Do RTL Representations Understand Circuits? A Diagnostic Benchmark for Functional Understanding and Implementation Awareness

Learning-based methods increasingly assist register-transfer level (RTL) circuit design and optimization, making effective RTL representations an important foundation for downstream applications. Such representations should capture shared functional semantics across equivalent implementations while remaining sensitive to implementation differences relevant to design quality. However, existing evaluations of RTL representations assess functional behavior and implementation quality separately and on different designs, leaving it unclear how well representations capture each capability and how the two interact. In this paper, we introduce UnderRTL, a unified diagnostic benchmark for evaluating these two capabilities, termed functional understanding and implementation awareness. Built on 7,075 curated RTL designs, UnderRTL provides verified functional variants, synthesis-based PPA labels across multiple PDKs and constraints, and natural-language queries for six linked tasks. Functionally equivalent implementations bridge the two axes, enabling controlled analysis of functional invariance and implementation sensitivity. UnderRTL integrates 24 engineered-feature, text, graph, and multimodal methods under standardized evaluation protocols. Our experiments reveal task-dependent limitations in both capabilities, as well as weak transfer to unseen designs, motivating more robust and generalizable RTL representations.