{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YY44VUGWUAS22WPVYNBP7M5PQQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fc7f5b29fd35b7a0faebe017ffd0a7677d7c15d4a19a50720384ad5ba560fcde","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2024-09-19T12:15:55Z","title_canon_sha256":"51f971200bb5fbf992fc7ed79c9cde108093df6845bb3024ff42aaf7055ade68"},"schema_version":"1.0","source":{"id":"2409.12993","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.12993","created_at":"2026-07-05T10:10:44Z"},{"alias_kind":"arxiv_version","alias_value":"2409.12993v2","created_at":"2026-07-05T10:10:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.12993","created_at":"2026-07-05T10:10:44Z"},{"alias_kind":"pith_short_12","alias_value":"YY44VUGWUAS2","created_at":"2026-07-05T10:10:44Z"},{"alias_kind":"pith_short_16","alias_value":"YY44VUGWUAS22WPV","created_at":"2026-07-05T10:10:44Z"},{"alias_kind":"pith_short_8","alias_value":"YY44VUGW","created_at":"2026-07-05T10:10:44Z"}],"graph_snapshots":[{"event_id":"sha256:fd50fd397c229d457d733faf679b926c10c9bfba427b34c3501396e1e35f7f1c","target":"graph","created_at":"2026-07-05T10:10:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2409.12993/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the significant progress made in code generation with large language models, challenges persist, especially with hardware description languages such as Verilog. This paper first presents an analysis of fine-tuned LLMs on Verilog coding, with synthetic data from prior methods. We identify two main issues: difficulties in handling non-textual representations (Karnaugh maps, state-transition diagrams and waveforms) and significant variability during training with models randomly making \"minor\" mistakes. To address these limitations, we enhance data curation by creating correct-by-construc","authors_text":"Haoxing Ren, Mingjie Liu, Wenfei Zhou, Yun-Da Tsai","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2024-09-19T12:15:55Z","title":"CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.12993","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:308867d986196cdc712baef6e18f641d05bd146bd8fa0605b29ec317f1b1afa9","target":"record","created_at":"2026-07-05T10:10:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fc7f5b29fd35b7a0faebe017ffd0a7677d7c15d4a19a50720384ad5ba560fcde","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2024-09-19T12:15:55Z","title_canon_sha256":"51f971200bb5fbf992fc7ed79c9cde108093df6845bb3024ff42aaf7055ade68"},"schema_version":"1.0","source":{"id":"2409.12993","kind":"arxiv","version":2}},"canonical_sha256":"c639cad0d6a025ad59f5c342ffb3af843bff412ca3fea0d257048fc19a35c60f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c639cad0d6a025ad59f5c342ffb3af843bff412ca3fea0d257048fc19a35c60f","first_computed_at":"2026-07-05T10:10:44.052918Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:10:44.052918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ykf0EQSRoegP4JGMh8oVo09Bk4UykafAABHTrtPjYmoW43toa51o7/M8hjWn+CZlqBzeqtlsN3JmuayxiGYKCg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:10:44.053358Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.12993","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:308867d986196cdc712baef6e18f641d05bd146bd8fa0605b29ec317f1b1afa9","sha256:fd50fd397c229d457d733faf679b926c10c9bfba427b34c3501396e1e35f7f1c"],"state_sha256":"d88c0858bf81679ed2d5ac02290054ca06f5b97028c0d17f5abd7c91e4c84d21"}