{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IH5XJ65KQEQLNJMLQRQIVKQOZT","short_pith_number":"pith:IH5XJ65K","schema_version":"1.0","canonical_sha256":"41fb74fbaa8120b6a58b84608aaa0ecccf81bb6a47d06c2ab2c39fd2711b6441","source":{"kind":"arxiv","id":"2405.11040","version":1},"attestation_state":"computed","paper":{"title":"From Generalist to Specialist: Improving Large Language Models for Medical Physics Using ARCoT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"cs.CL","authors_text":"Jace Grandinetti, Rafe Mcbeth","submitted_at":"2024-05-17T18:31:38Z","abstract_excerpt":"Large Language Models (LLMs) have achieved remarkable progress, yet their application in specialized fields, such as medical physics, remains challenging due to the need for domain-specific knowledge. This study introduces ARCoT (Adaptable Retrieval-based Chain of Thought), a framework designed to enhance the domain-specific accuracy of LLMs without requiring fine-tuning or extensive retraining. ARCoT integrates a retrieval mechanism to access relevant domain-specific information and employs step-back and chain-of-thought prompting techniques to guide the LLM's reasoning process, ensuring more"},"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":"2405.11040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-17T18:31:38Z","cross_cats_sorted":["physics.med-ph"],"title_canon_sha256":"319ef20b22bd263240f2fc67e74d2bcbce4b2b72f135bb7f577342f06eb26293","abstract_canon_sha256":"68d044dc11d031f5d3e446a0ab2f2fd95f4cb75771dc140e7459411828445a53"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:20:32.173285Z","signature_b64":"fSi7KkYOR4qINXkA+3tfhRO0J/gAxci7yOdSeJGbwzaL7rPDuiH3gI6Tv/LKqkce9I41QP8+itF3xaoLNuAJBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41fb74fbaa8120b6a58b84608aaa0ecccf81bb6a47d06c2ab2c39fd2711b6441","last_reissued_at":"2026-07-05T08:20:32.172741Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:20:32.172741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Generalist to Specialist: Improving Large Language Models for Medical Physics Using ARCoT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"cs.CL","authors_text":"Jace Grandinetti, Rafe Mcbeth","submitted_at":"2024-05-17T18:31:38Z","abstract_excerpt":"Large Language Models (LLMs) have achieved remarkable progress, yet their application in specialized fields, such as medical physics, remains challenging due to the need for domain-specific knowledge. This study introduces ARCoT (Adaptable Retrieval-based Chain of Thought), a framework designed to enhance the domain-specific accuracy of LLMs without requiring fine-tuning or extensive retraining. ARCoT integrates a retrieval mechanism to access relevant domain-specific information and employs step-back and chain-of-thought prompting techniques to guide the LLM's reasoning process, ensuring more"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.11040","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/2405.11040/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":"2405.11040","created_at":"2026-07-05T08:20:32.172798+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.11040v1","created_at":"2026-07-05T08:20:32.172798+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.11040","created_at":"2026-07-05T08:20:32.172798+00:00"},{"alias_kind":"pith_short_12","alias_value":"IH5XJ65KQEQL","created_at":"2026-07-05T08:20:32.172798+00:00"},{"alias_kind":"pith_short_16","alias_value":"IH5XJ65KQEQLNJML","created_at":"2026-07-05T08:20:32.172798+00:00"},{"alias_kind":"pith_short_8","alias_value":"IH5XJ65K","created_at":"2026-07-05T08:20:32.172798+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.14304","citing_title":"Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT","json":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT.json","graph_json":"https://pith.science/api/pith-number/IH5XJ65KQEQLNJMLQRQIVKQOZT/graph.json","events_json":"https://pith.science/api/pith-number/IH5XJ65KQEQLNJMLQRQIVKQOZT/events.json","paper":"https://pith.science/paper/IH5XJ65K"},"agent_actions":{"view_html":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT","download_json":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT.json","view_paper":"https://pith.science/paper/IH5XJ65K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.11040&json=true","fetch_graph":"https://pith.science/api/pith-number/IH5XJ65KQEQLNJMLQRQIVKQOZT/graph.json","fetch_events":"https://pith.science/api/pith-number/IH5XJ65KQEQLNJMLQRQIVKQOZT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT/action/storage_attestation","attest_author":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT/action/author_attestation","sign_citation":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT/action/citation_signature","submit_replication":"https://pith.science/pith/IH5XJ65KQEQLNJMLQRQIVKQOZT/action/replication_record"}},"created_at":"2026-07-05T08:20:32.172798+00:00","updated_at":"2026-07-05T08:20:32.172798+00:00"}