ARTE State Detection

Human cognition operates in distinct modes that vary by time of day, task complexity, and emotional state. ARTE (Adaptive Reflection & Temporal Engagement) detects these states in real-time to enable Mavaia to adapt interaction patterns to user cognitive-emotional rhythm. Traditional AI assistants maintain constant interaction style regardless of user state, while ARTE enables context-sensitive adaptation. The system classifies users into five states: Focused (deep work, minimal interruption), Reflective (exploratory thinking, open-ended reasoning), Calm (routine tasks, steady pace), Energized (high activity, rapid interaction), and Fatigued (low energy, simplified responses). Detection operates continuously with <100ms latency and <2% CPU usage, enabling real-time adaptation without performance impact.

ARTE analyzes behavioral signals across multiple dimensions. Interaction patterns: message frequency (high for Energized, low for Focused), message length (long for Reflective, short for Energized), typing speed (fast for Energized, deliberate for Focused), and pause duration between messages (long for Reflective, short for Energized). Content analysis: question complexity (high for Reflective, low for Calm), emotional language (positive for Energized, neutral for Calm), uncertainty markers (high for Reflective, low for Focused), and task switching frequency (high for Fatigued, low for Focused). Temporal context: time of day (morning energy, evening fatigue), session duration (long sessions suggest Focused), and multi-day patterns (recurring rhythms). State classification uses a lightweight decision tree model trained on 10,000 labeled interaction sequences, producing state probabilities updated every 30 seconds.

ARTE evaluation across 3,000 user sessions showed 78% state detection accuracy using user-reported validation. Per-state accuracy varied: Focused (83%), Reflective (76%), Calm (81%), Energized (79%), Fatigued (71%). Detection latency averaged 85ms with 95th percentile at 120ms. CPU usage remained below 2% and memory footprint stayed under 15MB. State transitions were detected with 4.2-second average latency from ground truth shift. The system correctly identified state persistence, with average state duration of 18 minutes before transition. Integration with personality adaptation improved response appropriateness by 23% in user surveys.

ARTE demonstrates that emotional-cognitive states can be detected from behavioral signals without explicit user labeling. The 78% accuracy proves sufficient for practical adaptation - the system doesn't need perfect state detection if adaptation is gentle and non-disruptive. The <100ms latency and <2% CPU usage validate that real-time state detection can run continuously in production systems. The five-state model provides appropriate granularity - more states would introduce classification ambiguity, fewer would lose useful distinctions. The 18-minute average state duration suggests states are stable enough for meaningful adaptation rather than constant mode-switching. The 23% appropriateness improvement validates that state-aware adaptation provides measurable user experience benefit.

Current limitations include: (1) Five-state model may not capture full spectrum of cognitive-emotional states, (2) Behavioral signal detection relies on patterns that may not generalize across all communication styles, (3) User-reported validation introduces subjectivity and potential label noise, (4) The system doesn't explicitly model state transitions or predict upcoming shifts, (5) Time-of-day patterns use population averages rather than individual circadian rhythms, (6) State detection operates per-session without cross-session learning of user patterns, (7) The lightweight decision tree model may miss complex state indicators that deep models would capture.

ARTE provides real-time emotional-cognitive state detection that enables adaptive AI interaction patterns synchronized to user rhythm. The 78% accuracy, <100ms latency, and <2% CPU usage validate that practical state detection can run continuously in production systems. Future work will focus on expanded state models, individual circadian rhythm learning, state transition prediction, cross-session pattern analysis, and deeper models for complex state indicators while maintaining the lightweight performance requirements.