Multi-agent systems communicate mostly through text, paying a lossy and expensive decode and re-encode cost. KV-cache communication is a promising alternative, yet most prior work is homogeneous, using duplicate copies of the same model, and avoids the central challenge of cross-model latent alignment; existing heterogeneous methods are also restrictive, typically assuming shared input and using transferred caches mainly for steering. We study a more fundamental question: can heterogeneous agents be aligned well enough to perform real "mind reading" and transfer both what one agent sees and how it thinks? Our information-structure analysis reveals a duality: context-aware transfer is driven by sparse reasoning signals, while context-unaware transfer, where the receiver sees no input, requires dense contextual knowledge preservation. Motivated by this, we propose dense alignment for heterogeneous KV-cache communication via a lightweight cross-model cache transformation and two-phase training: reconstruction followed by generation. Across all six directions of {Qwen3-4B, 8B, 14B} and six in-domain and out-of-domain benchmarks, our method outperforms prior heterogeneous baselines, matches or exceeds text communication in context-aware settings at roughly 2 to 3 times lower compute, and remains effective in context-unaware transfer where prior methods collapse.

翻译：多智能体系统主要通过文本通信，需付出有损且昂贵的解码与重编码代价。KV缓存通信是一种有前景的替代方案，但现有研究大多基于同构假设——使用同一模型的重复副本，回避了跨模型潜在对齐这一核心挑战；现有的异构方法也存在局限性，通常假定共享输入，并将迁移的缓存主要用于引导。我们研究了一个更根本的问题：异构智能体能否被充分对齐，以真正实现“读心”功能，即同时迁移一个智能体所见的内容及其思考方式？我们的信息结构分析揭示了一种对偶性：上下文感知迁移由稀疏推理信号驱动，而上下文无关迁移（接收者看不到任何输入）则需要稠密上下文知识的保留。受此启发，我们提出通过轻量级跨模型缓存变换与两阶段训练（先重建后生成）实现异构KV缓存通信的稠密对齐。在{Qwen3-4B, 8B, 14B}全部六个方向以及六个领域内与领域外基准测试中，我们的方法均优于先前的异构基线方法，在上下文感知场景下以约2至3倍更低的计算量匹配或超越文本通信，并在先前方法完全失效的上下文无关迁移中保持有效。