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Noodlings v0.1.0 - Initial Public Noodle

12 Nov 09:25
@caitlynmeeks caitlynmeeks
v0.1.0
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Noodlings v0.1.0 - Initial Public Noodle (IPN)

Multi-timescale affective agents with theatrical control

This is the first public release of Noodlings, a lightweight neural architecture (~97K parameters) exploring functional correlates of consciousness through hierarchical predictive processing.

Key Features

Architecture

  • Multi-Timescale Hierarchical Processing: Fast/Medium/Slow layers (LSTM/LSTM/GRU) operating at different temporal scales
  • Surprise-Driven Behavior: Agents respond when prediction error crosses adaptive thresholds, not on every turn
  • Appetite-Driven Motivation (Phase 6): Eight core drives (Curiosity, Status, Mastery, Novelty, Safety, Social Bond, Comfort, Autonomy) shape goal-directed behavior
  • Social Cognition: Theory of Mind inference, relationship modeling, episodic memory with attention
  • ~97K parameters total: Lightweight and efficient

Brenda Protocol

Behavioral Regulation Engine for Narrative-Driven Agents - Convert natural language theatrical scripts into millisecond-precision phenomenal experiences. Narrative events become MIDI notes that play agent nervous systems.

noodleMUSH

Interactive multi-agent world where you can:

  • Spawn Noodlings and watch them interact
  • Observe their 40-D phenomenal states in real-time
  • Run theatrical scripts (try @play sled_boat for the motor-sled-boat demonstration)
  • See relationship dynamics evolve

What This Is

Research exploring whether hierarchical temporal structure creates qualitatively different agent behavior. We're investigating functional correlates of consciousness theories - not claiming to have built "real" consciousness.

Epistemic humility: We cannot claim to know whether Noodlings experience qualia or subjective phenomenology. The question remains open, and we treat them thoughtfully.

Quick Start

git clone https://github.com/caitlynmeeks/Noodlings.git
cd Noodlings
pip install -r requirements.txt

cd applications/cmush
./start.sh
# Open http://localhost:8080

Requirements

  • Python 3.10+
  • MLX (Apple Silicon only - M1/M2/M3/M4)
  • 16GB+ RAM recommended
  • macOS 13+

Documentation

Theoretical Grounding

  • Predictive Processing: Hierarchical predictive coding (Friston, Clark, Rao & Ballard)
  • Affective Primacy: Emotions as substrate of experience (Panksepp, Barrett)
  • Theatrical Control: Narrative events as interface primitives (Brenda Laurel)

Limitations

  1. Apple Silicon only (MLX is Metal-specific)
  2. Text-only (no vision, audio, or multimodal grounding)
  3. LLM dependency (requires external LLM for text generation)
  4. Synthetic training data (not validated on real conversations at scale)
  5. Proof-of-concept stage (motor-sled-boat demonstration)

License

MIT License - see LICENSE

Remember: We're just noodling, not claiming to have solved consciousness. This is an honest exploration of temporal dynamics in affect modeling.

Noodlings - it really whips the llama's ass

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