As generative artificial intelligence evolves, autonomous agent networks present a powerful paradigm for interactive covert communication. However, because agents dynamically update internal memories via environmental interactions, existing methods face a critical structural vulnerability: cognitive asymmetry. Conventional approaches demand strict cognitive symmetry, requiring identical sequence prefixes between the encoder and decoder. In dynamic deployments, inevitable prefix discrepancies destroy synchronization, inducing severe channel degradation. To address this core challenge of cognitive asymmetry, we propose the Asymmetric Collaborative Framework (ACF), which structurally decouples covert communication from semantic reasoning via orthogonal statistical and cognitive layers. By deploying a prefix-independent decoding paradigm governed by a shared steganographic configuration, ACF eliminates the reliance on cognitive symmetry. Evaluations on realistic memory-augmented workflows demonstrate that under severe cognitive asymmetry, symmetric baselines suffer severe channel degradation, whereas ACF uniquely excels across both semantic fidelity and covert communication. It maintains computational indistinguishability, enabling reliable secret extraction with provable error bounds, and providing robust Effective Information Capacity guarantees for modern agent networks.

翻译：随着生成式人工智能的发展，自主智能体网络为交互式隐蔽通信提供了强大的范式。然而，由于智能体通过环境交互动态更新内部记忆，现有方法面临一个关键的结构性漏洞：认知不对称。传统方法要求严格的认知对称性，即编码器和解码器之间需使用相同的序列前缀。在动态部署中，不可避免的前缀差异会破坏同步性，导致严重的信道退化。为应对认知不对称这一核心挑战，我们提出非对称协作框架（ACF），通过正交统计层与认知层的结构分离，将隐蔽通信与语义推理解耦。通过部署由共享隐写配置主导的、独立于前缀的解码范式，ACF消除了对认知对称性的依赖。在基于记忆增强工作流的实际评估中，严重认知不对称条件下，对称基线方法遭受严重的信道退化，而ACF在语义保真度与隐蔽通信两方面均表现卓越。它维持了计算不可区分性，能够以可证明的误差界实现可靠的秘密提取，并为现代智能体网络提供了鲁棒的有效信息容量保障。