{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:WFBHR2RX7IIKP5WOG2CRDKNRJO","short_pith_number":"pith:WFBHR2RX","canonical_record":{"source":{"id":"2511.14271","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-18T09:05:26Z","cross_cats_sorted":[],"title_canon_sha256":"4aa3259e48f8ad2746845356dcda26fb588dfdd906da3271f22ee5454db1471a","abstract_canon_sha256":"526196577fb067c15ea92f0363c56056090565a46a2f38b51db33d2c77cef47a"},"schema_version":"1.0"},"canonical_sha256":"b14278ea37fa10a7f6ce368511a9b14b94c4f1901ce2cc1623d00b6e3da5f8ca","source":{"kind":"arxiv","id":"2511.14271","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.14271","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"arxiv_version","alias_value":"2511.14271v2","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.14271","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"pith_short_12","alias_value":"WFBHR2RX7IIK","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"pith_short_16","alias_value":"WFBHR2RX7IIKP5WO","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"pith_short_8","alias_value":"WFBHR2RX","created_at":"2026-06-29T01:14:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:WFBHR2RX7IIKP5WOG2CRDKNRJO","target":"record","payload":{"canonical_record":{"source":{"id":"2511.14271","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-18T09:05:26Z","cross_cats_sorted":[],"title_canon_sha256":"4aa3259e48f8ad2746845356dcda26fb588dfdd906da3271f22ee5454db1471a","abstract_canon_sha256":"526196577fb067c15ea92f0363c56056090565a46a2f38b51db33d2c77cef47a"},"schema_version":"1.0"},"canonical_sha256":"b14278ea37fa10a7f6ce368511a9b14b94c4f1901ce2cc1623d00b6e3da5f8ca","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T01:14:26.491986Z","signature_b64":"sju/ncC/KE9g9ur2kiCzcjK/Aic1WChhXXOgzGQ3TIvZ0zJs1Ffp3vyoUB0mWWJNHh7vbN0LugUE92/6uFZBAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b14278ea37fa10a7f6ce368511a9b14b94c4f1901ce2cc1623d00b6e3da5f8ca","last_reissued_at":"2026-06-29T01:14:26.491496Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T01:14:26.491496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2511.14271","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-29T01:14:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EK0mI5Vai2uU7VXh43MhxiSZHio12sQd8I6we1LBeRohAHfvuGHMS7BDTRRF75zb8fhNIvfysQrtKoqJZM4hCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T08:12:00.484367Z"},"content_sha256":"4ac692b53f79cb0e28bf5ea0ab1f5f929cc46d9c88ddd218a934f9dd3dab80b7","schema_version":"1.0","event_id":"sha256:4ac692b53f79cb0e28bf5ea0ab1f5f929cc46d9c88ddd218a934f9dd3dab80b7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:WFBHR2RX7IIKP5WOG2CRDKNRJO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"He Sun, Weijian Luo, Weimin Bai, Wenzheng Chen, Yequan Wang, Yubo Li, Zeqiang Lai","submitted_at":"2025-11-18T09:05:26Z","abstract_excerpt":"Text-to-3D generation has advanced rapidly, yet state-of-the-art models, encompassing both optimization-based and feed-forward architectures, still face two fundamental limitations. First, they struggle with coarse semantic alignment, often failing to capture fine-grained prompt details. Second, they lack robust 3D spatial understanding, leading to geometric inconsistencies and catastrophic failures in part assembly and spatial relationships. To address these challenges, we propose VLM3D, a general framework that repurposes large vision-language models (VLMs) as powerful, differentiable semant"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.14271","kind":"arxiv","version":2},"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/2511.14271/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":"2026-06-29T01:14:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1CutMf6lmFhwhdlvNMiDBTW2AZiELjKwdlCHWiKAH6NHd1Jo+WUmJ5FoNOQj/eTguZvQHocBT/xFjEOxY+wFCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T08:12:00.484749Z"},"content_sha256":"4f22e9e36bc7db8d6e58448656a6eb5e0246ed7402f9c75668e41d0c23980e7c","schema_version":"1.0","event_id":"sha256:4f22e9e36bc7db8d6e58448656a6eb5e0246ed7402f9c75668e41d0c23980e7c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WFBHR2RX7IIKP5WOG2CRDKNRJO/bundle.json","state_url":"https://pith.science/pith/WFBHR2RX7IIKP5WOG2CRDKNRJO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WFBHR2RX7IIKP5WOG2CRDKNRJO/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":"2026-06-30T08:12:00Z","links":{"resolver":"https://pith.science/pith/WFBHR2RX7IIKP5WOG2CRDKNRJO","bundle":"https://pith.science/pith/WFBHR2RX7IIKP5WOG2CRDKNRJO/bundle.json","state":"https://pith.science/pith/WFBHR2RX7IIKP5WOG2CRDKNRJO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WFBHR2RX7IIKP5WOG2CRDKNRJO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:WFBHR2RX7IIKP5WOG2CRDKNRJO","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":"526196577fb067c15ea92f0363c56056090565a46a2f38b51db33d2c77cef47a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-18T09:05:26Z","title_canon_sha256":"4aa3259e48f8ad2746845356dcda26fb588dfdd906da3271f22ee5454db1471a"},"schema_version":"1.0","source":{"id":"2511.14271","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.14271","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"arxiv_version","alias_value":"2511.14271v2","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.14271","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"pith_short_12","alias_value":"WFBHR2RX7IIK","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"pith_short_16","alias_value":"WFBHR2RX7IIKP5WO","created_at":"2026-06-29T01:14:26Z"},{"alias_kind":"pith_short_8","alias_value":"WFBHR2RX","created_at":"2026-06-29T01:14:26Z"}],"graph_snapshots":[{"event_id":"sha256:4f22e9e36bc7db8d6e58448656a6eb5e0246ed7402f9c75668e41d0c23980e7c","target":"graph","created_at":"2026-06-29T01:14:26Z","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/2511.14271/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-to-3D generation has advanced rapidly, yet state-of-the-art models, encompassing both optimization-based and feed-forward architectures, still face two fundamental limitations. First, they struggle with coarse semantic alignment, often failing to capture fine-grained prompt details. Second, they lack robust 3D spatial understanding, leading to geometric inconsistencies and catastrophic failures in part assembly and spatial relationships. To address these challenges, we propose VLM3D, a general framework that repurposes large vision-language models (VLMs) as powerful, differentiable semant","authors_text":"He Sun, Weijian Luo, Weimin Bai, Wenzheng Chen, Yequan Wang, Yubo Li, Zeqiang Lai","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-18T09:05:26Z","title":"Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.14271","kind":"arxiv","version":2},"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":"sha256:4ac692b53f79cb0e28bf5ea0ab1f5f929cc46d9c88ddd218a934f9dd3dab80b7","target":"record","created_at":"2026-06-29T01:14:26Z","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":"526196577fb067c15ea92f0363c56056090565a46a2f38b51db33d2c77cef47a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-18T09:05:26Z","title_canon_sha256":"4aa3259e48f8ad2746845356dcda26fb588dfdd906da3271f22ee5454db1471a"},"schema_version":"1.0","source":{"id":"2511.14271","kind":"arxiv","version":2}},"canonical_sha256":"b14278ea37fa10a7f6ce368511a9b14b94c4f1901ce2cc1623d00b6e3da5f8ca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b14278ea37fa10a7f6ce368511a9b14b94c4f1901ce2cc1623d00b6e3da5f8ca","first_computed_at":"2026-06-29T01:14:26.491496Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-29T01:14:26.491496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sju/ncC/KE9g9ur2kiCzcjK/Aic1WChhXXOgzGQ3TIvZ0zJs1Ffp3vyoUB0mWWJNHh7vbN0LugUE92/6uFZBAQ==","signature_status":"signed_v1","signed_at":"2026-06-29T01:14:26.491986Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.14271","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ac692b53f79cb0e28bf5ea0ab1f5f929cc46d9c88ddd218a934f9dd3dab80b7","sha256:4f22e9e36bc7db8d6e58448656a6eb5e0246ed7402f9c75668e41d0c23980e7c"],"state_sha256":"1a6be32e3e9c39bef802c582c69b3c73e09bd11f339826d44b2eefbd45de3373"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zYT4DkR2rtRsp3ZTjkJ3PFcY2bOkaXP+rl0ULGnqRt+wyjuxrYc0rQuU3JX/uzdGya7zqahpCkVPXeOwB8g+Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T08:12:00.486750Z","bundle_sha256":"50e691aa11c7f71e5a363da3520278e81f771bdc4ba33ea533e870f4103a1341"}}