{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ACVKITZJA6YUNKSNE7GDI3QEYT","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":"2327f4d46b9dd4e3f208d022fec9fc246c92a66a364a0806d8900af10c851fec","cross_cats_sorted":["cs.AI","cs.FL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-01T14:40:07Z","title_canon_sha256":"b33b1a32e6db491bbce26a3bb41f4b91df41c7aa6ac1c2a284f429e5ab29df50"},"schema_version":"1.0","source":{"id":"2505.00562","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.00562","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"arxiv_version","alias_value":"2505.00562v1","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00562","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_12","alias_value":"ACVKITZJA6YU","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_16","alias_value":"ACVKITZJA6YUNKSN","created_at":"2026-07-05T10:57:22Z"},{"alias_kind":"pith_short_8","alias_value":"ACVKITZJ","created_at":"2026-07-05T10:57:22Z"}],"graph_snapshots":[{"event_id":"sha256:fc263731f0153ae34eb3958305957360dfd8f2b65bee780f57b5a760c4a33558","target":"graph","created_at":"2026-07-05T10:57:22Z","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/2505.00562/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning to solve complex tasks with signal temporal logic (STL) specifications is crucial to many real-world applications. However, most previous works only consider fixed or parametrized STL specifications due to the lack of a diverse STL dataset and encoders to effectively extract temporal logic information for downstream tasks. In this paper, we propose TeLoGraF, Temporal Logic Graph-encoded Flow, which utilizes Graph Neural Networks (GNN) encoder and flow-matching to learn solutions for general STL specifications. We identify four commonly used STL templates and collect a total of 200K sp","authors_text":"Chuchu Fan, Yue Meng","cross_cats":["cs.AI","cs.FL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-01T14:40:07Z","title":"TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00562","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:ea50df340acc44c15df77282386346ef8f2ecfdc03e17ec001440d4c43bf0f10","target":"record","created_at":"2026-07-05T10:57:22Z","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":"2327f4d46b9dd4e3f208d022fec9fc246c92a66a364a0806d8900af10c851fec","cross_cats_sorted":["cs.AI","cs.FL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-01T14:40:07Z","title_canon_sha256":"b33b1a32e6db491bbce26a3bb41f4b91df41c7aa6ac1c2a284f429e5ab29df50"},"schema_version":"1.0","source":{"id":"2505.00562","kind":"arxiv","version":1}},"canonical_sha256":"00aaa44f2907b146aa4d27cc346e04c4e00e52976f93a2cf251406685514f158","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"00aaa44f2907b146aa4d27cc346e04c4e00e52976f93a2cf251406685514f158","first_computed_at":"2026-07-05T10:57:22.140242Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:57:22.140242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xM6An5SSLG1mHsNjkQWRHZDjC0Wej2eGRQSU6FRU1wn+njcKPdgGDqEirM6TA+FkW4l0K9Go2vq6NyMw63bfBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:57:22.140734Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.00562","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea50df340acc44c15df77282386346ef8f2ecfdc03e17ec001440d4c43bf0f10","sha256:fc263731f0153ae34eb3958305957360dfd8f2b65bee780f57b5a760c4a33558"],"state_sha256":"3c42dadc293edfe32b8e44a7d30e77c56d94b8ca423085a81041cddef5d5c1cc"}