{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SO5DGOG4FZHZVELR2HDCYMVAE2","short_pith_number":"pith:SO5DGOG4","schema_version":"1.0","canonical_sha256":"93ba3338dc2e4f9a9171d1c62c32a026ab7bbbe62a5b8d8539784346a9f5748c","source":{"kind":"arxiv","id":"2605.12271","version":2},"attestation_state":"computed","paper":{"title":"Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Visual specification pages replace text prompts in frozen generators without any retraining.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoxuan Che, Jean-Michel Morel, Kangning Cui, Meng Chu, Raymond H. Chan, Rui Liu, Suiyun Zhang, Xiaodong Cun, Yaofang Liu, Zhaoqing Li","submitted_at":"2026-05-12T15:35:34Z","abstract_excerpt":"Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generators still ask users to serialize this intent into text, a bottleneck that compresses signals like spatial structure, exact appearance, and glyph shape. We propose \\textbf{\\emph{visual-to-visual} (V2V)} generation, in which the user conditions a generative model with a visual specification page rather than a text prompt. The page is not an edit target, but a visual document that specifies the desired output. We introduce \\textbf{V2V-Zero}, a tra"},"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":"2605.12271","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T15:35:34Z","cross_cats_sorted":[],"title_canon_sha256":"39c3e977f8fa68dc787afc434839e9eba9fd09d849d59fa67a1a2ebd616fd377","abstract_canon_sha256":"c0e33f53768481e2eedf292a036100c81e7148492ac9c18095e105d126df48e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:05:57.096600Z","signature_b64":"lMy0oasEtdNc5UVTjQGNd3HPOPRtg4qTL30TTrZwn5TQSX3o2+DvsKai36M0HMczKqg6PCEPLnFdb3mLF32ECw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93ba3338dc2e4f9a9171d1c62c32a026ab7bbbe62a5b8d8539784346a9f5748c","last_reissued_at":"2026-05-27T01:05:57.095844Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:05:57.095844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Visual specification pages replace text prompts in frozen generators without any retraining.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoxuan Che, Jean-Michel Morel, Kangning Cui, Meng Chu, Raymond H. Chan, Rui Liu, Suiyun Zhang, Xiaodong Cun, Yaofang Liu, Zhaoqing Li","submitted_at":"2026-05-12T15:35:34Z","abstract_excerpt":"Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generators still ask users to serialize this intent into text, a bottleneck that compresses signals like spatial structure, exact appearance, and glyph shape. We propose \\textbf{\\emph{visual-to-visual} (V2V)} generation, in which the user conditions a generative model with a visual specification page rather than a text prompt. The page is not an edit target, but a visual document that specifies the desired output. We introduce \\textbf{V2V-Zero}, a tra"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"V2V-Zero reaches 0.85 on GenEval with a frozen Qwen-Image backbone, closely matching its optimized text-to-image performance without fine-tuning, and scores 32.7/100 on Simple-V2V Bench while a video extension scores 20.2/100.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The frozen VLM already maps both text and images into the generator's conditioning space so that final-layer hidden states from visual pages can replace text conditioning without any fine-tuning or architectural changes.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"V2V-Zero adapts frozen VLMs for visual conditioning via hidden states from specification pages, scoring 0.85 on GenEval and 32.7 on a new seven-task benchmark while revealing capability hierarchies in attribute binding and structural control.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Visual specification pages replace text prompts in frozen generators without any retraining.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8274e763bce1d3161b4a9cacccf7d804a20e8f7e95d6e45c2976c625ad3f3f6e"},"source":{"id":"2605.12271","kind":"arxiv","version":2},"verdict":{"id":"a5ab15ab-d024-4dc1-944f-22d1a68cae8e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T05:56:50.607649Z","strongest_claim":"V2V-Zero reaches 0.85 on GenEval with a frozen Qwen-Image backbone, closely matching its optimized text-to-image performance without fine-tuning, and scores 32.7/100 on Simple-V2V Bench while a video extension scores 20.2/100.","one_line_summary":"V2V-Zero adapts frozen VLMs for visual conditioning via hidden states from specification pages, scoring 0.85 on GenEval and 32.7 on a new seven-task benchmark while revealing capability hierarchies in attribute binding and structural control.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The frozen VLM already maps both text and images into the generator's conditioning space so that final-layer hidden states from visual pages can replace text conditioning without any fine-tuning or architectural changes.","pith_extraction_headline":"Visual specification pages replace text prompts in frozen generators without any retraining."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.12271/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-26T14:45:24.437485Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-20T14:01:25.467010Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-20T10:17:13.945599Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T22:41:58.321139Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"06ac31b9b5c5d3c1c8e7e01e86603ebd62e45f52bf211ba126be209800a564cf"},"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":"2605.12271","created_at":"2026-05-27T01:05:57.095971+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.12271v2","created_at":"2026-05-27T01:05:57.095971+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12271","created_at":"2026-05-27T01:05:57.095971+00:00"},{"alias_kind":"pith_short_12","alias_value":"SO5DGOG4FZHZ","created_at":"2026-05-27T01:05:57.095971+00:00"},{"alias_kind":"pith_short_16","alias_value":"SO5DGOG4FZHZVELR","created_at":"2026-05-27T01:05:57.095971+00:00"},{"alias_kind":"pith_short_8","alias_value":"SO5DGOG4","created_at":"2026-05-27T01:05:57.095971+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/SO5DGOG4FZHZVELR2HDCYMVAE2","json":"https://pith.science/pith/SO5DGOG4FZHZVELR2HDCYMVAE2.json","graph_json":"https://pith.science/api/pith-number/SO5DGOG4FZHZVELR2HDCYMVAE2/graph.json","events_json":"https://pith.science/api/pith-number/SO5DGOG4FZHZVELR2HDCYMVAE2/events.json","paper":"https://pith.science/paper/SO5DGOG4"},"agent_actions":{"view_html":"https://pith.science/pith/SO5DGOG4FZHZVELR2HDCYMVAE2","download_json":"https://pith.science/pith/SO5DGOG4FZHZVELR2HDCYMVAE2.json","view_paper":"https://pith.science/paper/SO5DGOG4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.12271&json=true","fetch_graph":"https://pith.science/api/pith-number/SO5DGOG4FZHZVELR2HDCYMVAE2/graph.json","fetch_events":"https://pith.science/api/pith-number/SO5DGOG4FZHZVELR2HDCYMVAE2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SO5DGOG4FZHZVELR2HDCYMVAE2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SO5DGOG4FZHZVELR2HDCYMVAE2/action/storage_attestation","attest_author":"https://pith.science/pith/SO5DGOG4FZHZVELR2HDCYMVAE2/action/author_attestation","sign_citation":"https://pith.science/pith/SO5DGOG4FZHZVELR2HDCYMVAE2/action/citation_signature","submit_replication":"https://pith.science/pith/SO5DGOG4FZHZVELR2HDCYMVAE2/action/replication_record"}},"created_at":"2026-05-27T01:05:57.095971+00:00","updated_at":"2026-05-27T01:05:57.095971+00:00"}