Beyond Tokens: Model Communication through Common States

Language models can communicate through internal representations, but transfer depends on how the receiver uses them. We introduce common-state conditioning (CS-QKV): a feed-forward interface maps sharer key-value features to a continuous message added before a frozen receiver's attention-input normalization. Native projections construct the conditioned queries, keys, and values, while the direct residual carries the current layer input. Communication occurs during prefix processing; the receiver then generates independently. Across 15 larger-to-smaller Qwen3 pairs, Full CS exceeds default Cache-to-Cache (C2C) in 13. At the largest size gap, 32B$\to$0.6B, it raises four-task mean accuracy from 32.40% to 75.84%, gaining 43.44 points over the native receiver and 19.55 over C2C. Under the primary T0 protocol, single-run matched controls with sharer-KV-only inputs (No-H) gain 3.80 and 2.14 points over directly predicting Q/K/V increments. Receiving controls examine how native computations construct these updates; a separately trained first-order variant reaches 75.23% on 8B$\to$0.6B. Selected cross-family applications and a locked SciQ test extend the evidence, while prompt and readout comparisons delimit relative advantages. The results support using native normalization and projections to parameterize cross-model attention updates.