{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HUFQMH7SJNOWXIYZV5WZT5RV6O","short_pith_number":"pith:HUFQMH7S","schema_version":"1.0","canonical_sha256":"3d0b061ff24b5d6ba319af6d99f635f3a81dd06218a933b3118b70a8ce80c9e6","source":{"kind":"arxiv","id":"2511.19780","version":2},"attestation_state":"computed","paper":{"title":"NOEM$^{3}$A: a Neuro-symbolic Ontology-Enhanced Method for Multi-intent understanding in Mobile Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Aifen Sui, Ioannis Tzachristas","submitted_at":"2025-11-24T23:14:45Z","abstract_excerpt":"Mobile agents must map natural-language requests to executable intents under tight latency and privacy constraints. Scaling the language model is often an inefficient way to improve this component. We present NOEM$^{3}$A, a lightweight neuro-symbolic layer that augments compact language models with an intent ontology. For each query, NOEM$^{3}$A retrieves a small ontology neighborhood, injects candidate action labels into the prompt and applies a token-level decoding prior toward valid labels. This injects symbolic intent structure into both input and output representations while keeping infer"},"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":"2511.19780","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-11-24T23:14:45Z","cross_cats_sorted":[],"title_canon_sha256":"4273e764ba9156d852acb1e48c84fdd1dd5d9e258916928c4892fcf232ba156b","abstract_canon_sha256":"3326d484e8dafb5652eacd73496907bde4e4abcf73ea5d4cfba5ea7ba2c24cff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:12:35.564493Z","signature_b64":"sAsOOi8xaXV4l9eUawnOLuIATlQmQZIzwV9oGtfQZpZuqS/PQM2PoD2gUwUbV/qNVh4McATh7xhqcPpFeCd7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d0b061ff24b5d6ba319af6d99f635f3a81dd06218a933b3118b70a8ce80c9e6","last_reissued_at":"2026-06-23T02:12:35.564027Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:12:35.564027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NOEM$^{3}$A: a Neuro-symbolic Ontology-Enhanced Method for Multi-intent understanding in Mobile Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Aifen Sui, Ioannis Tzachristas","submitted_at":"2025-11-24T23:14:45Z","abstract_excerpt":"Mobile agents must map natural-language requests to executable intents under tight latency and privacy constraints. Scaling the language model is often an inefficient way to improve this component. We present NOEM$^{3}$A, a lightweight neuro-symbolic layer that augments compact language models with an intent ontology. For each query, NOEM$^{3}$A retrieves a small ontology neighborhood, injects candidate action labels into the prompt and applies a token-level decoding prior toward valid labels. This injects symbolic intent structure into both input and output representations while keeping infer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.19780","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/2511.19780/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":"2511.19780","created_at":"2026-06-23T02:12:35.564084+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.19780v2","created_at":"2026-06-23T02:12:35.564084+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.19780","created_at":"2026-06-23T02:12:35.564084+00:00"},{"alias_kind":"pith_short_12","alias_value":"HUFQMH7SJNOW","created_at":"2026-06-23T02:12:35.564084+00:00"},{"alias_kind":"pith_short_16","alias_value":"HUFQMH7SJNOWXIYZ","created_at":"2026-06-23T02:12:35.564084+00:00"},{"alias_kind":"pith_short_8","alias_value":"HUFQMH7S","created_at":"2026-06-23T02:12:35.564084+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/HUFQMH7SJNOWXIYZV5WZT5RV6O","json":"https://pith.science/pith/HUFQMH7SJNOWXIYZV5WZT5RV6O.json","graph_json":"https://pith.science/api/pith-number/HUFQMH7SJNOWXIYZV5WZT5RV6O/graph.json","events_json":"https://pith.science/api/pith-number/HUFQMH7SJNOWXIYZV5WZT5RV6O/events.json","paper":"https://pith.science/paper/HUFQMH7S"},"agent_actions":{"view_html":"https://pith.science/pith/HUFQMH7SJNOWXIYZV5WZT5RV6O","download_json":"https://pith.science/pith/HUFQMH7SJNOWXIYZV5WZT5RV6O.json","view_paper":"https://pith.science/paper/HUFQMH7S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.19780&json=true","fetch_graph":"https://pith.science/api/pith-number/HUFQMH7SJNOWXIYZV5WZT5RV6O/graph.json","fetch_events":"https://pith.science/api/pith-number/HUFQMH7SJNOWXIYZV5WZT5RV6O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HUFQMH7SJNOWXIYZV5WZT5RV6O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HUFQMH7SJNOWXIYZV5WZT5RV6O/action/storage_attestation","attest_author":"https://pith.science/pith/HUFQMH7SJNOWXIYZV5WZT5RV6O/action/author_attestation","sign_citation":"https://pith.science/pith/HUFQMH7SJNOWXIYZV5WZT5RV6O/action/citation_signature","submit_replication":"https://pith.science/pith/HUFQMH7SJNOWXIYZV5WZT5RV6O/action/replication_record"}},"created_at":"2026-06-23T02:12:35.564084+00:00","updated_at":"2026-06-23T02:12:35.564084+00:00"}