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

ACF addresses a specific technical hurdle in LLM-based steganography: the requirement for identical history (prefixes) between encoder and decoder. In multi-agent systems, agents' memories naturally diverge, which breaks traditional linguistic steganography techniques like ADG (Adaptive Dynamic Grouping). The project is currently at a research stage (0 stars, 1 day old, 5 forks likely from the research team). While the 'cognitive asymmetry' angle is a legitimate theoretical contribution to the field of AI safety and security, it currently lacks a developer ecosystem or production-ready implementation. Its defensibility is low because it is a reference implementation of a paper rather than a platform. Frontier labs are unlikely to adopt 'covert' communication features directly due to safety and alignment policies; in fact, they are more likely to build detection mechanisms (steganalysis) to prevent exactly this kind of behavior. The project's value lies in its niche positioning within the AI security/privacy research community, but it faces a high risk of being bypassed by new LLM watermarking or monitoring techniques within a 1-2 year window.