Thynaptic Human-AI Interface Protocol

The Thynaptic Human-AI Interface Protocol defines the communication framework between users and Mavaia's Adaptive Cognitive Layer within FocusOS. Unlike traditional chat interfaces that treat conversations as isolated exchanges, the protocol structures bidirectional communication where context flows across sessions, user intent is classified before processing, and responses are validated against safety and accuracy constraints. The protocol operates in two modes: Execution Mode for immediate task completion and Reflection Mode for exploratory reasoning. This dual-mode design enables the system to distinguish between 'do this now' and 'help me think about this' interactions, routing each appropriately through the ACL pipeline.

The protocol implementation spans three layers: Intent Classification (natural language parsing into structured intent objects), Context Assembly (combining conversation history, workspace state, and memory retrieval), and Action Translation (converting model outputs into executable operations). Intent classification uses a lightweight classifier that analyzes query structure, temporal indicators, and task markers to categorize requests into 12 intent types. Context assembly queries ARTE for relevant memories, reads current workspace state from FocusOS, and assembles a structured context window. Action translation parses model outputs for tool calls, file operations, and system commands, validating each against user permissions before execution.

Protocol evaluation across 5,000 interactions showed 89% intent classification accuracy, with execution/reflection mode selection correct in 94% of cases. Context assembly latency averaged 180ms for simple queries and 420ms for complex multi-workspace requests. Action translation successfully parsed and validated 97% of model-generated tool calls, with the remaining 3% requiring user confirmation due to ambiguous permissions. The dual-mode routing reduced unnecessary reasoning activation by 31% compared to single-mode baselines.

The protocol's strength lies in its explicit separation of communication concerns. Rather than treating the interface as a passthrough layer, it structures interaction at the architectural level. The 89% intent classification accuracy, while strong, reveals edge cases where query ambiguity requires fallback to user confirmation. The 420ms context assembly latency for complex requests suggests opportunities for predictive context loading. The action translation layer's 97% success rate demonstrates that structured output parsing can reliably convert natural language model responses into safe system operations.

Current limitations include: (1) Intent classification struggles with mixed-mode queries that blend execution and reflection, (2) Context assembly does not yet implement predictive loading, resulting in cold-start latency on complex workspace switches, (3) Action translation requires model outputs to follow specific formatting conventions, limiting model flexibility, (4) The protocol currently lacks native support for multi-turn negotiation when user intent is ambiguous.

The Thynaptic Human-AI Interface Protocol provides a structured foundation for human-AI interaction that extends beyond conversational interfaces. By explicitly modeling intent, context, and action translation, the protocol enables systems like Mavaia to integrate into complex workflows while maintaining safety and accuracy constraints. Future work will focus on adaptive intent classification, predictive context loading, and multi-turn negotiation for ambiguous requests.