{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FGE2G5PEGNVFUIQ3ISEBV4WWHI","short_pith_number":"pith:FGE2G5PE","schema_version":"1.0","canonical_sha256":"2989a375e4336a5a221b44881af2d63a3a99ecede21ab6212eca7834c0ee0e10","source":{"kind":"arxiv","id":"2608.08344","version":1},"attestation_state":"computed","paper":{"title":"PRISM: A Predictive Protocol for Permutation Optimization via Landscape Diagnostics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.OC"],"primary_cat":"cs.LG","authors_text":"Blessings Mambwe","submitted_at":"2026-08-08T21:52:04Z","abstract_excerpt":"Permutation optimization arises whenever the components of a system are fixed but their ordering affects performance. We introduce PRISM, a predictive protocol for permutation optimization that measures a fitness landscape before selecting a search strategy. PRISM uses inexpensive landscape diagnostics, including one-step move autocorrelation and fitness-distance correlation, to predict useful mutation operators, identify when structured search is likely to outperform random sampling, and detect regimes in which search provides little advantage. Across synthetic permutation landscapes, neural "},"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":"2608.08344","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-08T21:52:04Z","cross_cats_sorted":["cs.AI","math.OC"],"title_canon_sha256":"1c583e3cd945c3b45db86f7916d9bf8534d544bf9145e71137074eeb6c95157c","abstract_canon_sha256":"42cd3e9241e52efc5929304d937a4d9922c66158f27363e191d1b731bec2534c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T01:22:22.709241Z","signature_b64":"8HNM+8INlS+5oPnUa7OFQu31WxEBBAP8pDedgFudjjiuIp5d1MrTosPhsebzg1u+NYxpOTo+LVyqDg+rSawBCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2989a375e4336a5a221b44881af2d63a3a99ecede21ab6212eca7834c0ee0e10","last_reissued_at":"2026-08-11T01:22:22.706786Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T01:22:22.706786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PRISM: A Predictive Protocol for Permutation Optimization via Landscape Diagnostics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.OC"],"primary_cat":"cs.LG","authors_text":"Blessings Mambwe","submitted_at":"2026-08-08T21:52:04Z","abstract_excerpt":"Permutation optimization arises whenever the components of a system are fixed but their ordering affects performance. We introduce PRISM, a predictive protocol for permutation optimization that measures a fitness landscape before selecting a search strategy. PRISM uses inexpensive landscape diagnostics, including one-step move autocorrelation and fitness-distance correlation, to predict useful mutation operators, identify when structured search is likely to outperform random sampling, and detect regimes in which search provides little advantage. Across synthetic permutation landscapes, neural "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.08344","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/2608.08344/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":"2608.08344","created_at":"2026-08-11T01:22:22.707535+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.08344v1","created_at":"2026-08-11T01:22:22.707535+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.08344","created_at":"2026-08-11T01:22:22.707535+00:00"},{"alias_kind":"pith_short_12","alias_value":"FGE2G5PEGNVF","created_at":"2026-08-11T01:22:22.707535+00:00"},{"alias_kind":"pith_short_16","alias_value":"FGE2G5PEGNVFUIQ3","created_at":"2026-08-11T01:22:22.707535+00:00"},{"alias_kind":"pith_short_8","alias_value":"FGE2G5PE","created_at":"2026-08-11T01:22:22.707535+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/FGE2G5PEGNVFUIQ3ISEBV4WWHI","json":"https://pith.science/pith/FGE2G5PEGNVFUIQ3ISEBV4WWHI.json","graph_json":"https://pith.science/api/pith-number/FGE2G5PEGNVFUIQ3ISEBV4WWHI/graph.json","events_json":"https://pith.science/api/pith-number/FGE2G5PEGNVFUIQ3ISEBV4WWHI/events.json","paper":"https://pith.science/paper/FGE2G5PE"},"agent_actions":{"view_html":"https://pith.science/pith/FGE2G5PEGNVFUIQ3ISEBV4WWHI","download_json":"https://pith.science/pith/FGE2G5PEGNVFUIQ3ISEBV4WWHI.json","view_paper":"https://pith.science/paper/FGE2G5PE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.08344&json=true","fetch_graph":"https://pith.science/api/pith-number/FGE2G5PEGNVFUIQ3ISEBV4WWHI/graph.json","fetch_events":"https://pith.science/api/pith-number/FGE2G5PEGNVFUIQ3ISEBV4WWHI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FGE2G5PEGNVFUIQ3ISEBV4WWHI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FGE2G5PEGNVFUIQ3ISEBV4WWHI/action/storage_attestation","attest_author":"https://pith.science/pith/FGE2G5PEGNVFUIQ3ISEBV4WWHI/action/author_attestation","sign_citation":"https://pith.science/pith/FGE2G5PEGNVFUIQ3ISEBV4WWHI/action/citation_signature","submit_replication":"https://pith.science/pith/FGE2G5PEGNVFUIQ3ISEBV4WWHI/action/replication_record"}},"created_at":"2026-08-11T01:22:22.707535+00:00","updated_at":"2026-08-11T01:22:22.707535+00:00"}