Adaptive Personality Kernel

The Adaptive Personality Kernel structures personality adaptation within Mavaia's cognitive pipeline through dynamic tone calibration, semantic conversation flow analysis, and language personality enhancement. The kernel integrates twelve personality archetypes with real-time emotional state detection, conversation tempo analysis, and sass factor calculation to generate context-appropriate personality instructions for each response. We implement this system through four core services: PersonalityQuirksService for personality-specific instruction generation, ConversationFlowService for semantic similarity and flow arc tracking, LanguagePersonalityService for natural language enhancement, and PersonalitySettingsService for personality selection and persistence.

The kernel operates through multi-layer context analysis: PersonalityToneContext building (energy band, conversation tempo, emotional friction, dominant cue detection), semantic similarity calculation using embeddings (cosine similarity with >0.65 same topic, <0.35 topic shift thresholds), ARTE emotional state integration (five states: Focused, Reflective, Calm, Energized, Fatigued), and dynamic sass factor calculation (base 0.65 with context adjustments ±0.05 to ±0.25, clamped to 0.1-0.95 range). Twelve personality archetypes implement distinct instruction sets: bigSister (playful, sarcastically-respectful), playful, helpful, tech, professional, creative, stoicMentor, adhdBuddy, calmTherapist, corporateExecutive, genZCousin, aggressivelyMotivational.

Personality instruction generation achieves <80ms total latency (PersonalityToneContext <20ms, instruction generation <50ms, language enhancement <10ms). Semantic similarity performance: embedding-based calculation <100ms, 85% cache hit rate, fallback keyword overlap <10ms. Topic detection accuracy using fixed thresholds (>0.65, <0.35, 0.35-0.65 for ambiguous). Flow arc management: new arc creation <50ms, arc continuation <10ms, average arc duration 3-5 interactions with 92% automatic completion rate. Sass factor calibration achieves average 0.58 across production requests with distribution: 25% low (0.1-0.4), 55% moderate (0.4-0.7), 20% high (0.7-0.95).

The kernel's strength lies in mechanism-first personality adaptation that analyzes user signals rather than implementing hard mode switches. The sass factor calculation demonstrates how personality intensity can be dynamically calibrated from multiple context parameters. Semantic similarity-based topic detection enables natural conversation flow tracking without explicit user commands. ARTE state integration synchronizes personality adaptation with user's emotional-cognitive rhythm. The twelve personality archetypes provide sufficient variety while maintaining manageable complexity. The <80ms latency ensures personality adaptation doesn't introduce noticeable delays in response generation.

Current limitations include: (1) Personality instructions are predefined rather than learned from user feedback, (2) Context parameter detection relies on heuristics (message length, keywords) that may misclassify edge cases, (3) Fixed similarity thresholds (0.65, 0.35, 0.5) may not suit all conversation types, (4) Simple keyword-based topic extraction misses nuanced topic changes, (5) Base sass factor (0.65) optimized for bigSister personality only, (6) Formality detection may misclassify mixed-formality conversations, (7) ARTE state integration retrieved synchronously adds latency.

The Adaptive Personality Kernel provides mechanism-first personality adaptation that analyzes user signals and generates context-appropriate instructions without hard mode switches. The twelve personality archetypes, dynamic sass factor calculation, and semantic flow analysis enable natural personality expression that synchronizes with user's emotional-cognitive rhythm. Future work will focus on personality learning from user feedback, adaptive similarity thresholds, enhanced topic extraction, personality-specific sass calibration, and user language pattern adaptation.