{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:DJEL2H4HN2BZQJ3T36VWIL7K3L","short_pith_number":"pith:DJEL2H4H","canonical_record":{"source":{"id":"2403.14613","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:58:04Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"ce3723363bc6b330496ef924799fb4e47ac835d2a34f5c8834514f4df27e1a9c","abstract_canon_sha256":"d494a6390351b8baccd3a860b149b51800dc4831672d095819f66432fa4f2748"},"schema_version":"1.0"},"canonical_sha256":"1a48bd1f876e83982773dfab642feadac482b30f45673f536448a877f9ddc476","source":{"kind":"arxiv","id":"2403.14613","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.14613","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"arxiv_version","alias_value":"2403.14613v1","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.14613","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"pith_short_12","alias_value":"DJEL2H4HN2BZ","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"pith_short_16","alias_value":"DJEL2H4HN2BZQJ3T","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"pith_short_8","alias_value":"DJEL2H4H","created_at":"2026-07-05T07:59:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:DJEL2H4HN2BZQJ3T36VWIL7K3L","target":"record","payload":{"canonical_record":{"source":{"id":"2403.14613","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:58:04Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"ce3723363bc6b330496ef924799fb4e47ac835d2a34f5c8834514f4df27e1a9c","abstract_canon_sha256":"d494a6390351b8baccd3a860b149b51800dc4831672d095819f66432fa4f2748"},"schema_version":"1.0"},"canonical_sha256":"1a48bd1f876e83982773dfab642feadac482b30f45673f536448a877f9ddc476","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:59:11.424523Z","signature_b64":"rIiT1cd7NF1Vy+/RwNrZrlJPW2Usu0MRvukrSztB4IHVFqoJfVG9xi+4lE8C4b7A0jubeBabiCQQxDAELmNkDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a48bd1f876e83982773dfab642feadac482b30f45673f536448a877f9ddc476","last_reissued_at":"2026-07-05T07:59:11.424042Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:59:11.424042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.14613","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":"2026-07-05T07:59:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9fyMZxu7HKXFOXOif4n19lUpoUdcVNtmV9Xeh6XAUp3bwOBpTIxguL3YfgSdrsY7Gd6dJFuEUFxqIRG2MWG6Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T02:45:44.077605Z"},"content_sha256":"1211db45cc86f70ca69bacb52396eb38443e69b819dfc0faf7d93489d525b815","schema_version":"1.0","event_id":"sha256:1211db45cc86f70ca69bacb52396eb38443e69b819dfc0faf7d93489d525b815"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:DJEL2H4HN2BZQJ3T36VWIL7K3L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DreamReward: Text-to-3D Generation with Human Preference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CV","authors_text":"Fangfu Liu, Junliang Ye, Jun Zhu, Qixiu Li, Xinzhou Wang, Yikai Wang, Yueqi Duan, Zhengyi Wang","submitted_at":"2024-03-21T17:58:04Z","abstract_excerpt":"3D content creation from text prompts has shown remarkable success recently. However, current text-to-3D methods often generate 3D results that do not align well with human preferences. In this paper, we present a comprehensive framework, coined DreamReward, to learn and improve text-to-3D models from human preference feedback. To begin with, we collect 25k expert comparisons based on a systematic annotation pipeline including rating and ranking. Then, we build Reward3D -- the first general-purpose text-to-3D human preference reward model to effectively encode human preferences. Building upon "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.14613","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/2403.14613/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":"2026-07-05T07:59:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7S+fDfpFxbIY/cvZKrCEtO9HQQ1DdXRbLEAbrK4gXYDRrqt01EWLhCtpkUWfCkL9x92XmkfEQbhvpbWrkqZuCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T02:45:44.078135Z"},"content_sha256":"c3c10ea0c55493487c0ec270337c488b9975438304e1d3eeafe00fcfd9c07c7b","schema_version":"1.0","event_id":"sha256:c3c10ea0c55493487c0ec270337c488b9975438304e1d3eeafe00fcfd9c07c7b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DJEL2H4HN2BZQJ3T36VWIL7K3L/bundle.json","state_url":"https://pith.science/pith/DJEL2H4HN2BZQJ3T36VWIL7K3L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DJEL2H4HN2BZQJ3T36VWIL7K3L/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":"2026-08-16T02:45:44Z","links":{"resolver":"https://pith.science/pith/DJEL2H4HN2BZQJ3T36VWIL7K3L","bundle":"https://pith.science/pith/DJEL2H4HN2BZQJ3T36VWIL7K3L/bundle.json","state":"https://pith.science/pith/DJEL2H4HN2BZQJ3T36VWIL7K3L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DJEL2H4HN2BZQJ3T36VWIL7K3L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DJEL2H4HN2BZQJ3T36VWIL7K3L","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":"d494a6390351b8baccd3a860b149b51800dc4831672d095819f66432fa4f2748","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:58:04Z","title_canon_sha256":"ce3723363bc6b330496ef924799fb4e47ac835d2a34f5c8834514f4df27e1a9c"},"schema_version":"1.0","source":{"id":"2403.14613","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.14613","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"arxiv_version","alias_value":"2403.14613v1","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.14613","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"pith_short_12","alias_value":"DJEL2H4HN2BZ","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"pith_short_16","alias_value":"DJEL2H4HN2BZQJ3T","created_at":"2026-07-05T07:59:11Z"},{"alias_kind":"pith_short_8","alias_value":"DJEL2H4H","created_at":"2026-07-05T07:59:11Z"}],"graph_snapshots":[{"event_id":"sha256:c3c10ea0c55493487c0ec270337c488b9975438304e1d3eeafe00fcfd9c07c7b","target":"graph","created_at":"2026-07-05T07:59:11Z","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/2403.14613/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"3D content creation from text prompts has shown remarkable success recently. However, current text-to-3D methods often generate 3D results that do not align well with human preferences. In this paper, we present a comprehensive framework, coined DreamReward, to learn and improve text-to-3D models from human preference feedback. To begin with, we collect 25k expert comparisons based on a systematic annotation pipeline including rating and ranking. Then, we build Reward3D -- the first general-purpose text-to-3D human preference reward model to effectively encode human preferences. Building upon ","authors_text":"Fangfu Liu, Junliang Ye, Jun Zhu, Qixiu Li, Xinzhou Wang, Yikai Wang, Yueqi Duan, Zhengyi Wang","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:58:04Z","title":"DreamReward: Text-to-3D Generation with Human Preference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.14613","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":"sha256:1211db45cc86f70ca69bacb52396eb38443e69b819dfc0faf7d93489d525b815","target":"record","created_at":"2026-07-05T07:59:11Z","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":"d494a6390351b8baccd3a860b149b51800dc4831672d095819f66432fa4f2748","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:58:04Z","title_canon_sha256":"ce3723363bc6b330496ef924799fb4e47ac835d2a34f5c8834514f4df27e1a9c"},"schema_version":"1.0","source":{"id":"2403.14613","kind":"arxiv","version":1}},"canonical_sha256":"1a48bd1f876e83982773dfab642feadac482b30f45673f536448a877f9ddc476","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a48bd1f876e83982773dfab642feadac482b30f45673f536448a877f9ddc476","first_computed_at":"2026-07-05T07:59:11.424042Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:59:11.424042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rIiT1cd7NF1Vy+/RwNrZrlJPW2Usu0MRvukrSztB4IHVFqoJfVG9xi+4lE8C4b7A0jubeBabiCQQxDAELmNkDw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:59:11.424523Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.14613","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1211db45cc86f70ca69bacb52396eb38443e69b819dfc0faf7d93489d525b815","sha256:c3c10ea0c55493487c0ec270337c488b9975438304e1d3eeafe00fcfd9c07c7b"],"state_sha256":"29f33bc4f5b2c83ae79737279978c598a46268b1a8df55b5e74b355e495d9e26"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u9X4JoJnoPXMCoTqfmmolBugsyt4Ku4xH6KjPmYQYhQcxwxEDP6Q/2XWcgDrIkPX/Lc+3bOeeLrJ3COe+kjkBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T02:45:44.081480Z","bundle_sha256":"3f006af4d951cf0076c92a4bbd324cdac035bea41cc1074e27245d6168047add"}}