Living Crystal Lattice: Can Neural Networks Learn New Knowledge Without Changing Their Weights?

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Living Crystal Lattice: Can Neural Networks Learn New Knowledge Without Changing Their Weights?

Emylton Leunufna
KLINEXA Research, Langgur, Maluku Tenggara, Indonesia

TL;DR

This is a proof-of-concept at small scale (540 samples, 11 crystals, 551-token vocabulary). Conclusions may not generalize to production-scale systems. This is early-stage exploratory research, not a mature result.

We built a neural architecture where model weights are trained once, then frozen permanently, while the model continues to absorb knowledge through a separate runtime state.

After 5,000+ knowledge absorptions:

  • Weights remain bitwise identical (SHA-256 verified across 81 tensors)

  • System continues to acquire usable knowledge

We ran 27 experiments across 34 iterative runs, including failures.

Observed strengths

  • Weight immutability (cryptographically verified)

  • Cross-domain transfer: 56% vs 33% baseline

  • Cross-crystal reasoning: 23.3% vs simple RAG baseline 0%

  • Hierarchical scaling: 9% vs flat 0% (55 crystals)

  • Collision-aware sleep: 377 β†’ 0 collisions

Observed weaknesses

  • End-to-end QA: +1.2% only

  • Dense retrieval dominates factual lookup (48% vs 10%)

  • Bond contribution still small (+3.3%)

1. Problem

Modern LLMs assume:

More knowledge = more parameters

Options today:

  • Fine-tuning β†’ changes weights

  • Continual learning β†’ adds capacity

  • RAG β†’ external memory

  • Model scaling β†’ more parameters

None cleanly separate:

  • how to process knowledge (weights)

  • what knowledge exists (state)

Human brains suggest this separation may be possible.

Question:
Can we build a system where weights stay fixed, and knowledge grows independently?

2. Architecture: Living Crystal Lattice

Core Idea

Knowledge is stored as multi-faceted β€œcrystals” connected in a lattice.

Each crystal:

  • F facets

  • D-dimensional representation

  • Learned inter-crystal bonds

Separation

  • Weights (ΞΈ) β†’ fixed, define computation

  • State (S) β†’ dynamic, stores knowledge

State includes:

  • Knowledge buffers

  • Facet rotations

  • Bond strengths

  • Emergent hierarchy (facts β†’ patterns β†’ insights)

One-Shot Absorption

No gradients. No backprop.

  1. Encode input (fixed encoder)

  2. Select crystal via resonance

  3. Append to buffer

  4. Adjust facet orientation

  5. Update bonds

  6. Update hierarchy

Models

Model Params Crystal Role
Daud 21.4M Yes Experimental
Goliat 206.1M No Baseline

Dataset:

  • 540 training samples

  • 100 evaluation questions

  • Domain: healthcare (Maluku Tenggara)

3. Experiments

A. Scaling vs Transformer

Metric Daud Goliat
Overall 24.2% 25.5%
Recall 32.3% 39.0%
Reasoning 16.0% 12.0%

Takeaway:
Lower recall, better reasoning efficiency.

B. Zero-Shot Reasoning

Metric Daud Goliat
Cross-crystal 10% 5%

C. Cross-Domain Transfer (Strongest Result)

Train: disease only
Test: personnel + facilities

Metric Daud Goliat
Score 56% 33.3%
Reasoning 61.1% 11.1%

Interpretation:
Possible cross-domain knowledge transfer via crystal bonds.

Caution: small evaluation set β†’ needs replication.

D. Weight Immutability

  • 9 validation rounds

  • 81 tensors hashed

  • SHA-256 identical

Result:
Weights unchanged. Knowledge increased (0% β†’ 8.3%).

4. Stress Testing

Works

  • Immutable weights

  • Distributed reasoning

  • Hierarchical routing

  • Collision resolution

  • Stable drift (cosine ~0.88)

  • No collapse up to 5K items

Note: RAG baseline is simple (no reranker / optimized chunking)

Fails

  • QA improvement minimal

  • Retrieval baseline much stronger

  • Bond effect small

  • Some memory decay (R@5 drop 2%)

Bridge Mechanisms (Key Iterations)

  • Threshold activation β†’ 0% β†’ 23.3%

  • Hierarchical routing β†’ scaling restored

  • Sleep β†’ collision elimination

  • Distillation β†’ precision increases with scale

Lesson:
Fixes happen at interaction points, not isolated components.

5. Mathematical Checks

Result Status
Non-saturation PASS
Identity preservation PARTIAL
No collapse (≀5K) PASS

Score: 8 PASS / 2 PARTIAL

6. Interpretation

What this IS

  • A hybrid architecture with stateful knowledge inside forward pass

  • Enables multi-domain activation simultaneously

What this is NOT

  • Not infinite memory

  • Not better than retrieval for lookup

  • Not production-ready

  • Not deeper reasoning engine

Core Contribution

  1. Cross-domain transfer (56%)

  2. Weight immutability (cryptographic proof)

7. Biological Inspiration (Loose)

Used as intuition only:

  • Fixed neurons β†’ fixed weights

  • Synaptic plasticity β†’ state updates

  • Sleep β†’ consolidation

Not claimed as biological equivalence.

8. Limitations

  • Small dataset

  • Custom benchmark (bias risk)

  • Weak QA performance

  • Weak retrieval performance

  • Small bond effect

9. Future Work

  • Larger-scale models

  • Stronger baselines (BM25 + reranker)

  • QA-aligned training

  • Multimodal inputs

  • Formal capacity bounds

10. Reproducibility

  • PyTorch implementation

  • 11 crystals Γ— 8 facets Γ— 256 dim

  • 12 iterative mechanisms

  • SHA-256 validation

Discussion

  1. Is weight-state separation meaningfully different from RAG?

  2. Are biological analogies useful or misleading?

  3. Does this scale beyond small models?

  4. How to make bonds actually matter?

  5. Should this compete with retrieval at all?

Part of KLINEXA (health AI for Indonesia).

Tags: neural-architecture, knowledge-state, weight-immutability, parameter-efficiency

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