{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CWXQ7IIQM65L3DDGSCWPGYWWO2","short_pith_number":"pith:CWXQ7IIQ","schema_version":"1.0","canonical_sha256":"15af0fa11067babd8c6690acf362d67680d2fa2770f0ba3ff1cec1e802c40b77","source":{"kind":"arxiv","id":"2309.15943","version":2},"attestation_state":"computed","paper":{"title":"Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chuchu Fan, Jacob Arkin, Nicholas Roy, Yang Zhang, Yongchao Chen","submitted_at":"2023-09-27T18:40:36Z","abstract_excerpt":"A flurry of recent work has demonstrated that pre-trained large language models (LLMs) can be effective task planners for a variety of single-robot tasks. The planning performance of LLMs is significantly improved via prompting techniques, such as in-context learning or re-prompting with state feedback, placing new importance on the token budget for the context window. An under-explored but natural next direction is to investigate LLMs as multi-robot task planners. However, long-horizon, heterogeneous multi-robot planning introduces new challenges of coordination while also pushing up against "},"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":"2309.15943","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.RO","submitted_at":"2023-09-27T18:40:36Z","cross_cats_sorted":[],"title_canon_sha256":"6b62b34a0d732ca68cff3543cf06133e9b6155cc74251fbe6564e006b0ba8f19","abstract_canon_sha256":"e1260009a2b8e569fe404ef4bfe3c8b06ac431309698616e7e8871c17c9e2f7f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:59:18.301370Z","signature_b64":"nmRwR+OwEhIJe4q8NmDtE+FImEYkoXUG/UyswTYE/anLm+pskezYNEqd+9T9MBuraQGw3b1MXzW1lRH9OkBeBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15af0fa11067babd8c6690acf362d67680d2fa2770f0ba3ff1cec1e802c40b77","last_reissued_at":"2026-07-05T07:59:18.300873Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:59:18.300873Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chuchu Fan, Jacob Arkin, Nicholas Roy, Yang Zhang, Yongchao Chen","submitted_at":"2023-09-27T18:40:36Z","abstract_excerpt":"A flurry of recent work has demonstrated that pre-trained large language models (LLMs) can be effective task planners for a variety of single-robot tasks. The planning performance of LLMs is significantly improved via prompting techniques, such as in-context learning or re-prompting with state feedback, placing new importance on the token budget for the context window. An under-explored but natural next direction is to investigate LLMs as multi-robot task planners. However, long-horizon, heterogeneous multi-robot planning introduces new challenges of coordination while also pushing up against "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.15943","kind":"arxiv","version":2},"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/2309.15943/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":"2309.15943","created_at":"2026-07-05T07:59:18.300937+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.15943v2","created_at":"2026-07-05T07:59:18.300937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.15943","created_at":"2026-07-05T07:59:18.300937+00:00"},{"alias_kind":"pith_short_12","alias_value":"CWXQ7IIQM65L","created_at":"2026-07-05T07:59:18.300937+00:00"},{"alias_kind":"pith_short_16","alias_value":"CWXQ7IIQM65L3DDG","created_at":"2026-07-05T07:59:18.300937+00:00"},{"alias_kind":"pith_short_8","alias_value":"CWXQ7IIQ","created_at":"2026-07-05T07:59:18.300937+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06990","citing_title":"A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2402.03578","citing_title":"LLM Multi-Agent Systems: Challenges and Open Problems","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2404.13501","citing_title":"A Survey on the Memory Mechanism of Large Language Model based Agents","ref_index":170,"is_internal_anchor":false},{"citing_arxiv_id":"2402.01680","citing_title":"Large Language Model based Multi-Agents: A Survey of Progress and Challenges","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2","json":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2.json","graph_json":"https://pith.science/api/pith-number/CWXQ7IIQM65L3DDGSCWPGYWWO2/graph.json","events_json":"https://pith.science/api/pith-number/CWXQ7IIQM65L3DDGSCWPGYWWO2/events.json","paper":"https://pith.science/paper/CWXQ7IIQ"},"agent_actions":{"view_html":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2","download_json":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2.json","view_paper":"https://pith.science/paper/CWXQ7IIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.15943&json=true","fetch_graph":"https://pith.science/api/pith-number/CWXQ7IIQM65L3DDGSCWPGYWWO2/graph.json","fetch_events":"https://pith.science/api/pith-number/CWXQ7IIQM65L3DDGSCWPGYWWO2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2/action/storage_attestation","attest_author":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2/action/author_attestation","sign_citation":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2/action/citation_signature","submit_replication":"https://pith.science/pith/CWXQ7IIQM65L3DDGSCWPGYWWO2/action/replication_record"}},"created_at":"2026-07-05T07:59:18.300937+00:00","updated_at":"2026-07-05T07:59:18.300937+00:00"}