ACF: A Collaborative Framework for Agent Covert Communication under Cognitive Asymmetry
Wansheng Wu, Kaibo Huang, Yukun Wei, Zhongliang Yang, and Linna Zhou

TL;DR
This paper introduces ACF, a new framework for covert communication among autonomous agents that overcomes cognitive asymmetry issues by decoupling semantic reasoning from statistical layers, ensuring reliable secret exchange.
Contribution
ACF is the first framework to structurally decouple covert communication from semantic reasoning, enabling effective communication despite cognitive asymmetry in agent networks.
Findings
ACF outperforms symmetric baselines under severe cognitive asymmetry.
ACF maintains semantic fidelity and covert capacity with provable error bounds.
ACF guarantees robust effective information capacity in agent networks.
Abstract
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,…
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