{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XSDQV7T352Q2TZJOCZUGQKUR7O","short_pith_number":"pith:XSDQV7T3","schema_version":"1.0","canonical_sha256":"bc870afe7beea1a9e52e1668682a91fb8fbfcc1643ac81e2f2910d2c250bf5f4","source":{"kind":"arxiv","id":"2607.19910","version":1},"attestation_state":"computed","paper":{"title":"MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CV","authors_text":"Chao Wang, Feiyu Wang, Hongxu Liu, Qiong Zeng, Tong Ge, Xiaoyu Yang, Yue Zhao, Zhen Yang","submitted_at":"2026-07-22T08:42:55Z","abstract_excerpt":"Multimodal large language models (MLLMs) are increasingly expected to automate visualization development by generating code directly from visual designs. However, existing evaluations mainly focus on single-chart generation and overlook coordinated multi-view interface construction, which requires joint reasoning about data semantics, view coordination, and interaction logic. Consequently, MLLM capabilities in this setting remain underexplored, and the field lacks a dedicated benchmark for systematic assessment. We introduce MV-Bench, a benchmark for evaluating MLLMs on coordinated multi-view "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.19910","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-22T08:42:55Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"76877852dfd8508e30b97b3fc628f5e7349f8137a80bdcd0c8a0595d8bf75f9d","abstract_canon_sha256":"1a359b30497081ed54d9acd1e164ddbde0909e5b0f8bb91bc34eb4c479b85f83"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-23T01:24:36.826518Z","signature_b64":"zLl168YTB8B+9Ef5fTqkXRATAGtJOm9lwpZtJeiIByELYr+uPHlVz72DEoMOeEEsd+LwkR+NofhZRjd6gzR9CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc870afe7beea1a9e52e1668682a91fb8fbfcc1643ac81e2f2910d2c250bf5f4","last_reissued_at":"2026-07-23T01:24:36.825601Z","signature_status":"signed_v1","first_computed_at":"2026-07-23T01:24:36.825601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CV","authors_text":"Chao Wang, Feiyu Wang, Hongxu Liu, Qiong Zeng, Tong Ge, Xiaoyu Yang, Yue Zhao, Zhen Yang","submitted_at":"2026-07-22T08:42:55Z","abstract_excerpt":"Multimodal large language models (MLLMs) are increasingly expected to automate visualization development by generating code directly from visual designs. However, existing evaluations mainly focus on single-chart generation and overlook coordinated multi-view interface construction, which requires joint reasoning about data semantics, view coordination, and interaction logic. Consequently, MLLM capabilities in this setting remain underexplored, and the field lacks a dedicated benchmark for systematic assessment. We introduce MV-Bench, a benchmark for evaluating MLLMs on coordinated multi-view "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.19910","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/2607.19910/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.19910","created_at":"2026-07-23T01:24:36.826047+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.19910v1","created_at":"2026-07-23T01:24:36.826047+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.19910","created_at":"2026-07-23T01:24:36.826047+00:00"},{"alias_kind":"pith_short_12","alias_value":"XSDQV7T352Q2","created_at":"2026-07-23T01:24:36.826047+00:00"},{"alias_kind":"pith_short_16","alias_value":"XSDQV7T352Q2TZJO","created_at":"2026-07-23T01:24:36.826047+00:00"},{"alias_kind":"pith_short_8","alias_value":"XSDQV7T3","created_at":"2026-07-23T01:24:36.826047+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O","json":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O.json","graph_json":"https://pith.science/api/pith-number/XSDQV7T352Q2TZJOCZUGQKUR7O/graph.json","events_json":"https://pith.science/api/pith-number/XSDQV7T352Q2TZJOCZUGQKUR7O/events.json","paper":"https://pith.science/paper/XSDQV7T3"},"agent_actions":{"view_html":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O","download_json":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O.json","view_paper":"https://pith.science/paper/XSDQV7T3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.19910&json=true","fetch_graph":"https://pith.science/api/pith-number/XSDQV7T352Q2TZJOCZUGQKUR7O/graph.json","fetch_events":"https://pith.science/api/pith-number/XSDQV7T352Q2TZJOCZUGQKUR7O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O/action/storage_attestation","attest_author":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O/action/author_attestation","sign_citation":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O/action/citation_signature","submit_replication":"https://pith.science/pith/XSDQV7T352Q2TZJOCZUGQKUR7O/action/replication_record"}},"created_at":"2026-07-23T01:24:36.826047+00:00","updated_at":"2026-07-23T01:24:36.826047+00:00"}