Where Does a Hardware Change Act? Intent-Conditioned Modification Support Recovery in Evolving RTL Designs

Reliable hardware evolution requires identifying the register-transfer-level (RTL) locations affected by a natural-language change request. This is challenging because changes often span multiple locations coupled by hardware dependencies, beyond independent request–to–code matching. We formulate this task as $\Delta \mathrm{Spec}$-to-RTL localization and introduce EvoRTL-Bench, the first benchmark aligning natural-language change requests with fine-grained modification support sets from real RTL revisions. Our analysis shows that modification support sets are sparse yet structurally cohesive, exhibit non-random dependency patterns, and have sizes and topologies predictable from change requests. Motivated by these findings, we propose RTLocating, which fuses textual semantics, functional behavior, and architectural context into selective semantic seeds, then performs intent-conditioned impact propagation over directed, typed RTL dependencies with adaptive channel and scope selection. Counterfactual decision supervision trains these decisions using the support-recovery utility of alternative evidence allocations and propagation actions. On EvoRTL-Bench, RTLocating improves mean average precision by 17.3% relative to the strongest baseline. End-to-end editing experiments further indicate that its localization guidance improves downstream RTL editing.