{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XHCLHFJEPXSRA3D5TKUF76HIGW","short_pith_number":"pith:XHCLHFJE","schema_version":"1.0","canonical_sha256":"b9c4b395247de5106c7d9aa85ff8e835a36e534d825ab778cc76fa11d526e87d","source":{"kind":"arxiv","id":"2606.04688","version":1},"attestation_state":"computed","paper":{"title":"MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiale Xu, Wang Zhao, Ying Shan","submitted_at":"2026-06-03T10:15:43Z","abstract_excerpt":"Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and prevents scaling to high-poly meshes, and (ii) absence of geometry-aware guidance, as generation is conditioned only on global shape embeddings rather than local surface cues. We introduce MeshWeaver, an autoregressive framework that treats mesh generation as a surface weaving process by directly predicting the next v"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.04688","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T10:15:43Z","cross_cats_sorted":[],"title_canon_sha256":"00d5affc2e73fd505612624f961095b78623f7d4ac8d3f0c53a7d398fc515c22","abstract_canon_sha256":"cafda451ff23c833593e5ccf119a5f11715c683cb362b979db1de4254b16c64c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:09:25.044447Z","signature_b64":"4+P3sYJ0HE/7b4kRVNyZrpil6k+44WbBKGIxcUXp1XSG1yxK7OmQB04YP6IPp9eIbDQsMc30FYlzEwPihTdcCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9c4b395247de5106c7d9aa85ff8e835a36e534d825ab778cc76fa11d526e87d","last_reissued_at":"2026-06-04T01:09:25.043840Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:09:25.043840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiale Xu, Wang Zhao, Ying Shan","submitted_at":"2026-06-03T10:15:43Z","abstract_excerpt":"Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and prevents scaling to high-poly meshes, and (ii) absence of geometry-aware guidance, as generation is conditioned only on global shape embeddings rather than local surface cues. We introduce MeshWeaver, an autoregressive framework that treats mesh generation as a surface weaving process by directly predicting the next v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04688","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/2606.04688/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.04688","created_at":"2026-06-04T01:09:25.043926+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.04688v1","created_at":"2026-06-04T01:09:25.043926+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04688","created_at":"2026-06-04T01:09:25.043926+00:00"},{"alias_kind":"pith_short_12","alias_value":"XHCLHFJEPXSR","created_at":"2026-06-04T01:09:25.043926+00:00"},{"alias_kind":"pith_short_16","alias_value":"XHCLHFJEPXSRA3D5","created_at":"2026-06-04T01:09:25.043926+00:00"},{"alias_kind":"pith_short_8","alias_value":"XHCLHFJE","created_at":"2026-06-04T01:09:25.043926+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW","json":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW.json","graph_json":"https://pith.science/api/pith-number/XHCLHFJEPXSRA3D5TKUF76HIGW/graph.json","events_json":"https://pith.science/api/pith-number/XHCLHFJEPXSRA3D5TKUF76HIGW/events.json","paper":"https://pith.science/paper/XHCLHFJE"},"agent_actions":{"view_html":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW","download_json":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW.json","view_paper":"https://pith.science/paper/XHCLHFJE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.04688&json=true","fetch_graph":"https://pith.science/api/pith-number/XHCLHFJEPXSRA3D5TKUF76HIGW/graph.json","fetch_events":"https://pith.science/api/pith-number/XHCLHFJEPXSRA3D5TKUF76HIGW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW/action/storage_attestation","attest_author":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW/action/author_attestation","sign_citation":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW/action/citation_signature","submit_replication":"https://pith.science/pith/XHCLHFJEPXSRA3D5TKUF76HIGW/action/replication_record"}},"created_at":"2026-06-04T01:09:25.043926+00:00","updated_at":"2026-06-04T01:09:25.043926+00:00"}