{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IEKNEQTOVQGQLCXB6M3UXO3Q6P","short_pith_number":"pith:IEKNEQTO","canonical_record":{"source":{"id":"2403.14624","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:59:50Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"87352c938c6afc2dea3ac47841997565364989bda833e8bd9032db8ca5aa4b05","abstract_canon_sha256":"bd2d944b86d298df85475113d4d792ced4626b8f40ed7ecd677b10b501932bf1"},"schema_version":"1.0"},"canonical_sha256":"4114d2426eac0d058ae1f3374bbb70f3d118e7729ca5530f450ef123757c0850","source":{"kind":"arxiv","id":"2403.14624","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.14624","created_at":"2026-05-17T23:38:45Z"},{"alias_kind":"arxiv_version","alias_value":"2403.14624v2","created_at":"2026-05-17T23:38:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.14624","created_at":"2026-05-17T23:38:45Z"},{"alias_kind":"pith_short_12","alias_value":"IEKNEQTOVQGQ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"IEKNEQTOVQGQLCXB","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"IEKNEQTO","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IEKNEQTOVQGQLCXB6M3UXO3Q6P","target":"record","payload":{"canonical_record":{"source":{"id":"2403.14624","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:59:50Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"87352c938c6afc2dea3ac47841997565364989bda833e8bd9032db8ca5aa4b05","abstract_canon_sha256":"bd2d944b86d298df85475113d4d792ced4626b8f40ed7ecd677b10b501932bf1"},"schema_version":"1.0"},"canonical_sha256":"4114d2426eac0d058ae1f3374bbb70f3d118e7729ca5530f450ef123757c0850","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:45.983014Z","signature_b64":"RhIEPHj/LsVMnZx/ntEFhSKiujIb6bIe886FeDY17IAjPxRgOagCY5yjWe8cQbsiyUVNgD0BTCfuwhCBrJ56CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4114d2426eac0d058ae1f3374bbb70f3d118e7729ca5530f450ef123757c0850","last_reissued_at":"2026-05-17T23:38:45.982351Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:45.982351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.14624","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":"2026-05-17T23:38:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/zpLNPwP8ybovjHqmPINe3pekDRt/+PeM10ZfUUlDcpLyJKRmvtv0aXTZFU3mbpsTMc5YE+vBMSWZLwD4FMHAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T01:15:46.991722Z"},"content_sha256":"4ec558d10e02dd13c6e01a1c87eacec603253c352a7d78a7b4fb8ceb46dccfe2","schema_version":"1.0","event_id":"sha256:4ec558d10e02dd13c6e01a1c87eacec603253c352a7d78a7b4fb8ceb46dccfe2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IEKNEQTOVQGQLCXB6M3UXO3Q6P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"MathVerse shows multi-modal LLMs often solve visual math problems using text rather than diagrams.","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.CV","authors_text":"Aojun Zhou, Dongzhi Jiang, Haokun Lin, Hongsheng Li, Kai-Wei Chang, Pan Lu, Peng Gao, Pengshuo Qiu, Renrui Zhang, Yichi Zhang, Ziyu Guo","submitted_at":"2024-03-21T17:59:50Z","abstract_excerpt":"The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capabilities in visual math problem-solving remain insufficiently evaluated and understood. We investigate current benchmarks to incorporate excessive visual content within textual questions, which potentially assist MLLMs in deducing answers without truly interpreting the input diagrams. To this end, we introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Current visual math benchmarks contain excessive textual information that allows MLLMs to deduce answers without truly interpreting the input diagrams; MathVerse's multi-version design enables equitable evaluation of whether and how much MLLMs understand visual diagrams for mathematical reasoning.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The human-annotated transformations into six versions with varying degrees of multi-modal information accurately isolate visual understanding without introducing new biases or inconsistencies in problem difficulty or meaning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MathVerse is a benchmark that tests multi-modal LLMs on visual math by providing each problem in six versions with progressively less diagram and text information to measure true visual understanding.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MathVerse shows multi-modal LLMs often solve visual math problems using text rather than diagrams.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1fdc86d36ed5619e2f961941d0e63503ad392c2819d70140102c2c0484eeed9b"},"source":{"id":"2403.14624","kind":"arxiv","version":2},"verdict":{"id":"0ae6c1a3-8056-4682-8ec2-00e02dd0a14e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T01:22:47.288047Z","strongest_claim":"Current visual math benchmarks contain excessive textual information that allows MLLMs to deduce answers without truly interpreting the input diagrams; MathVerse's multi-version design enables equitable evaluation of whether and how much MLLMs understand visual diagrams for mathematical reasoning.","one_line_summary":"MathVerse is a benchmark that tests multi-modal LLMs on visual math by providing each problem in six versions with progressively less diagram and text information to measure true visual understanding.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The human-annotated transformations into six versions with varying degrees of multi-modal information accurately isolate visual understanding without introducing new biases or inconsistencies in problem difficulty or meaning.","pith_extraction_headline":"MathVerse shows multi-modal LLMs often solve visual math problems using text rather than diagrams."},"references":{"count":83,"sample":[{"doi":"","year":2022,"title":"Advances in Neural Information Processing Systems 35, 23716–23736 (2022)","work_id":"344ce97d-7e21-422e-ae14-69876c1801c3","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1905,"title":"arXiv preprint arXiv:1905.13319 , year=","work_id":"4539c966-2fd4-4238-88a9-60be171a99da","ref_index":2,"cited_arxiv_id":"1905.13319","is_internal_anchor":true},{"doi":"","year":2023,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","ref_index":3,"cited_arxiv_id":"2308.12966","is_internal_anchor":true},{"doi":"","year":1901,"title":"In: Advances in neural information processing systems","work_id":"81fd517a-6af8-4c9b-ad27-27c1b5fe5b95","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"In: Proceedings of the 29th International Conference on Computa- tional Linguistics","work_id":"31f970b8-cc4f-4384-8146-2fa989010e48","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":83,"snapshot_sha256":"e53a5d187e31b42fb1ed930049b6558b4361b81354645298f72cbc018f630a85","internal_anchors":26},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c76510fd3646433624a688a5a003c48cd6a7cd572ba2995c0d5e348a87936dc4"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"0ae6c1a3-8056-4682-8ec2-00e02dd0a14e"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rwoqT+zvYRVSUs2op2sLBCapB1GzF7jDqQqKEt7InxWttR183HUfOyTh8ohTBV+Y82M6gqtpYNBT593iJBCOAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T01:15:46.992326Z"},"content_sha256":"ebee5f4de2f7ef9adcb0baf98b7cbde14cf27b3e70304f56ab878332d19908fe","schema_version":"1.0","event_id":"sha256:ebee5f4de2f7ef9adcb0baf98b7cbde14cf27b3e70304f56ab878332d19908fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IEKNEQTOVQGQLCXB6M3UXO3Q6P/bundle.json","state_url":"https://pith.science/pith/IEKNEQTOVQGQLCXB6M3UXO3Q6P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IEKNEQTOVQGQLCXB6M3UXO3Q6P/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":"2026-06-06T01:15:46Z","links":{"resolver":"https://pith.science/pith/IEKNEQTOVQGQLCXB6M3UXO3Q6P","bundle":"https://pith.science/pith/IEKNEQTOVQGQLCXB6M3UXO3Q6P/bundle.json","state":"https://pith.science/pith/IEKNEQTOVQGQLCXB6M3UXO3Q6P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IEKNEQTOVQGQLCXB6M3UXO3Q6P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IEKNEQTOVQGQLCXB6M3UXO3Q6P","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":"bd2d944b86d298df85475113d4d792ced4626b8f40ed7ecd677b10b501932bf1","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:59:50Z","title_canon_sha256":"87352c938c6afc2dea3ac47841997565364989bda833e8bd9032db8ca5aa4b05"},"schema_version":"1.0","source":{"id":"2403.14624","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.14624","created_at":"2026-05-17T23:38:45Z"},{"alias_kind":"arxiv_version","alias_value":"2403.14624v2","created_at":"2026-05-17T23:38:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.14624","created_at":"2026-05-17T23:38:45Z"},{"alias_kind":"pith_short_12","alias_value":"IEKNEQTOVQGQ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"IEKNEQTOVQGQLCXB","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"IEKNEQTO","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:ebee5f4de2f7ef9adcb0baf98b7cbde14cf27b3e70304f56ab878332d19908fe","target":"graph","created_at":"2026-05-17T23:38:45Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Current visual math benchmarks contain excessive textual information that allows MLLMs to deduce answers without truly interpreting the input diagrams; MathVerse's multi-version design enables equitable evaluation of whether and how much MLLMs understand visual diagrams for mathematical reasoning."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The human-annotated transformations into six versions with varying degrees of multi-modal information accurately isolate visual understanding without introducing new biases or inconsistencies in problem difficulty or meaning."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MathVerse is a benchmark that tests multi-modal LLMs on visual math by providing each problem in six versions with progressively less diagram and text information to measure true visual understanding."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"MathVerse shows multi-modal LLMs often solve visual math problems using text rather than diagrams."}],"snapshot_sha256":"1fdc86d36ed5619e2f961941d0e63503ad392c2819d70140102c2c0484eeed9b"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c76510fd3646433624a688a5a003c48cd6a7cd572ba2995c0d5e348a87936dc4"},"paper":{"abstract_excerpt":"The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capabilities in visual math problem-solving remain insufficiently evaluated and understood. We investigate current benchmarks to incorporate excessive visual content within textual questions, which potentially assist MLLMs in deducing answers without truly interpreting the input diagrams. To this end, we introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We","authors_text":"Aojun Zhou, Dongzhi Jiang, Haokun Lin, Hongsheng Li, Kai-Wei Chang, Pan Lu, Peng Gao, Pengshuo Qiu, Renrui Zhang, Yichi Zhang, Ziyu Guo","cross_cats":["cs.AI","cs.CL","cs.LG"],"headline":"MathVerse shows multi-modal LLMs often solve visual math problems using text rather than diagrams.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:59:50Z","title":"MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?"},"references":{"count":83,"internal_anchors":26,"resolved_work":83,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Advances in Neural Information Processing Systems 35, 23716–23736 (2022)","work_id":"344ce97d-7e21-422e-ae14-69876c1801c3","year":2022},{"cited_arxiv_id":"1905.13319","doi":"","is_internal_anchor":true,"ref_index":2,"title":"arXiv preprint arXiv:1905.13319 , year=","work_id":"4539c966-2fd4-4238-88a9-60be171a99da","year":1905},{"cited_arxiv_id":"2308.12966","doi":"","is_internal_anchor":true,"ref_index":3,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"In: Advances in neural information processing systems","work_id":"81fd517a-6af8-4c9b-ad27-27c1b5fe5b95","year":1901},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"In: Proceedings of the 29th International Conference on Computa- tional Linguistics","work_id":"31f970b8-cc4f-4384-8146-2fa989010e48","year":2022}],"snapshot_sha256":"e53a5d187e31b42fb1ed930049b6558b4361b81354645298f72cbc018f630a85"},"source":{"id":"2403.14624","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-17T01:22:47.288047Z","id":"0ae6c1a3-8056-4682-8ec2-00e02dd0a14e","model_set":{"reader":"grok-4.3"},"one_line_summary":"MathVerse is a benchmark that tests multi-modal LLMs on visual math by providing each problem in six versions with progressively less diagram and text information to measure true visual understanding.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"MathVerse shows multi-modal LLMs often solve visual math problems using text rather than diagrams.","strongest_claim":"Current visual math benchmarks contain excessive textual information that allows MLLMs to deduce answers without truly interpreting the input diagrams; MathVerse's multi-version design enables equitable evaluation of whether and how much MLLMs understand visual diagrams for mathematical reasoning.","weakest_assumption":"The human-annotated transformations into six versions with varying degrees of multi-modal information accurately isolate visual understanding without introducing new biases or inconsistencies in problem difficulty or meaning."}},"verdict_id":"0ae6c1a3-8056-4682-8ec2-00e02dd0a14e"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4ec558d10e02dd13c6e01a1c87eacec603253c352a7d78a7b4fb8ceb46dccfe2","target":"record","created_at":"2026-05-17T23:38:45Z","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":"bd2d944b86d298df85475113d4d792ced4626b8f40ed7ecd677b10b501932bf1","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-21T17:59:50Z","title_canon_sha256":"87352c938c6afc2dea3ac47841997565364989bda833e8bd9032db8ca5aa4b05"},"schema_version":"1.0","source":{"id":"2403.14624","kind":"arxiv","version":2}},"canonical_sha256":"4114d2426eac0d058ae1f3374bbb70f3d118e7729ca5530f450ef123757c0850","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4114d2426eac0d058ae1f3374bbb70f3d118e7729ca5530f450ef123757c0850","first_computed_at":"2026-05-17T23:38:45.982351Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:45.982351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RhIEPHj/LsVMnZx/ntEFhSKiujIb6bIe886FeDY17IAjPxRgOagCY5yjWe8cQbsiyUVNgD0BTCfuwhCBrJ56CA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:45.983014Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.14624","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ec558d10e02dd13c6e01a1c87eacec603253c352a7d78a7b4fb8ceb46dccfe2","sha256:ebee5f4de2f7ef9adcb0baf98b7cbde14cf27b3e70304f56ab878332d19908fe"],"state_sha256":"1a32f35ea206c7debfb26dade008365a4b68a83fd335333bf92825600e22cfcd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RoGnb98eyt0pxBLoD9B+leE+oUSJbg3mbaL1vb2cIh+YjARZ01RAkOyVREz5E603QFBEY+M9jKh3PpMzRw+dAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T01:15:46.995241Z","bundle_sha256":"b5689f3472850dcd4dd0521c0d137244c859a4fe9049632a826ffde0e54c6bf6"}}