{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YSDMYKEKBRH5SLTLBP6W2YVGW5","short_pith_number":"pith:YSDMYKEK","schema_version":"1.0","canonical_sha256":"c486cc288a0c4fd92e6b0bfd6d62a6b774a231b2519b31ef4b4017e2fa78f331","source":{"kind":"arxiv","id":"2605.26763","version":1},"attestation_state":"computed","paper":{"title":"Adversarial Training for Robust Coverage Network under Worst-case Facility Losses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Changhao Miao, Chen Chen, Fang Deng, Tongyu Wu, Yuntian Zhang","submitted_at":"2026-05-26T09:36:37Z","abstract_excerpt":"The Maximal Covering Location-Interdiction Problem (MCLIP) is a classic bi-level optimization problem, which is fundamental to resilient infrastructure planning yet remains computationally intractable. Specifically, the upper level determines facility locations to maximize coverage, while the lower level executes worst-case interdiction to minimize the coverage. The strong coupling between the upper and lower levels, combined with their respective high combinatorial complexity, renders traditional methods ineffective. To bridge this gap, we propose a Dual-Agent Deep Reinforcement Learning (DAD"},"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":"2605.26763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-26T09:36:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1a9acb0bd6f340a1fea222b5c653f89f7d8710a8d83053376f8e0a58541ae9d9","abstract_canon_sha256":"9e3a2c2ee831a40cd27ee65d0d55c4857d89e76c73b57901fdd6b3aab1c2d78e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:06:11.416100Z","signature_b64":"Qlkoq/Vugv2Kdzxb65H1o479TnQsaZAqgjFDrzYi1YSJRuQZwtZ8wIbT0NQKv3N5Cb1OAK7IKLJb+0qF9KcRCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c486cc288a0c4fd92e6b0bfd6d62a6b774a231b2519b31ef4b4017e2fa78f331","last_reissued_at":"2026-05-27T01:06:11.414846Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:06:11.414846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Training for Robust Coverage Network under Worst-case Facility Losses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Changhao Miao, Chen Chen, Fang Deng, Tongyu Wu, Yuntian Zhang","submitted_at":"2026-05-26T09:36:37Z","abstract_excerpt":"The Maximal Covering Location-Interdiction Problem (MCLIP) is a classic bi-level optimization problem, which is fundamental to resilient infrastructure planning yet remains computationally intractable. Specifically, the upper level determines facility locations to maximize coverage, while the lower level executes worst-case interdiction to minimize the coverage. The strong coupling between the upper and lower levels, combined with their respective high combinatorial complexity, renders traditional methods ineffective. To bridge this gap, we propose a Dual-Agent Deep Reinforcement Learning (DAD"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26763","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/2605.26763/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":"2605.26763","created_at":"2026-05-27T01:06:11.415072+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.26763v1","created_at":"2026-05-27T01:06:11.415072+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26763","created_at":"2026-05-27T01:06:11.415072+00:00"},{"alias_kind":"pith_short_12","alias_value":"YSDMYKEKBRH5","created_at":"2026-05-27T01:06:11.415072+00:00"},{"alias_kind":"pith_short_16","alias_value":"YSDMYKEKBRH5SLTL","created_at":"2026-05-27T01:06:11.415072+00:00"},{"alias_kind":"pith_short_8","alias_value":"YSDMYKEK","created_at":"2026-05-27T01:06:11.415072+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/YSDMYKEKBRH5SLTLBP6W2YVGW5","json":"https://pith.science/pith/YSDMYKEKBRH5SLTLBP6W2YVGW5.json","graph_json":"https://pith.science/api/pith-number/YSDMYKEKBRH5SLTLBP6W2YVGW5/graph.json","events_json":"https://pith.science/api/pith-number/YSDMYKEKBRH5SLTLBP6W2YVGW5/events.json","paper":"https://pith.science/paper/YSDMYKEK"},"agent_actions":{"view_html":"https://pith.science/pith/YSDMYKEKBRH5SLTLBP6W2YVGW5","download_json":"https://pith.science/pith/YSDMYKEKBRH5SLTLBP6W2YVGW5.json","view_paper":"https://pith.science/paper/YSDMYKEK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.26763&json=true","fetch_graph":"https://pith.science/api/pith-number/YSDMYKEKBRH5SLTLBP6W2YVGW5/graph.json","fetch_events":"https://pith.science/api/pith-number/YSDMYKEKBRH5SLTLBP6W2YVGW5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YSDMYKEKBRH5SLTLBP6W2YVGW5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YSDMYKEKBRH5SLTLBP6W2YVGW5/action/storage_attestation","attest_author":"https://pith.science/pith/YSDMYKEKBRH5SLTLBP6W2YVGW5/action/author_attestation","sign_citation":"https://pith.science/pith/YSDMYKEKBRH5SLTLBP6W2YVGW5/action/citation_signature","submit_replication":"https://pith.science/pith/YSDMYKEKBRH5SLTLBP6W2YVGW5/action/replication_record"}},"created_at":"2026-05-27T01:06:11.415072+00:00","updated_at":"2026-05-27T01:06:11.415072+00:00"}