{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:2BRX35TCR7CQOOEER5IWO4LBL2","short_pith_number":"pith:2BRX35TC","canonical_record":{"source":{"id":"2606.07654","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T02:57:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4c6a2280a75713a1845b47fb955d06a3176d041fef4316919d4fb64630c6f488","abstract_canon_sha256":"2f2780dc8f05196d06c40d06e2229096f86fc29765bef2f69f557c675781bc03"},"schema_version":"1.0"},"canonical_sha256":"d0637df6628fc50738848f516771615ebed89e1775d8130f8a3c1db0dfebe50f","source":{"kind":"arxiv","id":"2606.07654","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07654","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07654v1","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07654","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"pith_short_12","alias_value":"2BRX35TCR7CQ","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"pith_short_16","alias_value":"2BRX35TCR7CQOOEE","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"pith_short_8","alias_value":"2BRX35TC","created_at":"2026-06-09T00:04:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:2BRX35TCR7CQOOEER5IWO4LBL2","target":"record","payload":{"canonical_record":{"source":{"id":"2606.07654","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T02:57:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4c6a2280a75713a1845b47fb955d06a3176d041fef4316919d4fb64630c6f488","abstract_canon_sha256":"2f2780dc8f05196d06c40d06e2229096f86fc29765bef2f69f557c675781bc03"},"schema_version":"1.0"},"canonical_sha256":"d0637df6628fc50738848f516771615ebed89e1775d8130f8a3c1db0dfebe50f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T00:04:45.928724Z","signature_b64":"F7LlX0kIYgNIMGvqFGdejO3uN0/MtKd7mrP1FOATmmNylKF7YrbiZ7r+bJeDX9RPdruhITw9bNYzZyGyRxIyDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0637df6628fc50738848f516771615ebed89e1775d8130f8a3c1db0dfebe50f","last_reissued_at":"2026-06-09T00:04:45.928310Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T00:04:45.928310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.07654","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":"2026-06-09T00:04:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pPTOaTzaKFdKOBd2OYB0leaEOCgEBKTgeMK5uKLYBfL9excBVYiOh655i2M9G4JmkhOQrA7KAHJj5RFpzlzIAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T08:44:20.066194Z"},"content_sha256":"6f43f8ab36e9d5afa9b2f3a191ab39710391b838fb6a80c8c1369a29be2b2090","schema_version":"1.0","event_id":"sha256:6f43f8ab36e9d5afa9b2f3a191ab39710391b838fb6a80c8c1369a29be2b2090"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:2BRX35TCR7CQOOEER5IWO4LBL2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Haowen Xiang, Jiahao Huo, Mingdong Ou, Xuming Hu, Yibo Yan, Yi Cao, Yu Huang","submitted_at":"2026-06-03T02:57:09Z","abstract_excerpt":"Multi-vector visual document retrievers achieve strong fine-grained matching by representing each page with multiple vectors from deep Vision-Language Models (VLMs), but this design makes deployment expensive in both storage and computational overhead. Existing efficiency techniques usually optimize only part of this budget, leaving multimodal retrievers without a unified way to trade accuracy for both vector width and encoder depth. Therefore, we propose MM-Matryoshka, a 2D Matryoshka training framework for budget-elastic Visual Document Retrieval (VDR), enabling ColPali-style multi-vector re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07654","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/2606.07654/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":"2026-06-09T00:04:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rm1Jv8C/1LEy4GOtKn0OuVaKvCwgMH3dbWWo2FD2Lc60Ih0HLXzQ1t9oJw+sd1Eu8fVN28NwvqQkrTxxVgxYBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T08:44:20.066611Z"},"content_sha256":"18e7064657ba4432368194aefc7bfe7ebbb0b8421fd9eb41aa3e18e7f110f6f0","schema_version":"1.0","event_id":"sha256:18e7064657ba4432368194aefc7bfe7ebbb0b8421fd9eb41aa3e18e7f110f6f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2BRX35TCR7CQOOEER5IWO4LBL2/bundle.json","state_url":"https://pith.science/pith/2BRX35TCR7CQOOEER5IWO4LBL2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2BRX35TCR7CQOOEER5IWO4LBL2/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":"2026-06-10T08:44:20Z","links":{"resolver":"https://pith.science/pith/2BRX35TCR7CQOOEER5IWO4LBL2","bundle":"https://pith.science/pith/2BRX35TCR7CQOOEER5IWO4LBL2/bundle.json","state":"https://pith.science/pith/2BRX35TCR7CQOOEER5IWO4LBL2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2BRX35TCR7CQOOEER5IWO4LBL2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2BRX35TCR7CQOOEER5IWO4LBL2","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":"2f2780dc8f05196d06c40d06e2229096f86fc29765bef2f69f557c675781bc03","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T02:57:09Z","title_canon_sha256":"4c6a2280a75713a1845b47fb955d06a3176d041fef4316919d4fb64630c6f488"},"schema_version":"1.0","source":{"id":"2606.07654","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07654","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07654v1","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07654","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"pith_short_12","alias_value":"2BRX35TCR7CQ","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"pith_short_16","alias_value":"2BRX35TCR7CQOOEE","created_at":"2026-06-09T00:04:45Z"},{"alias_kind":"pith_short_8","alias_value":"2BRX35TC","created_at":"2026-06-09T00:04:45Z"}],"graph_snapshots":[{"event_id":"sha256:18e7064657ba4432368194aefc7bfe7ebbb0b8421fd9eb41aa3e18e7f110f6f0","target":"graph","created_at":"2026-06-09T00:04:45Z","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/2606.07654/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-vector visual document retrievers achieve strong fine-grained matching by representing each page with multiple vectors from deep Vision-Language Models (VLMs), but this design makes deployment expensive in both storage and computational overhead. Existing efficiency techniques usually optimize only part of this budget, leaving multimodal retrievers without a unified way to trade accuracy for both vector width and encoder depth. Therefore, we propose MM-Matryoshka, a 2D Matryoshka training framework for budget-elastic Visual Document Retrieval (VDR), enabling ColPali-style multi-vector re","authors_text":"Haowen Xiang, Jiahao Huo, Mingdong Ou, Xuming Hu, Yibo Yan, Yi Cao, Yu Huang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T02:57:09Z","title":"MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07654","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":"sha256:6f43f8ab36e9d5afa9b2f3a191ab39710391b838fb6a80c8c1369a29be2b2090","target":"record","created_at":"2026-06-09T00:04:45Z","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":"2f2780dc8f05196d06c40d06e2229096f86fc29765bef2f69f557c675781bc03","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T02:57:09Z","title_canon_sha256":"4c6a2280a75713a1845b47fb955d06a3176d041fef4316919d4fb64630c6f488"},"schema_version":"1.0","source":{"id":"2606.07654","kind":"arxiv","version":1}},"canonical_sha256":"d0637df6628fc50738848f516771615ebed89e1775d8130f8a3c1db0dfebe50f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0637df6628fc50738848f516771615ebed89e1775d8130f8a3c1db0dfebe50f","first_computed_at":"2026-06-09T00:04:45.928310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T00:04:45.928310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F7LlX0kIYgNIMGvqFGdejO3uN0/MtKd7mrP1FOATmmNylKF7YrbiZ7r+bJeDX9RPdruhITw9bNYzZyGyRxIyDA==","signature_status":"signed_v1","signed_at":"2026-06-09T00:04:45.928724Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07654","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6f43f8ab36e9d5afa9b2f3a191ab39710391b838fb6a80c8c1369a29be2b2090","sha256:18e7064657ba4432368194aefc7bfe7ebbb0b8421fd9eb41aa3e18e7f110f6f0"],"state_sha256":"a67e1d2cdcc96cb1b1b1ee29ae115350cac345194a7b48c5fc91dd78a3115124"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"snP4Kd4ExmKSHUvHg+aIZBXng5DheQfQrvcYL6eLADm2njKBwE6aQ5dzbzc73arKdC1JNx3alImZGculVSNUDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T08:44:20.069066Z","bundle_sha256":"854decc63c6e9f8dbbdf4a3b5f04b4b811176d61b282a7afc92ddc38aafba573"}}