{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RXMERQSXVD23J6IFODBYVTACOH","short_pith_number":"pith:RXMERQSX","schema_version":"1.0","canonical_sha256":"8dd848c257a8f5b4f90570c38acc0271e120e6d86bd710468993bcec4be93ec8","source":{"kind":"arxiv","id":"2602.02849","version":2},"attestation_state":"computed","paper":{"title":"AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dmitrii Torbunov, Soumyajit Mandal, Xi Yu, Yihui Ren","submitted_at":"2026-02-02T21:51:55Z","abstract_excerpt":"The design of Analog and Mixed-Signal (AMS) integrated circuits remains heavily reliant on expert knowledge, with transistor sizing a major bottleneck due to nonlinear behavior, high-dimensional design spaces, and strict performance constraints. Existing Electronic Design Automation (EDA) methods typically frame sizing as static black-box optimization, resulting in inefficient and less robust solutions. Although Large Language Models (LLMs) exhibit strong reasoning abilities, they are not suited for precise numerical optimization in AMS sizing. To address this gap, we propose AutoSizer, a refl"},"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":"2602.02849","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-02-02T21:51:55Z","cross_cats_sorted":[],"title_canon_sha256":"c0841bed2d2389c641e284ad07e661790a27a088809e8e6dc5b917d7780b3461","abstract_canon_sha256":"93cdf675b80ecf6b44420bbed6f788b088508fdaa3e8cca0f8553786389a6b85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:05:40.303472Z","signature_b64":"eNw8asd1cXbW6tU4I7Dmgv4KO1kquZinSAICl0nbmY99zp3zrizIOn7Vsiz7nJqGjG3/nIr3wTgzr40vI2VzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8dd848c257a8f5b4f90570c38acc0271e120e6d86bd710468993bcec4be93ec8","last_reissued_at":"2026-05-29T02:05:40.302836Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:05:40.302836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dmitrii Torbunov, Soumyajit Mandal, Xi Yu, Yihui Ren","submitted_at":"2026-02-02T21:51:55Z","abstract_excerpt":"The design of Analog and Mixed-Signal (AMS) integrated circuits remains heavily reliant on expert knowledge, with transistor sizing a major bottleneck due to nonlinear behavior, high-dimensional design spaces, and strict performance constraints. Existing Electronic Design Automation (EDA) methods typically frame sizing as static black-box optimization, resulting in inefficient and less robust solutions. Although Large Language Models (LLMs) exhibit strong reasoning abilities, they are not suited for precise numerical optimization in AMS sizing. To address this gap, we propose AutoSizer, a refl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.02849","kind":"arxiv","version":2},"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/2602.02849/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":"2602.02849","created_at":"2026-05-29T02:05:40.302921+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.02849v2","created_at":"2026-05-29T02:05:40.302921+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.02849","created_at":"2026-05-29T02:05:40.302921+00:00"},{"alias_kind":"pith_short_12","alias_value":"RXMERQSXVD23","created_at":"2026-05-29T02:05:40.302921+00:00"},{"alias_kind":"pith_short_16","alias_value":"RXMERQSXVD23J6IF","created_at":"2026-05-29T02:05:40.302921+00:00"},{"alias_kind":"pith_short_8","alias_value":"RXMERQSX","created_at":"2026-05-29T02:05:40.302921+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2604.07387","citing_title":"A Self-Calibrating Framework for Analog Circuit Sizing Using LLM-Derived Analytical Equations","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH","json":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH.json","graph_json":"https://pith.science/api/pith-number/RXMERQSXVD23J6IFODBYVTACOH/graph.json","events_json":"https://pith.science/api/pith-number/RXMERQSXVD23J6IFODBYVTACOH/events.json","paper":"https://pith.science/paper/RXMERQSX"},"agent_actions":{"view_html":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH","download_json":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH.json","view_paper":"https://pith.science/paper/RXMERQSX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.02849&json=true","fetch_graph":"https://pith.science/api/pith-number/RXMERQSXVD23J6IFODBYVTACOH/graph.json","fetch_events":"https://pith.science/api/pith-number/RXMERQSXVD23J6IFODBYVTACOH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH/action/storage_attestation","attest_author":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH/action/author_attestation","sign_citation":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH/action/citation_signature","submit_replication":"https://pith.science/pith/RXMERQSXVD23J6IFODBYVTACOH/action/replication_record"}},"created_at":"2026-05-29T02:05:40.302921+00:00","updated_at":"2026-05-29T02:05:40.302921+00:00"}