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MCP
(Model Context Protocol)
MCP is a specialized communication framework for maintaining and exchanging contextual information between AI models and subsystems. This protocol enables complex AI architectures to share state, memory, and attention contexts across different model components like LLMs, CNNs, and reinforcement learning agents.
Key features include:
- Context preservation across inference calls
- Dynamic model composition capabilities
- Shared attention mechanisms between specialized submodels
- Distributed memory management for multi-model systems
MCP is particularly valuable in modular AI systems where different models (e.g., vision + language + reasoning) need to maintain consistent contextual understanding. The protocol helps address the 'context boundary' problem in composite AI systems, enabling more sophisticated applications than standalone models can achieve. Modern implementations often use tensor-based context representations compatible with frameworks like TensorFlow and PyTorch.
Key features include:
- Context preservation across inference calls
- Dynamic model composition capabilities
- Shared attention mechanisms between specialized submodels
- Distributed memory management for multi-model systems
MCP is particularly valuable in modular AI systems where different models (e.g., vision + language + reasoning) need to maintain consistent contextual understanding. The protocol helps address the 'context boundary' problem in composite AI systems, enabling more sophisticated applications than standalone models can achieve. Modern implementations often use tensor-based context representations compatible with frameworks like TensorFlow and PyTorch.