{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TABDJPPXVLDSBF4QJUL5JQBVFI","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":"0c8e7f5a8b0732257e27dea206ab86ce1542f86780d1ae2db2b52f8af78e64d9","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-26T10:24:43Z","title_canon_sha256":"35a6e720c60f8c080ddccf9762420fc4de427cd2abeefa8ef11f9973074ff178"},"schema_version":"1.0","source":{"id":"2605.26808","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.26808","created_at":"2026-05-27T01:06:13Z"},{"alias_kind":"arxiv_version","alias_value":"2605.26808v1","created_at":"2026-05-27T01:06:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26808","created_at":"2026-05-27T01:06:13Z"},{"alias_kind":"pith_short_12","alias_value":"TABDJPPXVLDS","created_at":"2026-05-27T01:06:13Z"},{"alias_kind":"pith_short_16","alias_value":"TABDJPPXVLDSBF4Q","created_at":"2026-05-27T01:06:13Z"},{"alias_kind":"pith_short_8","alias_value":"TABDJPPX","created_at":"2026-05-27T01:06:13Z"}],"graph_snapshots":[{"event_id":"sha256:17bd3a34af9bd5d786dc383acbbcf87c762eccd5152d6ffd47a4cb9d92f1efa8","target":"graph","created_at":"2026-05-27T01:06:13Z","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/2605.26808/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hallucination is a central limitation of large language models (LLMs), and substantial effort has been devoted to understanding and mitigating it. Towards this, Kalai and Vempala (STOC 2024) introduced a probabilistic framework formalizing calibration and hallucination, and showed that, with high probability, calibrated LLMs hallucinate roughly at the rate of the \"missing mass\", a measure of how incomplete the training data is relative to its source. This raises two fundamental questions: (i) what property of a calibrated LLM makes hallucinations unavoidable? and (ii) can hallucinations be avo","authors_text":"Nishant P. Das, Piyush Srivastava","cross_cats":["cs.AI","cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-26T10:24:43Z","title":"Innovation: An Almost Characterization of Hallucination"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26808","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":"sha256:0d6465d6c80a25ecbed57269f1927ccef6d0f7f7b685701703556d2bfe00c8ec","target":"record","created_at":"2026-05-27T01:06:13Z","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":"0c8e7f5a8b0732257e27dea206ab86ce1542f86780d1ae2db2b52f8af78e64d9","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-26T10:24:43Z","title_canon_sha256":"35a6e720c60f8c080ddccf9762420fc4de427cd2abeefa8ef11f9973074ff178"},"schema_version":"1.0","source":{"id":"2605.26808","kind":"arxiv","version":1}},"canonical_sha256":"980234bdf7aac72097904d17d4c0352a245edc86bcd679c4e8e6347904f9083f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"980234bdf7aac72097904d17d4c0352a245edc86bcd679c4e8e6347904f9083f","first_computed_at":"2026-05-27T01:06:13.767188Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-27T01:06:13.767188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OlKO/vLnTkY//lIr0ZO1qjlhWS5tqFYNyLSRqnD6ARH6iyY7zEZ0W4UaXwE6QYSk0Jv1mBo2ET9xexD6S7ehDA==","signature_status":"signed_v1","signed_at":"2026-05-27T01:06:13.767793Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.26808","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0d6465d6c80a25ecbed57269f1927ccef6d0f7f7b685701703556d2bfe00c8ec","sha256:17bd3a34af9bd5d786dc383acbbcf87c762eccd5152d6ffd47a4cb9d92f1efa8"],"state_sha256":"81a773a607f837e72c63a59a7e0dc64f6de7e16841840ac628b9c4213dbc2d38"}