Predictable Cross-Model Control Without Shared Coordinates

Can information from one model guide a predictable intervention in another? We apply a source model's signed relation weights over paired examples to the receiver's own states, so no hidden vector crosses models. Across twelve directed paths among four decoder-only Transformer families and four controlled tasks, the resulting directions are closer to each receiver's relational reference than randomly permuted donors in every path-task cell. With identical donors, source weights produce larger held-out effects than a target-neutral control. A receiver score from disjoint audit items ranks held-out additive effects across the source construction and three controls, with Spearman correlations of $0.837$ and $0.825$ at two doses. In natural-context tests including an encoder-decoder model, source responses reduce receiver-response prediction error by $32.5\%$ beyond label, action, and clean-output baselines. These results establish two concrete uses of cross-model information: constructing receiver-native directions whose additive effects can be ranked by a local measurement, and predicting output changes from observed source responses.