{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EW5OQL3KQVMVEPGTWCVJZ3GLEU","short_pith_number":"pith:EW5OQL3K","schema_version":"1.0","canonical_sha256":"25bae82f6a8559523cd3b0aa9ceccb25230b9af7fb2bdbfd82e65608fe1000c0","source":{"kind":"arxiv","id":"2502.04392","version":1},"attestation_state":"computed","paper":{"title":"Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chenyang Shao, Fengli Xu, Xinyuan Hu, Yutang Lin","submitted_at":"2025-02-06T02:40:25Z","abstract_excerpt":"The rapid expansion of web content has made on-device AI assistants indispensable for helping users manage the increasing complexity of online tasks. The emergent reasoning ability in large language models offer a promising path for next-generation on-device AI agents. However, deploying full-scale Large Language Models (LLMs) on resource-limited local devices is challenging. In this paper, we propose Division-of-Thoughts (DoT), a collaborative reasoning framework leveraging the synergy between locally deployed Smaller-scale Language Models (SLMs) and cloud-based LLMs. DoT leverages a Task Dec"},"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":"2502.04392","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-06T02:40:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"885708bcca2c0614098d0186c1e47a503da187e5a6258b90fcde96c7dc264e18","abstract_canon_sha256":"aeb11080b121826a642e81c7e28cbc5d8a15e9cebd695bec98b3b9c292fc8d40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:50.879565Z","signature_b64":"Aiv8wT5g+ke9Xr+yk81+U2ZFEMj/7Vzi27exXtgXIcwSY4j+6LUqC5Lm8Vm5+knaeK47K1dFiGkDE07CJevVBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25bae82f6a8559523cd3b0aa9ceccb25230b9af7fb2bdbfd82e65608fe1000c0","last_reissued_at":"2026-07-05T10:10:50.879065Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:50.879065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chenyang Shao, Fengli Xu, Xinyuan Hu, Yutang Lin","submitted_at":"2025-02-06T02:40:25Z","abstract_excerpt":"The rapid expansion of web content has made on-device AI assistants indispensable for helping users manage the increasing complexity of online tasks. The emergent reasoning ability in large language models offer a promising path for next-generation on-device AI agents. However, deploying full-scale Large Language Models (LLMs) on resource-limited local devices is challenging. In this paper, we propose Division-of-Thoughts (DoT), a collaborative reasoning framework leveraging the synergy between locally deployed Smaller-scale Language Models (SLMs) and cloud-based LLMs. DoT leverages a Task Dec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.04392","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/2502.04392/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":"2502.04392","created_at":"2026-07-05T10:10:50.879138+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.04392v1","created_at":"2026-07-05T10:10:50.879138+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.04392","created_at":"2026-07-05T10:10:50.879138+00:00"},{"alias_kind":"pith_short_12","alias_value":"EW5OQL3KQVMV","created_at":"2026-07-05T10:10:50.879138+00:00"},{"alias_kind":"pith_short_16","alias_value":"EW5OQL3KQVMVEPGT","created_at":"2026-07-05T10:10:50.879138+00:00"},{"alias_kind":"pith_short_8","alias_value":"EW5OQL3K","created_at":"2026-07-05T10:10:50.879138+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.00082","citing_title":"Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU","json":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU.json","graph_json":"https://pith.science/api/pith-number/EW5OQL3KQVMVEPGTWCVJZ3GLEU/graph.json","events_json":"https://pith.science/api/pith-number/EW5OQL3KQVMVEPGTWCVJZ3GLEU/events.json","paper":"https://pith.science/paper/EW5OQL3K"},"agent_actions":{"view_html":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU","download_json":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU.json","view_paper":"https://pith.science/paper/EW5OQL3K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.04392&json=true","fetch_graph":"https://pith.science/api/pith-number/EW5OQL3KQVMVEPGTWCVJZ3GLEU/graph.json","fetch_events":"https://pith.science/api/pith-number/EW5OQL3KQVMVEPGTWCVJZ3GLEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU/action/storage_attestation","attest_author":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU/action/author_attestation","sign_citation":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU/action/citation_signature","submit_replication":"https://pith.science/pith/EW5OQL3KQVMVEPGTWCVJZ3GLEU/action/replication_record"}},"created_at":"2026-07-05T10:10:50.879138+00:00","updated_at":"2026-07-05T10:10:50.879138+00:00"}