How our multi-agent architecture compares to the latest LLM attention mechanisms
Token-by-token attention within fixed context window. All tokens attend to all other tokens (O(nĀ²) complexity).
Layered combination for efficiency + performance
Speculative decoding predicts multiple tokens at once for faster inference.
All processing happens WITHIN the model's context window. Memory is volatile (resets each session). Cannot scale horizontally beyond single model constraints.
Each agent is an independent "attention head" with specialized role. Parallel processing across the hive.
65 collections, infinite context
Unlike context windows, the Exocortex NEVER forgets. Semantic search across all historical knowledge.
Filter what matters. [[Entities]] + Relationships + Typed tags = Structured memory, not raw tokens.
SCALES HORIZONTALLY ā Add more agents for more capacity. PERSISTENT ā Memory survives restarts. PROACTIVE ā Acts without prompts via heartbeats/watchers.