{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:U4YRYUER6RYAWTB3G3ODWFIJRZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"cf5e6310fa38557b7ae95d14d24be8ea9c7aa05efaeacb66d71b9887381f4db9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2026-06-28T12:34:24Z","title_canon_sha256":"d7399c213aa4f00902b773732e89d55b205f053f13ee674d5dec03b4e5e6ecf0"},"schema_version":"1.0","source":{"id":"2606.29371","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29371","created_at":"2026-06-30T01:18:03Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29371v1","created_at":"2026-06-30T01:18:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29371","created_at":"2026-06-30T01:18:03Z"},{"alias_kind":"pith_short_12","alias_value":"U4YRYUER6RYA","created_at":"2026-06-30T01:18:03Z"},{"alias_kind":"pith_short_16","alias_value":"U4YRYUER6RYAWTB3","created_at":"2026-06-30T01:18:03Z"},{"alias_kind":"pith_short_8","alias_value":"U4YRYUER","created_at":"2026-06-30T01:18:03Z"}],"graph_snapshots":[{"event_id":"sha256:edc8378f0815206df780e67d7d2c4b9aaad9de678b4a3ba8d16b0deb216b9952","target":"graph","created_at":"2026-06-30T01:18:03Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.29371/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Classical actuarial pricing models, such as the generalized linear model, are valued for transparency and ease of governance, but they use interactions among risk factors only when these are supplied through explicit feature engineering. We study whether embeddings from a pre-trained large language model, computed from a natural-language description of each policyholder, can replace hand-crafted features as inputs to a standard actuarial pricing model, taking Poisson claim-frequency regression as the main example. The language model is used only to construct deterministic embedding covariates;","authors_text":"Christopher Blier-Wong, Derek Kusmenko","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2026-06-28T12:34:24Z","title":"Semantic insurance pricing with large language models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29371","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:77ea55b5a34daa111da21de6ed9396834b44bb5b66cd996e53e85786778e0fa5","target":"record","created_at":"2026-06-30T01:18:03Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"cf5e6310fa38557b7ae95d14d24be8ea9c7aa05efaeacb66d71b9887381f4db9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2026-06-28T12:34:24Z","title_canon_sha256":"d7399c213aa4f00902b773732e89d55b205f053f13ee674d5dec03b4e5e6ecf0"},"schema_version":"1.0","source":{"id":"2606.29371","kind":"arxiv","version":1}},"canonical_sha256":"a7311c5091f4700b4c3b36dc3b15098e52dfcc9a40b3d32a75c3efb290599ebc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7311c5091f4700b4c3b36dc3b15098e52dfcc9a40b3d32a75c3efb290599ebc","first_computed_at":"2026-06-30T01:18:03.242090Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T01:18:03.242090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9FazLQK5BcGlnv71JWh3EDUiiJTqMUYxzunxEHjnNRQ5C6M+q4oPVkvhJXQxlwN6G+MD2wwxJ3DVEOMJcVr1DA==","signature_status":"signed_v1","signed_at":"2026-06-30T01:18:03.245074Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.29371","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:77ea55b5a34daa111da21de6ed9396834b44bb5b66cd996e53e85786778e0fa5","sha256:edc8378f0815206df780e67d7d2c4b9aaad9de678b4a3ba8d16b0deb216b9952"],"state_sha256":"243362dd6e80e77217c6582e44d0c757f2246cfa9f2ab93e8beaf9da346d161f"}