{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:USZ7P6DXCLYRNR6BP4FQM4FCTF","short_pith_number":"pith:USZ7P6DX","schema_version":"1.0","canonical_sha256":"a4b3f7f87712f116c7c17f0b0670a2996f227b260ef603e98a4a05116a74918f","source":{"kind":"arxiv","id":"2606.26880","version":1},"attestation_state":"computed","paper":{"title":"Heterogeneous Neural Predictivity from Language Models During Naturalistic Comprehension","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Xiao Jia","submitted_at":"2026-06-25T11:08:49Z","abstract_excerpt":"Language-model representations provide structured, high-dimensional annotations of naturalistic language stimuli and can serve as informative neural predictors during comprehension. We analyzed locked derived data from Brain Treebank, MEG-MASC, and Podcast ECoG with eight frozen language models, blocked encoding models, and matched temporal, nuisance, and representation-capacity controls. Positive held-out prediction and gains over low-level baselines were widespread in source-level summaries. Across Brain Treebank and Podcast ECoG, 67 of 432 evaluable rows met a controlled predictive-only cri"},"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":"2606.26880","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-25T11:08:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"fec7b4763bea612f2c278754c661170a4b497919df6fb7e6cf1ffa373bb63051","abstract_canon_sha256":"b08eac7906e51abf76f885d0a357752c59941584df348bce6f3fa31505ffc01b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:02.810360Z","signature_b64":"ooC7EAff6P47kCM7zMl1OfllP6XlSbbhFX0jgLXwwlueTVktsMVMoR4VMmZ56qetfN+I1BJ5zizTSiP87jUjDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4b3f7f87712f116c7c17f0b0670a2996f227b260ef603e98a4a05116a74918f","last_reissued_at":"2026-06-26T01:16:02.809662Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:02.809662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Heterogeneous Neural Predictivity from Language Models During Naturalistic Comprehension","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Xiao Jia","submitted_at":"2026-06-25T11:08:49Z","abstract_excerpt":"Language-model representations provide structured, high-dimensional annotations of naturalistic language stimuli and can serve as informative neural predictors during comprehension. We analyzed locked derived data from Brain Treebank, MEG-MASC, and Podcast ECoG with eight frozen language models, blocked encoding models, and matched temporal, nuisance, and representation-capacity controls. Positive held-out prediction and gains over low-level baselines were widespread in source-level summaries. Across Brain Treebank and Podcast ECoG, 67 of 432 evaluable rows met a controlled predictive-only cri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26880","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/2606.26880/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":"2606.26880","created_at":"2026-06-26T01:16:02.809752+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.26880v1","created_at":"2026-06-26T01:16:02.809752+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26880","created_at":"2026-06-26T01:16:02.809752+00:00"},{"alias_kind":"pith_short_12","alias_value":"USZ7P6DXCLYR","created_at":"2026-06-26T01:16:02.809752+00:00"},{"alias_kind":"pith_short_16","alias_value":"USZ7P6DXCLYRNR6B","created_at":"2026-06-26T01:16:02.809752+00:00"},{"alias_kind":"pith_short_8","alias_value":"USZ7P6DX","created_at":"2026-06-26T01:16:02.809752+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/USZ7P6DXCLYRNR6BP4FQM4FCTF","json":"https://pith.science/pith/USZ7P6DXCLYRNR6BP4FQM4FCTF.json","graph_json":"https://pith.science/api/pith-number/USZ7P6DXCLYRNR6BP4FQM4FCTF/graph.json","events_json":"https://pith.science/api/pith-number/USZ7P6DXCLYRNR6BP4FQM4FCTF/events.json","paper":"https://pith.science/paper/USZ7P6DX"},"agent_actions":{"view_html":"https://pith.science/pith/USZ7P6DXCLYRNR6BP4FQM4FCTF","download_json":"https://pith.science/pith/USZ7P6DXCLYRNR6BP4FQM4FCTF.json","view_paper":"https://pith.science/paper/USZ7P6DX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.26880&json=true","fetch_graph":"https://pith.science/api/pith-number/USZ7P6DXCLYRNR6BP4FQM4FCTF/graph.json","fetch_events":"https://pith.science/api/pith-number/USZ7P6DXCLYRNR6BP4FQM4FCTF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/USZ7P6DXCLYRNR6BP4FQM4FCTF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/USZ7P6DXCLYRNR6BP4FQM4FCTF/action/storage_attestation","attest_author":"https://pith.science/pith/USZ7P6DXCLYRNR6BP4FQM4FCTF/action/author_attestation","sign_citation":"https://pith.science/pith/USZ7P6DXCLYRNR6BP4FQM4FCTF/action/citation_signature","submit_replication":"https://pith.science/pith/USZ7P6DXCLYRNR6BP4FQM4FCTF/action/replication_record"}},"created_at":"2026-06-26T01:16:02.809752+00:00","updated_at":"2026-06-26T01:16:02.809752+00:00"}