{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BIABXCMA24E6CQ2ANGOVNKND2R","short_pith_number":"pith:BIABXCMA","schema_version":"1.0","canonical_sha256":"0a001b8980d709e14340699d56a9a3d4791f6d83b670c755db057d2759ee06ad","source":{"kind":"arxiv","id":"2505.13729","version":1},"attestation_state":"computed","paper":{"title":"SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Abhinav Rajvanshi, Han-Pang Chiu, Karan Sikka, Pritish Sahu, Tixiao Shan","submitted_at":"2025-05-19T20:58:06Z","abstract_excerpt":"Adaptive collaboration is critical to a team of autonomous robots to perform complicated navigation tasks in large-scale unknown environments. An effective collaboration strategy should be determined and adapted according to each robot's skills and current status to successfully achieve the shared goal. We present SayCoNav, a new approach that leverages large language models (LLMs) for automatically generating this collaboration strategy among a team of robots. Building on the collaboration strategy, each robot uses the LLM to generate its plans and actions in a decentralized way. By sharing i"},"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":"2505.13729","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-19T20:58:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"854f98584c661cfba9ff64456768ab351a758cacab8dcd3e3736dd422e8e479f","abstract_canon_sha256":"935b1d0fe408ffe5ea262e871a15b86ec4ad089f9176783d3a884a872ec44a0f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:50.119125Z","signature_b64":"Dr1CnEPjTbrF8q+08H1YZe2EOv5jM17q5gF8I4J2jUUKkanZmZYZIdYs0M79D27TxDxSU2chXdavz6fQxsYyCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a001b8980d709e14340699d56a9a3d4791f6d83b670c755db057d2759ee06ad","last_reissued_at":"2026-07-05T11:05:50.118641Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:50.118641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Abhinav Rajvanshi, Han-Pang Chiu, Karan Sikka, Pritish Sahu, Tixiao Shan","submitted_at":"2025-05-19T20:58:06Z","abstract_excerpt":"Adaptive collaboration is critical to a team of autonomous robots to perform complicated navigation tasks in large-scale unknown environments. An effective collaboration strategy should be determined and adapted according to each robot's skills and current status to successfully achieve the shared goal. We present SayCoNav, a new approach that leverages large language models (LLMs) for automatically generating this collaboration strategy among a team of robots. Building on the collaboration strategy, each robot uses the LLM to generate its plans and actions in a decentralized way. By sharing i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13729","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/2505.13729/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":"2505.13729","created_at":"2026-07-05T11:05:50.118696+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.13729v1","created_at":"2026-07-05T11:05:50.118696+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13729","created_at":"2026-07-05T11:05:50.118696+00:00"},{"alias_kind":"pith_short_12","alias_value":"BIABXCMA24E6","created_at":"2026-07-05T11:05:50.118696+00:00"},{"alias_kind":"pith_short_16","alias_value":"BIABXCMA24E6CQ2A","created_at":"2026-07-05T11:05:50.118696+00:00"},{"alias_kind":"pith_short_8","alias_value":"BIABXCMA","created_at":"2026-07-05T11:05:50.118696+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19383","citing_title":"3D Scene Graphs: Open Challenges and Future Directions","ref_index":115,"is_internal_anchor":false},{"citing_arxiv_id":"2502.03814","citing_title":"Large Language Models for Multi-Robot Systems: A Survey","ref_index":96,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R","json":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R.json","graph_json":"https://pith.science/api/pith-number/BIABXCMA24E6CQ2ANGOVNKND2R/graph.json","events_json":"https://pith.science/api/pith-number/BIABXCMA24E6CQ2ANGOVNKND2R/events.json","paper":"https://pith.science/paper/BIABXCMA"},"agent_actions":{"view_html":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R","download_json":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R.json","view_paper":"https://pith.science/paper/BIABXCMA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.13729&json=true","fetch_graph":"https://pith.science/api/pith-number/BIABXCMA24E6CQ2ANGOVNKND2R/graph.json","fetch_events":"https://pith.science/api/pith-number/BIABXCMA24E6CQ2ANGOVNKND2R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R/action/storage_attestation","attest_author":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R/action/author_attestation","sign_citation":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R/action/citation_signature","submit_replication":"https://pith.science/pith/BIABXCMA24E6CQ2ANGOVNKND2R/action/replication_record"}},"created_at":"2026-07-05T11:05:50.118696+00:00","updated_at":"2026-07-05T11:05:50.118696+00:00"}