Demystifying Cache-to-Cache Communication: When Latent Transfer Reduces to Output Calibration

Recent work proposes cache-to-cache (C2C) communication for heterogeneous LLM agents, directly transferring KV caches to convey question-specific semantics more efficiently than text-based communication. In this paper, we revisit this mechanism and uncover a different explanation: C2C gains arise primarily from a question-independent distributional shift that corrects a degenerate receiver output distribution, rather than from question-specific semantic transfer. Using the authors' released weights and evaluation protocol, we first reproduce the reported gains, then conduct four controlled experiments. First, in a setting where only the sender holds the answer, nothing crosses between the released model pair. Second, replacing the sender KV cache with zeros preserves nearly the full improvement, showing that sender-specific semantic content is unnecessary. Third, decomposing the induced logit shift shows that a single question-independent vector reproduces the gain, while the question-dependent residual is ineffective or harmful. Fourth, varying receiver scale shows that gains appear only when the receiver output distribution is degenerate and disappear after directly centring it. The effect has a boundary: fusers trained on task data do carry the sender's answer when the sender is much stronger or the receiver has no bias to correct. Together, these results challenge the interpretation of C2C as semantic communication and instead attribute its benchmark gains to a content-independent correction of the receiver distribution. Our findings highlight the need to distinguish semantic information transfer from distributional calibration when evaluating latent communication between LLMs. Code, per-item outputs and the data of the constructed tasks are available at https://anonymous.4open.science/r/gains-without-transfer.