CircuitWeave: Topology-Behavior Alignment for Executable Multimodal RTL Generation

Jiahao Feng, Haiyan Qin, Zhiwei Xie, Wang Kang

Text-only LLMs generate RTL from natural-language specifications, but prose can leave connectivity, register boundaries, and state-output relations implicit even when interfaces and cycle-level behavior are specified. Schematics can make these structural relations explicit and thereby complement the behavioral constraints conveyed by text. Yet simply adding an image creates a fusion challenge: direct multimodal decoding does not explicitly separate the evidence roles of text and schematics or make missing and conflicting constraints explicit before code generation.We present CircuitWeave, a contract-mediated multimodal framework that extracts a topology contract from the schematic and a behavior contract from the text. It fuses these records into a circuit contract that serializes correspondences, missing evidence, and conflicts, then generates RTL only from this contract. A joint objective supervises both contracts, serialized fusion, contract-conditioned RTL generation, and reverse reconstruction of covered contract fields from reference RTL.We construct 5,000 executable-qualified packages, each containing a specification, generated schematic, structured contracts, reference RTL, and self-checking testbench, and use the training split to adapt Qwen with LoRA. On VerilogEval-Human, CircuitWeave reaches 46.60% pass@1, 61.49% pass@5, and 65.39% pass@10. These point estimates are 8.46, 5.85, and 2.57 percentage points above those of the same adapted checkpoint without the schematic. On RTLLM, it reaches 40.00%, 48.00%, and 52.00%, two percentage points above the adapted text-only condition at each cutoff.The dataset is publicly available at https://huggingface.co/datasets/fengjiahao0421/CircuitWeave.