{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GAVLWFCCGN26XDSQR3AM5SKA6Q","short_pith_number":"pith:GAVLWFCC","schema_version":"1.0","canonical_sha256":"302abb14423375eb8e508ec0cec940f41a1d52c58a178b52baeb1bf1547f2f5d","source":{"kind":"arxiv","id":"2411.13787","version":2},"attestation_state":"computed","paper":{"title":"Adaptive Routing of Text-to-Image Generation Requests Between Large Cloud Model and Light-Weight Edge Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chaoyue Niu, Fan Wu, Guihai Chen, Qinya Li, Zewei Xin","submitted_at":"2024-11-21T02:18:06Z","abstract_excerpt":"Large text-to-image models demonstrate impressive generation capabilities; however, their substantial size necessitates expensive cloud servers for deployment. Conversely, light-weight models can be deployed on edge devices at lower cost but often with inferior generation quality for complex user prompts. To strike a balance between performance and cost, we propose a routing framework, called RouteT2I, which dynamically selects either the large cloud model or the light-weight edge model for each user prompt. Since generated image quality is challenging to measure and compare directly, RouteT2I"},"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":"2411.13787","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T02:18:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"54b00b03fc6a7906a7f3b264f0de1cfed5ed7e31b941c1adc9fec0e65bf598ed","abstract_canon_sha256":"964e6482189dc068ee33f62cc8356492309b7834186b5e2cd3577396414ed0c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:51.160267Z","signature_b64":"NyDn5FrC5euqx+3OwjhKRe61IxnyEJcP3JZzx+QkoJS9y2hudQBnorZyF3QoMiY515Tim8rOTu/GMeM8hICLDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"302abb14423375eb8e508ec0cec940f41a1d52c58a178b52baeb1bf1547f2f5d","last_reissued_at":"2026-07-05T11:56:51.159733Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:51.159733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Routing of Text-to-Image Generation Requests Between Large Cloud Model and Light-Weight Edge Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chaoyue Niu, Fan Wu, Guihai Chen, Qinya Li, Zewei Xin","submitted_at":"2024-11-21T02:18:06Z","abstract_excerpt":"Large text-to-image models demonstrate impressive generation capabilities; however, their substantial size necessitates expensive cloud servers for deployment. Conversely, light-weight models can be deployed on edge devices at lower cost but often with inferior generation quality for complex user prompts. To strike a balance between performance and cost, we propose a routing framework, called RouteT2I, which dynamically selects either the large cloud model or the light-weight edge model for each user prompt. Since generated image quality is challenging to measure and compare directly, RouteT2I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.13787","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/2411.13787/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":"2411.13787","created_at":"2026-07-05T11:56:51.159798+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.13787v2","created_at":"2026-07-05T11:56:51.159798+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.13787","created_at":"2026-07-05T11:56:51.159798+00:00"},{"alias_kind":"pith_short_12","alias_value":"GAVLWFCCGN26","created_at":"2026-07-05T11:56:51.159798+00:00"},{"alias_kind":"pith_short_16","alias_value":"GAVLWFCCGN26XDSQ","created_at":"2026-07-05T11:56:51.159798+00:00"},{"alias_kind":"pith_short_8","alias_value":"GAVLWFCC","created_at":"2026-07-05T11:56:51.159798+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.16731","citing_title":"Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges","ref_index":165,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q","json":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q.json","graph_json":"https://pith.science/api/pith-number/GAVLWFCCGN26XDSQR3AM5SKA6Q/graph.json","events_json":"https://pith.science/api/pith-number/GAVLWFCCGN26XDSQR3AM5SKA6Q/events.json","paper":"https://pith.science/paper/GAVLWFCC"},"agent_actions":{"view_html":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q","download_json":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q.json","view_paper":"https://pith.science/paper/GAVLWFCC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.13787&json=true","fetch_graph":"https://pith.science/api/pith-number/GAVLWFCCGN26XDSQR3AM5SKA6Q/graph.json","fetch_events":"https://pith.science/api/pith-number/GAVLWFCCGN26XDSQR3AM5SKA6Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q/action/storage_attestation","attest_author":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q/action/author_attestation","sign_citation":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q/action/citation_signature","submit_replication":"https://pith.science/pith/GAVLWFCCGN26XDSQR3AM5SKA6Q/action/replication_record"}},"created_at":"2026-07-05T11:56:51.159798+00:00","updated_at":"2026-07-05T11:56:51.159798+00:00"}