{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7EOXSKCF7LH7S4KWMLEO7OZCQ5","short_pith_number":"pith:7EOXSKCF","schema_version":"1.0","canonical_sha256":"f91d792845facff9715662c8efbb22875d671489c4b8d4d55af449b0cd72702f","source":{"kind":"arxiv","id":"2308.14363","version":3},"attestation_state":"computed","paper":{"title":"Mobile Foundation Model as Firmware","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chen Yang, Dingge Zhang, Dongqi Cai, Hanzi Mei, Jinliang Yuan, Mengwei Xu, Shangguang Wang, Shihe Wang, Xiang Li, Xianqing Jia, Xin Yuan, Zeling Zhang","submitted_at":"2023-08-28T07:21:26Z","abstract_excerpt":"In today's landscape, smartphones have evolved into hubs for hosting a multitude of deep learning models aimed at local execution. A key realization driving this work is the notable fragmentation among these models, characterized by varied architectures, operators, and implementations. This fragmentation imposes a significant burden on the comprehensive optimization of hardware, system settings, and algorithms.\n  Buoyed by the recent strides in large foundation models, this work introduces a pioneering paradigm for mobile AI: a collaborative management approach between the mobile OS and hardwa"},"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":"2308.14363","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-08-28T07:21:26Z","cross_cats_sorted":[],"title_canon_sha256":"c6f9f6658765f47b945f7704004e9a6b633a7a34b77910665f1c1e7c07774ac3","abstract_canon_sha256":"133255e7ac3a649ecd4db688c206708426cefca891564aa18d992b2284985312"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:50.402057Z","signature_b64":"TnY1FVg28GQG5hDvnqYZ6XgGY1T9ebTnvcasUw5oQbpFcFe8/dQffdX8D4WKkxH6E7F7eW/JLlUx/gi2W4KcCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f91d792845facff9715662c8efbb22875d671489c4b8d4d55af449b0cd72702f","last_reissued_at":"2026-07-05T07:54:50.401636Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:50.401636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mobile Foundation Model as Firmware","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chen Yang, Dingge Zhang, Dongqi Cai, Hanzi Mei, Jinliang Yuan, Mengwei Xu, Shangguang Wang, Shihe Wang, Xiang Li, Xianqing Jia, Xin Yuan, Zeling Zhang","submitted_at":"2023-08-28T07:21:26Z","abstract_excerpt":"In today's landscape, smartphones have evolved into hubs for hosting a multitude of deep learning models aimed at local execution. A key realization driving this work is the notable fragmentation among these models, characterized by varied architectures, operators, and implementations. This fragmentation imposes a significant burden on the comprehensive optimization of hardware, system settings, and algorithms.\n  Buoyed by the recent strides in large foundation models, this work introduces a pioneering paradigm for mobile AI: a collaborative management approach between the mobile OS and hardwa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.14363","kind":"arxiv","version":3},"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/2308.14363/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":"2308.14363","created_at":"2026-07-05T07:54:50.401693+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.14363v3","created_at":"2026-07-05T07:54:50.401693+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.14363","created_at":"2026-07-05T07:54:50.401693+00:00"},{"alias_kind":"pith_short_12","alias_value":"7EOXSKCF7LH7","created_at":"2026-07-05T07:54:50.401693+00:00"},{"alias_kind":"pith_short_16","alias_value":"7EOXSKCF7LH7S4KW","created_at":"2026-07-05T07:54:50.401693+00:00"},{"alias_kind":"pith_short_8","alias_value":"7EOXSKCF","created_at":"2026-07-05T07:54:50.401693+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.17138","citing_title":"RAP: Runtime Adaptive Pruning for LLM Inference","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5","json":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5.json","graph_json":"https://pith.science/api/pith-number/7EOXSKCF7LH7S4KWMLEO7OZCQ5/graph.json","events_json":"https://pith.science/api/pith-number/7EOXSKCF7LH7S4KWMLEO7OZCQ5/events.json","paper":"https://pith.science/paper/7EOXSKCF"},"agent_actions":{"view_html":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5","download_json":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5.json","view_paper":"https://pith.science/paper/7EOXSKCF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.14363&json=true","fetch_graph":"https://pith.science/api/pith-number/7EOXSKCF7LH7S4KWMLEO7OZCQ5/graph.json","fetch_events":"https://pith.science/api/pith-number/7EOXSKCF7LH7S4KWMLEO7OZCQ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5/action/storage_attestation","attest_author":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5/action/author_attestation","sign_citation":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5/action/citation_signature","submit_replication":"https://pith.science/pith/7EOXSKCF7LH7S4KWMLEO7OZCQ5/action/replication_record"}},"created_at":"2026-07-05T07:54:50.401693+00:00","updated_at":"2026-07-05T07:54:50.401693+00:00"}