{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:PSLHV75ILBUJNFX5476VRCXXK5","short_pith_number":"pith:PSLHV75I","canonical_record":{"source":{"id":"2502.20508","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-27T20:33:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eb698a833848f7e6ab071a0116f889c201cc6b13527840af6ab5cece4b796f52","abstract_canon_sha256":"ac4fc49f48dc36026a86011a2337a5ff9a55bc4fe01807e1e55473ebc67869d4"},"schema_version":"1.0"},"canonical_sha256":"7c967affa858689696fde7fd588af75745ab0d9227a7be0246479e39589f0e48","source":{"kind":"arxiv","id":"2502.20508","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.20508","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"arxiv_version","alias_value":"2502.20508v1","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.20508","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_12","alias_value":"PSLHV75ILBUJ","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_16","alias_value":"PSLHV75ILBUJNFX5","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_8","alias_value":"PSLHV75I","created_at":"2026-07-05T10:21:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:PSLHV75ILBUJNFX5476VRCXXK5","target":"record","payload":{"canonical_record":{"source":{"id":"2502.20508","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-27T20:33:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eb698a833848f7e6ab071a0116f889c201cc6b13527840af6ab5cece4b796f52","abstract_canon_sha256":"ac4fc49f48dc36026a86011a2337a5ff9a55bc4fe01807e1e55473ebc67869d4"},"schema_version":"1.0"},"canonical_sha256":"7c967affa858689696fde7fd588af75745ab0d9227a7be0246479e39589f0e48","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:21:34.332721Z","signature_b64":"HOHgy4nHiCAqHO4IrAijRnpz35mK4M4VHRe1WlXStkyyEFrCzQ8GnG8yh93EfE4QJGFsmpC3lSuC9WgrLiNhBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c967affa858689696fde7fd588af75745ab0d9227a7be0246479e39589f0e48","last_reissued_at":"2026-07-05T10:21:34.331981Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:21:34.331981Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.20508","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":"2026-07-05T10:21:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gti9auq/qsDWqvlwQMUFLJSw90Qz/acKh6JdQ72uLXkBNfyqTZi2eVsVXUYj7WWJQx/T+9MMFA2dSIjKz5JYBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:37:51.236269Z"},"content_sha256":"89ad587ac5fb67dc57cbdba71dfd7db9a9c391290c0beee0b0c6eeb844ab1389","schema_version":"1.0","event_id":"sha256:89ad587ac5fb67dc57cbdba71dfd7db9a9c391290c0beee0b0c6eeb844ab1389"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:PSLHV75ILBUJNFX5476VRCXXK5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Abhik Jana, Manish Gupta, Pranav Purkar, Ritwik Raghav, Shreya Ghosh, Shubhojit Mallick, Soumyabrata Chaudhuri","submitted_at":"2025-02-27T20:33:28Z","abstract_excerpt":"Recent advancements in probing Large Language Models (LLMs) have explored their latent potential as personalized travel planning agents, yet existing benchmarks remain limited in real world applicability. Existing datasets, such as TravelPlanner and TravelPlanner+, suffer from semi synthetic data reliance, spatial inconsistencies, and a lack of key travel constraints, making them inadequate for practical itinerary generation. To address these gaps, we introduce TripCraft, a spatiotemporally coherent travel planning dataset that integrates real world constraints, including public transit schedu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.20508","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/2502.20508/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":"2026-07-05T10:21:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LmbzUM26SMYseTp7xw4o3l9TelpCjFdyqM0lNWILo++B3138JYgEdnCZ+6HrN/RBLQIjXb9w4+hyXKZ0n0wZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:37:51.236886Z"},"content_sha256":"4fd72b2acd80a981fd92c00d0fa6b5ec6e90f7000938f3f41904722c6401b4de","schema_version":"1.0","event_id":"sha256:4fd72b2acd80a981fd92c00d0fa6b5ec6e90f7000938f3f41904722c6401b4de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PSLHV75ILBUJNFX5476VRCXXK5/bundle.json","state_url":"https://pith.science/pith/PSLHV75ILBUJNFX5476VRCXXK5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PSLHV75ILBUJNFX5476VRCXXK5/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":"2026-08-09T01:37:51Z","links":{"resolver":"https://pith.science/pith/PSLHV75ILBUJNFX5476VRCXXK5","bundle":"https://pith.science/pith/PSLHV75ILBUJNFX5476VRCXXK5/bundle.json","state":"https://pith.science/pith/PSLHV75ILBUJNFX5476VRCXXK5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PSLHV75ILBUJNFX5476VRCXXK5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PSLHV75ILBUJNFX5476VRCXXK5","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":"ac4fc49f48dc36026a86011a2337a5ff9a55bc4fe01807e1e55473ebc67869d4","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-27T20:33:28Z","title_canon_sha256":"eb698a833848f7e6ab071a0116f889c201cc6b13527840af6ab5cece4b796f52"},"schema_version":"1.0","source":{"id":"2502.20508","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.20508","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"arxiv_version","alias_value":"2502.20508v1","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.20508","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_12","alias_value":"PSLHV75ILBUJ","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_16","alias_value":"PSLHV75ILBUJNFX5","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_8","alias_value":"PSLHV75I","created_at":"2026-07-05T10:21:34Z"}],"graph_snapshots":[{"event_id":"sha256:4fd72b2acd80a981fd92c00d0fa6b5ec6e90f7000938f3f41904722c6401b4de","target":"graph","created_at":"2026-07-05T10:21:34Z","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/2502.20508/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in probing Large Language Models (LLMs) have explored their latent potential as personalized travel planning agents, yet existing benchmarks remain limited in real world applicability. Existing datasets, such as TravelPlanner and TravelPlanner+, suffer from semi synthetic data reliance, spatial inconsistencies, and a lack of key travel constraints, making them inadequate for practical itinerary generation. To address these gaps, we introduce TripCraft, a spatiotemporally coherent travel planning dataset that integrates real world constraints, including public transit schedu","authors_text":"Abhik Jana, Manish Gupta, Pranav Purkar, Ritwik Raghav, Shreya Ghosh, Shubhojit Mallick, Soumyabrata Chaudhuri","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-27T20:33:28Z","title":"TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.20508","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":"sha256:89ad587ac5fb67dc57cbdba71dfd7db9a9c391290c0beee0b0c6eeb844ab1389","target":"record","created_at":"2026-07-05T10:21:34Z","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":"ac4fc49f48dc36026a86011a2337a5ff9a55bc4fe01807e1e55473ebc67869d4","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-27T20:33:28Z","title_canon_sha256":"eb698a833848f7e6ab071a0116f889c201cc6b13527840af6ab5cece4b796f52"},"schema_version":"1.0","source":{"id":"2502.20508","kind":"arxiv","version":1}},"canonical_sha256":"7c967affa858689696fde7fd588af75745ab0d9227a7be0246479e39589f0e48","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c967affa858689696fde7fd588af75745ab0d9227a7be0246479e39589f0e48","first_computed_at":"2026-07-05T10:21:34.331981Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:21:34.331981Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HOHgy4nHiCAqHO4IrAijRnpz35mK4M4VHRe1WlXStkyyEFrCzQ8GnG8yh93EfE4QJGFsmpC3lSuC9WgrLiNhBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:21:34.332721Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.20508","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:89ad587ac5fb67dc57cbdba71dfd7db9a9c391290c0beee0b0c6eeb844ab1389","sha256:4fd72b2acd80a981fd92c00d0fa6b5ec6e90f7000938f3f41904722c6401b4de"],"state_sha256":"e1dfbbceec55cda16389c4096a85b53f304fea3dc7c79ce9fcebc16642ec3c53"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AKLTiA9AiHeJ21rJruFSDwpZ1wSJhC7P3y3KC9RUrYgA9f8bjfWCM6UQHJr5mp03n4jlnnGBrJkLisBOcLPHDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T01:37:51.242317Z","bundle_sha256":"76780f4c93de81201a7922a9a65248d0ca3eeff4b2f563f85ed93d30b3efa41f"}}