{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:UXGQYTUZX7FESK2TM6BB6VFHPI","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":"9a07ee88226effc6e824565962a1079667aac4fe606a0b0b8fd11e87d3927916","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-17T02:44:11Z","title_canon_sha256":"b664b78cb39256a10ff3aaf2b46a24190a377c22bbbb32fb310916bf496bab7c"},"schema_version":"1.0","source":{"id":"2512.17951","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.17951","created_at":"2026-07-24T01:24:05Z"},{"alias_kind":"arxiv_version","alias_value":"2512.17951v3","created_at":"2026-07-24T01:24:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.17951","created_at":"2026-07-24T01:24:05Z"},{"alias_kind":"pith_short_12","alias_value":"UXGQYTUZX7FE","created_at":"2026-07-24T01:24:05Z"},{"alias_kind":"pith_short_16","alias_value":"UXGQYTUZX7FESK2T","created_at":"2026-07-24T01:24:05Z"},{"alias_kind":"pith_short_8","alias_value":"UXGQYTUZ","created_at":"2026-07-24T01:24:05Z"}],"graph_snapshots":[{"event_id":"sha256:b4cae1952f952af22bfdd9e762e59222a644eb4b5e0d9763ea92dfb27acfc3ad","target":"graph","created_at":"2026-07-24T01:24:05Z","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/2512.17951/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent progress in flow-based generative models and reinforcement learning (RL) has improved text-image alignment and visual quality. However, current RL training for flow models still has two main problems: (i) GRPO-style fixed per-prompt group sizes ignore variation in sampling importance across prompts, which leads to inefficient sampling and slower training; and (ii) trajectory-level advantages are reused as per-step estimates, which biases credit assignment along the flow. We propose SuperFlow, an RL training framework for flow-based models that adjusts group sizes with variance-aware sam","authors_text":"Kaijie Chen, Lifu Huang, Ying Shen, Yuguang Yao, Zhiyang Xu, Zihao Lin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-17T02:44:11Z","title":"SuperFlow: Training Flow Matching Models with RL on the Fly"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.17951","kind":"arxiv","version":3},"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":"sha256:9d7fea2f4ce11feee083828b2b1a3a9488631dbea8ceff39507906aab5d20351","target":"record","created_at":"2026-07-24T01:24:05Z","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":"9a07ee88226effc6e824565962a1079667aac4fe606a0b0b8fd11e87d3927916","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-17T02:44:11Z","title_canon_sha256":"b664b78cb39256a10ff3aaf2b46a24190a377c22bbbb32fb310916bf496bab7c"},"schema_version":"1.0","source":{"id":"2512.17951","kind":"arxiv","version":3}},"canonical_sha256":"a5cd0c4e99bfca492b5367821f54a77a197c73026d8318c238f12a4de0f7f330","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5cd0c4e99bfca492b5367821f54a77a197c73026d8318c238f12a4de0f7f330","first_computed_at":"2026-07-24T01:24:05.909174Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-24T01:24:05.909174Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Rk4Gh4itNRwNLuNlOkG6+DOtJdDrguts0sU9A2cufqTBF/hnrwGXs8UoDKlH6awKoX54Zc+l1W4H18P8Wm5FCw==","signature_status":"signed_v1","signed_at":"2026-07-24T01:24:05.910113Z","signed_message":"canonical_sha256_bytes"},"source_id":"2512.17951","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d7fea2f4ce11feee083828b2b1a3a9488631dbea8ceff39507906aab5d20351","sha256:b4cae1952f952af22bfdd9e762e59222a644eb4b5e0d9763ea92dfb27acfc3ad"],"state_sha256":"bf65d0cc8a55444cb5c6067303a976475ec6601320f88700ea6d99946dc04b0d"}