{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IWKN5E3CJIHBTPGT5I3YUX3NTE","short_pith_number":"pith:IWKN5E3C","canonical_record":{"source":{"id":"2505.10922","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2025-05-16T06:54:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"fc3f67eda846c57ab81a4c2c436bd4e1983b549d2c0a01b75bb7fe4a0a67955c","abstract_canon_sha256":"b7349d4802cf508b1700f6ef44b3268e3edfdce45b7c4f9ace78a477ef13cb4f"},"schema_version":"1.0"},"canonical_sha256":"4594de93624a0e19bcd3ea378a5f6d99028688c12e7c4181a4bbe0b903dcf493","source":{"kind":"arxiv","id":"2505.10922","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.10922","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"arxiv_version","alias_value":"2505.10922v1","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10922","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"pith_short_12","alias_value":"IWKN5E3CJIHB","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"pith_short_16","alias_value":"IWKN5E3CJIHBTPGT","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"pith_short_8","alias_value":"IWKN5E3C","created_at":"2026-07-05T11:04:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IWKN5E3CJIHBTPGT5I3YUX3NTE","target":"record","payload":{"canonical_record":{"source":{"id":"2505.10922","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2025-05-16T06:54:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"fc3f67eda846c57ab81a4c2c436bd4e1983b549d2c0a01b75bb7fe4a0a67955c","abstract_canon_sha256":"b7349d4802cf508b1700f6ef44b3268e3edfdce45b7c4f9ace78a477ef13cb4f"},"schema_version":"1.0"},"canonical_sha256":"4594de93624a0e19bcd3ea378a5f6d99028688c12e7c4181a4bbe0b903dcf493","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:08.047921Z","signature_b64":"u9/9YJD1ovTtUiy8j+qqEZtXYv5h6nWcdNHYPYLq4IpekQrQwd9OvNcgrQVcxvQm4f/Du5gD5V40iAKktcv/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4594de93624a0e19bcd3ea378a5f6d99028688c12e7c4181a4bbe0b903dcf493","last_reissued_at":"2026-07-05T11:04:08.047514Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:08.047514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.10922","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":"2026-07-05T11:04:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uMNwHBWXm+f1jUBpJYbQWqhUBpagTJVV/kdY4T9/QGf4Pwe+VGT5hrx06/RgFfrA+t0ubJ3Trg1f97byPyqsCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T05:09:25.753809Z"},"content_sha256":"a1b49d2737ca975ddf14e4569746fec5ea2740cce9b1e9aac2c7b7becb2aa4df","schema_version":"1.0","event_id":"sha256:a1b49d2737ca975ddf14e4569746fec5ea2740cce9b1e9aac2c7b7becb2aa4df"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IWKN5E3CJIHBTPGT5I3YUX3NTE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Vaiage: A Multi-Agent Solution to Personalized Travel Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Binwen Liu, Jiamin Wang, Jiexi Ge","submitted_at":"2025-05-16T06:54:52Z","abstract_excerpt":"Planning trips is a cognitively intensive task involving conflicting user preferences, dynamic external information, and multi-step temporal-spatial optimization. Traditional platforms often fall short - they provide static results, lack contextual adaptation, and fail to support real-time interaction or intent refinement.\n  Our approach, Vaiage, addresses these challenges through a graph-structured multi-agent framework built around large language models (LLMs) that serve as both goal-conditioned recommenders and sequential planners. LLMs infer user intent, suggest personalized destinations a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10922","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/2505.10922/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":"2026-07-05T11:04:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xvDWv+5P+DCb6IT1Eetr9AdM5SeNIkgxs9P7NkJQP62GCzZZr+kbp//lZwXk0YT9vCDQSZYAeNECTjXXw8ayBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T05:09:25.754184Z"},"content_sha256":"9c6b3b74c035a7fb98602868e17a5e374a01d20184e9d8a0938cdc19f30860c0","schema_version":"1.0","event_id":"sha256:9c6b3b74c035a7fb98602868e17a5e374a01d20184e9d8a0938cdc19f30860c0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IWKN5E3CJIHBTPGT5I3YUX3NTE/bundle.json","state_url":"https://pith.science/pith/IWKN5E3CJIHBTPGT5I3YUX3NTE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IWKN5E3CJIHBTPGT5I3YUX3NTE/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":"2026-07-06T05:09:25Z","links":{"resolver":"https://pith.science/pith/IWKN5E3CJIHBTPGT5I3YUX3NTE","bundle":"https://pith.science/pith/IWKN5E3CJIHBTPGT5I3YUX3NTE/bundle.json","state":"https://pith.science/pith/IWKN5E3CJIHBTPGT5I3YUX3NTE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IWKN5E3CJIHBTPGT5I3YUX3NTE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IWKN5E3CJIHBTPGT5I3YUX3NTE","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":"b7349d4802cf508b1700f6ef44b3268e3edfdce45b7c4f9ace78a477ef13cb4f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2025-05-16T06:54:52Z","title_canon_sha256":"fc3f67eda846c57ab81a4c2c436bd4e1983b549d2c0a01b75bb7fe4a0a67955c"},"schema_version":"1.0","source":{"id":"2505.10922","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.10922","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"arxiv_version","alias_value":"2505.10922v1","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10922","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"pith_short_12","alias_value":"IWKN5E3CJIHB","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"pith_short_16","alias_value":"IWKN5E3CJIHBTPGT","created_at":"2026-07-05T11:04:08Z"},{"alias_kind":"pith_short_8","alias_value":"IWKN5E3C","created_at":"2026-07-05T11:04:08Z"}],"graph_snapshots":[{"event_id":"sha256:9c6b3b74c035a7fb98602868e17a5e374a01d20184e9d8a0938cdc19f30860c0","target":"graph","created_at":"2026-07-05T11:04:08Z","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/2505.10922/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Planning trips is a cognitively intensive task involving conflicting user preferences, dynamic external information, and multi-step temporal-spatial optimization. Traditional platforms often fall short - they provide static results, lack contextual adaptation, and fail to support real-time interaction or intent refinement.\n  Our approach, Vaiage, addresses these challenges through a graph-structured multi-agent framework built around large language models (LLMs) that serve as both goal-conditioned recommenders and sequential planners. LLMs infer user intent, suggest personalized destinations a","authors_text":"Binwen Liu, Jiamin Wang, Jiexi Ge","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2025-05-16T06:54:52Z","title":"Vaiage: A Multi-Agent Solution to Personalized Travel Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10922","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:a1b49d2737ca975ddf14e4569746fec5ea2740cce9b1e9aac2c7b7becb2aa4df","target":"record","created_at":"2026-07-05T11:04:08Z","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":"b7349d4802cf508b1700f6ef44b3268e3edfdce45b7c4f9ace78a477ef13cb4f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2025-05-16T06:54:52Z","title_canon_sha256":"fc3f67eda846c57ab81a4c2c436bd4e1983b549d2c0a01b75bb7fe4a0a67955c"},"schema_version":"1.0","source":{"id":"2505.10922","kind":"arxiv","version":1}},"canonical_sha256":"4594de93624a0e19bcd3ea378a5f6d99028688c12e7c4181a4bbe0b903dcf493","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4594de93624a0e19bcd3ea378a5f6d99028688c12e7c4181a4bbe0b903dcf493","first_computed_at":"2026-07-05T11:04:08.047514Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:04:08.047514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u9/9YJD1ovTtUiy8j+qqEZtXYv5h6nWcdNHYPYLq4IpekQrQwd9OvNcgrQVcxvQm4f/Du5gD5V40iAKktcv/CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:04:08.047921Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.10922","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a1b49d2737ca975ddf14e4569746fec5ea2740cce9b1e9aac2c7b7becb2aa4df","sha256:9c6b3b74c035a7fb98602868e17a5e374a01d20184e9d8a0938cdc19f30860c0"],"state_sha256":"005b85de66d42d73cfe2fd30d643b5d1fed2a9fa36d521bd9df8c6736a3a6fde"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6TLd/GdfYwpIXhFm9utHlPIsADusTtaehpxd0vVndY6dxTm34O5p4qZa/AUbfG+l5/qSLAPMXlAUZ2bFEOV5CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T05:09:25.756578Z","bundle_sha256":"02555a1d428e8a0d2dbe284df394a0c4d7d71b53a2282e8f75b3492e75b7531e"}}