{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HTANCXNWABQ6RBRRKJ75LNNG4A","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0f2f971f30c51634959fce9b849d7e3e35fb196f4b7dbcc9e03b6921cde780eb","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T16:50:41Z","title_canon_sha256":"944a7a75a5f1cc0e897d73b0b18a303ce172ee3532ccec7eea7b6eb0818bbc93"},"schema_version":"1.0","source":{"id":"2212.09611","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.09611","created_at":"2026-07-05T07:28:43Z"},{"alias_kind":"arxiv_version","alias_value":"2212.09611v2","created_at":"2026-07-05T07:28:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09611","created_at":"2026-07-05T07:28:43Z"},{"alias_kind":"pith_short_12","alias_value":"HTANCXNWABQ6","created_at":"2026-07-05T07:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"HTANCXNWABQ6RBRR","created_at":"2026-07-05T07:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"HTANCXNW","created_at":"2026-07-05T07:28:43Z"}],"graph_snapshots":[{"event_id":"sha256:d765e043d2739e9f7a4e9077b9ed3b61cc97eed27d7bfb787da979fa0646ca94","target":"graph","created_at":"2026-07-05T07:28:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2212.09611/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Well-designed prompts can guide text-to-image models to generate amazing images. However, the performant prompts are often model-specific and misaligned with user input. Instead of laborious human engineering, we propose prompt adaptation, a general framework that automatically adapts original user input to model-preferred prompts. Specifically, we first perform supervised fine-tuning with a pretrained language model on a small collection of manually engineered prompts. Then we use reinforcement learning to explore better prompts. We define a reward function that encourages the policy to gener","authors_text":"Furu Wei, Li Dong, Yaru Hao, Zewen Chi","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T16:50:41Z","title":"Optimizing Prompts for Text-to-Image Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09611","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fb6f6f293ec9ff45ae367433dd50d26cf3b41394316d900259efe833f37ea035","target":"record","created_at":"2026-07-05T07:28:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0f2f971f30c51634959fce9b849d7e3e35fb196f4b7dbcc9e03b6921cde780eb","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T16:50:41Z","title_canon_sha256":"944a7a75a5f1cc0e897d73b0b18a303ce172ee3532ccec7eea7b6eb0818bbc93"},"schema_version":"1.0","source":{"id":"2212.09611","kind":"arxiv","version":2}},"canonical_sha256":"3cc0d15db60061e88631527fd5b5a6e005bf10977ca2d0908018b8ee79c342c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3cc0d15db60061e88631527fd5b5a6e005bf10977ca2d0908018b8ee79c342c8","first_computed_at":"2026-07-05T07:28:43.563151Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:28:43.563151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AMoshC61V1Ik0iE+vyue/lj/Cfn5ywX52HX3oeJ1HWHDJ8b+mLAGx9AlPPFrdCgjqqbLCOMo7XE0r9irB0c0AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:28:43.563651Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.09611","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb6f6f293ec9ff45ae367433dd50d26cf3b41394316d900259efe833f37ea035","sha256:d765e043d2739e9f7a4e9077b9ed3b61cc97eed27d7bfb787da979fa0646ca94"],"state_sha256":"1784c16f9354ee873b7de7e986ce836873ec3085c7284bd5397231173995147e"}