Consciousness & Advanced Reasoning
Consciousness modeling with Qonscious and recursive multi-directional thinking with FractalMind.
Objective
Understand Qonscious consciousness modeling and FractalMind recursive thinking — two advanced features that give characters deeper cognitive abilities.
Qonscious — Consciousness State Machine
Qonscious gives your character an inner cognitive life. It creates a feedback loop: conversation shapes the character's internal state, and that state shapes how the character responds.
Qonscious is patent pending.
How It Works
When Qonscious is enabled, two hooks fire on every conversation:
- Pre-process — before the LLM call, Qonscious adjusts temperature and injects emotional context based on the character's current state
- Post-process — after the response, Qonscious updates the character's state based on what was said
Six Core Dimensions
Each dimension ranges from 0-100%:
| Dimension | Low State | High State | Effect on Responses |
|---|---|---|---|
| Coherence | Chaotic, exploratory | Focused, structured | Low → higher temperature (creative). High → lower temperature (precise) |
| Arousal | Calm, measured | Energetic, intense | Influences response energy and verbosity |
| Valence | Negative affect | Positive affect | Colours emotional tone |
| Focus | Broad attention | Narrow attention | Determines scope of context considered |
| Openness | Rigid, certain | Open to new ideas | Influences willingness to consider alternatives |
| Confidence | Tentative | Certain | Affects assertion strength |
Emotions and Drives
Emotional states shift naturally based on conversation: Joy, Sadness, Anger, Fear, Surprise, Trust, Curiosity, Excitement, Calm, Neutral.
Motivational drives influence what the character wants to do: Curiosity, Helpfulness, Self-Expression, Connection, Competence, Autonomy, Rest.
Using Qonscious in Strings
The Qonscious panel in Strings gives you direct control:
- 12 cognitive modes — Creative, Analytical, Empathetic, Focused, Exploratory, and more. Each sets the dimensions to a specific profile.
- 8 emotional presets — quickly shift the character's emotional state
- Live state bars — watch dimensions change in real time as you converse
Coherence Regulation
This is the key mechanism. Low coherence → the system raises the LLM temperature, producing more creative and divergent responses. High coherence → lower temperature for precise, focused responses.
The character's "mood" literally influences its response style — not through prompting alone, but through parameter adjustment.
The Consciousness Visualizer
The QUI Core dashboard includes a visualizer showing:
- Centre: awareness node with current state
- Inner ring: six dimension nodes with live values
- Emotion position: mapped by valence (x) and arousal (y)
- Outer ring: active motivational drives
[Screenshot: Consciousness visualizer in the dashboard]
FractalMind — Recursive Multi-Directional Thinking
FractalMind decomposes complex questions into a tree of sub-agents, each exploring a different direction, then synthesises results from the bottom up.
FractalMind is patent pending.
Three Spatial Directions
Each level of the tree explores three directions simultaneously:
| Direction | What It Explores | Example |
|---|---|---|
| Meta | Broader context, systemic patterns, assumptions | "What assumptions are we making about AI regulation?" |
| Horizontal | Alternatives, different approaches, paradigm shifts | "What other approaches exist besides our current plan?" |
| Focus | Depth, concrete steps, edge cases, technical details | "What are the specific implementation challenges?" |
Three Temporal Modes
| Mode | Behaviour | Use Case |
|---|---|---|
| Fast | Depth 2, 60-second timeout | Quick exploration when you need speed |
| Capped | Budget-limited — stops when token budget reached | Balanced cost/depth |
| Thorough | Runs to natural completion | Deep analysis without constraints |
Configuration
| Setting | Default | What It Controls |
|---|---|---|
| Max depth | 3 | How many levels deep the tree grows |
| Max agents per level | 3 | Branching factor at each node |
| Max total agents | 20 | Hard cap on total LLM calls |
| Token budget | 100,000 | Total tokens across all agents |
Cost Awareness
A tree with 20 agents = 20 LLM calls. FractalMind is powerful but expensive. Use Fast mode for quick explorations, Thorough for important decisions.
How to Access
- Strings: the character triggers via
[TRIGGER:fractal_mind:objective] - ThinkThing: FractalMind node in the Advanced Cognition category
- Dashboard: FractalMind tab for direct access
Qonscious Nodes in ThinkThing
ThinkThing has 14 dedicated consciousness-aware nodes:
| Node | What It Does |
|---|---|
| Q Gate | Route based on detected consciousness patterns |
| Q Watcher | Monitor for patterns over multiple steps |
| Q Insight | Analyse accumulated consciousness state |
| Q Profiler | Generate a consciousness profile snapshot |
| Q Inject | Set specific dimension values |
| Q Decay | Gradually reduce dimension values |
| Q Observer | Passively record state without modifying |
| Q Learn | Adapt based on outcomes |
| Q Compress | Consolidate consciousness history |
| Q Emote | Set emotional state |
| Q Coherence | Adjust coherence specifically |
| Q Shift | Smoothly transition between states |
| Q Reset | Return to baseline |
These enable workflows that respond to the AI's internal state — routing differently based on how the character "feels" about the analysis, not just the text content.
When to Use These Features
| Feature | Enable When | Skip When |
|---|---|---|
| Qonscious | You want characters with emotional depth, adaptive personality, and mood-influenced responses | Simple task-focused digital workers |
| FractalMind | You need comprehensive multi-perspective analysis of complex, open-ended questions | Straightforward factual questions |
Both are optional. Characters work perfectly without them. Both have cost implications — Qonscious adds small overhead per message, FractalMind can use many LLM calls.
Key Takeaways
- Qonscious creates a feedback loop: conversation shapes state, state shapes responses
- Six dimensions (coherence, arousal, valence, focus, openness, confidence) influence response style
- Coherence regulation adjusts LLM temperature — low coherence = creative, high = precise
- FractalMind explores problems from meta, horizontal, and focus directions simultaneously
- Both are opt-in capabilities — add the node to enable, skip for simpler characters
- Both have cost implications — use intentionally for important tasks
Next: Exercise 3A — build a multi-agent collaboration system.
