{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:2IBY5H3423TU7UBALE45FIUGSS","short_pith_number":"pith:2IBY5H34","canonical_record":{"source":{"id":"2606.01334","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-31T16:36:49Z","cross_cats_sorted":[],"title_canon_sha256":"df42b6ced139450c67377872cf3476723220d0bb3f47c96c3da9e39f3aa21bf4","abstract_canon_sha256":"14e52a7c08ddd11fd603bb70010d137f137b32840c776c30f42dae337083777f"},"schema_version":"1.0"},"canonical_sha256":"d2038e9f7cd6e74fd0205939d2a286949798db82d5741e3c6b45f7014dda19f3","source":{"kind":"arxiv","id":"2606.01334","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.01334","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"arxiv_version","alias_value":"2606.01334v1","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01334","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"pith_short_12","alias_value":"2IBY5H3423TU","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"pith_short_16","alias_value":"2IBY5H3423TU7UBA","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"pith_short_8","alias_value":"2IBY5H34","created_at":"2026-06-02T02:04:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:2IBY5H3423TU7UBALE45FIUGSS","target":"record","payload":{"canonical_record":{"source":{"id":"2606.01334","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-31T16:36:49Z","cross_cats_sorted":[],"title_canon_sha256":"df42b6ced139450c67377872cf3476723220d0bb3f47c96c3da9e39f3aa21bf4","abstract_canon_sha256":"14e52a7c08ddd11fd603bb70010d137f137b32840c776c30f42dae337083777f"},"schema_version":"1.0"},"canonical_sha256":"d2038e9f7cd6e74fd0205939d2a286949798db82d5741e3c6b45f7014dda19f3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:30.514181Z","signature_b64":"U7SuzII1n0gXXUDT1EOY3s4BItlsEKFxVe6yWEdyoEh4aciHIoNq27a00lzASmk496aGahdDLRluIgK8prDKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2038e9f7cd6e74fd0205939d2a286949798db82d5741e3c6b45f7014dda19f3","last_reissued_at":"2026-06-02T02:04:30.513836Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:30.513836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.01334","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":"2026-06-02T02:04:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n74PQtbLnEGfqzT5f5XyZDwU+/W5VknevRut9UZCjBmR5p6qByB0x/pgvIMhhMY9uL32Un4dQ3HnTehaKBjRDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T04:55:13.948099Z"},"content_sha256":"b001886f228ce0f49c502a8679f2239c04087f48c856d730220c9a519fb2d17f","schema_version":"1.0","event_id":"sha256:b001886f228ce0f49c502a8679f2239c04087f48c856d730220c9a519fb2d17f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:2IBY5H3423TU7UBALE45FIUGSS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HOLA: Holistic Multi-Modal Alignment for Open-Set 3D Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayellet Tal, Koby Aharonov, Oren Shrout","submitted_at":"2026-05-31T16:36:49Z","abstract_excerpt":"Open-set 3D recognition requires models that generalize to rare or unseen categories. Recent approaches address this by distilling language-vision knowledge into 3D encoders, typically relying on heavy 2D ViTs and aligning each point cloud with a single image or caption, thus anchoring representations to partial views. We propose aligning each point cloud with multiple images and textual descriptions to capture a more holistic understanding of 3D objects. To realize this idea, it is essential to design a loss function capable of jointly aligning a 3D instance with multiple matched signals, mul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01334","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/2606.01334/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":"2026-06-02T02:04:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/5/TXY449FOkqsNqbNIbMHW0X2h0e+HdGd2rpM5qjV3LqrRbXWRmpDdGjr8WB658i/sg9+Jb1/5xj2UjbUHGAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T04:55:13.948500Z"},"content_sha256":"b35733d0b050a09b5ef3cd8073911fb98aebfafacd537df239a5a2689be58976","schema_version":"1.0","event_id":"sha256:b35733d0b050a09b5ef3cd8073911fb98aebfafacd537df239a5a2689be58976"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2IBY5H3423TU7UBALE45FIUGSS/bundle.json","state_url":"https://pith.science/pith/2IBY5H3423TU7UBALE45FIUGSS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2IBY5H3423TU7UBALE45FIUGSS/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":"2026-06-21T04:55:13Z","links":{"resolver":"https://pith.science/pith/2IBY5H3423TU7UBALE45FIUGSS","bundle":"https://pith.science/pith/2IBY5H3423TU7UBALE45FIUGSS/bundle.json","state":"https://pith.science/pith/2IBY5H3423TU7UBALE45FIUGSS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2IBY5H3423TU7UBALE45FIUGSS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2IBY5H3423TU7UBALE45FIUGSS","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":"14e52a7c08ddd11fd603bb70010d137f137b32840c776c30f42dae337083777f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-31T16:36:49Z","title_canon_sha256":"df42b6ced139450c67377872cf3476723220d0bb3f47c96c3da9e39f3aa21bf4"},"schema_version":"1.0","source":{"id":"2606.01334","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.01334","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"arxiv_version","alias_value":"2606.01334v1","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01334","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"pith_short_12","alias_value":"2IBY5H3423TU","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"pith_short_16","alias_value":"2IBY5H3423TU7UBA","created_at":"2026-06-02T02:04:30Z"},{"alias_kind":"pith_short_8","alias_value":"2IBY5H34","created_at":"2026-06-02T02:04:30Z"}],"graph_snapshots":[{"event_id":"sha256:b35733d0b050a09b5ef3cd8073911fb98aebfafacd537df239a5a2689be58976","target":"graph","created_at":"2026-06-02T02:04:30Z","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/2606.01334/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Open-set 3D recognition requires models that generalize to rare or unseen categories. Recent approaches address this by distilling language-vision knowledge into 3D encoders, typically relying on heavy 2D ViTs and aligning each point cloud with a single image or caption, thus anchoring representations to partial views. We propose aligning each point cloud with multiple images and textual descriptions to capture a more holistic understanding of 3D objects. To realize this idea, it is essential to design a loss function capable of jointly aligning a 3D instance with multiple matched signals, mul","authors_text":"Ayellet Tal, Koby Aharonov, Oren Shrout","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-31T16:36:49Z","title":"HOLA: Holistic Multi-Modal Alignment for Open-Set 3D Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01334","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:b001886f228ce0f49c502a8679f2239c04087f48c856d730220c9a519fb2d17f","target":"record","created_at":"2026-06-02T02:04:30Z","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":"14e52a7c08ddd11fd603bb70010d137f137b32840c776c30f42dae337083777f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-31T16:36:49Z","title_canon_sha256":"df42b6ced139450c67377872cf3476723220d0bb3f47c96c3da9e39f3aa21bf4"},"schema_version":"1.0","source":{"id":"2606.01334","kind":"arxiv","version":1}},"canonical_sha256":"d2038e9f7cd6e74fd0205939d2a286949798db82d5741e3c6b45f7014dda19f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d2038e9f7cd6e74fd0205939d2a286949798db82d5741e3c6b45f7014dda19f3","first_computed_at":"2026-06-02T02:04:30.513836Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T02:04:30.513836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U7SuzII1n0gXXUDT1EOY3s4BItlsEKFxVe6yWEdyoEh4aciHIoNq27a00lzASmk496aGahdDLRluIgK8prDKDA==","signature_status":"signed_v1","signed_at":"2026-06-02T02:04:30.514181Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.01334","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b001886f228ce0f49c502a8679f2239c04087f48c856d730220c9a519fb2d17f","sha256:b35733d0b050a09b5ef3cd8073911fb98aebfafacd537df239a5a2689be58976"],"state_sha256":"532bf3c9bd37f74c7510927b72360c4b3cf74dfaa790ece282cb5468e8b93830"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+NjyNKuts+M4Avz4dVmgUc/lTX3vyYVlkFjlxr9ux5bdXgJ3+86XRXk5hCXYUMUk8oVeGEdi2lPzRcFYE8nkBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T04:55:13.950617Z","bundle_sha256":"642cf43ba9f662e3b3ab7fc90e82dd481e2bbb79552a9107c7ceb53d63b08017"}}