{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ZTXKHUTEGVZXRJFIBR75J32VZ3","short_pith_number":"pith:ZTXKHUTE","canonical_record":{"source":{"id":"2605.24001","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T21:26:33Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8b693e3e46c18478e846511ee445dc892707634895c949ee40c2df94791f1255","abstract_canon_sha256":"bf1ae82645a0c8d5742462f7afedc9e417cb7ac2347fec1f64e26a0a1310f497"},"schema_version":"1.0"},"canonical_sha256":"cceea3d264357378a4a80c7fd4ef55ced59f4967ad1169f3700246b0bf896cf1","source":{"kind":"arxiv","id":"2605.24001","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24001","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24001v1","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24001","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"pith_short_12","alias_value":"ZTXKHUTEGVZX","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"pith_short_16","alias_value":"ZTXKHUTEGVZXRJFI","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"pith_short_8","alias_value":"ZTXKHUTE","created_at":"2026-05-26T01:02:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ZTXKHUTEGVZXRJFIBR75J32VZ3","target":"record","payload":{"canonical_record":{"source":{"id":"2605.24001","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T21:26:33Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8b693e3e46c18478e846511ee445dc892707634895c949ee40c2df94791f1255","abstract_canon_sha256":"bf1ae82645a0c8d5742462f7afedc9e417cb7ac2347fec1f64e26a0a1310f497"},"schema_version":"1.0"},"canonical_sha256":"cceea3d264357378a4a80c7fd4ef55ced59f4967ad1169f3700246b0bf896cf1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:02:40.249564Z","signature_b64":"/b24lJhwISohzuvqtuYv7vkOnZ64PjGkH1h3Tf3cUAPlXYEevzb7b6/pxNfJ+3ys9e7NYSnvhoP8pqjALGwzBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cceea3d264357378a4a80c7fd4ef55ced59f4967ad1169f3700246b0bf896cf1","last_reissued_at":"2026-05-26T01:02:40.248884Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:02:40.248884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.24001","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:02:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YvjbELO4lHmWaAVAmpHf7tgaKIOjIwA6cD3ZzKA5nkQhGS7mnrFFrtY6lxLAzpXlreB0ErfI/rGz+gnXnHx+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T17:20:12.001452Z"},"content_sha256":"2cb70fc52f3f4e172f13a5dc308c22b5613dc17745699a303e3a2e3b604d87f4","schema_version":"1.0","event_id":"sha256:2cb70fc52f3f4e172f13a5dc308c22b5613dc17745699a303e3a2e3b604d87f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ZTXKHUTEGVZXRJFIBR75J32VZ3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Guang Lin Haoyang Zheng Runzhe Zhang Guang Lin, Haoyang Zheng, Junyi Wu, Runzhe Zhang, Weijian Luo","submitted_at":"2026-05-18T21:26:33Z","abstract_excerpt":"Recent advances in one-step text-to-image generation have enabled real-time synthesis with remarkable efficiency and quality. Previous reinforcement learning methods for one-step generators combine image-space reward optimization with diffusion noisy-space distribution matching. This paradigm brings challenges due to a mismatch between terminal reward optimization and the underlying generative dynamics. As a result, optimization tends to exploit stochastic degrees of freedom, often improving reward at the expense of image fidelity. To address this issue, we propose Diff-Instruct with Diffused "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24001","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/2605.24001/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:02:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JK4zC39oUdgQSF+b+Q8fr4mCUOi+Rmt21VP9p8zqwxoXP0NySUV86HFkD33MUS1ZpFzPBz5Sw1qMNk3oZClXAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T17:20:12.001833Z"},"content_sha256":"977a32e8b38f298a210c8f7be2e6f5bfcbaf48f2d36b92f1c52bbc8a2834e640","schema_version":"1.0","event_id":"sha256:977a32e8b38f298a210c8f7be2e6f5bfcbaf48f2d36b92f1c52bbc8a2834e640"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZTXKHUTEGVZXRJFIBR75J32VZ3/bundle.json","state_url":"https://pith.science/pith/ZTXKHUTEGVZXRJFIBR75J32VZ3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZTXKHUTEGVZXRJFIBR75J32VZ3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-02T17:20:12Z","links":{"resolver":"https://pith.science/pith/ZTXKHUTEGVZXRJFIBR75J32VZ3","bundle":"https://pith.science/pith/ZTXKHUTEGVZXRJFIBR75J32VZ3/bundle.json","state":"https://pith.science/pith/ZTXKHUTEGVZXRJFIBR75J32VZ3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZTXKHUTEGVZXRJFIBR75J32VZ3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZTXKHUTEGVZXRJFIBR75J32VZ3","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":"bf1ae82645a0c8d5742462f7afedc9e417cb7ac2347fec1f64e26a0a1310f497","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T21:26:33Z","title_canon_sha256":"8b693e3e46c18478e846511ee445dc892707634895c949ee40c2df94791f1255"},"schema_version":"1.0","source":{"id":"2605.24001","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24001","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24001v1","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24001","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"pith_short_12","alias_value":"ZTXKHUTEGVZX","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"pith_short_16","alias_value":"ZTXKHUTEGVZXRJFI","created_at":"2026-05-26T01:02:40Z"},{"alias_kind":"pith_short_8","alias_value":"ZTXKHUTE","created_at":"2026-05-26T01:02:40Z"}],"graph_snapshots":[{"event_id":"sha256:977a32e8b38f298a210c8f7be2e6f5bfcbaf48f2d36b92f1c52bbc8a2834e640","target":"graph","created_at":"2026-05-26T01:02:40Z","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/2605.24001/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in one-step text-to-image generation have enabled real-time synthesis with remarkable efficiency and quality. Previous reinforcement learning methods for one-step generators combine image-space reward optimization with diffusion noisy-space distribution matching. This paradigm brings challenges due to a mismatch between terminal reward optimization and the underlying generative dynamics. As a result, optimization tends to exploit stochastic degrees of freedom, often improving reward at the expense of image fidelity. To address this issue, we propose Diff-Instruct with Diffused ","authors_text":"Guang Lin Haoyang Zheng Runzhe Zhang Guang Lin, Haoyang Zheng, Junyi Wu, Runzhe Zhang, Weijian Luo","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T21:26:33Z","title":"Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24001","kind":"arxiv","version":1},"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":"sha256:2cb70fc52f3f4e172f13a5dc308c22b5613dc17745699a303e3a2e3b604d87f4","target":"record","created_at":"2026-05-26T01:02:40Z","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":"bf1ae82645a0c8d5742462f7afedc9e417cb7ac2347fec1f64e26a0a1310f497","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T21:26:33Z","title_canon_sha256":"8b693e3e46c18478e846511ee445dc892707634895c949ee40c2df94791f1255"},"schema_version":"1.0","source":{"id":"2605.24001","kind":"arxiv","version":1}},"canonical_sha256":"cceea3d264357378a4a80c7fd4ef55ced59f4967ad1169f3700246b0bf896cf1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cceea3d264357378a4a80c7fd4ef55ced59f4967ad1169f3700246b0bf896cf1","first_computed_at":"2026-05-26T01:02:40.248884Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:02:40.248884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/b24lJhwISohzuvqtuYv7vkOnZ64PjGkH1h3Tf3cUAPlXYEevzb7b6/pxNfJ+3ys9e7NYSnvhoP8pqjALGwzBg==","signature_status":"signed_v1","signed_at":"2026-05-26T01:02:40.249564Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.24001","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2cb70fc52f3f4e172f13a5dc308c22b5613dc17745699a303e3a2e3b604d87f4","sha256:977a32e8b38f298a210c8f7be2e6f5bfcbaf48f2d36b92f1c52bbc8a2834e640"],"state_sha256":"6e32e28e5e53e64841cf34ecc74ebc1153ecddd630d9726cd50495baedef811c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0uvmRbFOAgfCuM2mElDhjUJvFv5yPjtcGMyhA8hNfdxtNAxtiNAdlCg1lv3AD4HB7jbEO0iEPzIoPJXdwlUbAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T17:20:12.003914Z","bundle_sha256":"cbc4e8b95c0e585d9bd357c558fd49593a07ee3ce4dcae60830345125476b0f6"}}