{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WSWKWZAECY5SIPV3GQ5CTHOMEI","short_pith_number":"pith:WSWKWZAE","schema_version":"1.0","canonical_sha256":"b4acab6404163b243ebb343a299dcc2221d022a2d48e5c2af9751128d5828cfe","source":{"kind":"arxiv","id":"2606.21350","version":1},"attestation_state":"computed","paper":{"title":"A Reward-Petri-Net Interpretation of Temporal Behavior Trees","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"G\\\"unther Waxenegger-Wilfing, Sebastian Schirmer, Till Schmeil","submitted_at":"2026-06-19T11:47:54Z","abstract_excerpt":"This paper introduces an interpretation of Temporal Behavior Trees (TBTs) as Reward-Petri-Nets (RPNs) for reinforcement learning (RL). Designing reward functions for complex, long-horizon robotic tasks is notoriously difficult, especially when tasks have hierarchical structure and temporal constraints. TBTs extend conventional behavior trees (BTs) used in robotic applications by incorporating temporal properties into their leaf nodes. This allows TBTs to represents not only the behavioral task structure defined by BT operators such as Sequence, Fallback, and Parallel, but also the task's tempo"},"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":"2606.21350","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-19T11:47:54Z","cross_cats_sorted":[],"title_canon_sha256":"e14c3d2f2e67be4abdc7c1729e0626767d262d3e38b22c0ecaa5c3d147d2a288","abstract_canon_sha256":"6cc1c2c29de35769ef8985290f9e09ec987f33a2f02998ef0e1cf25c156d337f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:12:38.159450Z","signature_b64":"hVEf+VjkEfBR7BOpel3GHON8pY65IWajeaEqL+rIz4v6d8+ReRv6C3PeiQA1bCRpdP/9o2dlao0kPEkqJjwkDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4acab6404163b243ebb343a299dcc2221d022a2d48e5c2af9751128d5828cfe","last_reissued_at":"2026-06-23T01:12:38.158953Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:12:38.158953Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Reward-Petri-Net Interpretation of Temporal Behavior Trees","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"G\\\"unther Waxenegger-Wilfing, Sebastian Schirmer, Till Schmeil","submitted_at":"2026-06-19T11:47:54Z","abstract_excerpt":"This paper introduces an interpretation of Temporal Behavior Trees (TBTs) as Reward-Petri-Nets (RPNs) for reinforcement learning (RL). Designing reward functions for complex, long-horizon robotic tasks is notoriously difficult, especially when tasks have hierarchical structure and temporal constraints. TBTs extend conventional behavior trees (BTs) used in robotic applications by incorporating temporal properties into their leaf nodes. This allows TBTs to represents not only the behavioral task structure defined by BT operators such as Sequence, Fallback, and Parallel, but also the task's tempo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21350","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/2606.21350/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":"2606.21350","created_at":"2026-06-23T01:12:38.159023+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21350v1","created_at":"2026-06-23T01:12:38.159023+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21350","created_at":"2026-06-23T01:12:38.159023+00:00"},{"alias_kind":"pith_short_12","alias_value":"WSWKWZAECY5S","created_at":"2026-06-23T01:12:38.159023+00:00"},{"alias_kind":"pith_short_16","alias_value":"WSWKWZAECY5SIPV3","created_at":"2026-06-23T01:12:38.159023+00:00"},{"alias_kind":"pith_short_8","alias_value":"WSWKWZAE","created_at":"2026-06-23T01:12:38.159023+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/WSWKWZAECY5SIPV3GQ5CTHOMEI","json":"https://pith.science/pith/WSWKWZAECY5SIPV3GQ5CTHOMEI.json","graph_json":"https://pith.science/api/pith-number/WSWKWZAECY5SIPV3GQ5CTHOMEI/graph.json","events_json":"https://pith.science/api/pith-number/WSWKWZAECY5SIPV3GQ5CTHOMEI/events.json","paper":"https://pith.science/paper/WSWKWZAE"},"agent_actions":{"view_html":"https://pith.science/pith/WSWKWZAECY5SIPV3GQ5CTHOMEI","download_json":"https://pith.science/pith/WSWKWZAECY5SIPV3GQ5CTHOMEI.json","view_paper":"https://pith.science/paper/WSWKWZAE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21350&json=true","fetch_graph":"https://pith.science/api/pith-number/WSWKWZAECY5SIPV3GQ5CTHOMEI/graph.json","fetch_events":"https://pith.science/api/pith-number/WSWKWZAECY5SIPV3GQ5CTHOMEI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WSWKWZAECY5SIPV3GQ5CTHOMEI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WSWKWZAECY5SIPV3GQ5CTHOMEI/action/storage_attestation","attest_author":"https://pith.science/pith/WSWKWZAECY5SIPV3GQ5CTHOMEI/action/author_attestation","sign_citation":"https://pith.science/pith/WSWKWZAECY5SIPV3GQ5CTHOMEI/action/citation_signature","submit_replication":"https://pith.science/pith/WSWKWZAECY5SIPV3GQ5CTHOMEI/action/replication_record"}},"created_at":"2026-06-23T01:12:38.159023+00:00","updated_at":"2026-06-23T01:12:38.159023+00:00"}