{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2AUURWZEHR3S3ADQ45TKWQHIQ4","short_pith_number":"pith:2AUURWZE","canonical_record":{"source":{"id":"2506.08990","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-10T17:02:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"472d1f5b559dc487ea055286a164fdb268a19a4dfc2bd1fec81dd472ec8e7b37","abstract_canon_sha256":"b12892d6bdc0f9a8ce317b1f9903f2550f630abf9a7cb7284cce6e2c71753d98"},"schema_version":"1.0"},"canonical_sha256":"d02948db243c772d8070e766ab40e8871286283ee5f793a3f735b4bdd23415a6","source":{"kind":"arxiv","id":"2506.08990","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.08990","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"arxiv_version","alias_value":"2506.08990v1","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08990","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"pith_short_12","alias_value":"2AUURWZEHR3S","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"pith_short_16","alias_value":"2AUURWZEHR3S3ADQ","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"pith_short_8","alias_value":"2AUURWZE","created_at":"2026-07-05T11:19:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2AUURWZEHR3S3ADQ45TKWQHIQ4","target":"record","payload":{"canonical_record":{"source":{"id":"2506.08990","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-10T17:02:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"472d1f5b559dc487ea055286a164fdb268a19a4dfc2bd1fec81dd472ec8e7b37","abstract_canon_sha256":"b12892d6bdc0f9a8ce317b1f9903f2550f630abf9a7cb7284cce6e2c71753d98"},"schema_version":"1.0"},"canonical_sha256":"d02948db243c772d8070e766ab40e8871286283ee5f793a3f735b4bdd23415a6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:16.523701Z","signature_b64":"e6BZRZ1eQXLpXUHEYc0AqKtp8Tnp+0bQPrJq2kRWTYs7dbhBdE6EO3814H8tRl9vkiTsUcaS4gy6lsV/lC6kCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d02948db243c772d8070e766ab40e8871286283ee5f793a3f735b4bdd23415a6","last_reissued_at":"2026-07-05T11:19:16.523282Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:16.523282Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.08990","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":"2026-07-05T11:19:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BLNJdJdBPHpM9AtrLvCiXnixzGecpmNq8GqUrD1eDOwZdyhuf5CKX8AfS65JGPMtZmniET3ZcAUTZ9OEz387BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T22:15:43.373531Z"},"content_sha256":"7885ea76415c01c9a2f5d154bcf33310ad69d6a6b3c3019c6eca956fca364bac","schema_version":"1.0","event_id":"sha256:7885ea76415c01c9a2f5d154bcf33310ad69d6a6b3c3019c6eca956fca364bac"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2AUURWZEHR3S3ADQ45TKWQHIQ4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Chenyu Lian, Dongyun Liang, Hong-Yu Zhou, Jing Qin, Liansheng Wang","submitted_at":"2025-06-10T17:02:27Z","abstract_excerpt":"Medical vision-language alignment through cross-modal contrastive learning shows promising performance in image-text matching tasks, such as retrieval and zero-shot classification. However, conventional cross-modal contrastive learning (CLIP-based) methods suffer from suboptimal visual representation capabilities, which also limits their effectiveness in vision-language alignment. In contrast, although the models pretrained via multimodal masked modeling struggle with direct cross-modal matching, they excel in visual representation. To address this contradiction, we propose ALTA (ALign Through"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08990","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/2506.08990/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":"2026-07-05T11:19:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yViLsKu3BUP5EWsd9HdZNICB880ZljReCi2nugwY3Pg2wHMSlM0Qe+dDVqeGTUNq82V8vOr03J7bFiKFojUGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T22:15:43.374052Z"},"content_sha256":"674415edb84d0cc080f46d13e6159ea4cdbdd41d52b8ef0bba186e6a4eb5e2cd","schema_version":"1.0","event_id":"sha256:674415edb84d0cc080f46d13e6159ea4cdbdd41d52b8ef0bba186e6a4eb5e2cd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2AUURWZEHR3S3ADQ45TKWQHIQ4/bundle.json","state_url":"https://pith.science/pith/2AUURWZEHR3S3ADQ45TKWQHIQ4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2AUURWZEHR3S3ADQ45TKWQHIQ4/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":"2026-08-23T22:15:43Z","links":{"resolver":"https://pith.science/pith/2AUURWZEHR3S3ADQ45TKWQHIQ4","bundle":"https://pith.science/pith/2AUURWZEHR3S3ADQ45TKWQHIQ4/bundle.json","state":"https://pith.science/pith/2AUURWZEHR3S3ADQ45TKWQHIQ4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2AUURWZEHR3S3ADQ45TKWQHIQ4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2AUURWZEHR3S3ADQ45TKWQHIQ4","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":"b12892d6bdc0f9a8ce317b1f9903f2550f630abf9a7cb7284cce6e2c71753d98","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-10T17:02:27Z","title_canon_sha256":"472d1f5b559dc487ea055286a164fdb268a19a4dfc2bd1fec81dd472ec8e7b37"},"schema_version":"1.0","source":{"id":"2506.08990","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.08990","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"arxiv_version","alias_value":"2506.08990v1","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08990","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"pith_short_12","alias_value":"2AUURWZEHR3S","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"pith_short_16","alias_value":"2AUURWZEHR3S3ADQ","created_at":"2026-07-05T11:19:16Z"},{"alias_kind":"pith_short_8","alias_value":"2AUURWZE","created_at":"2026-07-05T11:19:16Z"}],"graph_snapshots":[{"event_id":"sha256:674415edb84d0cc080f46d13e6159ea4cdbdd41d52b8ef0bba186e6a4eb5e2cd","target":"graph","created_at":"2026-07-05T11:19:16Z","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/2506.08990/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical vision-language alignment through cross-modal contrastive learning shows promising performance in image-text matching tasks, such as retrieval and zero-shot classification. However, conventional cross-modal contrastive learning (CLIP-based) methods suffer from suboptimal visual representation capabilities, which also limits their effectiveness in vision-language alignment. In contrast, although the models pretrained via multimodal masked modeling struggle with direct cross-modal matching, they excel in visual representation. To address this contradiction, we propose ALTA (ALign Through","authors_text":"Chenyu Lian, Dongyun Liang, Hong-Yu Zhou, Jing Qin, Liansheng Wang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-10T17:02:27Z","title":"Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08990","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":"sha256:7885ea76415c01c9a2f5d154bcf33310ad69d6a6b3c3019c6eca956fca364bac","target":"record","created_at":"2026-07-05T11:19:16Z","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":"b12892d6bdc0f9a8ce317b1f9903f2550f630abf9a7cb7284cce6e2c71753d98","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-10T17:02:27Z","title_canon_sha256":"472d1f5b559dc487ea055286a164fdb268a19a4dfc2bd1fec81dd472ec8e7b37"},"schema_version":"1.0","source":{"id":"2506.08990","kind":"arxiv","version":1}},"canonical_sha256":"d02948db243c772d8070e766ab40e8871286283ee5f793a3f735b4bdd23415a6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d02948db243c772d8070e766ab40e8871286283ee5f793a3f735b4bdd23415a6","first_computed_at":"2026-07-05T11:19:16.523282Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:19:16.523282Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e6BZRZ1eQXLpXUHEYc0AqKtp8Tnp+0bQPrJq2kRWTYs7dbhBdE6EO3814H8tRl9vkiTsUcaS4gy6lsV/lC6kCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:19:16.523701Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.08990","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7885ea76415c01c9a2f5d154bcf33310ad69d6a6b3c3019c6eca956fca364bac","sha256:674415edb84d0cc080f46d13e6159ea4cdbdd41d52b8ef0bba186e6a4eb5e2cd"],"state_sha256":"0ba6131330f8898799ec5796eba25638c5a9c3e31b4c5485f90945440e80a5da"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XcatJ6uLQJui/O/lC9DsaXjsi91soHYEhSVj9JkClh9ATeEu2zXcDpAHXH4lQjBoG1b7ujjNPw5Q6eqb0BQjAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T22:15:43.378970Z","bundle_sha256":"1e717febc54be5121b4cf1b12aa714dd73782611aafbc4a1fbf76f8c90ce116d"}}