{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IJGVA7UPC647PFQ2UY6YU3XTWT","short_pith_number":"pith:IJGVA7UP","canonical_record":{"source":{"id":"2506.17903","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-22T05:20:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6d5b6e03c44413676e68e6cf212cf2a6b962b3b56bd9a03d4ca586a82cffa8ea","abstract_canon_sha256":"6388b16ad5ac42b4902ebf1e687e734f8a09a4b53018a15c02e9f1a446d6f6c7"},"schema_version":"1.0"},"canonical_sha256":"424d507e8f17b9f7961aa63d8a6ef3b4c81c0b0a8102dbb36b279a5a35450ea9","source":{"kind":"arxiv","id":"2506.17903","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.17903","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"arxiv_version","alias_value":"2506.17903v1","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17903","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"pith_short_12","alias_value":"IJGVA7UPC647","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"pith_short_16","alias_value":"IJGVA7UPC647PFQ2","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"pith_short_8","alias_value":"IJGVA7UP","created_at":"2026-07-05T11:25:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IJGVA7UPC647PFQ2UY6YU3XTWT","target":"record","payload":{"canonical_record":{"source":{"id":"2506.17903","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-22T05:20:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6d5b6e03c44413676e68e6cf212cf2a6b962b3b56bd9a03d4ca586a82cffa8ea","abstract_canon_sha256":"6388b16ad5ac42b4902ebf1e687e734f8a09a4b53018a15c02e9f1a446d6f6c7"},"schema_version":"1.0"},"canonical_sha256":"424d507e8f17b9f7961aa63d8a6ef3b4c81c0b0a8102dbb36b279a5a35450ea9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:25.217809Z","signature_b64":"CMNsXuKKAIIdzKKxp7QTMfINyZBxvTkzAbM1T6dCffDGfA3W7Dh3xGgxKA7a2Wnr8wVcXHV3oNMACnTux9I8Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"424d507e8f17b9f7961aa63d8a6ef3b4c81c0b0a8102dbb36b279a5a35450ea9","last_reissued_at":"2026-07-05T11:25:25.217359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:25.217359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.17903","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":"2026-07-05T11:25:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QxpxdJlPhXK9pJ0xy9ZFo4LaJOpXxaYLruM77O92yv2BWlzmPZGh6H4n5PH19dijd36OEBgQQmDZyPs/Q/YFCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T16:32:31.816065Z"},"content_sha256":"4add19d07419a14c00d183d77697c78c12a346c41f58ddf80ac80cc4ecde7278","schema_version":"1.0","event_id":"sha256:4add19d07419a14c00d183d77697c78c12a346c41f58ddf80ac80cc4ecde7278"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IJGVA7UPC647PFQ2UY6YU3XTWT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cause-Effect Driven Optimization for Robust Medical Visual Question Answering with Language Biases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bingzhi Chen, Guangming Lu, Huanjia Zhu, Xiaozhao Fang, Yishu Liu","submitted_at":"2025-06-22T05:20:34Z","abstract_excerpt":"Existing Medical Visual Question Answering (Med-VQA) models often suffer from language biases, where spurious correlations between question types and answer categories are inadvertently established. To address these issues, we propose a novel Cause-Effect Driven Optimization framework called CEDO, that incorporates three well-established mechanisms, i.e., Modality-driven Heterogeneous Optimization (MHO), Gradient-guided Modality Synergy (GMS), and Distribution-adapted Loss Rescaling (DLR), for comprehensively mitigating language biases from both causal and effectual perspectives. Specifically,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17903","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/2506.17903/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":"2026-07-05T11:25:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nbqf0EPrZdB1yIiX6ESuW2mANiwlneulv00RB9qDF91bHEAoB3pRzqZfIyMRiBJBhUEuRDxdLd2xysYQ8C4mCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T16:32:31.816618Z"},"content_sha256":"4b72264572f0fb61a87fac6d3e71f305f07d6d639c4a5f9d776f5046ca2a7cd5","schema_version":"1.0","event_id":"sha256:4b72264572f0fb61a87fac6d3e71f305f07d6d639c4a5f9d776f5046ca2a7cd5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IJGVA7UPC647PFQ2UY6YU3XTWT/bundle.json","state_url":"https://pith.science/pith/IJGVA7UPC647PFQ2UY6YU3XTWT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IJGVA7UPC647PFQ2UY6YU3XTWT/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":"2026-08-17T16:32:31Z","links":{"resolver":"https://pith.science/pith/IJGVA7UPC647PFQ2UY6YU3XTWT","bundle":"https://pith.science/pith/IJGVA7UPC647PFQ2UY6YU3XTWT/bundle.json","state":"https://pith.science/pith/IJGVA7UPC647PFQ2UY6YU3XTWT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IJGVA7UPC647PFQ2UY6YU3XTWT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IJGVA7UPC647PFQ2UY6YU3XTWT","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":"6388b16ad5ac42b4902ebf1e687e734f8a09a4b53018a15c02e9f1a446d6f6c7","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-22T05:20:34Z","title_canon_sha256":"6d5b6e03c44413676e68e6cf212cf2a6b962b3b56bd9a03d4ca586a82cffa8ea"},"schema_version":"1.0","source":{"id":"2506.17903","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.17903","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"arxiv_version","alias_value":"2506.17903v1","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17903","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"pith_short_12","alias_value":"IJGVA7UPC647","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"pith_short_16","alias_value":"IJGVA7UPC647PFQ2","created_at":"2026-07-05T11:25:25Z"},{"alias_kind":"pith_short_8","alias_value":"IJGVA7UP","created_at":"2026-07-05T11:25:25Z"}],"graph_snapshots":[{"event_id":"sha256:4b72264572f0fb61a87fac6d3e71f305f07d6d639c4a5f9d776f5046ca2a7cd5","target":"graph","created_at":"2026-07-05T11:25:25Z","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/2506.17903/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing Medical Visual Question Answering (Med-VQA) models often suffer from language biases, where spurious correlations between question types and answer categories are inadvertently established. To address these issues, we propose a novel Cause-Effect Driven Optimization framework called CEDO, that incorporates three well-established mechanisms, i.e., Modality-driven Heterogeneous Optimization (MHO), Gradient-guided Modality Synergy (GMS), and Distribution-adapted Loss Rescaling (DLR), for comprehensively mitigating language biases from both causal and effectual perspectives. Specifically,","authors_text":"Bingzhi Chen, Guangming Lu, Huanjia Zhu, Xiaozhao Fang, Yishu Liu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-22T05:20:34Z","title":"Cause-Effect Driven Optimization for Robust Medical Visual Question Answering with Language Biases"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17903","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":"sha256:4add19d07419a14c00d183d77697c78c12a346c41f58ddf80ac80cc4ecde7278","target":"record","created_at":"2026-07-05T11:25:25Z","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":"6388b16ad5ac42b4902ebf1e687e734f8a09a4b53018a15c02e9f1a446d6f6c7","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-22T05:20:34Z","title_canon_sha256":"6d5b6e03c44413676e68e6cf212cf2a6b962b3b56bd9a03d4ca586a82cffa8ea"},"schema_version":"1.0","source":{"id":"2506.17903","kind":"arxiv","version":1}},"canonical_sha256":"424d507e8f17b9f7961aa63d8a6ef3b4c81c0b0a8102dbb36b279a5a35450ea9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"424d507e8f17b9f7961aa63d8a6ef3b4c81c0b0a8102dbb36b279a5a35450ea9","first_computed_at":"2026-07-05T11:25:25.217359Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:25.217359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CMNsXuKKAIIdzKKxp7QTMfINyZBxvTkzAbM1T6dCffDGfA3W7Dh3xGgxKA7a2Wnr8wVcXHV3oNMACnTux9I8Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:25.217809Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.17903","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4add19d07419a14c00d183d77697c78c12a346c41f58ddf80ac80cc4ecde7278","sha256:4b72264572f0fb61a87fac6d3e71f305f07d6d639c4a5f9d776f5046ca2a7cd5"],"state_sha256":"4099205c299dda84ca00bbe517dcbe97e5d0b892eed1e72d2cba82555f406ae1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8FI10eOkO6REPTio/R4BiYhksVLsSOeohhAV1dVYGk+9m1J+aYyclWhW1OY+wOmebY6PKUmkuHmyb3zQo0m+Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T16:32:31.826756Z","bundle_sha256":"38e3bb3363bf13b84bea36ae91da2bb9b8e544a2193a1a530460b20fe10543f2"}}