RTLScope: Fine-Grained Uncertainty-Aware RTL Retrieval with Probabilistic Embeddings

There is increasing deployment of LLM and agentic applications in the chip design industry, and fine-grained natural-language-to-RTL retrieval becomes essential as it grounds queries in the RTL code snippets that hardware-design workflows require. Such queries span abstraction levels and vary in detail, from local RTL operations to behavioral intent, and a single description often maps to multiple snippets that jointly implement a behavior; existing benchmarks, however, focus on RTL generation and leave this retrieval capability unmeasured. Because correctness is critical in chip design, measuring the uncertainty of retrieval is highly valuable. We introduce RTLScope, a repository-scale benchmark for fine-grained RTL retrieval built from practical SoC and other large-scale open-source designs; two complementary construction pipelines are built to obtain multi-level queries and one-to-many NL–RTL alignments. We further propose a probabilistic embedding method that learns alignment with a group-listwise multi-positive objective and quantifies query and retrieval uncertainty. With the proposed method, we achieve 15.0% and 15.2% increases on MRR and MAP over the strongest fine-tuned baseline. Together, the benchmark and method enable reliable RTL retrieval for specification-compliance analysis, agentic debugging, verification assistance, design comprehension, and retrieval-augmented hardware-design workflows.