{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZSSNQ5OC2MKQT7FVNNHO4UN4RV","short_pith_number":"pith:ZSSNQ5OC","schema_version":"1.0","canonical_sha256":"cca4d875c2d31509fcb56b4eee51bc8d5a9c991c84a65326ecf30381b02eadf6","source":{"kind":"arxiv","id":"2412.05311","version":1},"attestation_state":"computed","paper":{"title":"DRC-Coder: Automated DRC Checker Code Generation Using LLM Autonomous Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.AR","authors_text":"Chen-Chia Chang, Chia-Tung Ho, Haoxing Ren, Yaguang Li, Yiran Chen","submitted_at":"2024-11-28T04:29:17Z","abstract_excerpt":"In the advanced technology nodes, the integrated design rule checker (DRC) is often utilized in place and route tools for fast optimization loops for power-performance-area. Implementing integrated DRC checkers to meet the standard of commercial DRC tools demands extensive human expertise to interpret foundry specifications, analyze layouts, and debug code iteratively. However, this labor-intensive process, requiring to be repeated by every update of technology nodes, prolongs the turnaround time of designing circuits. In this paper, we present DRC-Coder, a multi-agent framework with vision ca"},"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":"2412.05311","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2024-11-28T04:29:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8dd1da538babadcb29ff71ec9ec11fe6b0525e13e5ad22542c015d97b9b9c527","abstract_canon_sha256":"436943bec2e088f57e5c1abd1876935a7739e153e8d8a5291942a55477c6b52e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:01.257106Z","signature_b64":"hLcPeyabPrVO0fxEfy5A8glQL5iAhVEJLAq5eBHPhhgGk5e5vkWMPgnQ/LMDp6enmk9E/YHVMTjJtYyp2EUtCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cca4d875c2d31509fcb56b4eee51bc8d5a9c991c84a65326ecf30381b02eadf6","last_reissued_at":"2026-07-05T09:46:01.256697Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:01.256697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DRC-Coder: Automated DRC Checker Code Generation Using LLM Autonomous Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.AR","authors_text":"Chen-Chia Chang, Chia-Tung Ho, Haoxing Ren, Yaguang Li, Yiran Chen","submitted_at":"2024-11-28T04:29:17Z","abstract_excerpt":"In the advanced technology nodes, the integrated design rule checker (DRC) is often utilized in place and route tools for fast optimization loops for power-performance-area. Implementing integrated DRC checkers to meet the standard of commercial DRC tools demands extensive human expertise to interpret foundry specifications, analyze layouts, and debug code iteratively. However, this labor-intensive process, requiring to be repeated by every update of technology nodes, prolongs the turnaround time of designing circuits. In this paper, we present DRC-Coder, a multi-agent framework with vision ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05311","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/2412.05311/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":"2412.05311","created_at":"2026-07-05T09:46:01.256752+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.05311v1","created_at":"2026-07-05T09:46:01.256752+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05311","created_at":"2026-07-05T09:46:01.256752+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZSSNQ5OC2MKQ","created_at":"2026-07-05T09:46:01.256752+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZSSNQ5OC2MKQT7FV","created_at":"2026-07-05T09:46:01.256752+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZSSNQ5OC","created_at":"2026-07-05T09:46:01.256752+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/ZSSNQ5OC2MKQT7FVNNHO4UN4RV","json":"https://pith.science/pith/ZSSNQ5OC2MKQT7FVNNHO4UN4RV.json","graph_json":"https://pith.science/api/pith-number/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/graph.json","events_json":"https://pith.science/api/pith-number/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/events.json","paper":"https://pith.science/paper/ZSSNQ5OC"},"agent_actions":{"view_html":"https://pith.science/pith/ZSSNQ5OC2MKQT7FVNNHO4UN4RV","download_json":"https://pith.science/pith/ZSSNQ5OC2MKQT7FVNNHO4UN4RV.json","view_paper":"https://pith.science/paper/ZSSNQ5OC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.05311&json=true","fetch_graph":"https://pith.science/api/pith-number/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/graph.json","fetch_events":"https://pith.science/api/pith-number/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/action/storage_attestation","attest_author":"https://pith.science/pith/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/action/author_attestation","sign_citation":"https://pith.science/pith/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/action/citation_signature","submit_replication":"https://pith.science/pith/ZSSNQ5OC2MKQT7FVNNHO4UN4RV/action/replication_record"}},"created_at":"2026-07-05T09:46:01.256752+00:00","updated_at":"2026-07-05T09:46:01.256752+00:00"}