Layered Architecture

A sophisticated multi-layered hierarchy that creates functional clinical intelligence through unified memory-knowledge-reasoning integration

Amigo's memory architecture employs a sophisticated, multi-layered hierarchy (L0, L1, L2, L3) that creates functional clinical intelligence with perfect memory as one of its capabilities. This isn't just a memory system—it's a complete cognitive framework that generates multiple interconnected feedback loops between global patient understanding and local processing. The architecture operates through distinct post-processing and live session phases, serving as a critical component of the unified Memory-Knowledge-Reasoning (M-K-R) system.

Live Conversation Processing

During live interactions, L3 provides memory at the right interpretation, precision, and depth to power knowledge application and reasoning without retrieval latency that would degrade reasoning quality:

1. L3 (Always Available)

Memory-Knowledge-Reasoning Integration: L3 provides memory at the precise interpretation depth needed for clinical knowledge application and reasoning

• Memory maintained at the specific precision and depth required for different clinical reasoning tasks with immediate availability

• Professional identity ensures memory interpretation matches knowledge application requirements without retrieval delays

• Healthcare decisions powered by memory-knowledge-reasoning unity where current symptoms connect to patterns through proper contextual depth AND zero-latency access

• Unified context with immediate availability enables high-quality reasoning because memory, knowledge application, and reasoning operate on consistently interpreted information without retrieval interruption

1. Rare Recontextualization (Adds Latency)

Perfect Reasoning Foundation: Rare expansion occurs only when genuinely new context emerges, not due to L3 limitations - L3 provides complete reasoning foundation

• Complete Memory-Reasoning Foundation: L3 provides complete memory at the interpretation depth needed for clinical reasoning with immediate availability

• Targeted Historical Insight Extraction: Expansion occurs when L3-guided reasoning identifies opportunities to extract additional insights from historical context

• Complete Context Expansion: Queries generated with full L3 context enable precise extraction of genuinely valuable historical insights

• Perfect Recontextualization: Past L0 conversations recontextualized through L3's complete unified context, enabling reasoning that extracts maximum value from historical information

• Complete Professional Integration: Clinical knowledge fully integrated through L3's comprehensive professional context

• Unified Memory-Knowledge-Reasoning: L3 enables complete coherent reasoning across all information with the precision depth required for clinical intelligence

Post-Processing Memory Management

Amigo implements a sophisticated post-processing cycle that creates L3 through progressive synthesis:

1

L0 → L1: Memory Extraction with L3 Anchoring

L3-Guided Extraction: Every memory extraction from raw transcripts operates with complete awareness of the existing L3

• Net-New Information Determination: L3 determines what constitutes genuinely new information worth extracting from L0 transcripts

• Contextual Interpretation: L3 provides the interpretive lens for understanding L0 conversations from complete historical perspective

• Professional Identity Targeting: Service provider background shapes what information is deemed critical for extraction

• Dimensional Blueprint Guidance: L3's dimensional framework guides extraction targeting based on functional importance

• Perfect Source Linking: Each L1 memory maintains linkage to source L0 for future recontextualization needs

2

L1 → L2: Episodic Synthesis when Accumulation Threshold Reached

Accumulation-Based Synthesis: When net-new information accumulation reaches threshold, L1 memories are synthesized into L2 episodic user model

• L3-Anchored Synthesis: L1 memories synthesized into L2 episodic model with complete L3 awareness

• Information Density Management: Prevents explosion while maintaining critical insights

• Dimensional Organization: Professional identity guides how information is structured in episodic model

• Temporal Coherence: Maintains chronological understanding while creating episodic synthesis

• Boundary Prevention: L3 anchoring prevents information loss at processing boundaries

3

L2 → L3: Global Model Evolution through Boundary-Crossing Synthesis

Global Model Merger: Multiple L2 episodic models merged to evolve L3 across all time

• Boundary-Crossing Synthesis: Merges L2 episodic models while preventing information density explosion

• Complete Temporal Coverage: Creates unified understanding across entire patient history

• Dimensional Evolution: User dimensions refined based on patterns discovered across episodes

• Professional Identity Integration: Maintains clinically relevant interpretation throughout merger

• Continuous Improvement: Each L3 evolution incorporates new insights while preserving historical understanding

Dimensional Evolution and Clinical Intelligence

The system creates multiple interconnected feedback loops between global patient understanding and local processing:

1. Professional Identity-Driven Evolution

• Professional identity guides interpretation at every level of the memory hierarchy

• System evolves attention patterns based on discovered patient patterns

• Drift Detection and Correction: When system detects drift between user dimension definitions and optimal interpretation patterns for a patient group, dimensional definitions can be modified

• Complete Temporal Backfill: Modified dimensional blueprints trigger replay-based reprocessing across all historical time, regenerating L0→L1 extraction, L1→L2 episodic synthesis, and L2→L3 global model evolution with superior interpretation framework

• Functional Optimization: This dimensional evolution and backfill process improves patient safety, clinical experience, and medical outcomes through evolved professional interpretation frameworks

• Population-Level Intelligence: Enables reinterpretation of entire patient populations with optimal information interpretation, depth, granularity, and angle based on discovered clinical patterns

• Clinical Outcome Optimization: As understanding of patient groups evolves, dimension definitions can be updated with system backfilling by recomputing interpretations based on new dimensional understanding

2. Global-Local Context Bridge

• Dual Anchoring Mechanism: L3 serves as anchor point for both expansion query generation (present context) and historical interpretation (past context)

• Query Generation: Expansion queries generated with respect to current L3 state against present live session context

• Historical Interpretation: Query answering from past L0 sessions is anchored against L3, merging past raw events with present global understanding across time

• Temporal Synthesis: This creates coherent interpretation where historical events are understood through the lens of complete current patient understanding, not isolated past context

• Professional identity creates better targeting for recontextualization during live sessions through this dual anchoring mechanism

3. Functional Clinical Intelligence

• This comprehensive contextual awareness is essential for medical intelligence performance

• Healthcare decisions require understanding how current symptoms connect to established patterns, medication interactions, family history, and treatment responses

• System knows everything already learned about a patient, enabling focus on genuinely new information

• Rapid clinical decision-making achieved with complete context through L3

Self-Improving System: This complete cycle creates a self-improving clinical intelligence system where discovered patterns in patient groups can retroactively improve the interpretation of all historical data through dimensional evolution and temporal backfill, ensuring optimal clinical understanding evolves across the entire patient population.

High-Bandwidth Cross-Layer Integrations

L3 ↔ L0 Direct Integration

• Contextualized Historical Access: L3 provides interpretive context for direct L0 access during recontextualization

• Temporal Bridging: L3 serves as bridge between present understanding and raw historical events

• Selective Retrieval: L3 dimensions guide which L0 sessions are relevant for expansion queries

• Interpretive Anchoring: Raw L0 data interpreted through L3 global context rather than isolated historical perspective

L3 ↔ L3 Temporal Evolution

• Cross-Time Integration: Current L3 integrates insights from previous L3 snapshots

• Dimensional Continuity: User dimensions evolve while maintaining continuity across L3 generations

• Pattern Accumulation: Long-range patterns emerge through L3-to-L3 synthesis over time

• Boundary-Crossing Intelligence: L3 evolution prevents information loss across processing boundaries

L3 ↔ L1 Extraction Guidance

• Net-New Determination: L3 determines what constitutes genuinely new information during L0→L1 extraction

• Interpretive Lens: L3 provides interpretive framework for understanding historical context during extraction

• Dimensional Anchoring: L1 extractions anchored against L3 to prevent misinterpretation

• Professional Identity Integration: L3 professional identity guides L1 extraction targeting and prioritization

L2 ↔ L3 Episodic Synthesis

• Coherent Aggregation: Multiple L2 episodic models synthesized into L3 through Boundary-Crossing Synthesis

• Context Preservation: L2→L3 synthesis maintains episodic insights while creating global coherence

• Dimensional Evolution: L3 dimensional framework evolves based on patterns discovered across L2 episodes

• Information Density Management: Synthesis prevents information density explosion while preserving critical insights

User Understanding ↔ Dimension Definition Feedback Loops

Object Level

Direct User Understanding

• Immediate Clinical Context: User understanding directly informs clinical decision-making in live sessions

• Dimensional Application: Current dimensional definitions applied to interpret patient information

• Professional Identity Filtering: User understanding filtered through professional identity lens

• Real-Time Contextualization: Present user state contextualized against historical understanding

Meta Level

Dimension Definition Evolution

• Pattern Recognition: System recognizes when dimensional definitions are suboptimal for patient groups

• Drift Detection: Meta-analysis identifies when user understanding patterns diverge from dimensional framework

• Adaptive Optimization: Dimensional definitions modified based on discovered user understanding patterns

• Professional Identity Evolution: Meta-level adaptation of how professional identity guides interpretation

Meta-Meta Level

Framework Evolution

• System Learning: Meta-meta analysis of how dimensional evolution patterns themselves can be optimized

• Attention Pattern Evolution: System evolves its own attention patterns based on meta-level insights

• Framework Optimization: Meta-meta feedback optimizes the dimensional evolution process itself

• Cross-Population Intelligence: Meta-meta insights applied across entire patient populations

Hierarchical Integration

Feedback Integration

• Object→Meta: Real user understanding patterns inform dimensional definition changes

• Meta→Object: Evolved dimensional definitions improve real-time user understanding quality

• Meta→Meta-Meta: Dimensional evolution patterns inform framework optimization strategies

• Meta-Meta→Meta: Optimized frameworks improve dimensional evolution effectiveness

• Cross-Temporal Integration: Feedback loops operate across multiple time horizons simultaneously

Continuous Enhancement: These nested feedback loops create a self-improving clinical intelligence system where user understanding, dimensional definitions, and framework optimization continuously enhance each other through high-bandwidth cross-layer integration.

Overcoming the Token Bottleneck

The Core Constraint: Current language models are constrained by the token bottleneck: they must squeeze high‑dimensional internal reasoning into low‑bandwidth text tokens, sharply limiting their ability to preserve complex state across steps.

The layered memory system functions as an external high-dimensional memory store that preserves information that would otherwise be lost in token-based reasoning. This is crucial for the integrated M-K-R cycle:

Perfect Information Preservation

While token-based reasoning loses significant information density, our L0 layer maintains complete, verbatim records with perfect fidelity, forming a reliable Memory base for the M-K-R system.

Rich Contextual Relationships

The system preserves multidimensional relationships that would be flattened in token externalization, providing richer Memory context for Knowledge and Reasoning.

Progressive Abstraction

While maintaining perfect ground truth (L0 Memory), the system creates increasingly abstracted representations (L1, L2 Memory) that optimize for efficient retrieval and integration into the M-K-R cycle, ensuring high-bandwidth interplay.

For details on the upcoming Memory‑Reasoning Bridge planned for Agent V2, see Advanced Topics › Memory‑Reasoning Bridge.