{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:T2JHRTZKKC6B627UY5XQNFXZGG","short_pith_number":"pith:T2JHRTZK","schema_version":"1.0","canonical_sha256":"9e9278cf2a50bc1f6bf4c76f0696f93195a1d0dd58ae808fd6613c58de800ac3","source":{"kind":"arxiv","id":"2411.00927","version":2},"attestation_state":"computed","paper":{"title":"ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Dilek Hakkani-T\\\"ur, Emre Can Acikgoz, Gokhan Tur, Suvodip Dey, Vardhan Dongre, Xiaocheng Yang","submitted_at":"2024-11-01T15:57:45Z","abstract_excerpt":"Large language model (LLM)-based agents are increasingly employed to interact with external environments (e.g., games, APIs, world models) to solve user-provided tasks. However, current frameworks often lack the ability to collaborate effectively with users in fully conversational settings. Conversations are essential for aligning on task details, achieving user-defined goals, and satisfying preferences. While existing agents address ambiguity through clarification questions, they underutilize the broader potential of an LLM's conversational capabilities. In this work, we introduce ReSpAct, an"},"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":"2411.00927","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-01T15:57:45Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"337ada007cbdec2f230f2bc1781ba365a38da77b53bd7733b2a51a4b998b6fcd","abstract_canon_sha256":"3d814d828b6afaed2b30fd76ea24ba7eae8ad6e072bc8ce1d4c662a8c969b106"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:27.804547Z","signature_b64":"LTOYTe0x7OUTVIlOFUJ9ZVjIHMbDQwhL2VOADQOgIvegVVQqroAFpyGAspaliKZWVKMsJFqLlR8x/HnNpf7QDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e9278cf2a50bc1f6bf4c76f0696f93195a1d0dd58ae808fd6613c58de800ac3","last_reissued_at":"2026-07-05T10:51:27.804014Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:27.804014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Dilek Hakkani-T\\\"ur, Emre Can Acikgoz, Gokhan Tur, Suvodip Dey, Vardhan Dongre, Xiaocheng Yang","submitted_at":"2024-11-01T15:57:45Z","abstract_excerpt":"Large language model (LLM)-based agents are increasingly employed to interact with external environments (e.g., games, APIs, world models) to solve user-provided tasks. However, current frameworks often lack the ability to collaborate effectively with users in fully conversational settings. Conversations are essential for aligning on task details, achieving user-defined goals, and satisfying preferences. While existing agents address ambiguity through clarification questions, they underutilize the broader potential of an LLM's conversational capabilities. In this work, we introduce ReSpAct, an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.00927","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/2411.00927/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":"2411.00927","created_at":"2026-07-05T10:51:27.804077+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.00927v2","created_at":"2026-07-05T10:51:27.804077+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.00927","created_at":"2026-07-05T10:51:27.804077+00:00"},{"alias_kind":"pith_short_12","alias_value":"T2JHRTZKKC6B","created_at":"2026-07-05T10:51:27.804077+00:00"},{"alias_kind":"pith_short_16","alias_value":"T2JHRTZKKC6B627U","created_at":"2026-07-05T10:51:27.804077+00:00"},{"alias_kind":"pith_short_8","alias_value":"T2JHRTZK","created_at":"2026-07-05T10:51:27.804077+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.23049","citing_title":"AURA: Agent for Understanding, Reasoning, and Automated Tool Use in Voice-Driven Tasks","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG","json":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG.json","graph_json":"https://pith.science/api/pith-number/T2JHRTZKKC6B627UY5XQNFXZGG/graph.json","events_json":"https://pith.science/api/pith-number/T2JHRTZKKC6B627UY5XQNFXZGG/events.json","paper":"https://pith.science/paper/T2JHRTZK"},"agent_actions":{"view_html":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG","download_json":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG.json","view_paper":"https://pith.science/paper/T2JHRTZK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.00927&json=true","fetch_graph":"https://pith.science/api/pith-number/T2JHRTZKKC6B627UY5XQNFXZGG/graph.json","fetch_events":"https://pith.science/api/pith-number/T2JHRTZKKC6B627UY5XQNFXZGG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG/action/storage_attestation","attest_author":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG/action/author_attestation","sign_citation":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG/action/citation_signature","submit_replication":"https://pith.science/pith/T2JHRTZKKC6B627UY5XQNFXZGG/action/replication_record"}},"created_at":"2026-07-05T10:51:27.804077+00:00","updated_at":"2026-07-05T10:51:27.804077+00:00"}