## 6 Conclusions and Future Direction
### Future Direction
#### 6.2.2 Architectural Improvement for Intelligent Communication Systems  
Future architectural designs for semantic communications have been discussed in [144; 145; 146; 147]. The integration of perceptual fidelity into the design of intelligent communication architectures represents a paradigm shift from traditional communication systems optimized solely for distortion. The development of systems that better align with human-centric quality assessments and support emerging applications requiring high perceptual fidelity is a promising direction for future research and development, particularly as applications increasingly demand high perceptual quality in transmitted data. By bridging the gap between theoretical advances in information theory and practical deployment considerations, the RDP framework can guide intelligent communication architectures that better serve human-centric needs. Interactive AI Agents  
In interactive AI agent scenarios, communication systems must support real-time decision making while maintaining perceptual consistency with human expectations. RDP trade-off provides a theoretical foundation for balancing the rate requirements against both distortion and perceptual constraints in these dynamic environments. The deployment of generative models at both transmitter and receiver nodes allows for the incorporation of common and private randomness, which has been shown to improve perceptual fidelity in RDP frameworks. For interactive agents, this translates to more natural human-AI interactions where the AI can generate responses that are not only factually accurate but also perceptually coherent with the user's expectations. Architecturally, this requires communication systems that can dynamically adjust their rate allocation based on the perceptual importance of different components of the transmitted information. For example, in multi-modal communication systems involving text, image, and video data, the RDP function can guide the allocation of bandwidth to prioritize perceptually critical features across different data types. Perceptually Optimized Data Transmission  
RDP frameworks enable the design of communication systems that explicitly optimize for perceptual quality rather than just fidelity to the original data. This is particularly important in applications where the end-user experience depends on how natural/realistic the reconstructed data appear rather than on strict pixel-wise accuracy. In image and video transmission, for example, RDP-informed architectures can prioritize the preservation of distributions that contribute significantly to human perception of quality, even if this requires slightly higher bitrates. Conversely, they can allocate fewer bits to components that have minimal impact on perceptual quality, achieving more efficient compression. For example, systems employing generative models like GANs can achieve high perceptual quality with manageable increases in bitrate by leveraging the models' ability to capture the underlying data distribution. Reinforcement learning has emerged as a potentially transformative approach for developing agent-based systems, including embodied AI, as downstream tasks for intelligent communications. Recently, the rate-reward trade-off has been introduced [148]. Moreover, the remote coordination problem has been introduced [149], driven by the necessity to develop coordinated policies among distributed agents [150]. In this context, exploring the fundamental limits of coordination under rate-constrained communication emerges as a significant area of investigation. Communication systems prioritizing human-centric quality should account for the subjective nature of human perception, which often differs from objective distortion metrics in current system design. This requires the development of communication architectures that can adapt to the specific perceptual requirements of different applications and user contexts.