{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5J2FULSIXMI2HUYDP47TMEHUL4","short_pith_number":"pith:5J2FULSI","schema_version":"1.0","canonical_sha256":"ea745a2e48bb11a3d3037f3f3610f45f2b2b5588aa47c2f6a06689bc4a84480c","source":{"kind":"arxiv","id":"2605.29683","version":1},"attestation_state":"computed","paper":{"title":"WF-Bench: A Benchmark for Neural Network WaveFunction Expressivity and Scaling Laws","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Di Luo, Guijing Duan, Lixing Zhang","submitted_at":"2026-05-28T09:48:02Z","abstract_excerpt":"We present a comprehensive benchmarking dataset and empirical scaling law analysis for neural network wavefunctions by matching them to a wide spectrum of famous many body target wavefunctions. The dataset, WF-Bench, spans multiple distinct regimes of strongly correlated quantum matter, including topological states, Wigner crystals, and superconducting wavefunctions, providing a diverse and challenging test bed for neural network wavefunction expressivity. We introduce a systematic and reproducible benchmarking protocol for target wavefunction matching, enabling consistent performance evaluati"},"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":"2605.29683","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2026-05-28T09:48:02Z","cross_cats_sorted":[],"title_canon_sha256":"f8b66ff4df873d0aa05b6a7710fa60f8201cd607f8d0e5b778dc27ddbb63b1c6","abstract_canon_sha256":"a75ca7ddf5a8300b372fbdc3146ace7c291f9f84c73e9fdae09131803a811e80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:55.373387Z","signature_b64":"aSmzXBTC5fy6jWiUJTIdsFmNvlzb9/OQyrO6KsVqw2OmBCUAo9Gy+is5wLRtqsqL+XTNbIj0DqLWak+r8ARaAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea745a2e48bb11a3d3037f3f3610f45f2b2b5588aa47c2f6a06689bc4a84480c","last_reissued_at":"2026-05-29T01:05:55.372841Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:55.372841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WF-Bench: A Benchmark for Neural Network WaveFunction Expressivity and Scaling Laws","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Di Luo, Guijing Duan, Lixing Zhang","submitted_at":"2026-05-28T09:48:02Z","abstract_excerpt":"We present a comprehensive benchmarking dataset and empirical scaling law analysis for neural network wavefunctions by matching them to a wide spectrum of famous many body target wavefunctions. The dataset, WF-Bench, spans multiple distinct regimes of strongly correlated quantum matter, including topological states, Wigner crystals, and superconducting wavefunctions, providing a diverse and challenging test bed for neural network wavefunction expressivity. We introduce a systematic and reproducible benchmarking protocol for target wavefunction matching, enabling consistent performance evaluati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29683","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/2605.29683/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":"2605.29683","created_at":"2026-05-29T01:05:55.372923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29683v1","created_at":"2026-05-29T01:05:55.372923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29683","created_at":"2026-05-29T01:05:55.372923+00:00"},{"alias_kind":"pith_short_12","alias_value":"5J2FULSIXMI2","created_at":"2026-05-29T01:05:55.372923+00:00"},{"alias_kind":"pith_short_16","alias_value":"5J2FULSIXMI2HUYD","created_at":"2026-05-29T01:05:55.372923+00:00"},{"alias_kind":"pith_short_8","alias_value":"5J2FULSI","created_at":"2026-05-29T01:05:55.372923+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/5J2FULSIXMI2HUYDP47TMEHUL4","json":"https://pith.science/pith/5J2FULSIXMI2HUYDP47TMEHUL4.json","graph_json":"https://pith.science/api/pith-number/5J2FULSIXMI2HUYDP47TMEHUL4/graph.json","events_json":"https://pith.science/api/pith-number/5J2FULSIXMI2HUYDP47TMEHUL4/events.json","paper":"https://pith.science/paper/5J2FULSI"},"agent_actions":{"view_html":"https://pith.science/pith/5J2FULSIXMI2HUYDP47TMEHUL4","download_json":"https://pith.science/pith/5J2FULSIXMI2HUYDP47TMEHUL4.json","view_paper":"https://pith.science/paper/5J2FULSI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29683&json=true","fetch_graph":"https://pith.science/api/pith-number/5J2FULSIXMI2HUYDP47TMEHUL4/graph.json","fetch_events":"https://pith.science/api/pith-number/5J2FULSIXMI2HUYDP47TMEHUL4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5J2FULSIXMI2HUYDP47TMEHUL4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5J2FULSIXMI2HUYDP47TMEHUL4/action/storage_attestation","attest_author":"https://pith.science/pith/5J2FULSIXMI2HUYDP47TMEHUL4/action/author_attestation","sign_citation":"https://pith.science/pith/5J2FULSIXMI2HUYDP47TMEHUL4/action/citation_signature","submit_replication":"https://pith.science/pith/5J2FULSIXMI2HUYDP47TMEHUL4/action/replication_record"}},"created_at":"2026-05-29T01:05:55.372923+00:00","updated_at":"2026-05-29T01:05:55.372923+00:00"}