Closing-parenthesis dictionary feature — seeds → Automatic feature circuit detection

The feature chosen to demonstrate circuit detection, feature 52027 in layer 5, was not picked at random: unembedding it shows its top tokens are all closing-parenthesis variants, which is possible to check directly because layer 5 is the model's final layer, whose job is to write directions the unembedding matrix reads out as tokens. That gives the target feature's meaning an unusually strong, weights-based confirmation before any circuit-tracing happens, unlike a mid-network feature whose semantics can only be inferred from activating text. The circuit-detection procedure then works backward from that anchor: find the feature's max activation M, sample 20 contexts where it fires in [M/2, M], ablate each layer-4 feature in turn and rank them by how much their ablation reduces the target's activation, optionally recursing to build the multi-layer causal tree shown in Figure 5. Because the closing-parenthesis feature's own identity is nearly unambiguous, the method's output, upstream features for dates, acronyms, and other parenthesis-preceding phrases, can be judged as sensible or not, which is what makes this feature a credible validation case rather than just an illustration.