{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NFREUPWHZ76DWOVVVMGNUA3GPL","short_pith_number":"pith:NFREUPWH","schema_version":"1.0","canonical_sha256":"69624a3ec7cffc3b3ab5ab0cda03667acc7f6c348cae32dc274367a9b5eb2989","source":{"kind":"arxiv","id":"2510.04120","version":2},"attestation_state":"computed","paper":{"title":"Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Derek F. Wong, Fengying Ye, Lidia S. Chao, Shanshan Wang","submitted_at":"2025-10-05T09:45:51Z","abstract_excerpt":"Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor processing. We present a diagnostic analysis that examines the limits of behavioral evidence by probing three complementary dimensions: semantic attribute alignment, lexical invariance, and syntactic sensitivity. Using geometric probing, we assess whether model-generated interpretations align with reference semantic attributes; through context-varying substitution, we analyze the stability of lexical associations betw"},"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":"2510.04120","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-10-05T09:45:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"38c0c1366ee6baec07e640e9569d78a0e6d7ea8aaa7b99bdc58ed97e3ea00d26","abstract_canon_sha256":"b78b3ddf03fc71294be69caf0102ccd5f181b38e10580ea06b1fd4ea7ad48389"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:13.779587Z","signature_b64":"uMjz63EnPUAvixpk+E8ie2PQkTPT1XpUi93d0Vn4ShClxdhUI4ANIyJZ0haQbtSAp00TzqSBVLSqbJTVye11DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69624a3ec7cffc3b3ab5ab0cda03667acc7f6c348cae32dc274367a9b5eb2989","last_reissued_at":"2026-06-19T16:11:13.779091Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:13.779091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Derek F. Wong, Fengying Ye, Lidia S. Chao, Shanshan Wang","submitted_at":"2025-10-05T09:45:51Z","abstract_excerpt":"Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor processing. We present a diagnostic analysis that examines the limits of behavioral evidence by probing three complementary dimensions: semantic attribute alignment, lexical invariance, and syntactic sensitivity. Using geometric probing, we assess whether model-generated interpretations align with reference semantic attributes; through context-varying substitution, we analyze the stability of lexical associations betw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.04120","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/2510.04120/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":"2510.04120","created_at":"2026-06-19T16:11:13.779147+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.04120v2","created_at":"2026-06-19T16:11:13.779147+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.04120","created_at":"2026-06-19T16:11:13.779147+00:00"},{"alias_kind":"pith_short_12","alias_value":"NFREUPWHZ76D","created_at":"2026-06-19T16:11:13.779147+00:00"},{"alias_kind":"pith_short_16","alias_value":"NFREUPWHZ76DWOVV","created_at":"2026-06-19T16:11:13.779147+00:00"},{"alias_kind":"pith_short_8","alias_value":"NFREUPWH","created_at":"2026-06-19T16:11:13.779147+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.22654","citing_title":"Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs","ref_index":115,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL","json":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL.json","graph_json":"https://pith.science/api/pith-number/NFREUPWHZ76DWOVVVMGNUA3GPL/graph.json","events_json":"https://pith.science/api/pith-number/NFREUPWHZ76DWOVVVMGNUA3GPL/events.json","paper":"https://pith.science/paper/NFREUPWH"},"agent_actions":{"view_html":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL","download_json":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL.json","view_paper":"https://pith.science/paper/NFREUPWH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.04120&json=true","fetch_graph":"https://pith.science/api/pith-number/NFREUPWHZ76DWOVVVMGNUA3GPL/graph.json","fetch_events":"https://pith.science/api/pith-number/NFREUPWHZ76DWOVVVMGNUA3GPL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL/action/storage_attestation","attest_author":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL/action/author_attestation","sign_citation":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL/action/citation_signature","submit_replication":"https://pith.science/pith/NFREUPWHZ76DWOVVVMGNUA3GPL/action/replication_record"}},"created_at":"2026-06-19T16:11:13.779147+00:00","updated_at":"2026-06-19T16:11:13.779147+00:00"}