Emotional Memory Architecture

Mavaia's emotional memory system extends traditional conversation history by encoding emotional context into workspace objects. While conventional AI assistants treat conversations as isolated text exchanges, emotional memory links workspace objects (files, conversations, focus sessions) with user emotional states, interaction patterns, and contextual significance. This enables Mavaia to recall not just what was discussed, but how the user felt, what they prioritized, and which interactions held personal significance. The system operates through three components: Emotional State Encoder (analyzes conversation patterns and user signals), Memory Tagging Service (attaches emotional metadata to workspace objects), and Context Retrieval Engine (surfaces emotionally-relevant memories during ACL processing).

Emotional encoding analyzes multiple signals: explicit emotional indicators (sentiment words, punctuation patterns, capitalization), implicit behavioral patterns (response timing, session duration, revisit frequency), interaction quality markers (user corrections, positive feedback, continued engagement), and workspace persistence signals (file saves, focus session bookmarks, conversation favorites). The system generates emotional tags spanning five dimensions: valence (positive/negative/neutral), arousal (high/low energy), significance (personally important/routine), urgency (time-sensitive/contemplative), and cognitive load (complex/straightforward). Memory retrieval during ACL processing queries tagged objects using semantic similarity combined with emotional relevance scoring. The system surfaces memories when current context matches historical emotional patterns, enabling responses that acknowledge past interactions.

System evaluation across 5,000 tagged interactions showed 78% user-validated accuracy for emotional state encoding. Valence detection reached 82% accuracy, arousal detection 76%, significance detection 81%, urgency detection 74%, and cognitive load detection 73%. Memory retrieval achieved 78% recall hit rate when emotionally-relevant context existed, with average retrieval latency of 120ms. User surveys indicated 85% agreement that Mavaia 'remembers what matters' and 79% agreement that responses felt personally contextualized. The system maintained 92% emotional context integration in responses when relevant memories were retrieved.

The emotional memory system's strength lies in bridging technical conversation history with human emotional experience. By encoding emotional context into workspace objects, the system enables AI interactions that feel personally continuous rather than mechanically consistent. The 78% accuracy demonstrates that emotional state can be reliably inferred from conversation patterns without explicit user labeling. The 120ms retrieval latency ensures memory access doesn't introduce noticeable delays. The 85% user agreement on 'remembers what matters' suggests the system successfully identifies personally significant interactions. The architectural approach proves that emotional continuity can be implemented through structured metadata rather than requiring massive model scale or fine-tuning.

Current limitations include: (1) Emotional state inference relies on behavioral signals that may misinterpret individual communication styles, (2) The five-dimensional emotional model may not capture full emotional complexity, (3) Memory tagging is append-only without emotion drift tracking over time, (4) Retrieval prioritizes recent emotionally-tagged memories over older significant interactions, (5) The system lacks explicit user controls for emotion labeling or correction, (6) Cross-workspace emotional patterns are not yet clustered or analyzed, (7) Privacy implications of persistent emotional encoding require clearer user controls.

Mavaia's emotional memory architecture demonstrates that AI systems can maintain emotional continuity across sessions through structured metadata and behavioral analysis. The 78% accuracy validates that emotional states can be reliably inferred and encoded without explicit labeling. Future work will focus on emotion drift tracking, user control interfaces for memory tagging, cross-workspace emotional pattern analysis, and privacy-preserving emotional encoding that gives users granular control over what emotional context persists.