{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YJPOXVYOI5OBMCQX6ASD6W5E4A","short_pith_number":"pith:YJPOXVYO","schema_version":"1.0","canonical_sha256":"c25eebd70e475c160a17f0243f5ba4e03735a6e85befe9104a90c78ac8eafb5d","source":{"kind":"arxiv","id":"2502.05383","version":3},"attestation_state":"computed","paper":{"title":"Is attention all you need to solve the correlated electron problem?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cond-mat.mes-hall","cs.AI"],"primary_cat":"cond-mat.str-el","authors_text":"Khachatur Nazaryan, Liang Fu, Max Geier, Timothy Zaklama","submitted_at":"2025-02-07T23:41:41Z","abstract_excerpt":"The attention mechanism has transformed artificial intelligence research by its ability to learn relations between objects. In this work, we explore how a many-body wavefunction ansatz constructed from a large-parameter self-attention neural network can be used to solve the interacting electron problem in solids. By a systematic neural-network variational Monte Carlo study on a moir\\'e quantum material, we demonstrate that the self-attention ansatz provides an accurate and efficient solution without human bias. Moreover, our numerical study finds that the required number of variational paramet"},"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":"2502.05383","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cond-mat.str-el","submitted_at":"2025-02-07T23:41:41Z","cross_cats_sorted":["cond-mat.mes-hall","cs.AI"],"title_canon_sha256":"872762071b7461d1f53a2d3b0a388d552d3bdfdced9191949617229b03f37a3e","abstract_canon_sha256":"5803fd178d0ae9a4f8de575c343f7a4ccc9677ffc210901beb983b5386da2624"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:27.886508Z","signature_b64":"iIQHKYzQb0OuN5jjoGhXmhLR2JOIyReXZDpPB70R9j8a2ZrwzgjBIzlAfzmLAGBg6HVRLXb1+Kqd/Eth1dsPCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c25eebd70e475c160a17f0243f5ba4e03735a6e85befe9104a90c78ac8eafb5d","last_reissued_at":"2026-07-05T11:36:27.885895Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:27.885895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Is attention all you need to solve the correlated electron problem?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cond-mat.mes-hall","cs.AI"],"primary_cat":"cond-mat.str-el","authors_text":"Khachatur Nazaryan, Liang Fu, Max Geier, Timothy Zaklama","submitted_at":"2025-02-07T23:41:41Z","abstract_excerpt":"The attention mechanism has transformed artificial intelligence research by its ability to learn relations between objects. In this work, we explore how a many-body wavefunction ansatz constructed from a large-parameter self-attention neural network can be used to solve the interacting electron problem in solids. By a systematic neural-network variational Monte Carlo study on a moir\\'e quantum material, we demonstrate that the self-attention ansatz provides an accurate and efficient solution without human bias. Moreover, our numerical study finds that the required number of variational paramet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05383","kind":"arxiv","version":3},"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/2502.05383/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":"2502.05383","created_at":"2026-07-05T11:36:27.885977+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.05383v3","created_at":"2026-07-05T11:36:27.885977+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05383","created_at":"2026-07-05T11:36:27.885977+00:00"},{"alias_kind":"pith_short_12","alias_value":"YJPOXVYOI5OB","created_at":"2026-07-05T11:36:27.885977+00:00"},{"alias_kind":"pith_short_16","alias_value":"YJPOXVYOI5OBMCQX","created_at":"2026-07-05T11:36:27.885977+00:00"},{"alias_kind":"pith_short_8","alias_value":"YJPOXVYO","created_at":"2026-07-05T11:36:27.885977+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08616","citing_title":"Accurate Self-Attention Wavefunctions at Large Scale","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"2606.08707","citing_title":"Simulating quantum circuits with a neural statebank","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06302","citing_title":"Quantum Electron Quasicrystal","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A","json":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A.json","graph_json":"https://pith.science/api/pith-number/YJPOXVYOI5OBMCQX6ASD6W5E4A/graph.json","events_json":"https://pith.science/api/pith-number/YJPOXVYOI5OBMCQX6ASD6W5E4A/events.json","paper":"https://pith.science/paper/YJPOXVYO"},"agent_actions":{"view_html":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A","download_json":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A.json","view_paper":"https://pith.science/paper/YJPOXVYO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.05383&json=true","fetch_graph":"https://pith.science/api/pith-number/YJPOXVYOI5OBMCQX6ASD6W5E4A/graph.json","fetch_events":"https://pith.science/api/pith-number/YJPOXVYOI5OBMCQX6ASD6W5E4A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A/action/storage_attestation","attest_author":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A/action/author_attestation","sign_citation":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A/action/citation_signature","submit_replication":"https://pith.science/pith/YJPOXVYOI5OBMCQX6ASD6W5E4A/action/replication_record"}},"created_at":"2026-07-05T11:36:27.885977+00:00","updated_at":"2026-07-05T11:36:27.885977+00:00"}