{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KIMVHYBK24UFRMP7RAACDGLXJH","short_pith_number":"pith:KIMVHYBK","schema_version":"1.0","canonical_sha256":"521953e02ad72858b1ff880021997749fc2c4cfbdd26c2697c94c0ef707e0746","source":{"kind":"arxiv","id":"2505.05762","version":1},"attestation_state":"computed","paper":{"title":"Multi-Agent Systems for Robotic Autonomy with LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Dandan Zhang, George Mylonas, Haoyuan G Xu, Junhong Chen, Ziqi Yang","submitted_at":"2025-05-09T03:52:37Z","abstract_excerpt":"Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generali"},"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":"2505.05762","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-05-09T03:52:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b3593eef5e3bb879d2b164ea1387933eb20d14ab3296871df6ff84c2ea54f89d","abstract_canon_sha256":"7d56b7ddc5710e8882c22e118e8363833214849f5887210b81bf75b3cea5a39f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:00:47.096786Z","signature_b64":"f9cqcswLMedos9PzvjvSmIS+soVoZyex6AMlgc8Bj30G9WcfOFW2svLRXet/gHDprYBLIvAa1rUb6HYsc4TyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"521953e02ad72858b1ff880021997749fc2c4cfbdd26c2697c94c0ef707e0746","last_reissued_at":"2026-07-05T11:00:47.096260Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:00:47.096260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Agent Systems for Robotic Autonomy with LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Dandan Zhang, George Mylonas, Haoyuan G Xu, Junhong Chen, Ziqi Yang","submitted_at":"2025-05-09T03:52:37Z","abstract_excerpt":"Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.05762","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/2505.05762/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":"2505.05762","created_at":"2026-07-05T11:00:47.096324+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.05762v1","created_at":"2026-07-05T11:00:47.096324+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.05762","created_at":"2026-07-05T11:00:47.096324+00:00"},{"alias_kind":"pith_short_12","alias_value":"KIMVHYBK24UF","created_at":"2026-07-05T11:00:47.096324+00:00"},{"alias_kind":"pith_short_16","alias_value":"KIMVHYBK24UFRMP7","created_at":"2026-07-05T11:00:47.096324+00:00"},{"alias_kind":"pith_short_8","alias_value":"KIMVHYBK","created_at":"2026-07-05T11:00:47.096324+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/KIMVHYBK24UFRMP7RAACDGLXJH","json":"https://pith.science/pith/KIMVHYBK24UFRMP7RAACDGLXJH.json","graph_json":"https://pith.science/api/pith-number/KIMVHYBK24UFRMP7RAACDGLXJH/graph.json","events_json":"https://pith.science/api/pith-number/KIMVHYBK24UFRMP7RAACDGLXJH/events.json","paper":"https://pith.science/paper/KIMVHYBK"},"agent_actions":{"view_html":"https://pith.science/pith/KIMVHYBK24UFRMP7RAACDGLXJH","download_json":"https://pith.science/pith/KIMVHYBK24UFRMP7RAACDGLXJH.json","view_paper":"https://pith.science/paper/KIMVHYBK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.05762&json=true","fetch_graph":"https://pith.science/api/pith-number/KIMVHYBK24UFRMP7RAACDGLXJH/graph.json","fetch_events":"https://pith.science/api/pith-number/KIMVHYBK24UFRMP7RAACDGLXJH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KIMVHYBK24UFRMP7RAACDGLXJH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KIMVHYBK24UFRMP7RAACDGLXJH/action/storage_attestation","attest_author":"https://pith.science/pith/KIMVHYBK24UFRMP7RAACDGLXJH/action/author_attestation","sign_citation":"https://pith.science/pith/KIMVHYBK24UFRMP7RAACDGLXJH/action/citation_signature","submit_replication":"https://pith.science/pith/KIMVHYBK24UFRMP7RAACDGLXJH/action/replication_record"}},"created_at":"2026-07-05T11:00:47.096324+00:00","updated_at":"2026-07-05T11:00:47.096324+00:00"}