{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:A5JRGNSLOUUTAWAERMBQMP3JV5","short_pith_number":"pith:A5JRGNSL","schema_version":"1.0","canonical_sha256":"075313364b75293058048b03063f69af6f37bd0b01f387547a27ac4882b83a73","source":{"kind":"arxiv","id":"2606.30464","version":1},"attestation_state":"computed","paper":{"title":"NQS-Agent: Health-Aware Agentic Hyperparameter Optimization for Neural-Network Quantum States","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","physics.comp-ph"],"primary_cat":"cond-mat.str-el","authors_text":"Jia-Qi Wang, Rong-Qiang He, Xiao-Qi Han, Ze-Feng Gao, Zhong-Yi Lu","submitted_at":"2026-06-29T15:28:27Z","abstract_excerpt":"Neural-network quantum states (NQS) provide expressive variational representations for strongly correlated quantum many-body systems, but their practical accuracy depends sensitively on architecture-level hyperparameters and optimization schedules. Here we develop NQS-Agent, an implemented open-source software framework for health-aware hyperparameter optimization (HPO) in NQS calculations. Its workflow monitors energy trajectories, detects destructive optimization events, stops unstable calculations, modifies the learning-rate schedule, resumes optimization from safe checkpoints, and ranks ca"},"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":"2606.30464","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.str-el","submitted_at":"2026-06-29T15:28:27Z","cross_cats_sorted":["cond-mat.dis-nn","physics.comp-ph"],"title_canon_sha256":"b9bc9bfbc03f8ad5ef5912d174d841da426a14220e0555dac0394bbfbf618979","abstract_canon_sha256":"e34a7af6b1e562d054a628467b4658c13467bbd00ee2526ab875c7553ae461d7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:18:16.489730Z","signature_b64":"XiAr0FG6lkRQAODfLR6gZ8vrxx6t5/Ruy6HnHJxwxrEJAwh4j60G5lrcPxdcJTHJnGD8UQhSfy8KVjSjIpSjBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"075313364b75293058048b03063f69af6f37bd0b01f387547a27ac4882b83a73","last_reissued_at":"2026-06-30T02:18:16.488090Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:18:16.488090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NQS-Agent: Health-Aware Agentic Hyperparameter Optimization for Neural-Network Quantum States","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","physics.comp-ph"],"primary_cat":"cond-mat.str-el","authors_text":"Jia-Qi Wang, Rong-Qiang He, Xiao-Qi Han, Ze-Feng Gao, Zhong-Yi Lu","submitted_at":"2026-06-29T15:28:27Z","abstract_excerpt":"Neural-network quantum states (NQS) provide expressive variational representations for strongly correlated quantum many-body systems, but their practical accuracy depends sensitively on architecture-level hyperparameters and optimization schedules. Here we develop NQS-Agent, an implemented open-source software framework for health-aware hyperparameter optimization (HPO) in NQS calculations. Its workflow monitors energy trajectories, detects destructive optimization events, stops unstable calculations, modifies the learning-rate schedule, resumes optimization from safe checkpoints, and ranks ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30464","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/2606.30464/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":"2606.30464","created_at":"2026-06-30T02:18:16.488980+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30464v1","created_at":"2026-06-30T02:18:16.488980+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30464","created_at":"2026-06-30T02:18:16.488980+00:00"},{"alias_kind":"pith_short_12","alias_value":"A5JRGNSLOUUT","created_at":"2026-06-30T02:18:16.488980+00:00"},{"alias_kind":"pith_short_16","alias_value":"A5JRGNSLOUUTAWAE","created_at":"2026-06-30T02:18:16.488980+00:00"},{"alias_kind":"pith_short_8","alias_value":"A5JRGNSL","created_at":"2026-06-30T02:18:16.488980+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/A5JRGNSLOUUTAWAERMBQMP3JV5","json":"https://pith.science/pith/A5JRGNSLOUUTAWAERMBQMP3JV5.json","graph_json":"https://pith.science/api/pith-number/A5JRGNSLOUUTAWAERMBQMP3JV5/graph.json","events_json":"https://pith.science/api/pith-number/A5JRGNSLOUUTAWAERMBQMP3JV5/events.json","paper":"https://pith.science/paper/A5JRGNSL"},"agent_actions":{"view_html":"https://pith.science/pith/A5JRGNSLOUUTAWAERMBQMP3JV5","download_json":"https://pith.science/pith/A5JRGNSLOUUTAWAERMBQMP3JV5.json","view_paper":"https://pith.science/paper/A5JRGNSL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30464&json=true","fetch_graph":"https://pith.science/api/pith-number/A5JRGNSLOUUTAWAERMBQMP3JV5/graph.json","fetch_events":"https://pith.science/api/pith-number/A5JRGNSLOUUTAWAERMBQMP3JV5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A5JRGNSLOUUTAWAERMBQMP3JV5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A5JRGNSLOUUTAWAERMBQMP3JV5/action/storage_attestation","attest_author":"https://pith.science/pith/A5JRGNSLOUUTAWAERMBQMP3JV5/action/author_attestation","sign_citation":"https://pith.science/pith/A5JRGNSLOUUTAWAERMBQMP3JV5/action/citation_signature","submit_replication":"https://pith.science/pith/A5JRGNSLOUUTAWAERMBQMP3JV5/action/replication_record"}},"created_at":"2026-06-30T02:18:16.488980+00:00","updated_at":"2026-06-30T02:18:16.488980+00:00"}