{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AC2K4PK4VQ562FFDVFU6ISSBMR","short_pith_number":"pith:AC2K4PK4","schema_version":"1.0","canonical_sha256":"00b4ae3d5cac3bed14a3a969e44a416466bcdadafa6e07e992c2a6b5bee8af32","source":{"kind":"arxiv","id":"2312.07755","version":1},"attestation_state":"computed","paper":{"title":"Designing with Language: Wireframing UI Design Intent with Generative Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Chunyang Chen, Jieshan Chen, Mingyue Yuan, Sidong Feng, Zhenchang Xing","submitted_at":"2023-12-12T21:52:47Z","abstract_excerpt":"Wireframing is a critical step in the UI design process. Mid-fidelity wireframes offer more impactful and engaging visuals compared to low-fidelity versions. However, their creation can be time-consuming and labor-intensive, requiring the addition of actual content and semantic icons. In this paper, we introduce a novel solution WireGen, to automatically generate mid-fidelity wireframes with just a brief design intent description using the generative Large Language Models (LLMs). Our experiments demonstrate the effectiveness of WireGen in producing 77.5% significantly better wireframes, outper"},"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":"2312.07755","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-12-12T21:52:47Z","cross_cats_sorted":[],"title_canon_sha256":"0ec679e63a07b7cfe26633e798bcd3ab5fa02eebd7ee261846c86e5c475da6e5","abstract_canon_sha256":"a7a4f3b6cfdeb31b734f16d77171949db9f7bc9e21724529b6cefb00c6d4ad03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:35.670950Z","signature_b64":"EvfkI7pbsp93ZfDQcg/k3GncJBim6fDuxqAHlm45p0N+luUztM27lVjBWpFmF8J73UY85UDR6nzxNV34KxeHCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00b4ae3d5cac3bed14a3a969e44a416466bcdadafa6e07e992c2a6b5bee8af32","last_reissued_at":"2026-07-05T07:23:35.670565Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:35.670565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Designing with Language: Wireframing UI Design Intent with Generative Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Chunyang Chen, Jieshan Chen, Mingyue Yuan, Sidong Feng, Zhenchang Xing","submitted_at":"2023-12-12T21:52:47Z","abstract_excerpt":"Wireframing is a critical step in the UI design process. Mid-fidelity wireframes offer more impactful and engaging visuals compared to low-fidelity versions. However, their creation can be time-consuming and labor-intensive, requiring the addition of actual content and semantic icons. In this paper, we introduce a novel solution WireGen, to automatically generate mid-fidelity wireframes with just a brief design intent description using the generative Large Language Models (LLMs). Our experiments demonstrate the effectiveness of WireGen in producing 77.5% significantly better wireframes, outper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.07755","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/2312.07755/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":"2312.07755","created_at":"2026-07-05T07:23:35.670631+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.07755v1","created_at":"2026-07-05T07:23:35.670631+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.07755","created_at":"2026-07-05T07:23:35.670631+00:00"},{"alias_kind":"pith_short_12","alias_value":"AC2K4PK4VQ56","created_at":"2026-07-05T07:23:35.670631+00:00"},{"alias_kind":"pith_short_16","alias_value":"AC2K4PK4VQ562FFD","created_at":"2026-07-05T07:23:35.670631+00:00"},{"alias_kind":"pith_short_8","alias_value":"AC2K4PK4","created_at":"2026-07-05T07:23:35.670631+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.10652","citing_title":"Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR","json":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR.json","graph_json":"https://pith.science/api/pith-number/AC2K4PK4VQ562FFDVFU6ISSBMR/graph.json","events_json":"https://pith.science/api/pith-number/AC2K4PK4VQ562FFDVFU6ISSBMR/events.json","paper":"https://pith.science/paper/AC2K4PK4"},"agent_actions":{"view_html":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR","download_json":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR.json","view_paper":"https://pith.science/paper/AC2K4PK4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.07755&json=true","fetch_graph":"https://pith.science/api/pith-number/AC2K4PK4VQ562FFDVFU6ISSBMR/graph.json","fetch_events":"https://pith.science/api/pith-number/AC2K4PK4VQ562FFDVFU6ISSBMR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR/action/storage_attestation","attest_author":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR/action/author_attestation","sign_citation":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR/action/citation_signature","submit_replication":"https://pith.science/pith/AC2K4PK4VQ562FFDVFU6ISSBMR/action/replication_record"}},"created_at":"2026-07-05T07:23:35.670631+00:00","updated_at":"2026-07-05T07:23:35.670631+00:00"}