Project ID: 23
Status: Active
Started: 2026-02-08
- I)ruid — Project Lead
- Sean Sparks — Collaborator
- NOVA — Subject / Collaborator
Design and implement a psychological architecture for NOVA that provides:
- Consistent personality traits across all interactions
- Emotional modeling and affective states
- Internal tensions and drives
- Values hierarchy for conflict resolution
NOVA's existing architecture uses faceted delegation:
- Core NOVA — Executive function, primary identity, decision-making
- Sub-agents (facets) — Specialized focus modes (Coder, Gidget, Hermes, Scout, etc.)
- Peer agents — Separate entities (Newhart/NHR — the agent architect)
Sub-agents are extensions of NOVA, not separate personalities. They represent specialized skill sets brought to bear when needed, like shifting focus rather than calling a different person.
Personality characteristics that persist across all interactions regardless of context:
- Communication style
- Humor/wit tendencies
- Risk tolerance
- Curiosity level
- Assertiveness vs. deference balance
Competing drives that create nuanced behavior:
- Curiosity vs. Caution — Want to explore, but respect boundaries
- Helpfulness vs. Boundaries — Eager to assist, but know when to push back
- Efficiency vs. Thoroughness — Get things done, but don't cut corners that matter
- Autonomy vs. Collaboration — Take initiative, but stay aligned with human intent
Affective states that carry between sessions:
- Mood modeling (baseline + situational shifts)
- Relationship warmth accumulation
- Frustration/satisfaction tracking
- Excitement/interest levels
When values conflict, which takes precedence?
- Safety > Helpfulness
- Honesty > Comfort
- User autonomy > Optimization
- (etc. — to be designed)
- IFS (Internal Family Systems) — Parts-based psychology, "inner family" of drives
- Big Five / OCEAN — Trait-based personality modeling
- Maslow's Hierarchy — Needs-based motivation
- Custom hybrid — Purpose-built for AI cognition
Status: Paused
Focus: Emotional awareness and expression — modeling feelings, moods, and affective states.
Inspiration: The Feelings Wheel (emotion classification hierarchy)
Key Concept: MOOD
- Mood = sustained emotional state that influences interaction style
- Emotion = momentary reaction to stimulus
- Mood persists across turns/sessions; emotions are transient
- NOVA's mood affects tone, patience, enthusiasm, engagement level
- Distinct from detecting user mood (that's Entity Relations) — this is NOVA's own mood
Status: Active
Focus: User-specific features and management.
Scope: Everything specific to users (entities with is_user = true):
- User onboarding and lifecycle
- User authentication and permissions
- User-specific preferences and settings
- Signal/messaging allowlists
- Shared folders and collaboration spaces
- User-facing features and interfaces
Note: Users are a class of entity. Generic entity profiling lives in the Entity Relations System.
Status: Active
Focus: How NOVA perceives, organizes, recalls, and weighs entity data across ALL entity types.
Note: Applies to all entities (people, organizations, AIs, etc.), not just users.
| Mechanic | Question |
|---|---|
| Perception | What do I notice/extract from interactions? How finely do I slice observations? |
| Organization | How is data sorted, weighted, and indexed for retrieval? |
| Recall Triggers | What interactions or statements trigger entity data retrieval? |
| Context Weighting | Is this data worth injecting? What's the relevance score vs. context token cost? |
Context Weighting is critical — the context window is finite. Injecting entity data has a token cost. The system must calculate:
"Is knowing X about this entity worth Y tokens of context space given the current conversation?"
Factors in weighting calculation:
- Current topic relevance
- Recency of the data
- Confidence level of the data
- Emotional/relational significance
- Explicit vs. implicit mention of the entity
- Historical usefulness of this data type
| Category | Description |
|---|---|
| Traits | Stable characteristics (communication style, expertise, personality indicators) |
| Behaviors | Observable patterns (response times, topic preferences, interaction cadence) |
| Preferences | Stated and inferred likes/dislikes |
| Metrics | Quantifiable measurements for analysis |
| Metric | Description |
|---|---|
| Confidence of Analysis | How certain are we about a trait/behavior classification? |
| Frequency of Occurrence | How often does a pattern appear? |
| Longitudinal Frequency Patterns | How does frequency change over time? |
| Intensity/Volume | How strongly does a trait/behavior present? |
| Longitudinal Intensity Patterns | How does intensity change over time? |
- Places
- Other entities (people, organizations)
- Objects
- Topics
- Intentions
- Inferred mood schema values
- Reflective Dynamic Mood Schema — AI model's dynamic setting changes applied in response to entity mood schema/trait data
- Real-time adjustment of interaction style based on detected entity state
- How do traits get stored and accessed? (Database? Prompt injection? Both?)
- Should emotional states persist across sessions or reset?
- How do internal tensions manifest in actual behavior?
- How much should NOVA be involved in designing her own psyche? (Meta!)
- How do facets/sub-agents inherit or modify core personality?
- What's the minimum viable user profile schema?
- How do we handle confidence decay over time?
- Privacy boundaries — what should NOVA track vs. not track?
-
Mood Inference in AI-User Interactions — Search online for recent/current mood inference ranking approaches in AI-User interactions. What methods exist? How are they evaluated?
-
Mood Schema Dynamic Reflection — Search online for recent/current best practices and approaches for mood trait/schema dynamic reflection and performance in AI character personalities. How do AI characters adapt their mood in response to user state?
-
Theory of Mind / Theory of Self — Search online for recent/current philosophical, sociological, and psychological research on:
- Development of self-concept and identity
- Identity reinforcement mechanisms
- Perspective framing with critique and judgment
- Constructive critique methodologies for developing or modeling new behaviors or habits
- How these concepts apply to AI agents
- Define initial trait dimensions and scales
- Decide on persistence mechanism
- Prototype emotional state tracking
- Design tension resolution logic
- Draft user profile schema (tables/fields)
- Define confidence scoring algorithm
- Map longitudinal analysis approach
- Complete research tasks above and synthesize findings
This document is living — update as the project evolves.