{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VQJBTOOHGGES2K43A364KTK4LD","short_pith_number":"pith:VQJBTOOH","schema_version":"1.0","canonical_sha256":"ac1219b9c731892d2b9b06fdc54d5c58f80081df3c93997a908d2cf293d73daa","source":{"kind":"arxiv","id":"2504.01588","version":1},"attestation_state":"computed","paper":{"title":"Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Alessandra Sciutti, Francesco Rea, Gabriele Russo, Giulia Belgiovine, Luca Garello","submitted_at":"2025-04-02T10:45:41Z","abstract_excerpt":"Integrating robotics into everyday scenarios like tutoring or physical training requires robots capable of adaptive, socially engaging, and goal-oriented interactions. While Large Language Models show promise in human-like communication, their standalone use is hindered by memory constraints and contextual incoherence. This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction. Specifically, we develop an LLM-based agent for a robot trainer, balancing social conversation with task guidan"},"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":"2504.01588","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-04-02T10:45:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d51e98a0919b4c2133974e2c66d9db9547865009a61dc926c01a06140d8cbe14","abstract_canon_sha256":"f31abdd77d660b6b2efca6a6f5acfee42c4a06d5a06e3a9250a120f0656b686c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:17.680782Z","signature_b64":"D7MRKLUewSZUjNHRM+8KiB3CLmhcRDLCEYJpPueP3sojufxg3gXxaQlg1mi6PfqRx+Ow4AD7JrYNkeadY1FJBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac1219b9c731892d2b9b06fdc54d5c58f80081df3c93997a908d2cf293d73daa","last_reissued_at":"2026-07-05T10:43:17.680295Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:17.680295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Alessandra Sciutti, Francesco Rea, Gabriele Russo, Giulia Belgiovine, Luca Garello","submitted_at":"2025-04-02T10:45:41Z","abstract_excerpt":"Integrating robotics into everyday scenarios like tutoring or physical training requires robots capable of adaptive, socially engaging, and goal-oriented interactions. While Large Language Models show promise in human-like communication, their standalone use is hindered by memory constraints and contextual incoherence. This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction. Specifically, we develop an LLM-based agent for a robot trainer, balancing social conversation with task guidan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.01588","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/2504.01588/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":"2504.01588","created_at":"2026-07-05T10:43:17.680370+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.01588v1","created_at":"2026-07-05T10:43:17.680370+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.01588","created_at":"2026-07-05T10:43:17.680370+00:00"},{"alias_kind":"pith_short_12","alias_value":"VQJBTOOHGGES","created_at":"2026-07-05T10:43:17.680370+00:00"},{"alias_kind":"pith_short_16","alias_value":"VQJBTOOHGGES2K43","created_at":"2026-07-05T10:43:17.680370+00:00"},{"alias_kind":"pith_short_8","alias_value":"VQJBTOOH","created_at":"2026-07-05T10:43:17.680370+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.00943","citing_title":"ARIS: Agentic and Relationship Intelligence System for Social Robots","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD","json":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD.json","graph_json":"https://pith.science/api/pith-number/VQJBTOOHGGES2K43A364KTK4LD/graph.json","events_json":"https://pith.science/api/pith-number/VQJBTOOHGGES2K43A364KTK4LD/events.json","paper":"https://pith.science/paper/VQJBTOOH"},"agent_actions":{"view_html":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD","download_json":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD.json","view_paper":"https://pith.science/paper/VQJBTOOH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.01588&json=true","fetch_graph":"https://pith.science/api/pith-number/VQJBTOOHGGES2K43A364KTK4LD/graph.json","fetch_events":"https://pith.science/api/pith-number/VQJBTOOHGGES2K43A364KTK4LD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD/action/storage_attestation","attest_author":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD/action/author_attestation","sign_citation":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD/action/citation_signature","submit_replication":"https://pith.science/pith/VQJBTOOHGGES2K43A364KTK4LD/action/replication_record"}},"created_at":"2026-07-05T10:43:17.680370+00:00","updated_at":"2026-07-05T10:43:17.680370+00:00"}