{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XJRLC5J63UA5EUOPONQ2SKWTHH","short_pith_number":"pith:XJRLC5J6","schema_version":"1.0","canonical_sha256":"ba62b1753edd01d251cf7361a92ad339df27cb7d5dd97d6e82652f089c96ba14","source":{"kind":"arxiv","id":"2606.24416","version":1},"attestation_state":"computed","paper":{"title":"Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bingnan Xiao, Chenhao Yang, Tony Q. S. Quek, Wei Ni, Xin Wang","submitted_at":"2026-06-23T10:53:12Z","abstract_excerpt":"Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective. This paper presents Agentic long-term performance optimization (Agentic-LTPO), a nested bilevel optimization framework that can be applied to adaptive physical layer problem configuration. The key idea is to employ agentic AI to generate upper-level configurations in a bilevel optimization structure, where evolving operator policies, environment summaries, and historical experiences are translated into structured low"},"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":"2606.24416","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-23T10:53:12Z","cross_cats_sorted":[],"title_canon_sha256":"44cabcca3f5a957acacbd3190d0c986f2a39b936be454a8142fe8b22cc73b34d","abstract_canon_sha256":"b8ad7544ed36981a6b6d35da22060d1e3a3b134fa9992bebed0b24982cf9fbf7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:15:29.894089Z","signature_b64":"iisSM48V5dIdtGHw840BWwAhxCS3d0BKCbSRq79icfBQpli41UhoEjnf6J4GUGdH1iLfJpYqxe4Pl6n4fU1eAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba62b1753edd01d251cf7361a92ad339df27cb7d5dd97d6e82652f089c96ba14","last_reissued_at":"2026-06-24T01:15:29.893739Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:15:29.893739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bingnan Xiao, Chenhao Yang, Tony Q. S. Quek, Wei Ni, Xin Wang","submitted_at":"2026-06-23T10:53:12Z","abstract_excerpt":"Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective. This paper presents Agentic long-term performance optimization (Agentic-LTPO), a nested bilevel optimization framework that can be applied to adaptive physical layer problem configuration. The key idea is to employ agentic AI to generate upper-level configurations in a bilevel optimization structure, where evolving operator policies, environment summaries, and historical experiences are translated into structured low"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24416","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/2606.24416/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":"2606.24416","created_at":"2026-06-24T01:15:29.893801+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24416v1","created_at":"2026-06-24T01:15:29.893801+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24416","created_at":"2026-06-24T01:15:29.893801+00:00"},{"alias_kind":"pith_short_12","alias_value":"XJRLC5J63UA5","created_at":"2026-06-24T01:15:29.893801+00:00"},{"alias_kind":"pith_short_16","alias_value":"XJRLC5J63UA5EUOP","created_at":"2026-06-24T01:15:29.893801+00:00"},{"alias_kind":"pith_short_8","alias_value":"XJRLC5J6","created_at":"2026-06-24T01:15:29.893801+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/XJRLC5J63UA5EUOPONQ2SKWTHH","json":"https://pith.science/pith/XJRLC5J63UA5EUOPONQ2SKWTHH.json","graph_json":"https://pith.science/api/pith-number/XJRLC5J63UA5EUOPONQ2SKWTHH/graph.json","events_json":"https://pith.science/api/pith-number/XJRLC5J63UA5EUOPONQ2SKWTHH/events.json","paper":"https://pith.science/paper/XJRLC5J6"},"agent_actions":{"view_html":"https://pith.science/pith/XJRLC5J63UA5EUOPONQ2SKWTHH","download_json":"https://pith.science/pith/XJRLC5J63UA5EUOPONQ2SKWTHH.json","view_paper":"https://pith.science/paper/XJRLC5J6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24416&json=true","fetch_graph":"https://pith.science/api/pith-number/XJRLC5J63UA5EUOPONQ2SKWTHH/graph.json","fetch_events":"https://pith.science/api/pith-number/XJRLC5J63UA5EUOPONQ2SKWTHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XJRLC5J63UA5EUOPONQ2SKWTHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XJRLC5J63UA5EUOPONQ2SKWTHH/action/storage_attestation","attest_author":"https://pith.science/pith/XJRLC5J63UA5EUOPONQ2SKWTHH/action/author_attestation","sign_citation":"https://pith.science/pith/XJRLC5J63UA5EUOPONQ2SKWTHH/action/citation_signature","submit_replication":"https://pith.science/pith/XJRLC5J63UA5EUOPONQ2SKWTHH/action/replication_record"}},"created_at":"2026-06-24T01:15:29.893801+00:00","updated_at":"2026-06-24T01:15:29.893801+00:00"}