{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RF5V6DHQFGFLCJCTWAWZE7MQ2P","short_pith_number":"pith:RF5V6DHQ","schema_version":"1.0","canonical_sha256":"897b5f0cf0298ab12453b02d927d90d3e15dcff1864b203cb1c401b7cc13c8e3","source":{"kind":"arxiv","id":"2606.26930","version":1},"attestation_state":"computed","paper":{"title":"PortraitGen: Exemplar-Driven GRPO with Dual-Reward Guidance for Photorealistic Portrait Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Li, Huchuan Lu, Jing Lyu, Qian Liang, Xiaomin Li, Xu Jia, Yinan Li, Ying Zhang","submitted_at":"2026-06-25T12:05:40Z","abstract_excerpt":"Reinforcement Learning like Group Relative Policy Optimization (GRPO) has significantly advanced text-to-image post-training. However, current methods often favor superficial aesthetics, such as over-saturated colors, leaving critical flaws like AI artifacts and biological implausibilities unresolved. We attribute these limitations to two primary factors: (1) The absence of real images during post-training confines GRPO sampling to the original distribution, failing to break inherent generative boundaries; (2) the optimization process lacks specific rewards targeting fine-grained artifacts lik"},"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":"2606.26930","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T12:05:40Z","cross_cats_sorted":[],"title_canon_sha256":"c8d4e677a327c3167762920a061b754dff53df970df03f4ee1240d0c60e95903","abstract_canon_sha256":"04121bfa39635b0a064e6a86293b899856d0e6f3f1d143aad7bf8b4b4f12c07d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:04.410465Z","signature_b64":"1dy1tVf33pqHqv8xxuLRHZjOmWV6DaHg96NWKH03AaIxB7QAmTVIM4zjt+bauXg3nY6BPLATvg3fhyE5jKy6Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"897b5f0cf0298ab12453b02d927d90d3e15dcff1864b203cb1c401b7cc13c8e3","last_reissued_at":"2026-06-26T01:16:04.410073Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:04.410073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PortraitGen: Exemplar-Driven GRPO with Dual-Reward Guidance for Photorealistic Portrait Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Li, Huchuan Lu, Jing Lyu, Qian Liang, Xiaomin Li, Xu Jia, Yinan Li, Ying Zhang","submitted_at":"2026-06-25T12:05:40Z","abstract_excerpt":"Reinforcement Learning like Group Relative Policy Optimization (GRPO) has significantly advanced text-to-image post-training. However, current methods often favor superficial aesthetics, such as over-saturated colors, leaving critical flaws like AI artifacts and biological implausibilities unresolved. We attribute these limitations to two primary factors: (1) The absence of real images during post-training confines GRPO sampling to the original distribution, failing to break inherent generative boundaries; (2) the optimization process lacks specific rewards targeting fine-grained artifacts lik"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26930","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/2606.26930/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":"2606.26930","created_at":"2026-06-26T01:16:04.410125+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.26930v1","created_at":"2026-06-26T01:16:04.410125+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26930","created_at":"2026-06-26T01:16:04.410125+00:00"},{"alias_kind":"pith_short_12","alias_value":"RF5V6DHQFGFL","created_at":"2026-06-26T01:16:04.410125+00:00"},{"alias_kind":"pith_short_16","alias_value":"RF5V6DHQFGFLCJCT","created_at":"2026-06-26T01:16:04.410125+00:00"},{"alias_kind":"pith_short_8","alias_value":"RF5V6DHQ","created_at":"2026-06-26T01:16:04.410125+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/RF5V6DHQFGFLCJCTWAWZE7MQ2P","json":"https://pith.science/pith/RF5V6DHQFGFLCJCTWAWZE7MQ2P.json","graph_json":"https://pith.science/api/pith-number/RF5V6DHQFGFLCJCTWAWZE7MQ2P/graph.json","events_json":"https://pith.science/api/pith-number/RF5V6DHQFGFLCJCTWAWZE7MQ2P/events.json","paper":"https://pith.science/paper/RF5V6DHQ"},"agent_actions":{"view_html":"https://pith.science/pith/RF5V6DHQFGFLCJCTWAWZE7MQ2P","download_json":"https://pith.science/pith/RF5V6DHQFGFLCJCTWAWZE7MQ2P.json","view_paper":"https://pith.science/paper/RF5V6DHQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.26930&json=true","fetch_graph":"https://pith.science/api/pith-number/RF5V6DHQFGFLCJCTWAWZE7MQ2P/graph.json","fetch_events":"https://pith.science/api/pith-number/RF5V6DHQFGFLCJCTWAWZE7MQ2P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RF5V6DHQFGFLCJCTWAWZE7MQ2P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RF5V6DHQFGFLCJCTWAWZE7MQ2P/action/storage_attestation","attest_author":"https://pith.science/pith/RF5V6DHQFGFLCJCTWAWZE7MQ2P/action/author_attestation","sign_citation":"https://pith.science/pith/RF5V6DHQFGFLCJCTWAWZE7MQ2P/action/citation_signature","submit_replication":"https://pith.science/pith/RF5V6DHQFGFLCJCTWAWZE7MQ2P/action/replication_record"}},"created_at":"2026-06-26T01:16:04.410125+00:00","updated_at":"2026-06-26T01:16:04.410125+00:00"}