The bottleneck in LLM-assisted RTL generation is not the generation step. It is knowing whether the output is correct before you spend the time to write a testbench. NoTB removes that constraint. The system generates RTL from the same spec using multiple independently trained LLM families, then applies Sequential Equivalence Checking across them. Designs that reach formal consensus across model families are accepted at high confidence. The rest get deferred.
On 78 CVDP RTL-generation tasks, four-family consensus achieves 94.7% precision at 27% coverage. Drop to three families and precision falls to 87% while coverage rises to 33%. These are tunable operating points: tighten the consensus requirement and you raise confidence; loosen it and you triage more output at the cost of some false accepts. Both modes give designers a concrete accept/defer rule before any trusted testbench or golden RTL exists.
The constraint being removed is the circular dependency at the start of any LLM-RTL workflow: you need a testbench to know if the RTL is good, but writing a good testbench requires understanding the design well enough that you could have written the RTL yourself. NoTB sidesteps the oracle problem entirely by treating cross-model formal agreement as the signal. It is not a replacement for full verification; it is a gating step that determines whether a candidate RTL is worth the effort of full verification.
RTL teams integrating LLMs into design flows now have a principled accept/defer rule at the top of the funnel. The teams that will struggle are those still routing all LLM RTL output to manual review as the first step, with no automated triage. That review queue will not scale as LLM generation throughput increases.