# Draft-KV

The work first audits existing latent communication interfaces: across five method-dataset pairs, replacing each message with one from an unrelated question changes accuracy by at most 0.60 points even when communication adds 15.44 points over the receiver alone, indicating the gain can come from interface adaptation rather than message content; it then introduces Draft-KV, which sends the key-value states formed while the sharer drafts an answer through linear projections into a side memory read by a gated attention branch, trained progressively from message reconstruction to answer supervision under a one-sided guard on harm from mismatched messages, with both models frozen and only 1.05M interface parameters trained (348x fewer than C2C); with a Qwen3-8B sharer and a frozen Qwen2.5-0.

The work first audits existing latent communication interfaces: across five method-dataset pairs, replacing each message with one from an unrelated question changes accuracy by at most 0.60 points even when communication adds 15.44 points over the receiver alone, indicating the gain can come from interface adaptation rather than message content; it then introduces Draft-KV, which sends the key-value states formed while the sharer drafts an answer through linear projections into a side memory read by a gated attention branch, trained progressively from message reconstruction to answer supervision under a one-sided guard on harm from mismatched messages, with both models frozen and only 1.05M interface parameters trained (348x fewer than C2C); with a Qwen3-8B sharer and a frozen Qwen2.5-0.