{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6UP5UDUZOXAAYIFCNI5OK3UOOL","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":"be6e866b801660dd1922e6817e0e45eba26cb651d0faf88ad7aafb7a73fd53f4","cross_cats_sorted":["cs.AI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-06-18T06:28:22Z","title_canon_sha256":"7efcf61a0213629ce3e05aa15c6e438716194d54ed902fcbf5ac683495612b4a"},"schema_version":"1.0","source":{"id":"2506.15167","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.15167","created_at":"2026-07-05T11:34:09Z"},{"alias_kind":"arxiv_version","alias_value":"2506.15167v2","created_at":"2026-07-05T11:34:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.15167","created_at":"2026-07-05T11:34:09Z"},{"alias_kind":"pith_short_12","alias_value":"6UP5UDUZOXAA","created_at":"2026-07-05T11:34:09Z"},{"alias_kind":"pith_short_16","alias_value":"6UP5UDUZOXAAYIFC","created_at":"2026-07-05T11:34:09Z"},{"alias_kind":"pith_short_8","alias_value":"6UP5UDUZ","created_at":"2026-07-05T11:34:09Z"}],"graph_snapshots":[{"event_id":"sha256:385e59a131602772b7164196e9fa1cb6995266d4a694288f2c52e483d3831bf9","target":"graph","created_at":"2026-07-05T11:34:09Z","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/2506.15167/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyper-parameters are essential and critical for the performance of communication algorithms. However, current hyper-parameters optimization approaches for Warm-Start Particles Swarm Optimization with Crossover and Mutation (WS-PSO-CM) algorithm, designed for radio map-enabled unmanned aerial vehicle (UAV) trajectory and communication, are primarily heuristic-based, exhibiting low levels of automation and improvable performance. In this paper, we design an Large Language Model (LLM) agent for automatic hyper-parameters-tuning, where an iterative framework and Model Context Protocol (MCP) are ap","authors_text":"Jianqiu Peng, Menghao Hu, Mingjie Shao, Shuai Wang, Tong Zhang, Wanli Ni, Wanzhe Wang, Weihuang Zhong, Yixin Zhang","cross_cats":["cs.AI","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-06-18T06:28:22Z","title":"LLM Agent for Hyper-Parameter Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.15167","kind":"arxiv","version":2},"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":"sha256:75f52ae99327635b057dc3bddc59df473a096c304d9313231fc371adc2fe94df","target":"record","created_at":"2026-07-05T11:34:09Z","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":"be6e866b801660dd1922e6817e0e45eba26cb651d0faf88ad7aafb7a73fd53f4","cross_cats_sorted":["cs.AI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-06-18T06:28:22Z","title_canon_sha256":"7efcf61a0213629ce3e05aa15c6e438716194d54ed902fcbf5ac683495612b4a"},"schema_version":"1.0","source":{"id":"2506.15167","kind":"arxiv","version":2}},"canonical_sha256":"f51fda0e9975c00c20a26a3ae56e8e72c888456f77399eb4951259fc65854bfb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f51fda0e9975c00c20a26a3ae56e8e72c888456f77399eb4951259fc65854bfb","first_computed_at":"2026-07-05T11:34:09.505985Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:34:09.505985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nTYwpUvQSCbjUQRE0nWmrhNzgEDS877fktuZPesf1a7B0t4atVq+LifbzQ4jGmL8WJwHfYR0aeRf/P7+bBV9AA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:34:09.506543Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.15167","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75f52ae99327635b057dc3bddc59df473a096c304d9313231fc371adc2fe94df","sha256:385e59a131602772b7164196e9fa1cb6995266d4a694288f2c52e483d3831bf9"],"state_sha256":"e1aa37873e4c860542baf05f0189f3802d71ad79ab43c8dec61eb6cf1b738bff"}