Recall Mechanisms

The memory system uses two distinct recall methods, enhancing proactive and context-aware responsiveness.

Implicit Recall

Triggered by: Information gap detection during real-time conversation analysis.

Process:

• Agent autonomously identifies information gaps by evaluating current conversation against user model

• Drills down to L0 (perfect context) layer when required information isn't in the user model

• Applies contextual reasoning to synthesize relevant insights from raw conversation data

• Seamlessly integrates retrieved information while maintaining conversation flow

The power of implicit recall comes from the user model's comprehensive coverage—approximately 90% of important information is already available with proper contextualization and precision at the start of each session. This makes information gaps relatively rare but immediately obvious when they occur.

When the agent detects an information gap (by analyzing the context graph, interaction logs, and user model against the current conversation), it automatically initiates a targeted retrieval process to maintain perfect contextual understanding without disrupting conversation flow.

This recall process leverages dimensional importance hierarchies to prioritize information retrieval, ensuring that critical data (marked with higher precision requirements) is retrieved first and with complete contextual preservation. The system employs proximity-based search methods that focus on contextually relevant information rather than simple keyword matching.

Explicit Recall

Triggered by: Predetermined recall points defined in the context graph structure.

Process:

• Activates at specific decision points predefined in the context graph

• Generates precisely targeted queries based on functional requirements

• Recontextualizes requested information through user model integration

• Guarantees consistent information access for critical decision points

Unlike implicit recall, explicit recall is deliberately engineered into the conversation flow through the context graph. These recall points ensure critical information is always available at key decision moments, rather than relying on gap detection.

The integration with the user model transforms raw recall queries into contextually-rich retrievals. Each explicit recall query is recontextualized against the user model dimensions, ensuring the retrieved information maintains perfect context preservation and functional relevance.

This dual-mechanism approach ensures both responsive, dynamic information retrieval (implicit) and guaranteed access to critical information at predetermined points (explicit), creating a robust memory system optimized for enterprise reliability.