{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VOPZY5LGP4BG2GLCHL4OLFV3KX","short_pith_number":"pith:VOPZY5LG","schema_version":"1.0","canonical_sha256":"ab9f9c75667f026d19623af8e596bb55c6b6a04de66cb87370ad8fadbaf21b39","source":{"kind":"arxiv","id":"2606.12983","version":1},"attestation_state":"computed","paper":{"title":"Structured Testbench Generation for LLM-Driven HDL Design and Verification-Oriented Data Curation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Cheng Liang, En-Ming Huang, Hsiang-Yu Tsou, H.T. Kung, Mu-Chi Chen, Po-Hsuang Huang, Ren-Hao Deng, Shao-Chun Ho, Shih-Hao Hung, Wei-Po Hsin, Yao-Ting Hsieh, Yu-Hung Kao, Yu-Kai Hung","submitted_at":"2026-06-11T07:19:41Z","abstract_excerpt":"Automated testbench generation has become a critical bottleneck in large language model (LLM)-driven Register Transfer Level (RTL) workflows, where large numbers of candidate designs must be verified rapidly and reliably. Existing prompt-based approaches treat testbench generation as unconstrained code synthesis, yielding stochastic outputs with high token cost, low reproducibility, and insufficient coverage. To address this gap, we present STG, a Structured Testbench Generation framework that exploits the inherent structure of hardware designs to generate deterministic testbenches. As a direc"},"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":"2606.12983","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-11T07:19:41Z","cross_cats_sorted":[],"title_canon_sha256":"c175041986e372633f136d80fc7d3238bc728337146f3c93092f9137869561e3","abstract_canon_sha256":"bbc48b86919b7e8ad1322d4c4eb1db979fa458b5158daf5d1ad944ea86fb6a00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:09:36.116347Z","signature_b64":"7ZnX8XdKMVFKOV6otEfQm7aTnYeBq1TvYdnIMny069fuK1QfsJ7y9cfGMRHm01F9wmQAns2KnF24nCHNUau/Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab9f9c75667f026d19623af8e596bb55c6b6a04de66cb87370ad8fadbaf21b39","last_reissued_at":"2026-06-12T01:09:36.115933Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:09:36.115933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Structured Testbench Generation for LLM-Driven HDL Design and Verification-Oriented Data Curation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Cheng Liang, En-Ming Huang, Hsiang-Yu Tsou, H.T. Kung, Mu-Chi Chen, Po-Hsuang Huang, Ren-Hao Deng, Shao-Chun Ho, Shih-Hao Hung, Wei-Po Hsin, Yao-Ting Hsieh, Yu-Hung Kao, Yu-Kai Hung","submitted_at":"2026-06-11T07:19:41Z","abstract_excerpt":"Automated testbench generation has become a critical bottleneck in large language model (LLM)-driven Register Transfer Level (RTL) workflows, where large numbers of candidate designs must be verified rapidly and reliably. Existing prompt-based approaches treat testbench generation as unconstrained code synthesis, yielding stochastic outputs with high token cost, low reproducibility, and insufficient coverage. To address this gap, we present STG, a Structured Testbench Generation framework that exploits the inherent structure of hardware designs to generate deterministic testbenches. As a direc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12983","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/2606.12983/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":"2606.12983","created_at":"2026-06-12T01:09:36.115992+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.12983v1","created_at":"2026-06-12T01:09:36.115992+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12983","created_at":"2026-06-12T01:09:36.115992+00:00"},{"alias_kind":"pith_short_12","alias_value":"VOPZY5LGP4BG","created_at":"2026-06-12T01:09:36.115992+00:00"},{"alias_kind":"pith_short_16","alias_value":"VOPZY5LGP4BG2GLC","created_at":"2026-06-12T01:09:36.115992+00:00"},{"alias_kind":"pith_short_8","alias_value":"VOPZY5LG","created_at":"2026-06-12T01:09:36.115992+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/VOPZY5LGP4BG2GLCHL4OLFV3KX","json":"https://pith.science/pith/VOPZY5LGP4BG2GLCHL4OLFV3KX.json","graph_json":"https://pith.science/api/pith-number/VOPZY5LGP4BG2GLCHL4OLFV3KX/graph.json","events_json":"https://pith.science/api/pith-number/VOPZY5LGP4BG2GLCHL4OLFV3KX/events.json","paper":"https://pith.science/paper/VOPZY5LG"},"agent_actions":{"view_html":"https://pith.science/pith/VOPZY5LGP4BG2GLCHL4OLFV3KX","download_json":"https://pith.science/pith/VOPZY5LGP4BG2GLCHL4OLFV3KX.json","view_paper":"https://pith.science/paper/VOPZY5LG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.12983&json=true","fetch_graph":"https://pith.science/api/pith-number/VOPZY5LGP4BG2GLCHL4OLFV3KX/graph.json","fetch_events":"https://pith.science/api/pith-number/VOPZY5LGP4BG2GLCHL4OLFV3KX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VOPZY5LGP4BG2GLCHL4OLFV3KX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VOPZY5LGP4BG2GLCHL4OLFV3KX/action/storage_attestation","attest_author":"https://pith.science/pith/VOPZY5LGP4BG2GLCHL4OLFV3KX/action/author_attestation","sign_citation":"https://pith.science/pith/VOPZY5LGP4BG2GLCHL4OLFV3KX/action/citation_signature","submit_replication":"https://pith.science/pith/VOPZY5LGP4BG2GLCHL4OLFV3KX/action/replication_record"}},"created_at":"2026-06-12T01:09:36.115992+00:00","updated_at":"2026-06-12T01:09:36.115992+00:00"}