DigiMind-Modular-LLM-architecture

DigiMind: A Modular Cognitive Architecture for Continual Learning and Factual Coherence

Status: Preliminary Draft (November 2025)
Author: Chaiya Tantisukarom (Independent Researcher)
Contact: [email protected]

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📖 Overview

DigiMind is a proposed unified theoretical and architectural framework designed to address the fundamental limitations of modern monolithic Large Language Models (LLMs): Catastrophic Forgetting, Super-linear Computational Costs, and Factual Incoherence (Hallucination).

The framework re-imagines the cognitive process as a computational Analog-to-Digital Conversion (ADC) problem. By replacing the monolithic model with a hierarchical Hard-Switch Mixture-of-Experts (H-MoE), DigiMind creates a blueprint for sustainable Artificial General Intelligence (AGI) capable of:

1. Continual Learning: Learning new tasks without degrading previous knowledge.
2. Sparse Computation: Massive reduction in energy and latency per inference.
3. Factual Stability: Decoupling procedural "know-how" from epistemic "know-that."

🧠 The "Crisis of Monolithic Memory"

Current Transformer architectures face three critical bottlenecks that DigiMind aims to solve:

1. Catastrophic Forgetting: Sequential training overwrites previous weights (the plasticity-stability dilemma).
2. Computational Inefficiency: Every query activates 100% of parameters ($\mathbf{P}_{\text{Total}}$).
3. Generative Error: Retrieval is probabilistic, lacking a grounded "truth" mechanism.

⚙️ Architecture: The ADC/DAC Framework

DigiMind splits the cognitive workload into two distinct phases: ADC (We Learn/Route) and DAC (We Talk).

1. ADC: The Router ($\mathbf{R}$) & Semantic Index

• The Router: A fixed-size Gating Transformer that performs "quantization." It maps continuous input embeddings to a precise Digital Address ($\mathbf{A}$). It uses Hierarchical Contrastive Loss ($\mathcal{L}_{\text{HCL}}$) to enforce high-margin separation between concepts.
• Semantic Index (SI): A non-volatile, vector-based lookup table for Epistemic Memory (facts).
  • Feature: Factual Override. If the SI returns a high-confidence match ($\mathbf{A}_{\text{gold}}$), the neural routing is overridden, eliminating hallucination.
  • Validation: Connected to Stack.AI, a conceptual external loop where human reviewers validate facts before they are committed to memory.

2. Specialist Modules ($\mathbf{M}_i$)

• Decoupled, independent neural networks holding Procedural Knowledge.
• Activated via Hard-Switch: Non-selected modules consume zero compute.
• Updated via Localized PEFT, ensuring no interference with other knowledge domains.

3. DAC: The Synthesis Decoder ($\mathbf{D}_{\text{synth}}$)

• A dedicated, medium-weight Multimodal LLM (1B–5B parameters).
• Role: A knowledge-agnostic "Syntactic Engine." Its base weights are permanently frozen.
• Mechanism: Fuses the sparse outputs ($O_i$) from experts with the original context using Multi-Head Cross-Attention (MHXA) to generate coherent, stylistically fluid output.

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🔬 Key Theoretical Concepts

Knowledge Entropy ($\mathcal{H}_{\text{K}}$)

A formal metric quantifying the relational complexity and conceptual overlap of a domain. It determines the optimal bit-depth required to resolve ambiguity.

$$ \mathcal{H}{\text{K}, j} = - \log_2 \left( \min{c, k \in C_j, c \neq k} \left( \frac{| \mathbf{c}{c} - \mathbf{c}{k} |_2 + \delta}{\sigma_j} \right) \right) $$

Granular Evolution

The architecture is not static. It utilizes Vertical Flexibility to dynamically adapt its structure:

• Split Operation: If a module's $\mathcal{H}_{\text{K}}$ exceeds a threshold, it splits into a new router and sub-modules.
• Result: The system grows its depth ($D$) and bit-resolution ($B$) only where complexity demands it.

Hierarchical Contrastive Loss ($\mathcal{L}_{\text{HCL}}$)

Unlike standard Load Balancing losses, $\mathcal{L}_{\text{HCL}}$ actively forces conceptual clusters apart in the embedding space to ensure strict path sparsity.

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📉 Economic Analysis & Efficiency

DigiMind shifts the paradigm from parameter count to architectural complexity.

Simulated Cost-Savings per Inference: Comparison of a 220B Monolithic Baseline vs. a 227B DigiMind System (distributed across $N=1000$ modules).

Result: A projected 30x to 60x reduction in computational cost/latency per inference.

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📚 Terminology

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⚖️ License & Citation

This work is currently a preliminary draft made available as an open idea.

Citation:

Tantisukarom, C. (2025). DigiMind: A Modular Cognitive Architecture for Continual Learning and Factual Coherence. Preliminary Draft.