{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YJMUXZNTUREUQ4GF4A2JXOUPUS","short_pith_number":"pith:YJMUXZNT","canonical_record":{"source":{"id":"2507.07802","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-10T14:28:12Z","cross_cats_sorted":[],"title_canon_sha256":"14ddeb83c4994908aa084b5021f9c28d02dd12ad413a8343bb4d68a22a929449","abstract_canon_sha256":"7431c1e390d775f56bd854f3eda0c3178c77beff0607bdb9ba9c49eba16b5539"},"schema_version":"1.0"},"canonical_sha256":"c2594be5b3a4494870c5e0349bba8fa4b90ddd8e0f3a23afc766a3feaac062a4","source":{"kind":"arxiv","id":"2507.07802","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.07802","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"arxiv_version","alias_value":"2507.07802v2","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07802","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"pith_short_12","alias_value":"YJMUXZNTUREU","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"pith_short_16","alias_value":"YJMUXZNTUREUQ4GF","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"pith_short_8","alias_value":"YJMUXZNT","created_at":"2026-07-05T11:35:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YJMUXZNTUREUQ4GF4A2JXOUPUS","target":"record","payload":{"canonical_record":{"source":{"id":"2507.07802","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-10T14:28:12Z","cross_cats_sorted":[],"title_canon_sha256":"14ddeb83c4994908aa084b5021f9c28d02dd12ad413a8343bb4d68a22a929449","abstract_canon_sha256":"7431c1e390d775f56bd854f3eda0c3178c77beff0607bdb9ba9c49eba16b5539"},"schema_version":"1.0"},"canonical_sha256":"c2594be5b3a4494870c5e0349bba8fa4b90ddd8e0f3a23afc766a3feaac062a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:36.707188Z","signature_b64":"xcGrF4pRV/vfCJtb8qj8sR7hno5D+0UW6ptqZtwnrh6vd1LXQUryIQjt7iClc3th09yikgSOr1gvbtsdo0DpCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2594be5b3a4494870c5e0349bba8fa4b90ddd8e0f3a23afc766a3feaac062a4","last_reissued_at":"2026-07-05T11:35:36.706692Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:36.706692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.07802","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:35:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q2QLIt75fLIGiM79BjlpcBmXhiAsWNQSbYhgY2YSbfMlp1EFL6ColWSp7TD4bIRd+e8P2csM7CSLKhpZQdYVBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T09:29:22.030899Z"},"content_sha256":"74707b8c2fd77788a21422617d92b1725ac005c7021d9ef22a610ed7070dafca","schema_version":"1.0","event_id":"sha256:74707b8c2fd77788a21422617d92b1725ac005c7021d9ef22a610ed7070dafca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YJMUXZNTUREUQ4GF4A2JXOUPUS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Synergistic Prompting for Robust Visual Recognition with Missing Modalities","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangyin Jin, Jing Zhang, Luanyuan Dai, Qika Lin, Xiaoshuai Hao, Yufei Guo, Yunfeng Diao, Zhihui Zhang","submitted_at":"2025-07-10T14:28:12Z","abstract_excerpt":"Large-scale multi-modal models have demonstrated remarkable performance across various visual recognition tasks by leveraging extensive paired multi-modal training data. However, in real-world applications, the presence of missing or incomplete modality inputs often leads to significant performance degradation. Recent research has focused on prompt-based strategies to tackle this issue; however, existing methods are hindered by two major limitations: (1) static prompts lack the flexibility to adapt to varying missing-data conditions, and (2) basic prompt-tuning methods struggle to ensure relia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07802","kind":"arxiv","version":2},"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/2507.07802/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":"2026-07-05T11:35:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nDHTV9ock37ejAUswUkIUddfPgpN2fE+I6q56O15Re/Y6L3kkLlRuBkm0DYoRBr9VaYZ5YHeZ0fmCyztFHY7Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T09:29:22.031709Z"},"content_sha256":"77e270058426913ce5e427afc716ce288358a5f8e0394e58912bf10f658237f5","schema_version":"1.0","event_id":"sha256:77e270058426913ce5e427afc716ce288358a5f8e0394e58912bf10f658237f5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YJMUXZNTUREUQ4GF4A2JXOUPUS/bundle.json","state_url":"https://pith.science/pith/YJMUXZNTUREUQ4GF4A2JXOUPUS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YJMUXZNTUREUQ4GF4A2JXOUPUS/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":"2026-08-10T09:29:22Z","links":{"resolver":"https://pith.science/pith/YJMUXZNTUREUQ4GF4A2JXOUPUS","bundle":"https://pith.science/pith/YJMUXZNTUREUQ4GF4A2JXOUPUS/bundle.json","state":"https://pith.science/pith/YJMUXZNTUREUQ4GF4A2JXOUPUS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YJMUXZNTUREUQ4GF4A2JXOUPUS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YJMUXZNTUREUQ4GF4A2JXOUPUS","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":"7431c1e390d775f56bd854f3eda0c3178c77beff0607bdb9ba9c49eba16b5539","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-10T14:28:12Z","title_canon_sha256":"14ddeb83c4994908aa084b5021f9c28d02dd12ad413a8343bb4d68a22a929449"},"schema_version":"1.0","source":{"id":"2507.07802","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.07802","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"arxiv_version","alias_value":"2507.07802v2","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07802","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"pith_short_12","alias_value":"YJMUXZNTUREU","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"pith_short_16","alias_value":"YJMUXZNTUREUQ4GF","created_at":"2026-07-05T11:35:36Z"},{"alias_kind":"pith_short_8","alias_value":"YJMUXZNT","created_at":"2026-07-05T11:35:36Z"}],"graph_snapshots":[{"event_id":"sha256:77e270058426913ce5e427afc716ce288358a5f8e0394e58912bf10f658237f5","target":"graph","created_at":"2026-07-05T11:35:36Z","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/2507.07802/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale multi-modal models have demonstrated remarkable performance across various visual recognition tasks by leveraging extensive paired multi-modal training data. However, in real-world applications, the presence of missing or incomplete modality inputs often leads to significant performance degradation. Recent research has focused on prompt-based strategies to tackle this issue; however, existing methods are hindered by two major limitations: (1) static prompts lack the flexibility to adapt to varying missing-data conditions, and (2) basic prompt-tuning methods struggle to ensure relia","authors_text":"Guangyin Jin, Jing Zhang, Luanyuan Dai, Qika Lin, Xiaoshuai Hao, Yufei Guo, Yunfeng Diao, Zhihui Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-10T14:28:12Z","title":"Synergistic Prompting for Robust Visual Recognition with Missing Modalities"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07802","kind":"arxiv","version":2},"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":"sha256:74707b8c2fd77788a21422617d92b1725ac005c7021d9ef22a610ed7070dafca","target":"record","created_at":"2026-07-05T11:35:36Z","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":"7431c1e390d775f56bd854f3eda0c3178c77beff0607bdb9ba9c49eba16b5539","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-10T14:28:12Z","title_canon_sha256":"14ddeb83c4994908aa084b5021f9c28d02dd12ad413a8343bb4d68a22a929449"},"schema_version":"1.0","source":{"id":"2507.07802","kind":"arxiv","version":2}},"canonical_sha256":"c2594be5b3a4494870c5e0349bba8fa4b90ddd8e0f3a23afc766a3feaac062a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2594be5b3a4494870c5e0349bba8fa4b90ddd8e0f3a23afc766a3feaac062a4","first_computed_at":"2026-07-05T11:35:36.706692Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:35:36.706692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xcGrF4pRV/vfCJtb8qj8sR7hno5D+0UW6ptqZtwnrh6vd1LXQUryIQjt7iClc3th09yikgSOr1gvbtsdo0DpCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:35:36.707188Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.07802","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:74707b8c2fd77788a21422617d92b1725ac005c7021d9ef22a610ed7070dafca","sha256:77e270058426913ce5e427afc716ce288358a5f8e0394e58912bf10f658237f5"],"state_sha256":"e4d92bb3aa616266fb02b2617ad737adebd15fe3e02cfa61bb82aecfcc97d3fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DQbwFEbtrhaLNzNgmZwLrWcu/qb55jvOtJPXcmVWcv66yHvdXN/Xbqw2QJK26cQCGy5/eVl6EaiJ2EYorKKECA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T09:29:22.037503Z","bundle_sha256":"f24fc69065199024fb5af7c256f985bd21f86b4cf1ef55ed6fe2394dcaf38820"}}