc89926368b477e0647e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BL5SB5WBAGJRRFZ33FJO5QU5B4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jiangbo Yu, Jinhua Zhao, Sandy Pentland, Shenhao Wang, Yuebing Liang, Zhan Zhao","submitted_at":"2025-03-17T02:54:02Z","abstract_excerpt":"Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To analyze the sequential activity-travel decisions, deep inverse reinforcement learning (DIRL) has proven effective in learning the decision mechanisms by approximating a reward function to represent preferences and a policy function to replicate observed behavior using deep neural networks (DNNs). However, most existing research has focused on using DIRL to enhance only prediction accuracy, with limited exploration int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.12761","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/2503.12761/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":"2026-07-05T10:32:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aaX6XjFBelpGLMJnjGMcNNr3clAkWW/+7TEZUtoMNcsPFgPG34Qh7zeboM0n9GTRioa2TW3OLkZqCjK72UdMAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:09:48.964690Z"},"content_sha256":"bf7fa0dbf32a3bf5fc3ebba4e205d27b3be16218c64c0b487b2df38233ae3439","schema_version":"1.0","event_id":"sha256:bf7fa0dbf32a3bf5fc3ebba4e205d27b3be16218c64c0b487b2df38233ae3439"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BL5SB5WBAGJRRFZ33FJO5QU5B4/bundle.json","state_url":"https://pith.science/pith/BL5SB5WBAGJRRFZ33FJO5QU5B4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BL5SB5WBAGJRRFZ33FJO5QU5B4/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":"2026-08-08T23:09:48Z","links":{"resolver":"https://pith.science/pith/BL5SB5WBAGJRRFZ33FJO5QU5B4","bundle":"https://pith.science/pith/BL5SB5WBAGJRRFZ33FJO5QU5B4/bundle.json","state":"https://pith.science/pith/BL5SB5WBAGJRRFZ33FJO5QU5B4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BL5SB5WBAGJRRFZ33FJO5QU5B4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BL5SB5WBAGJRRFZ33FJO5QU5B4","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":"6c049ba3fef513e1d6aa7ac45d9ccf8246847cd6a476228f5b1551d633e635b6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-17T02:54:02Z","title_canon_sha256":"5987f9c046a6648760725cb0fd194136201c0eb0e1a5a56bd014f7a1b8164a56"},"schema_version":"1.0","source":{"id":"2503.12761","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.12761","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"arxiv_version","alias_value":"2503.12761v1","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.12761","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"pith_short_12","alias_value":"BL5SB5WBAGJR","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"pith_short_16","alias_value":"BL5SB5WBAGJRRFZ3","created_at":"2026-07-05T10:32:45Z"},{"alias_kind":"pith_short_8","alias_value":"BL5SB5WB","created_at":"2026-07-05T10:32:45Z"}],"graph_snapshots":[{"event_id":"sha256:bf7fa0dbf32a3bf5fc3ebba4e205d27b3be16218c64c0b487b2df38233ae3439","target":"graph","created_at":"2026-07-05T10:32:45Z","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/2503.12761/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To analyze the sequential activity-travel decisions, deep inverse reinforcement learning (DIRL) has proven effective in learning the decision mechanisms by approximating a reward function to represent preferences and a policy function to replicate observed behavior using deep neural networks (DNNs). However, most existing research has focused on using DIRL to enhance only prediction accuracy, with limited exploration int","authors_text":"Jiangbo Yu, Jinhua Zhao, Sandy Pentland, Shenhao Wang, Yuebing Liang, Zhan Zhao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-17T02:54:02Z","title":"Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.12761","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:a9f36ea6fd05b1ca3ccfdf0b00eb47477e9f73733dc2cc89926368b477e0647e","target":"record","created_at":"2026-07-05T10:32:45Z","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":"6c049ba3fef513e1d6aa7ac45d9ccf8246847cd6a476228f5b1551d633e635b6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-17T02:54:02Z","title_canon_sha256":"5987f9c046a6648760725cb0fd194136201c0eb0e1a5a56bd014f7a1b8164a56"},"schema_version":"1.0","source":{"id":"2503.12761","kind":"arxiv","version":1}},"canonical_sha256":"0afb20f6c1019318973bd952eec29d0f0cd3ac7efa877b5cc11eaf1c959b53d4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0afb20f6c1019318973bd952eec29d0f0cd3ac7efa877b5cc11eaf1c959b53d4","first_computed_at":"2026-07-05T10:32:45.668395Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:32:45.668395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"45ViHrnERGFO5M3xD+njvZHZAWMkmNE7CflTyeEXtHbmz2eSjLiK4RhaOO9O1VQSAX0XIcZlRNole3ENxX2EDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:32:45.669064Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.12761","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a9f36ea6fd05b1ca3ccfdf0b00eb47477e9f73733dc2cc89926368b477e0647e","sha256:bf7fa0dbf32a3bf5fc3ebba4e205d27b3be16218c64c0b487b2df38233ae3439"],"state_sha256":"e6028ba99a5856aad9d99cc16d762a4943777a83c4bc636d6342e84258b81de1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b2EDlf8orlY4VsP+0okJ2Vse1W58BOA/2XgEeHqr5F+Pr9SXDCFGDgV3UiZWg07FclT+Zriw1cBd5v1tOl87BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T23:09:48.968742Z","bundle_sha256":"d8b210b513f15dcf31ceb8f35b1d9e8c9a7f06cc3b8f4a3b7034f3693e05af11"}}