{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:DN6ZAATRIH5AWWOPKQFXOM5PWG","short_pith_number":"pith:DN6ZAATR","schema_version":"1.0","canonical_sha256":"1b7d90027141fa0b59cf540b7733afb1bbc37db7d1d976888702e91b664d42d2","source":{"kind":"arxiv","id":"2607.19341","version":1},"attestation_state":"computed","paper":{"title":"ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jinsong Lan, Mengting Chen, Xiaoyong Zhu, Xuetao Feng, Yongchao Du, Yuan Wang","submitted_at":"2026-07-21T17:59:02Z","abstract_excerpt":"Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However, these methods focus on explicit commonsense reasoning, shallow causal understanding, and direct knowledge recall, failing at knowledge-intensive generation. We develop \\textbf{ExpertVerse}, a capability-centric benchmark to evaluate generative models via knowledge-intensive lens. ExpertVerse stratifies reasoning generation across an orthogonal taxonomy of \\textit{9 cognitive capabilities} and \\textit{8 expert discipl"},"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":"2607.19341","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-21T17:59:02Z","cross_cats_sorted":[],"title_canon_sha256":"93840f322ffd72dc5b2bf8217b347ccd47076b30a7da0fd73aae1af82c85160b","abstract_canon_sha256":"ce8a4f077f5784ab70d9f026dedb5446d9f6416dc9e93f74896dee807e82a91e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T01:24:38.091570Z","signature_b64":"2LV6np+NISmRPblsEgB5x/oFT8/G0U9cZiaiRj6tdZEsQaRNUfx0H0OBzwIh1NcW2FyZpi8uvjrWfQBCJ6sKCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b7d90027141fa0b59cf540b7733afb1bbc37db7d1d976888702e91b664d42d2","last_reissued_at":"2026-07-22T01:24:38.090727Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T01:24:38.090727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jinsong Lan, Mengting Chen, Xiaoyong Zhu, Xuetao Feng, Yongchao Du, Yuan Wang","submitted_at":"2026-07-21T17:59:02Z","abstract_excerpt":"Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However, these methods focus on explicit commonsense reasoning, shallow causal understanding, and direct knowledge recall, failing at knowledge-intensive generation. We develop \\textbf{ExpertVerse}, a capability-centric benchmark to evaluate generative models via knowledge-intensive lens. ExpertVerse stratifies reasoning generation across an orthogonal taxonomy of \\textit{9 cognitive capabilities} and \\textit{8 expert discipl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.19341","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/2607.19341/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":"2607.19341","created_at":"2026-07-22T01:24:38.091169+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.19341v1","created_at":"2026-07-22T01:24:38.091169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.19341","created_at":"2026-07-22T01:24:38.091169+00:00"},{"alias_kind":"pith_short_12","alias_value":"DN6ZAATRIH5A","created_at":"2026-07-22T01:24:38.091169+00:00"},{"alias_kind":"pith_short_16","alias_value":"DN6ZAATRIH5AWWOP","created_at":"2026-07-22T01:24:38.091169+00:00"},{"alias_kind":"pith_short_8","alias_value":"DN6ZAATR","created_at":"2026-07-22T01:24:38.091169+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/DN6ZAATRIH5AWWOPKQFXOM5PWG","json":"https://pith.science/pith/DN6ZAATRIH5AWWOPKQFXOM5PWG.json","graph_json":"https://pith.science/api/pith-number/DN6ZAATRIH5AWWOPKQFXOM5PWG/graph.json","events_json":"https://pith.science/api/pith-number/DN6ZAATRIH5AWWOPKQFXOM5PWG/events.json","paper":"https://pith.science/paper/DN6ZAATR"},"agent_actions":{"view_html":"https://pith.science/pith/DN6ZAATRIH5AWWOPKQFXOM5PWG","download_json":"https://pith.science/pith/DN6ZAATRIH5AWWOPKQFXOM5PWG.json","view_paper":"https://pith.science/paper/DN6ZAATR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.19341&json=true","fetch_graph":"https://pith.science/api/pith-number/DN6ZAATRIH5AWWOPKQFXOM5PWG/graph.json","fetch_events":"https://pith.science/api/pith-number/DN6ZAATRIH5AWWOPKQFXOM5PWG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DN6ZAATRIH5AWWOPKQFXOM5PWG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DN6ZAATRIH5AWWOPKQFXOM5PWG/action/storage_attestation","attest_author":"https://pith.science/pith/DN6ZAATRIH5AWWOPKQFXOM5PWG/action/author_attestation","sign_citation":"https://pith.science/pith/DN6ZAATRIH5AWWOPKQFXOM5PWG/action/citation_signature","submit_replication":"https://pith.science/pith/DN6ZAATRIH5AWWOPKQFXOM5PWG/action/replication_record"}},"created_at":"2026-07-22T01:24:38.091169+00:00","updated_at":"2026-07-22T01:24:38.091169+00:00"}