{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7HF6MIMAXNRVIK45KGIWA6AUTF","short_pith_number":"pith:7HF6MIMA","schema_version":"1.0","canonical_sha256":"f9cbe62180bb63542b9d519160781499585ed13b4f562fd2c94885e44e5cf4f4","source":{"kind":"arxiv","id":"2504.12805","version":2},"attestation_state":"computed","paper":{"title":"Assessing LLMs in Art Contexts: Critique Generation and Theory of Mind Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.HC"],"primary_cat":"cs.CL","authors_text":"Fuminori Akiba, Reiji Suzuki, Takaya Arita, Wenxian Zheng","submitted_at":"2025-04-17T10:10:25Z","abstract_excerpt":"This study explored how large language models (LLMs) perform in two areas related to art: writing critiques of artworks and reasoning about mental states (Theory of Mind, or ToM) in art-related situations. For the critique generation part, we built a system that combines Noel Carroll's evaluative framework with a broad selection of art criticism theories. The model was prompted to first write a full-length critique and then shorter, more coherent versions using a step-by-step prompting process. These AI-generated critiques were then compared with those written by human experts in a Turing test"},"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":"2504.12805","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-04-17T10:10:25Z","cross_cats_sorted":["cs.CY","cs.HC"],"title_canon_sha256":"4864b09d4a8b7fa7d7b48d5a09f2c1aec52e5e42690132bdc36adbeea4d316e5","abstract_canon_sha256":"854f0dd02ad5bbb92cebfbfdd930ce5a728fae5aedd72fff181ad1d61cfd3c22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:17.146005Z","signature_b64":"qcQcHa/299PeiUc3BMOtz2ckKEO6KP9JPTLht/JqNtnxEtnXoGTpdE461veenID1cnnI1hJC1KygWpqxI3yIBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9cbe62180bb63542b9d519160781499585ed13b4f562fd2c94885e44e5cf4f4","last_reissued_at":"2026-07-05T12:11:17.145341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:17.145341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Assessing LLMs in Art Contexts: Critique Generation and Theory of Mind Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.HC"],"primary_cat":"cs.CL","authors_text":"Fuminori Akiba, Reiji Suzuki, Takaya Arita, Wenxian Zheng","submitted_at":"2025-04-17T10:10:25Z","abstract_excerpt":"This study explored how large language models (LLMs) perform in two areas related to art: writing critiques of artworks and reasoning about mental states (Theory of Mind, or ToM) in art-related situations. For the critique generation part, we built a system that combines Noel Carroll's evaluative framework with a broad selection of art criticism theories. The model was prompted to first write a full-length critique and then shorter, more coherent versions using a step-by-step prompting process. These AI-generated critiques were then compared with those written by human experts in a Turing test"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12805","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/2504.12805/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":"2504.12805","created_at":"2026-07-05T12:11:17.145423+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.12805v2","created_at":"2026-07-05T12:11:17.145423+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12805","created_at":"2026-07-05T12:11:17.145423+00:00"},{"alias_kind":"pith_short_12","alias_value":"7HF6MIMAXNRV","created_at":"2026-07-05T12:11:17.145423+00:00"},{"alias_kind":"pith_short_16","alias_value":"7HF6MIMAXNRVIK45","created_at":"2026-07-05T12:11:17.145423+00:00"},{"alias_kind":"pith_short_8","alias_value":"7HF6MIMA","created_at":"2026-07-05T12:11:17.145423+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.23788","citing_title":"MIRAGE: A Micro-Interaction Relational Architecture for Grounded Exploration in Multi-Figure Artworks","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF","json":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF.json","graph_json":"https://pith.science/api/pith-number/7HF6MIMAXNRVIK45KGIWA6AUTF/graph.json","events_json":"https://pith.science/api/pith-number/7HF6MIMAXNRVIK45KGIWA6AUTF/events.json","paper":"https://pith.science/paper/7HF6MIMA"},"agent_actions":{"view_html":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF","download_json":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF.json","view_paper":"https://pith.science/paper/7HF6MIMA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.12805&json=true","fetch_graph":"https://pith.science/api/pith-number/7HF6MIMAXNRVIK45KGIWA6AUTF/graph.json","fetch_events":"https://pith.science/api/pith-number/7HF6MIMAXNRVIK45KGIWA6AUTF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF/action/storage_attestation","attest_author":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF/action/author_attestation","sign_citation":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF/action/citation_signature","submit_replication":"https://pith.science/pith/7HF6MIMAXNRVIK45KGIWA6AUTF/action/replication_record"}},"created_at":"2026-07-05T12:11:17.145423+00:00","updated_at":"2026-07-05T12:11:17.145423+00:00"}