C7 is a dual-hemisphere cognitive architecture designed to move beyond classic pattern-matching AI systems. It introduces a self-regulating, multi-state reasoning core that dynamically switches between shallow and deep computation based on internal signals such as:
- predicted error
- coherence signals
- input intensity
- surprise response
- feedback sensitivity
- grounding stability
It was developed through iterative experiments from multimodal front-ends (audio, text, image) to a unified A7 integrator with a gated shallow/deep processor.
- Emb-C (collapsed multimodal embedding)
- Intensity estimation (audio/text/image energy)
- Associative arrays A1, A3, A5
- A7 integrator with stability regulation
- Shallow/Deep dual-mode processing
- Surprise-triggered gating
- Grounding layer for architectural stability
- Adaptive reasoning effort
- Experimental training loops
/core
c7_core_v1.py
c7_core_training.py
/modules
audio_frontend.py
text_frontend.py
image_frontend.py
emb_c.py
integration_a7.py
gating.py
/experiments
phase1_to_phase9/
surprise_gate/
intensity_tests/
coherence_tests/
/docs
whitepaper/
diagrams/
C7: Two-Hemisphere Grounded Intelligence
DOI: 10.5281/zenodo.17640165
- add datasets for real multimodal training
- build AudioBrain (C7-A)
- extend grounding layer
- create neuroscience-aligned version (C7-Neuro)
- open-source demo checkpoint
- release trained model v1.1
MercAIA Project
Mostafa Bahram