Hybrid Resonance Algorithm (GRA) + Large Language Models (LLM): Toward AGI/ASI with Ethical Realistic Reasoning and Catastrophic Forgetting Mitigation

olegbits
Mon, 03 Nov 2025 07:11:24 +0000

1. Overview

GRA is an iterative resonance-based algorithm that constructs a dynamic network (“foam of mind”) of knowledge objects from various domains, evolving toward a specified goal by strengthening resonant connections. LLMs specialize in generating structured templates or scenarios from heterogeneous data. The integration of GRA and LLM results in a hybrid system capable of addressing complex, multi-domain problems with high efficiency and ethical oversight.

2. Role of LLM and GRA in the Hybrid System

LLM: Given input data DDD, the LLM with parameters θ\thetaθ generates a set of hypotheses or templates:H=LLMθ(D)={h1,h2,…,hm}\mathcal{H} = \mathrm{LLM}_\theta(D) = { h_1, h_2, …, h_m }H=LLMθ(D)={h1,h2,…,hm}Each hih_ihi represents a structured representation (scenario, pattern, explanation) derived from raw data.

GRA: Utilizes the set H\mathcal{H}H as vertices in a resonance graph GR=(V,E)G_R = (V, E)GR=(V,E), where:V=HV = \mathcal{H}V=HEdges EEE carry weights determined by a resonance function RRR:R:H×H→,R(hi,hj)=resonance strength[11]R: \mathcal{H} \times \mathcal{H} \to, \quad R(h_i, h_j) = \text{resonance strength}[11]R:H×H→,R(hi,hj)=resonance strength[11]

3. Objective Function and Iterative Update

The goal GGG is defined via a quality metric QQQ over the resonance graph:

Q(GR)=∑(i,j)∈EwijR(hi,hj)Q(G_R) = \sum_{(i,j) \in E} w_{ij} R(h_i, h_j)Q(GR)=(i,j)∈E∑wijR(hi,hj)

where wijw_{ij}wij represent importance weights.

The resonance weights evolve iteratively:

Rijt+1=Rijt+η⋅∇Rij⋅reward(hi,hj)⋅Γ(hi,hj)R_{ij}^{t+1} = R_{ij}^t + \eta \cdot \nabla R_{ij} \cdot \mathrm{reward}(h_i, h_j) \cdot \Gamma(h_i, h_j)Rijt+1=Rijt+η⋅∇Rij⋅reward(hi,hj)⋅Γ(hi,hj)

η\etaη: learning rate,

reward(hi,hj)\mathrm{reward}(h_i, h_j)reward(hi,hj): feedback signal boosting effective resonances,

Γ(hi,hj)∈\Gamma(h_i, h_j) \in Γ(hi,hj)∈: ethical filter coefficient ensuring humane, acceptable solutions.gibridnyi-algoritm-s-etikoi-26.08.25-kven.txt​

4. Reducing Complexity: Exponential to Polynomial

GRA selectively strengthens only realistic and ethically acceptable resonances, pruning the search space and transforming inherently exponential problem complexity O(2n)O(2^n)O(2n) into polynomial scale O(nk)O(n^k)O(nk), where kkk is problem-dependent.

This pruning avoids combinatorial explosion and focuses resources on promising solution paths.

5. Addressing Catastrophic Forgetting

Unlike standard LLMs prone to catastrophic forgetting during retraining, the hybrid approach:

Maintains modular units (resonance templates hih_ihi) in memory,

Integrates new knowledge by controlled gradual resonance updates,

Preserves past knowledge by filtering new resonance connections through ethical and realistic criteria.

Mathematically:

M_{t+1} = M_t \cup {h_{new}}, \quad h_{new} \text{ passing realism & ethics thresholds}

6. Biological Plausibility

The architecture resembles hypothesized brain mechanisms:

LLM analogues correspond to cognitive subsystems producing a variety of hypotheses,

GRA reflects the brain’s resonance-based network harmonizing cognitive elements,

Ethical constraints serve as internal checks similar to moral cognition systems.

7. Practical Applications

Medicine: Generate diagnostic/treatment hypotheses using LLMs; prioritize and validate through GRA resonance/ethical filters.

Engineering: Explore design alternatives modeled as resonance scenarios, optimize under constraints with ethical considerations.

Social Sciences: Model complex human behaviors with ethics-aware resonance networks facilitating policy design.

Conclusion

The GRA+LLM hybrid system:

Combines structured generation with resonance-based optimization,

Enforces ethical standards via dynamic filtering,

Reduces computational complexity,

Mitigates standard LLM issues like catastrophic forgetting,

Provides a biologically inspired framework suitable for Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI).

This synthesis holds promise as a foundational architecture for universally capable, ethically aware AI systems.

If you would like, assistance can be provided for further mathematical refinements or specific coding examples.

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