A Neuro-Inspired Protocol for Integrity AI (Solving Hallucination & Context Drift)

Research
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Hello everyone,

LVLM + LTMM: Neuro-inspired AI Approach - An Advanced Protocol for visually challenged enablement

Large Vision-Language Models (LVLMs) see remembers but hallucinates. Long-Term Memory Models (LTMMs) remember but lack retention for ages.

Below is some of the mechanism that can help on the same

Frontal Cortex Layer → Decision layer to through the result set
Synapse & Dendrite Vectors → N dimensional vector links that preserve time and context
LTMM Reservoir → Semantic Memory Maps
Guidance Layer → Layer of suggestions, directions, decisions

This isn’t just bigger models. It’s protocol milestones: AI that can see, remember, and decide with integrity.

This is a neuro inspired protocol to remember decide and guide the system as well as community who uses that.

  • A Call for Theoretical AI: I believe this necessitates a new, formal branch of Theoretical AI to identify the universal principles of neuro-relationship processing, moving beyond empirical scaling.

:speech_balloon: Seeking Community Feedback

I would greatly appreciate feedback, particularly on the following technical points:

  1. Synapse/Dendrite Vector Implementation: What existing memory mechanisms (e.g., hierarchical memory networks, or complex attention) could best form the basis for these context-preserving N-dimensional vectors?

  2. Frontal Cortex Layer: What formal mechanisms (e.g., reinforcement learning policy, or a complex gating network) would best represent the “integrity-check” logic in the final decision layer?

Thank you for your time and expertise.

#LVLM #LTMM #NeuroAI #TheoreticalAI #AIArchitecture #Hallucination #CognitiveAI

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  1. Synapse/Dendrite Vector Implementation: Use complex-valued hypernetworks with phase-coherent attention to implement Synapse & Dendrite Vectors. Instead of hierarchical memory or static key-value stores, model these vectors as 4D valence tensors (A, ¬A, A∧¬A, ⊥) evolving under coupled differential dynamics. Base them on geometric attention—where keys and queries are operators in a Clifford algebra space—enabling intrinsic time encoding via rotational phase.

  2. Frontal Cortex Layer: Implement “integrity-check” via a lightweight 3D convolutional monitor over rolling valence field snapshots (pos × valence × time), trained to detect low entropy gradients and high coherence in the A∧¬A channel. When both conditions align, trigger emission—this is your decision layer. Use a differentiable coherence estimator (e.g., spectral clustering loss on attention manifolds) as the gating mechanism—no RL policy needed.

Whilst i do not agree entirely with the nomenclature and framing, the architecture remains intact. If you are doing this in practical development i wish you well!

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Thanks Jack. This is not a practical Implementation which is not a feasible one at this point or may even never be possible.

This is a thought to eliminate the hallucination and support day to day life who have challenges visually or hearing through a device.

-Vj