{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6HQEYYEXCRPHUTSTXGFSVV42PB","short_pith_number":"pith:6HQEYYEX","schema_version":"1.0","canonical_sha256":"f1e04c6097145e7a4e53b98b2ad79a7848ac71fc27d3a24ecf2b2cdb0ac6f619","source":{"kind":"arxiv","id":"2503.06791","version":2},"attestation_state":"computed","paper":{"title":"AutoMisty: A Multi-Agent LLM Framework for Automated Code Generation in the Misty Social Robot","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.MA"],"primary_cat":"cs.RO","authors_text":"Ifeoma Nwogu, Lu Dong, Sahana Rangasrinivasan, Srirangaraj Setlur, Venugopal Govindaraju, Xiao Wang","submitted_at":"2025-03-09T22:07:46Z","abstract_excerpt":"The social robot's open API allows users to customize open-domain interactions. However, it remains inaccessible to those without programming experience. In this work, we introduce AutoMisty, the first multi-agent collaboration framework powered by large language models (LLMs), to enable the seamless generation of executable Misty robot code from natural language instructions. AutoMisty incorporates four specialized agent modules to manage task decomposition, assignment, problem-solving, and result synthesis. Each agent incorporates a two-layer optimization mechanism, with self-reflection for "},"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":"2503.06791","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-09T22:07:46Z","cross_cats_sorted":["cs.AI","cs.HC","cs.MA"],"title_canon_sha256":"255bf53d20fc91ffccb6fe56036497fd3a57adf2b0fa66453992e0d61ad41cce","abstract_canon_sha256":"ba9c2239704e16bb772cdc1aa22695c3ecb40e8464a245afcc0bcca44b9d4c8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:25.888482Z","signature_b64":"MfLvSRcKR5dovEKTTi0ghp+gf83exjuOdInCqGBxF2Q66zefEblI++4u34135T1zzhdMM14JtTpFJS6Abi76BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1e04c6097145e7a4e53b98b2ad79a7848ac71fc27d3a24ecf2b2cdb0ac6f619","last_reissued_at":"2026-07-05T11:58:25.887903Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:25.887903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AutoMisty: A Multi-Agent LLM Framework for Automated Code Generation in the Misty Social Robot","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.MA"],"primary_cat":"cs.RO","authors_text":"Ifeoma Nwogu, Lu Dong, Sahana Rangasrinivasan, Srirangaraj Setlur, Venugopal Govindaraju, Xiao Wang","submitted_at":"2025-03-09T22:07:46Z","abstract_excerpt":"The social robot's open API allows users to customize open-domain interactions. However, it remains inaccessible to those without programming experience. In this work, we introduce AutoMisty, the first multi-agent collaboration framework powered by large language models (LLMs), to enable the seamless generation of executable Misty robot code from natural language instructions. AutoMisty incorporates four specialized agent modules to manage task decomposition, assignment, problem-solving, and result synthesis. Each agent incorporates a two-layer optimization mechanism, with self-reflection for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06791","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/2503.06791/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":"2503.06791","created_at":"2026-07-05T11:58:25.887967+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.06791v2","created_at":"2026-07-05T11:58:25.887967+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06791","created_at":"2026-07-05T11:58:25.887967+00:00"},{"alias_kind":"pith_short_12","alias_value":"6HQEYYEXCRPH","created_at":"2026-07-05T11:58:25.887967+00:00"},{"alias_kind":"pith_short_16","alias_value":"6HQEYYEXCRPHUTST","created_at":"2026-07-05T11:58:25.887967+00:00"},{"alias_kind":"pith_short_8","alias_value":"6HQEYYEX","created_at":"2026-07-05T11:58:25.887967+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/6HQEYYEXCRPHUTSTXGFSVV42PB","json":"https://pith.science/pith/6HQEYYEXCRPHUTSTXGFSVV42PB.json","graph_json":"https://pith.science/api/pith-number/6HQEYYEXCRPHUTSTXGFSVV42PB/graph.json","events_json":"https://pith.science/api/pith-number/6HQEYYEXCRPHUTSTXGFSVV42PB/events.json","paper":"https://pith.science/paper/6HQEYYEX"},"agent_actions":{"view_html":"https://pith.science/pith/6HQEYYEXCRPHUTSTXGFSVV42PB","download_json":"https://pith.science/pith/6HQEYYEXCRPHUTSTXGFSVV42PB.json","view_paper":"https://pith.science/paper/6HQEYYEX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.06791&json=true","fetch_graph":"https://pith.science/api/pith-number/6HQEYYEXCRPHUTSTXGFSVV42PB/graph.json","fetch_events":"https://pith.science/api/pith-number/6HQEYYEXCRPHUTSTXGFSVV42PB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6HQEYYEXCRPHUTSTXGFSVV42PB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6HQEYYEXCRPHUTSTXGFSVV42PB/action/storage_attestation","attest_author":"https://pith.science/pith/6HQEYYEXCRPHUTSTXGFSVV42PB/action/author_attestation","sign_citation":"https://pith.science/pith/6HQEYYEXCRPHUTSTXGFSVV42PB/action/citation_signature","submit_replication":"https://pith.science/pith/6HQEYYEXCRPHUTSTXGFSVV42PB/action/replication_record"}},"created_at":"2026-07-05T11:58:25.887967+00:00","updated_at":"2026-07-05T11:58:25.887967+00:00"}