{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:HTPULV2REY6CTHIQQFDRCQTPR7","short_pith_number":"pith:HTPULV2R","schema_version":"1.0","canonical_sha256":"3cdf45d751263c299d10814711426f8fefda52a6349b2b7dbae0ad500fab9bb1","source":{"kind":"arxiv","id":"2607.14165","version":1},"attestation_state":"computed","paper":{"title":"Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.AR","cs.LG"],"primary_cat":"cs.SE","authors_text":"Anantha P. Chandrakasan, Dimple Vijay Kochar, Hae-Seung Lee","submitted_at":"2026-07-15T05:19:35Z","abstract_excerpt":"While Large Language Models (LLMs) have demonstrated significant capability in software code generation, their application to analog Electronic Design Automation (EDA) is bottlenecked. Owing to limited circuit topology understanding and data, directly prompting LLMs and multimodal models leads to hallucinations and failure to produce schematics capable of passing rigorous SPICE simulations, as we show in our work. Instead, we propose an end-to-end, multi-step LLM agentic framework ATLAS, capable of generating a functional Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC"},"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":"2607.14165","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2026-07-15T05:19:35Z","cross_cats_sorted":["cs.AI","cs.AR","cs.LG"],"title_canon_sha256":"fdbc1e301cb9002968a5e25aa9367da7cce7d34f692cbf374e4d8102d6a7b440","abstract_canon_sha256":"978a7f9400d5e36a00e4ba1cfb46fd788670093eb140fdef75b9c300edd07330"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T00:20:54.860506Z","signature_b64":"Xr5pWdW7ddUu1jqQQ3AgjGp9Ut9aNKT3753AmCrAHe57lzM4v72MJfJy3Z8KWIjsNEof1JmbC2pjAHScQblADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3cdf45d751263c299d10814711426f8fefda52a6349b2b7dbae0ad500fab9bb1","last_reissued_at":"2026-07-17T00:20:54.859106Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T00:20:54.859106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.AR","cs.LG"],"primary_cat":"cs.SE","authors_text":"Anantha P. Chandrakasan, Dimple Vijay Kochar, Hae-Seung Lee","submitted_at":"2026-07-15T05:19:35Z","abstract_excerpt":"While Large Language Models (LLMs) have demonstrated significant capability in software code generation, their application to analog Electronic Design Automation (EDA) is bottlenecked. Owing to limited circuit topology understanding and data, directly prompting LLMs and multimodal models leads to hallucinations and failure to produce schematics capable of passing rigorous SPICE simulations, as we show in our work. Instead, we propose an end-to-end, multi-step LLM agentic framework ATLAS, capable of generating a functional Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14165","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/2607.14165/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":"2607.14165","created_at":"2026-07-17T00:20:54.860006+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.14165v1","created_at":"2026-07-17T00:20:54.860006+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14165","created_at":"2026-07-17T00:20:54.860006+00:00"},{"alias_kind":"pith_short_12","alias_value":"HTPULV2REY6C","created_at":"2026-07-17T00:20:54.860006+00:00"},{"alias_kind":"pith_short_16","alias_value":"HTPULV2REY6CTHIQ","created_at":"2026-07-17T00:20:54.860006+00:00"},{"alias_kind":"pith_short_8","alias_value":"HTPULV2R","created_at":"2026-07-17T00:20:54.860006+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/HTPULV2REY6CTHIQQFDRCQTPR7","json":"https://pith.science/pith/HTPULV2REY6CTHIQQFDRCQTPR7.json","graph_json":"https://pith.science/api/pith-number/HTPULV2REY6CTHIQQFDRCQTPR7/graph.json","events_json":"https://pith.science/api/pith-number/HTPULV2REY6CTHIQQFDRCQTPR7/events.json","paper":"https://pith.science/paper/HTPULV2R"},"agent_actions":{"view_html":"https://pith.science/pith/HTPULV2REY6CTHIQQFDRCQTPR7","download_json":"https://pith.science/pith/HTPULV2REY6CTHIQQFDRCQTPR7.json","view_paper":"https://pith.science/paper/HTPULV2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.14165&json=true","fetch_graph":"https://pith.science/api/pith-number/HTPULV2REY6CTHIQQFDRCQTPR7/graph.json","fetch_events":"https://pith.science/api/pith-number/HTPULV2REY6CTHIQQFDRCQTPR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HTPULV2REY6CTHIQQFDRCQTPR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HTPULV2REY6CTHIQQFDRCQTPR7/action/storage_attestation","attest_author":"https://pith.science/pith/HTPULV2REY6CTHIQQFDRCQTPR7/action/author_attestation","sign_citation":"https://pith.science/pith/HTPULV2REY6CTHIQQFDRCQTPR7/action/citation_signature","submit_replication":"https://pith.science/pith/HTPULV2REY6CTHIQQFDRCQTPR7/action/replication_record"}},"created_at":"2026-07-17T00:20:54.860006+00:00","updated_at":"2026-07-17T00:20:54.860006+00:00"}