{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:T5QXJU472E2V6WKIQE6DUZQJXX","short_pith_number":"pith:T5QXJU47","schema_version":"1.0","canonical_sha256":"9f6174d39fd1355f5948813c3a6609bdf1dfe41e52de00f502cda02d521a924d","source":{"kind":"arxiv","id":"2606.21974","version":1},"attestation_state":"computed","paper":{"title":"Fine-Tuning Large Language Models for Quantum Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"quant-ph","authors_text":"Casey R. Myers, James Quach, Katherine Ip, Peiyong Wang, Udaya Parampalli","submitted_at":"2026-06-20T10:06:29Z","abstract_excerpt":"Large language models (LLMs) exhibit abilities beyond natural language modelling and text generation. Recent advances in their reasoning capabilities have spurred interest in applying LLMs to complex scientific tasks requiring deep domain expertise and sophisticated reasoning. Quantum computing, as a highly specialised field with significant knowledge barriers and hardware constraints, could greatly benefit from such advancements. However, a key open question that first must be answered is: How can we develop fine-tuning pipelines that instil genuine quantum reasoning in LLMs, rather than task"},"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":"2606.21974","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-06-20T10:06:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6e5d9fa4f2921a8ec2d3132f6c4e8579206d97245ab4f47d545dee7fd8c0a06f","abstract_canon_sha256":"6de47e7d65103bbac738d7b6a1eca9d9fc0fc30302cf1cfce548277dd30469ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:04.714537Z","signature_b64":"fsEkkpw63kZg4K0vsEpnSqf4Np5mZgvLT7PKoW83oE6eyxCKf7F/AFKHRria4vJBAqFAuz29GB2d6vt+lWzvDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f6174d39fd1355f5948813c3a6609bdf1dfe41e52de00f502cda02d521a924d","last_reissued_at":"2026-06-23T02:13:04.714121Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:04.714121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fine-Tuning Large Language Models for Quantum Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"quant-ph","authors_text":"Casey R. Myers, James Quach, Katherine Ip, Peiyong Wang, Udaya Parampalli","submitted_at":"2026-06-20T10:06:29Z","abstract_excerpt":"Large language models (LLMs) exhibit abilities beyond natural language modelling and text generation. Recent advances in their reasoning capabilities have spurred interest in applying LLMs to complex scientific tasks requiring deep domain expertise and sophisticated reasoning. Quantum computing, as a highly specialised field with significant knowledge barriers and hardware constraints, could greatly benefit from such advancements. However, a key open question that first must be answered is: How can we develop fine-tuning pipelines that instil genuine quantum reasoning in LLMs, rather than task"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21974","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/2606.21974/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":"2606.21974","created_at":"2026-06-23T02:13:04.714186+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21974v1","created_at":"2026-06-23T02:13:04.714186+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21974","created_at":"2026-06-23T02:13:04.714186+00:00"},{"alias_kind":"pith_short_12","alias_value":"T5QXJU472E2V","created_at":"2026-06-23T02:13:04.714186+00:00"},{"alias_kind":"pith_short_16","alias_value":"T5QXJU472E2V6WKI","created_at":"2026-06-23T02:13:04.714186+00:00"},{"alias_kind":"pith_short_8","alias_value":"T5QXJU47","created_at":"2026-06-23T02:13:04.714186+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/T5QXJU472E2V6WKIQE6DUZQJXX","json":"https://pith.science/pith/T5QXJU472E2V6WKIQE6DUZQJXX.json","graph_json":"https://pith.science/api/pith-number/T5QXJU472E2V6WKIQE6DUZQJXX/graph.json","events_json":"https://pith.science/api/pith-number/T5QXJU472E2V6WKIQE6DUZQJXX/events.json","paper":"https://pith.science/paper/T5QXJU47"},"agent_actions":{"view_html":"https://pith.science/pith/T5QXJU472E2V6WKIQE6DUZQJXX","download_json":"https://pith.science/pith/T5QXJU472E2V6WKIQE6DUZQJXX.json","view_paper":"https://pith.science/paper/T5QXJU47","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21974&json=true","fetch_graph":"https://pith.science/api/pith-number/T5QXJU472E2V6WKIQE6DUZQJXX/graph.json","fetch_events":"https://pith.science/api/pith-number/T5QXJU472E2V6WKIQE6DUZQJXX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T5QXJU472E2V6WKIQE6DUZQJXX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T5QXJU472E2V6WKIQE6DUZQJXX/action/storage_attestation","attest_author":"https://pith.science/pith/T5QXJU472E2V6WKIQE6DUZQJXX/action/author_attestation","sign_citation":"https://pith.science/pith/T5QXJU472E2V6WKIQE6DUZQJXX/action/citation_signature","submit_replication":"https://pith.science/pith/T5QXJU472E2V6WKIQE6DUZQJXX/action/replication_record"}},"created_at":"2026-06-23T02:13:04.714186+00:00","updated_at":"2026-06-23T02:13:04.714186+00:00"}