{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XANHXY2IIWYEX72A3N24WASR4T","short_pith_number":"pith:XANHXY2I","schema_version":"1.0","canonical_sha256":"b81a7be34845b04bff40db75cb0251e4fae90d58abe8d3c7504d8cd3ee96182c","source":{"kind":"arxiv","id":"2507.12174","version":1},"attestation_state":"computed","paper":{"title":"Fast and Scalable Game-Theoretic Trajectory Planning with Intentional Uncertainties","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.RO","authors_text":"Benshan Ma, Jun Ma, Shaojie Shen, Yusen Xie, Zhenmin Huang","submitted_at":"2025-07-16T12:12:25Z","abstract_excerpt":"Trajectory planning involving multi-agent interactions has been a long-standing challenge in the field of robotics, primarily burdened by the inherent yet intricate interactions among agents. While game-theoretic methods are widely acknowledged for their effectiveness in managing multi-agent interactions, significant impediments persist when it comes to accommodating the intentional uncertainties of agents. In the context of intentional uncertainties, the heavy computational burdens associated with existing game-theoretic methods are induced, leading to inefficiencies and poor scalability. In "},"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":"2507.12174","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-07-16T12:12:25Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"e5069569aa7ce46009e9725c33e7c75d395ebf6835f1933aa883374b5e21025d","abstract_canon_sha256":"49f598b31b09eeadf23a72884a3ad83e45675e5c296b0c80c5350be0d91f87c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:15.943368Z","signature_b64":"Pi6+ZrX02CZGt8xuQMwbIhJHheEHHu3KJI/CgVHPXCzEwWb8xf09M0GOf9MGegtd8P4/CaNoyHb08uv1Gjw3CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b81a7be34845b04bff40db75cb0251e4fae90d58abe8d3c7504d8cd3ee96182c","last_reissued_at":"2026-07-05T11:38:15.942769Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:15.942769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast and Scalable Game-Theoretic Trajectory Planning with Intentional Uncertainties","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.RO","authors_text":"Benshan Ma, Jun Ma, Shaojie Shen, Yusen Xie, Zhenmin Huang","submitted_at":"2025-07-16T12:12:25Z","abstract_excerpt":"Trajectory planning involving multi-agent interactions has been a long-standing challenge in the field of robotics, primarily burdened by the inherent yet intricate interactions among agents. While game-theoretic methods are widely acknowledged for their effectiveness in managing multi-agent interactions, significant impediments persist when it comes to accommodating the intentional uncertainties of agents. In the context of intentional uncertainties, the heavy computational burdens associated with existing game-theoretic methods are induced, leading to inefficiencies and poor scalability. In "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.12174","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/2507.12174/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":"2507.12174","created_at":"2026-07-05T11:38:15.942837+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.12174v1","created_at":"2026-07-05T11:38:15.942837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.12174","created_at":"2026-07-05T11:38:15.942837+00:00"},{"alias_kind":"pith_short_12","alias_value":"XANHXY2IIWYE","created_at":"2026-07-05T11:38:15.942837+00:00"},{"alias_kind":"pith_short_16","alias_value":"XANHXY2IIWYEX72A","created_at":"2026-07-05T11:38:15.942837+00:00"},{"alias_kind":"pith_short_8","alias_value":"XANHXY2I","created_at":"2026-07-05T11:38:15.942837+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/XANHXY2IIWYEX72A3N24WASR4T","json":"https://pith.science/pith/XANHXY2IIWYEX72A3N24WASR4T.json","graph_json":"https://pith.science/api/pith-number/XANHXY2IIWYEX72A3N24WASR4T/graph.json","events_json":"https://pith.science/api/pith-number/XANHXY2IIWYEX72A3N24WASR4T/events.json","paper":"https://pith.science/paper/XANHXY2I"},"agent_actions":{"view_html":"https://pith.science/pith/XANHXY2IIWYEX72A3N24WASR4T","download_json":"https://pith.science/pith/XANHXY2IIWYEX72A3N24WASR4T.json","view_paper":"https://pith.science/paper/XANHXY2I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.12174&json=true","fetch_graph":"https://pith.science/api/pith-number/XANHXY2IIWYEX72A3N24WASR4T/graph.json","fetch_events":"https://pith.science/api/pith-number/XANHXY2IIWYEX72A3N24WASR4T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XANHXY2IIWYEX72A3N24WASR4T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XANHXY2IIWYEX72A3N24WASR4T/action/storage_attestation","attest_author":"https://pith.science/pith/XANHXY2IIWYEX72A3N24WASR4T/action/author_attestation","sign_citation":"https://pith.science/pith/XANHXY2IIWYEX72A3N24WASR4T/action/citation_signature","submit_replication":"https://pith.science/pith/XANHXY2IIWYEX72A3N24WASR4T/action/replication_record"}},"created_at":"2026-07-05T11:38:15.942837+00:00","updated_at":"2026-07-05T11:38:15.942837+00:00"}