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.
Seeking Community Feedback
I would greatly appreciate feedback, particularly on the following technical points:
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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?
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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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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.
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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
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Hello,
This represents a solid architectural split. Separating the generative capability (LVLM/Perception) from the executive control (Frontal Cortex/Decision) is the prerequisite for safe Agentic AI, especially for accessibility where hallucination carries physical risk (e.g., navigation).
To address your specific technical requests based on our work with Security Orchestration Architectures:
1. Synapse/Dendrite Vectors (Context Preservation)
You asked about mechanisms beyond standard embedding distance.
We are actively experimenting with Outcome-Weighted Retrieval (similar to the theoretical “Stone Retrieval Function”).
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The Concept: A vector link shouldn’t just store semantic similarity (Cosine), but also a mutable weight based on historical success/failure (Synaptic Weight).
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Implementation: Look into ColBERT (Late Interaction) for preserving fine-grained context, combined with a Time-Decay function on the weights. This mimics the “use-it-or-lose-it” nature of biological synapses (Long-Term Potentiation/Depression) better than static vector stores.
2. Frontal Cortex Layer (Integrity Logic)
You asked about RL Policy vs. Gating Networks.
Recommendation: For “Integrity” and “Safety”, do not rely on Reinforcement Learning (RL) alone. RL tends to reward-hack.
Instead, structure the “Frontal Cortex” as a Deterministic Orchestrator wrapping a Voting Ensemble:
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Gating: Use a “Judge Ensemble” (multiple small models evaluating the LVLM output).
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Logic: Implement a Fail-Closed Policy. If the “Synaptic Vectors” (Memory) and the LVLM (Vision) conflict, the Frontal Cortex must default to a safe state (“Stop/Wait”), rather than hallucinating a resolution.
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Architecture: We call this the “Intelligent Router” pattern. It acts as the conscious inhibitor of the subconscious generative model.
Moving from “empirical scaling” to “neuro-relationship processing” effectively means moving from end-to-end black boxes to modular, stateful systems. You are on the right track.
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Your neuro inspired framing is interesting, but I think you are solving it in the wrong place.
The failure mode is that we keep trying to make the model be the memory system. That forces you to replay more and more transcript, which drives context drift, hallucination pressure, and exploding token costs.
A better pattern is to externalize memory as infrastructure. Persist the full interaction as an append only event stream, index it, then retrieve only the small number of slices needed for the current decision. That gives you stable long term continuity without forcing the model to carry it in its prompt.
In your terms:
Synapse and dendrite vectors equal durable memory units plus retrieval keys.
LTMM reservoir equals the external store and its indexes.
Frontal cortex integrity check equals a gating step that requires evidence backed citations from retrieved memory before committing to an answer or action.
I built a Persistence Engine that does exactly this. It keeps memory outside the model, works with any LLM or VLM, survives restarts, and keeps per turn context fixed by retrieval budget instead of transcript replay. If you want I can outline the reference integration for a VLM plus agent stack in about ten bullets.
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That’s interesting aspect of externalization.
My aim is to build an agent that helps visually challenged people based on their patterns and help them.
Hence long term & logical sequencing is required with their own models.
This would be a smaller chunks of SLMs specific to their ecosystems.
It would be nice to know your details of externalization that can be shared..
-Vj
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I am launching a beta soon. I can’t do it until a lawyer has taken a look at all so I can start licensing it. The developer license is going to be free. Beta invite: Persistence engine for agents, cut token usage up to 95 percent as sessions age
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