{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:SFJXDVRTDSHL5NCSUPXB7P5SG4","short_pith_number":"pith:SFJXDVRT","canonical_record":{"source":{"id":"2606.27305","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T17:26:06Z","cross_cats_sorted":[],"title_canon_sha256":"3fb2dd26cbdb8cab6e4543996d13538b4bd778267468c6801762dd8aa32f28cf","abstract_canon_sha256":"1388f52aee2c64bd3903874a4d37dc4443fee52b44950dd7062c16c65ae8fe09"},"schema_version":"1.0"},"canonical_sha256":"915371d6331c8ebeb452a3ee1fbfb2370e482d48a8851b3969abf48c59a95a0e","source":{"kind":"arxiv","id":"2606.27305","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.27305","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"arxiv_version","alias_value":"2606.27305v1","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27305","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"pith_short_12","alias_value":"SFJXDVRTDSHL","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"pith_short_16","alias_value":"SFJXDVRTDSHL5NCS","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"pith_short_8","alias_value":"SFJXDVRT","created_at":"2026-06-26T01:16:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:SFJXDVRTDSHL5NCSUPXB7P5SG4","target":"record","payload":{"canonical_record":{"source":{"id":"2606.27305","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T17:26:06Z","cross_cats_sorted":[],"title_canon_sha256":"3fb2dd26cbdb8cab6e4543996d13538b4bd778267468c6801762dd8aa32f28cf","abstract_canon_sha256":"1388f52aee2c64bd3903874a4d37dc4443fee52b44950dd7062c16c65ae8fe09"},"schema_version":"1.0"},"canonical_sha256":"915371d6331c8ebeb452a3ee1fbfb2370e482d48a8851b3969abf48c59a95a0e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:18.250062Z","signature_b64":"b9c89EveXV8c1MW1yOUpkCclLFyniOkLh3iJSt4wrAAjx8IIP1VAUEScLyu+Jbm+Z/2El+270byiyYRU2m0UBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"915371d6331c8ebeb452a3ee1fbfb2370e482d48a8851b3969abf48c59a95a0e","last_reissued_at":"2026-06-26T01:16:18.249723Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:18.249723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.27305","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":"2026-06-26T01:16:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DMkKxIM3nJlXhtOrtjVHMFGbFs6Ez/r74EoTTYLfFJ7Bx1SMFQmGs7HXz7eQxZNqU4TCSQHywfI8VzagR7KZCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T07:31:46.243372Z"},"content_sha256":"d16c9344af05586137e56e1549c927aea0a365257de21eb605d72843e8d0c7be","schema_version":"1.0","event_id":"sha256:d16c9344af05586137e56e1549c927aea0a365257de21eb605d72843e8d0c7be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:SFJXDVRTDSHL5NCSUPXB7P5SG4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sculpting NeRF Geometry: Human-Preference Fine-Tuning of a 3D-Aware Face GAN","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Archer Moore, Liam Hodgkinson, Mingming Gong","submitted_at":"2026-06-25T17:26:06Z","abstract_excerpt":"Reinforcement learning from human feedback (RLHF) for 3D generation is now established across a number of works, but most existing pipelines optimise explicit surface representations, often by converting radiance fields into meshes and training heavily on surface-supervised data. We instead fine-tune a pretrained 3D-aware generative model directly from a learned reward over radiance-field density ($\\sigma$) values, with no externally supplied mesh or shape prior. The reward model requires no pretraining, trains easily on a small set of preference samples, and yields robust improvement in 3D ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27305","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/2606.27305/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":"2026-06-26T01:16:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x4M2YMEZGtWv0dhImVC013MEF0hcV7oRq3E3K1FKCIcLcHxEAsUvszi5hUoOpn70nwuDovySJOKYWwWA/Ml/Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T07:31:46.243967Z"},"content_sha256":"aaf2c6d448fc37266ea1c8aa13a5806ba4067f9037b14aa314a6b174e58e5779","schema_version":"1.0","event_id":"sha256:aaf2c6d448fc37266ea1c8aa13a5806ba4067f9037b14aa314a6b174e58e5779"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SFJXDVRTDSHL5NCSUPXB7P5SG4/bundle.json","state_url":"https://pith.science/pith/SFJXDVRTDSHL5NCSUPXB7P5SG4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SFJXDVRTDSHL5NCSUPXB7P5SG4/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":"2026-07-01T07:31:46Z","links":{"resolver":"https://pith.science/pith/SFJXDVRTDSHL5NCSUPXB7P5SG4","bundle":"https://pith.science/pith/SFJXDVRTDSHL5NCSUPXB7P5SG4/bundle.json","state":"https://pith.science/pith/SFJXDVRTDSHL5NCSUPXB7P5SG4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SFJXDVRTDSHL5NCSUPXB7P5SG4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:SFJXDVRTDSHL5NCSUPXB7P5SG4","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":"1388f52aee2c64bd3903874a4d37dc4443fee52b44950dd7062c16c65ae8fe09","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T17:26:06Z","title_canon_sha256":"3fb2dd26cbdb8cab6e4543996d13538b4bd778267468c6801762dd8aa32f28cf"},"schema_version":"1.0","source":{"id":"2606.27305","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.27305","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"arxiv_version","alias_value":"2606.27305v1","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27305","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"pith_short_12","alias_value":"SFJXDVRTDSHL","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"pith_short_16","alias_value":"SFJXDVRTDSHL5NCS","created_at":"2026-06-26T01:16:18Z"},{"alias_kind":"pith_short_8","alias_value":"SFJXDVRT","created_at":"2026-06-26T01:16:18Z"}],"graph_snapshots":[{"event_id":"sha256:aaf2c6d448fc37266ea1c8aa13a5806ba4067f9037b14aa314a6b174e58e5779","target":"graph","created_at":"2026-06-26T01:16:18Z","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/2606.27305/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reinforcement learning from human feedback (RLHF) for 3D generation is now established across a number of works, but most existing pipelines optimise explicit surface representations, often by converting radiance fields into meshes and training heavily on surface-supervised data. We instead fine-tune a pretrained 3D-aware generative model directly from a learned reward over radiance-field density ($\\sigma$) values, with no externally supplied mesh or shape prior. The reward model requires no pretraining, trains easily on a small set of preference samples, and yields robust improvement in 3D ge","authors_text":"Archer Moore, Liam Hodgkinson, Mingming Gong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T17:26:06Z","title":"Sculpting NeRF Geometry: Human-Preference Fine-Tuning of a 3D-Aware Face GAN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27305","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:d16c9344af05586137e56e1549c927aea0a365257de21eb605d72843e8d0c7be","target":"record","created_at":"2026-06-26T01:16:18Z","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":"1388f52aee2c64bd3903874a4d37dc4443fee52b44950dd7062c16c65ae8fe09","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T17:26:06Z","title_canon_sha256":"3fb2dd26cbdb8cab6e4543996d13538b4bd778267468c6801762dd8aa32f28cf"},"schema_version":"1.0","source":{"id":"2606.27305","kind":"arxiv","version":1}},"canonical_sha256":"915371d6331c8ebeb452a3ee1fbfb2370e482d48a8851b3969abf48c59a95a0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"915371d6331c8ebeb452a3ee1fbfb2370e482d48a8851b3969abf48c59a95a0e","first_computed_at":"2026-06-26T01:16:18.249723Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-26T01:16:18.249723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b9c89EveXV8c1MW1yOUpkCclLFyniOkLh3iJSt4wrAAjx8IIP1VAUEScLyu+Jbm+Z/2El+270byiyYRU2m0UBA==","signature_status":"signed_v1","signed_at":"2026-06-26T01:16:18.250062Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.27305","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d16c9344af05586137e56e1549c927aea0a365257de21eb605d72843e8d0c7be","sha256:aaf2c6d448fc37266ea1c8aa13a5806ba4067f9037b14aa314a6b174e58e5779"],"state_sha256":"e8a6f52fa3aa8a52fd84c12fd95b56610f90d4fefd92bd32c5295695ac47d876"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QbQkn+7YSnr4mkfeNM+aqCZW11yaTBU2pUYUfZgoGZb8MPV7HbO4fAsORY2iLv6Sb/fXmBkqVMkRxvX6VAqyBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T07:31:46.247273Z","bundle_sha256":"6804e8e415d5d6ee4fd6cc71a6255c5db0839c76e9da0d577f0e5e117e2ffd09"}}