{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MRNDXEZSDIV75LALMJBGEF55M4","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":"5560225942361165c012e32f0edc3a1fe4a2e7eeaf209270907fd096d99f4202","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.OT","submitted_at":"2026-06-16T19:19:22Z","title_canon_sha256":"def86270ec579ad272508d5670fa32a3381e6dd0ff39a4d1c4fd558299fb3cfd"},"schema_version":"1.0","source":{"id":"2606.18424","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.18424","created_at":"2026-06-19T16:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"2606.18424v1","created_at":"2026-06-19T16:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18424","created_at":"2026-06-19T16:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"MRNDXEZSDIV7","created_at":"2026-06-19T16:11:00Z"},{"alias_kind":"pith_short_16","alias_value":"MRNDXEZSDIV75LAL","created_at":"2026-06-19T16:11:00Z"},{"alias_kind":"pith_short_8","alias_value":"MRNDXEZS","created_at":"2026-06-19T16:11:00Z"}],"graph_snapshots":[{"event_id":"sha256:f4902e4a5c25a69054077c822985785e76f95f9d9d5daf8b8d3b52e0b7e17470","target":"graph","created_at":"2026-06-19T16:11:00Z","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/2606.18424/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper develops a variational framework for regulated language generation. Starting from autoregressive token sampling, we derive the induced distribution over complete messages and relate it to an entropy-regularized Gibbs law. Regulation is modeled as an optimal discriminator whose convex-dual value is an f-divergence, and the generator-regulator interaction is formulated as a saddle-point problem. The framework applies to moderation, censorship, AI deception detection, compliance auditing, phishing defense, and manipulation control, where regulation concerns a distribution over possible","authors_text":"Quanyan Zhu","cross_cats":["cs.AI","cs.IT","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.OT","submitted_at":"2026-06-16T19:19:22Z","title":"A Variational Framework for LLM Generator-Regulator Games"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18424","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:aae042c865879e103972f154400869892d468277953e0a37c0c4a5490e9b8bd1","target":"record","created_at":"2026-06-19T16:11:00Z","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":"5560225942361165c012e32f0edc3a1fe4a2e7eeaf209270907fd096d99f4202","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.OT","submitted_at":"2026-06-16T19:19:22Z","title_canon_sha256":"def86270ec579ad272508d5670fa32a3381e6dd0ff39a4d1c4fd558299fb3cfd"},"schema_version":"1.0","source":{"id":"2606.18424","kind":"arxiv","version":1}},"canonical_sha256":"645a3b93321a2bfeac0b62426217bd67311cfdf105fb99670ab23966480d4b7a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"645a3b93321a2bfeac0b62426217bd67311cfdf105fb99670ab23966480d4b7a","first_computed_at":"2026-06-19T16:11:00.904953Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:11:00.904953Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gBmr7YMN7h0hkoIetzJwW+TH1RsZYH3XHKDSd32YA96krQfkhPqa1ERK/PJk4QwjZ0SlelU/6S5dq/T1PKL9Dg==","signature_status":"signed_v1","signed_at":"2026-06-19T16:11:00.905294Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.18424","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aae042c865879e103972f154400869892d468277953e0a37c0c4a5490e9b8bd1","sha256:f4902e4a5c25a69054077c822985785e76f95f9d9d5daf8b8d3b52e0b7e17470"],"state_sha256":"3a13e7a4af4bdece7f4e5d90daadc827df3d877d5f2dad7a75182465e35be53d"}