{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TTBNQCLBHZNOEVVPNOUYCI2FXM","short_pith_number":"pith:TTBNQCLB","canonical_record":{"source":{"id":"2605.28995","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T18:53:09Z","cross_cats_sorted":[],"title_canon_sha256":"c69717569a5d7a44e1598dbdcaf5e34afe26e431f2dacc8a9028931a91ff9722","abstract_canon_sha256":"7bfda07397969e9f73a8c8976398620ec3de2727e50e2dd9047e7595e5f506bd"},"schema_version":"1.0"},"canonical_sha256":"9cc2d809613e5ae256af6ba9812345bb30c2f5e2e30ae569dc833dabfd3cb8bc","source":{"kind":"arxiv","id":"2605.28995","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28995","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28995v1","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28995","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"pith_short_12","alias_value":"TTBNQCLBHZNO","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"pith_short_16","alias_value":"TTBNQCLBHZNOEVVP","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"pith_short_8","alias_value":"TTBNQCLB","created_at":"2026-05-29T01:04:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TTBNQCLBHZNOEVVPNOUYCI2FXM","target":"record","payload":{"canonical_record":{"source":{"id":"2605.28995","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T18:53:09Z","cross_cats_sorted":[],"title_canon_sha256":"c69717569a5d7a44e1598dbdcaf5e34afe26e431f2dacc8a9028931a91ff9722","abstract_canon_sha256":"7bfda07397969e9f73a8c8976398620ec3de2727e50e2dd9047e7595e5f506bd"},"schema_version":"1.0"},"canonical_sha256":"9cc2d809613e5ae256af6ba9812345bb30c2f5e2e30ae569dc833dabfd3cb8bc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:04:42.966971Z","signature_b64":"aedjftlG9yf6tUG0pnRzjjDKd93obK4VwwMyvHAE0iOZgTXDWZDRFkN9v80ca0dpSlfMpp2QrrfnVHSfUrK1AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9cc2d809613e5ae256af6ba9812345bb30c2f5e2e30ae569dc833dabfd3cb8bc","last_reissued_at":"2026-05-29T01:04:42.966497Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:04:42.966497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.28995","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":"2026-05-29T01:04:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cs3bYskgtpyj3SFq6JCYvy79LtYyrRThYnUaPNXiAopwvHD6qa/qBucqKQTje/c7KZiHbAEAKBJ92tuK47JoAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T18:37:29.763592Z"},"content_sha256":"ae2d0a073bf1a9409f1b5d2f326845fcdf2dbf48937ae6d173d3e30000ed5d5a","schema_version":"1.0","event_id":"sha256:ae2d0a073bf1a9409f1b5d2f326845fcdf2dbf48937ae6d173d3e30000ed5d5a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TTBNQCLBHZNOEVVPNOUYCI2FXM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrii Zadaianchuk, Mohammad Mahdi Derakhshani, Polytimi Anna Gkotsi","submitted_at":"2026-05-27T18:53:09Z","abstract_excerpt":"Recent approaches integrating vision-language models (VLMs) as prompt encoders for generative model conditioning typically rely on expensive end-to-end training or map features to compressed representations, discarding the dense spatial structure required for geometry-aware tasks like 3D asset generation. To address this, we propose GAP3D, a modular, diffusion-based approach that aligns VLM-generated latents directly to the complete, patch-level feature space of a pre-trained image encoder, enabling a frozen downstream generative model to utilize a VLM as prompt encoder while maintaining a spa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28995","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/2605.28995/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":"2026-05-29T01:04:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LUDc2G//Sm0lYBd/qOATtljuZL44fiJUPUG7kRQr9/ley3xNTmxhbCVithgNsmqzQeuX1ApOF6C2L+r75U3LDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T18:37:29.763961Z"},"content_sha256":"dd4e0c3bd60ab98e501a11a9503c017f3d77d6af32f7489548f5a218aaf230ba","schema_version":"1.0","event_id":"sha256:dd4e0c3bd60ab98e501a11a9503c017f3d77d6af32f7489548f5a218aaf230ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TTBNQCLBHZNOEVVPNOUYCI2FXM/bundle.json","state_url":"https://pith.science/pith/TTBNQCLBHZNOEVVPNOUYCI2FXM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TTBNQCLBHZNOEVVPNOUYCI2FXM/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":"2026-06-29T18:37:29Z","links":{"resolver":"https://pith.science/pith/TTBNQCLBHZNOEVVPNOUYCI2FXM","bundle":"https://pith.science/pith/TTBNQCLBHZNOEVVPNOUYCI2FXM/bundle.json","state":"https://pith.science/pith/TTBNQCLBHZNOEVVPNOUYCI2FXM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TTBNQCLBHZNOEVVPNOUYCI2FXM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TTBNQCLBHZNOEVVPNOUYCI2FXM","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":"7bfda07397969e9f73a8c8976398620ec3de2727e50e2dd9047e7595e5f506bd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T18:53:09Z","title_canon_sha256":"c69717569a5d7a44e1598dbdcaf5e34afe26e431f2dacc8a9028931a91ff9722"},"schema_version":"1.0","source":{"id":"2605.28995","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28995","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28995v1","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28995","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"pith_short_12","alias_value":"TTBNQCLBHZNO","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"pith_short_16","alias_value":"TTBNQCLBHZNOEVVP","created_at":"2026-05-29T01:04:42Z"},{"alias_kind":"pith_short_8","alias_value":"TTBNQCLB","created_at":"2026-05-29T01:04:42Z"}],"graph_snapshots":[{"event_id":"sha256:dd4e0c3bd60ab98e501a11a9503c017f3d77d6af32f7489548f5a218aaf230ba","target":"graph","created_at":"2026-05-29T01:04:42Z","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/2605.28995/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent approaches integrating vision-language models (VLMs) as prompt encoders for generative model conditioning typically rely on expensive end-to-end training or map features to compressed representations, discarding the dense spatial structure required for geometry-aware tasks like 3D asset generation. To address this, we propose GAP3D, a modular, diffusion-based approach that aligns VLM-generated latents directly to the complete, patch-level feature space of a pre-trained image encoder, enabling a frozen downstream generative model to utilize a VLM as prompt encoder while maintaining a spa","authors_text":"Andrii Zadaianchuk, Mohammad Mahdi Derakhshani, Polytimi Anna Gkotsi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T18:53:09Z","title":"GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28995","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:ae2d0a073bf1a9409f1b5d2f326845fcdf2dbf48937ae6d173d3e30000ed5d5a","target":"record","created_at":"2026-05-29T01:04:42Z","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":"7bfda07397969e9f73a8c8976398620ec3de2727e50e2dd9047e7595e5f506bd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T18:53:09Z","title_canon_sha256":"c69717569a5d7a44e1598dbdcaf5e34afe26e431f2dacc8a9028931a91ff9722"},"schema_version":"1.0","source":{"id":"2605.28995","kind":"arxiv","version":1}},"canonical_sha256":"9cc2d809613e5ae256af6ba9812345bb30c2f5e2e30ae569dc833dabfd3cb8bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9cc2d809613e5ae256af6ba9812345bb30c2f5e2e30ae569dc833dabfd3cb8bc","first_computed_at":"2026-05-29T01:04:42.966497Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T01:04:42.966497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aedjftlG9yf6tUG0pnRzjjDKd93obK4VwwMyvHAE0iOZgTXDWZDRFkN9v80ca0dpSlfMpp2QrrfnVHSfUrK1AQ==","signature_status":"signed_v1","signed_at":"2026-05-29T01:04:42.966971Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.28995","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae2d0a073bf1a9409f1b5d2f326845fcdf2dbf48937ae6d173d3e30000ed5d5a","sha256:dd4e0c3bd60ab98e501a11a9503c017f3d77d6af32f7489548f5a218aaf230ba"],"state_sha256":"79b221a71503db8af8762047ada351c8f270943c3902e54c9a42ca19771bc495"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e46KTN/4fuatrF8mnGQVR6yabC9ys8Naewphq41OjvpSLKiXoGrRjMcALRIHIkP8NMbsj9B3LhUB67MOyWoGCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T18:37:29.765906Z","bundle_sha256":"fc21cc3bb76e5972c69c6b5b74a39d4660ac8bf125abedb02c9d24a62fd4d09e"}}