{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZQBCDUSFNXT4LPEMK7IHLGFU7Y","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":"fbd1dd6fceb4882368a638667cc1c701ba293846b5b9ec951fbc147375948b06","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-06-14T17:31:16Z","title_canon_sha256":"1c3cccbaaed31cd2903251e24c1d4e66ce7ed2a10ec2c254552c27870f4dc7a9"},"schema_version":"1.0","source":{"id":"2406.10196","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.10196","created_at":"2026-07-05T08:32:06Z"},{"alias_kind":"arxiv_version","alias_value":"2406.10196v1","created_at":"2026-07-05T08:32:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10196","created_at":"2026-07-05T08:32:06Z"},{"alias_kind":"pith_short_12","alias_value":"ZQBCDUSFNXT4","created_at":"2026-07-05T08:32:06Z"},{"alias_kind":"pith_short_16","alias_value":"ZQBCDUSFNXT4LPEM","created_at":"2026-07-05T08:32:06Z"},{"alias_kind":"pith_short_8","alias_value":"ZQBCDUSF","created_at":"2026-07-05T08:32:06Z"}],"graph_snapshots":[{"event_id":"sha256:c845de855e8d00fc350698bf16da6ba1e0f3406afdde36edc247054bb8527f70","target":"graph","created_at":"2026-07-05T08:32:06Z","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/2406.10196/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Travel planning is a complex task that involves generating a sequence of actions related to visiting places subject to constraints and maximizing some user satisfaction criteria. Traditional approaches rely on problem formulation in a given formal language, extracting relevant travel information from web sources, and use an adequate problem solver to generate a valid solution. As an alternative, recent Large Language Model (LLM) based approaches directly output plans from user requests using language. Although LLMs possess extensive travel domain knowledge and provide high-level information li","authors_text":"Alberto Pozanco, Daniel Borrajo, Sriram Gopalakrishnan, Tomas De la Rosa, Zhen Zeng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-06-14T17:31:16Z","title":"TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10196","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:dd9cb7f8ff22f805dc7722a818b20b2b9eb69854b12e5cbea9411d5eb46e9202","target":"record","created_at":"2026-07-05T08:32:06Z","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":"fbd1dd6fceb4882368a638667cc1c701ba293846b5b9ec951fbc147375948b06","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-06-14T17:31:16Z","title_canon_sha256":"1c3cccbaaed31cd2903251e24c1d4e66ce7ed2a10ec2c254552c27870f4dc7a9"},"schema_version":"1.0","source":{"id":"2406.10196","kind":"arxiv","version":1}},"canonical_sha256":"cc0221d2456de7c5bc8c57d07598b4fe27c34b30ebb48a9d2f1a8f4290bd4641","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc0221d2456de7c5bc8c57d07598b4fe27c34b30ebb48a9d2f1a8f4290bd4641","first_computed_at":"2026-07-05T08:32:06.613111Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:32:06.613111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FngU+MRMiXRux4nje+/evnE+l1+6CjX1FMFcjUz8YlHee6afi0xWTzZqaPobQHXZjhQhT6gSZ5rxeYtbheRGAw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:32:06.613703Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.10196","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd9cb7f8ff22f805dc7722a818b20b2b9eb69854b12e5cbea9411d5eb46e9202","sha256:c845de855e8d00fc350698bf16da6ba1e0f3406afdde36edc247054bb8527f70"],"state_sha256":"8105b7b89ad95c293d4bd52e447aa06368a1d8756d5121ba853750fc03f61dc1"}