I wanted to share a project I've been working on: CELN (C. Elegans Learning Network). It's a logical reasoning engine that uses Vector Symbolic Architectures (VSA) instead of neural networks.
I originally built this because I wanted to explore whether formal logical reasoning could be implemented entirely with deterministic vector algebra, rather than learned statistical models.
How it works (briefly):
- Concepts are encoded as 10,000-dimensional vectors
- A non-commutative binding operator (Projective Resonance) composes and decomposes logical statements
- The binding algebra produces a query-key similarity computation mathematically similar to Q·K^T attention, although without learned parameters
- Deduction happens through deterministic linear algebra, not probability
I evaluated CELN on the ProofWriter benchmark, which tests logical reasoning across three classes: True, False, and Unknown.
Results (Ryzen 2600):
- ProofWriter: 500/500 (100%)
- Stress test (5,000 examples): still 100%
- Latency: ~34.7ms per query
- RAM: 493MB peak
The "Unknown" class is interesting because CELN returns "no proof possible" whenever no derivation exists — the algebra simply doesn't resolve.
Limitations: CELN is a logic core, not a chatbot. It doesn't generate text fluently yet. Rules are currently hand-crafted; automatic extraction from natural language is the next step.
Background: I designed the architecture and math. The Python implementation was done with the help of AI assistants — I treated them as a compiler for the mathematical blueprint, reviewing and debugging every iteration.
I'm 15, from Brazil. No research lab, no GPU cluster, no advisor. Built this on a home PC.
Try it (no heavy downloads):
git clone https://github.com/Ravi4649/celn && cd celn && python examples/step_by_step_en.py
GitHub: https://github.com/Ravi4649/celn
Paper (DOI): https://doi.org/10.5281/zenodo.20836283
I'm especially interested in where people think this approach will fail. Happy to answer questions.
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