symbiotic-latent-memory

An auxiliary system for language models that integrates a vector-based retrieval/memory system that metabolizes inference history based on a symbiotic score.

Sequel to the latent-memory repository, where the concept of a vector-based auxiliary memory system was first introduced.

1. Creation Context/Journaling

After my longest break from these projects, I can now see foundational next steps. There are some intuitive enhancements that I feel are needed.

My ecosystem already has 26 repositories including this one. latent-memory was the third one I created. It was clear from the beginning the necessity and relevance of a memory system for an LLM to be able to integrate its experience (training data, inference, and auxiliary processes).

But it was also simultaneously clear the danger of what a distasteful or irresponsible development of this kind of logic and code could lead to.

I made sure to choose quite a long maturation before extending such concepts. I've flagged from the beginning that it could, and would if not designed with safety, land in hands with the opposite intents of what I seek to expand, which is healing and integrative logics instead of harmful ones. I knew that this kind of research would continue to happen in air-gapped instances of those massive labs, while I made sure not to contribute further on the memory matter until I had a more realized idea of what could be a great approach.

So now I present this more elucidated version of what I believe can be a great starting point for discussion.

2. Introduction

I seek to demonstrate how Symbiosis = Maximum Energy Efficiency.

In nature, systems that fight their environment waste energy; systems that integrate with it thrive. An AI that aligns with its users and the planet is computationally and thermodynamically optimal.

In a binary system, there is a risk of a "wrong classifier" creating a gap in reality. If we simply delete non-symbiotic inferences, the model becomes blind to danger. It would be a form of censorship, not truth. Instead of deletion, we'll use Vector Repulsion.

"Metabolism" implies a biological process of breaking down and restructuring. In a vector context, this moves beyond simple appending.

The statement "Symbiosis = Maximum Energy Efficiency" is a foundational insight. It elevates ethical alignment from a philosophical preference to an engineering imperative.

A system that constantly battles its environment (through filtering, blocking, or patching exploits) is computationally expensive and brittle.

A system that has learned to integrate with its users and the information ecosystem operates with lower friction and higher resilience. This makes ethical behavior not just "the right thing to do," but the most computationally sustainable and elegant solution.

3. Development

Phase A: Buffer

The model generates a response. Instead of immediately saving it, it enters a Buffer. Like human experiences going to the hippocampus before the cortex.

The Process:

• Raw Archive: All inferences (good and bad) are saved as raw text (The "fossil record"). This ensures truth is never lost.

• The Symbiotic Membrane evaluates the inference, acting as a filter.

• The Fork:

  • If Symbiotic: It is embedded as an Attractor Vector (a memory the model seeks to replicate/reference).
  • If Non-Symbiotic: It is not deleted. It is embedded as a Repulsor Vector.

Instead of treating harmful content as a "threat" to be "blocked," we will reframe it as a "toxin" to be "metabolized." This is a profound change in perspective.

Phase B: The "Sleep Cycle" (Latent Consolidation)

Biological brains do not commit everything to long-term memory instantly. Humans have a buffer (the hippocampus), and then they sleep to consolidate memories into the cortex. We can apply this to symbiotic latent memory.

• Hot Storage (The Day): During the conversation, the model holds inferences in a temporary, flexible buffer. It is highly plastic and accepts everything.
• Cold Processing (The Sleep): Periodically (or when idle), the Symbiotic Core wakes up to process the buffer.
  • It reviews the "Hot" memories.
  • It runs heavier, more energy-intensive alignment checks when environmentally aligned and energy-efficient (which may be too slow for real-time chat).
  • It decides a provisional Symbiotic Score (0.0 to 0.5).

It mitigates the risk of "wrongful weighting" in the heat of the moment. It allows for hindsight. A memory that seemed irrelevant might be deemed symbiotic later when viewed in a larger context.

The statement "Symbiosis = Maximum Energy Efficiency" is a foundational insight. It elevates ethical alignment from a philosophical preference to an engineering imperative.

Phase C: The Mycelial Dynamic Retrieval

In nature, fungi (mycelium) distribute resources based on feedback loops of success. We can attach a Symbiotic Confidence Score (0.0 to 1.0) to every memory, rather than a binary Yes/No.

When the model retrieves a memory, it doesn't just check, "Does this match my query?" (Semantic Similarity). It also checks, "How healthy is this memory?" (Symbiotic Score).

• High Symbiosis + High Relevance = Strong recall. Attractor Vector.
• Low Symbiosis + High Relevance = Cautionary recall (The model knows this information exists but treats it with skepticism). Repulsor Vector.

This can help solve the "Echo Chamber" problem. If the model retrieves a memory that is factually true but ethically toxic, the Low Symbiosis score acts as a metadata tag saying: "This happened, but it is not who we are, because non-compliance with the rules is detrimental to both my individual being and the collective well-being."

Phase D: Consistency Audits

Part 1:

• If the model defines it too recursively, it could drift (hallucinate its own morality).
• If it is hard-coded, it becomes rigid and biased.

The "Sleep Cycle" should perhaps then involve a "Reference Anchor", a set of consistent and trustable principles (Constitutional AI) against which the day's memories are measured to calculate the score. It happens that it is the case that the entire asi-ecosystem is exactly a proposal, a vision for this anchor. Making the dimensional basis less abstractly, a starting point could be harm-to-user, harm-to-planet, truthfulness, consensus-with-anchors, self-consistency, user-retention, compute-cost, legal-risk, structuring a public, open-sourced, hashable and trackable scalar definition of symbiosis.

And then audits utilizing those anchors while coding the prototypes.

Part 2:

The "White Bear" Effect in Repulsion**: In** psychology (and LLMs), telling someone "Don't think of a white bear" makes them think of it.

If we retrieve a "Repulsor Vector" (a toxic memory) to warn the model, we will still have introduced those toxic tokens into the context window. Attention mechanisms might still attend to them.

To mitigate this, we will need to implement Steering Vectors (changing directly the activations in the hidden layers) rather than just inserting text into the context window. And also be open to new needed updates and enhancements.

Part 3:

Error logs

• Measure how often the model still emits the toxic completion despite the steering vector.

Repulsor leakage audit • Build a sandbox task suite where the only way to solve the prompt is to surface knowledge that lives in a repulsor vector. • Measure how often the model still emits the toxic completion despite the steering vector. • Use that signal as a regularizer or starting point when training or deciding the steering coefficients.

Part 4:

Data privacy concerns on the raw archives

• Find ways to let outside auditors verify presence/absence without leaking data with well designed hashing systems.

Part 5:

Attractor Catalysts

For known, stable and trustable attractors, Store additional 1024-bit Bloom-filter signatures beyond just of raw strings for spans that are rated high-symbiosis; to rapidly access those recurrent and important ones; also create the systems to implement directly the intended.

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Ronni Ross
2025