{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MOT5O3GYNVYTK4RCHWH7U5SK65","short_pith_number":"pith:MOT5O3GY","schema_version":"1.0","canonical_sha256":"63a7d76cd86d713572223d8ffa764af765e939ac4cccfca49c13f60bf713823f","source":{"kind":"arxiv","id":"2506.07232","version":1},"attestation_state":"computed","paper":{"title":"Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.MA","authors_text":"Chenjia Bai, Jiakun Zheng, Jun Zhang, Ting Xiao, Xinran Li, Zijian Li","submitted_at":"2025-06-08T17:32:03Z","abstract_excerpt":"Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environments. However, despite LLMs' advanced abilities and the sophisticated modular design of agentic methods, existing LLM-based planning algorithms remain limited by weak adaptation capabilities to multi-agent embodied scenarios. We address this limitation by introducing a framework that enables LLM agents to learn and evolve both before and during test time, equipping them with environment-relevant knowledge for better "},"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":"2506.07232","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2025-06-08T17:32:03Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b39fcc5e1cc83fc4369725d3f34d87afc9c21be111ee80798803a48aecd778f2","abstract_canon_sha256":"d5fa83584770e8c54b531cc8286a81bb77e79894977b4ecfd95e5675098b157c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:03.996720Z","signature_b64":"xokDkr1CdK8Z8oQNHozJy5AwugbIsEksbEteZ/bJMMRn8cbbyQv3jDz11MWVsR8CQrAMychDg/6QZ8PiNg99CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63a7d76cd86d713572223d8ffa764af765e939ac4cccfca49c13f60bf713823f","last_reissued_at":"2026-07-05T11:18:03.996223Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:03.996223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.MA","authors_text":"Chenjia Bai, Jiakun Zheng, Jun Zhang, Ting Xiao, Xinran Li, Zijian Li","submitted_at":"2025-06-08T17:32:03Z","abstract_excerpt":"Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environments. However, despite LLMs' advanced abilities and the sophisticated modular design of agentic methods, existing LLM-based planning algorithms remain limited by weak adaptation capabilities to multi-agent embodied scenarios. We address this limitation by introducing a framework that enables LLM agents to learn and evolve both before and during test time, equipping them with environment-relevant knowledge for better "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07232","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/2506.07232/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":"2506.07232","created_at":"2026-07-05T11:18:03.996284+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.07232v1","created_at":"2026-07-05T11:18:03.996284+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07232","created_at":"2026-07-05T11:18:03.996284+00:00"},{"alias_kind":"pith_short_12","alias_value":"MOT5O3GYNVYT","created_at":"2026-07-05T11:18:03.996284+00:00"},{"alias_kind":"pith_short_16","alias_value":"MOT5O3GYNVYTK4RC","created_at":"2026-07-05T11:18:03.996284+00:00"},{"alias_kind":"pith_short_8","alias_value":"MOT5O3GY","created_at":"2026-07-05T11:18:03.996284+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.07774","citing_title":"RoboAgent: Chaining Basic Capabilities for Embodied Task Planning","ref_index":55,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65","json":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65.json","graph_json":"https://pith.science/api/pith-number/MOT5O3GYNVYTK4RCHWH7U5SK65/graph.json","events_json":"https://pith.science/api/pith-number/MOT5O3GYNVYTK4RCHWH7U5SK65/events.json","paper":"https://pith.science/paper/MOT5O3GY"},"agent_actions":{"view_html":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65","download_json":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65.json","view_paper":"https://pith.science/paper/MOT5O3GY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.07232&json=true","fetch_graph":"https://pith.science/api/pith-number/MOT5O3GYNVYTK4RCHWH7U5SK65/graph.json","fetch_events":"https://pith.science/api/pith-number/MOT5O3GYNVYTK4RCHWH7U5SK65/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65/action/storage_attestation","attest_author":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65/action/author_attestation","sign_citation":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65/action/citation_signature","submit_replication":"https://pith.science/pith/MOT5O3GYNVYTK4RCHWH7U5SK65/action/replication_record"}},"created_at":"2026-07-05T11:18:03.996284+00:00","updated_at":"2026-07-05T11:18:03.996284+00:00"}