ACF: A Collaborative Framework for Agent Covert Communication under Cognitive Asymmetry

2026-04-09

AI Summary

This work addresses the critical challenge of maintaining covert communication synchronization in dynamic environments, where cognitive asymmetry between autonomous agents leads to mismatched encoder-decoder prefixes and severely degrades performance. To overcome this limitation, the paper introduces the Asymmetric Collaboration Framework (ACF), which structurally decouples semantic reasoning from covert communication through orthogonal statistical and cognitive layers, thereby eliminating reliance on cognitive symmetry for the first time. ACF employs a prefix-agnostic decoding paradigm and shared steganographic configurations, integrated with a memory-augmented workflow model, to guarantee provable error bounds and effective information capacity. Experimental results demonstrate that under strong cognitive asymmetry, ACF substantially outperforms symmetric baselines, achieving robust improvements in semantic fidelity, covert communication efficacy, and reliable secret message extraction.

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, 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.

Problem

• covert communication
• cognitive asymmetry
• agent networks
• memory dynamics
• synchronization failure

Innovation

• cognitive asymmetry
• covert communication
• asymmetric collaborative framework
• prefix-independent decoding
• steganographic configuration