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    <title>DEV Community: Flavio Venturini</title>
    <description>The latest articles on DEV Community by Flavio Venturini (@ravi4649).</description>
    <link>https://dev.to/ravi4649</link>
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      <title>DEV Community: Flavio Venturini</title>
      <link>https://dev.to/ravi4649</link>
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      <title>CELN: A CPU-only deterministic reasoning engine using Vector Symbolic Architectures</title>
      <dc:creator>Flavio Venturini</dc:creator>
      <pubDate>Wed, 01 Jul 2026 18:01:28 +0000</pubDate>
      <link>https://dev.to/ravi4649/celn-a-cpu-only-deterministic-reasoning-engine-using-vector-symbolic-architectures-48om</link>
      <guid>https://dev.to/ravi4649/celn-a-cpu-only-deterministic-reasoning-engine-using-vector-symbolic-architectures-48om</guid>
      <description>&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;How it works (briefly):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Concepts are encoded as 10,000-dimensional vectors&lt;/li&gt;
&lt;li&gt;A non-commutative binding operator (Projective Resonance) composes and decomposes logical statements&lt;/li&gt;
&lt;li&gt;The binding algebra produces a query-key similarity computation mathematically similar to Q·K^T attention, although without learned parameters&lt;/li&gt;
&lt;li&gt;Deduction happens through deterministic linear algebra, not probability&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I evaluated CELN on the ProofWriter benchmark, which tests logical reasoning across three classes: True, False, and Unknown.&lt;/p&gt;

&lt;p&gt;Results (Ryzen 2600):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;ProofWriter: 500/500 (100%)&lt;/li&gt;
&lt;li&gt;Stress test (5,000 examples): still 100%&lt;/li&gt;
&lt;li&gt;Latency: ~34.7ms per query&lt;/li&gt;
&lt;li&gt;RAM: 493MB peak&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The "Unknown" class is interesting because CELN returns "no proof possible" whenever no derivation exists — the algebra simply doesn't resolve.&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;I'm 15, from Brazil. No research lab, no GPU cluster, no advisor. Built this on a home PC.&lt;/p&gt;

&lt;p&gt;Try it (no heavy downloads):&lt;br&gt;
&lt;code&gt;git clone https://github.com/Ravi4649/celn &amp;amp;&amp;amp; cd celn &amp;amp;&amp;amp; python examples/step_by_step_en.py&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;GitHub: &lt;a href="https://github.com/Ravi4649/celn" rel="noopener noreferrer"&gt;https://github.com/Ravi4649/celn&lt;/a&gt;&lt;br&gt;
Paper (DOI): &lt;a href="https://doi.org/10.5281/zenodo.20836283" rel="noopener noreferrer"&gt;https://doi.org/10.5281/zenodo.20836283&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I'm especially interested in where people think this approach will fail. Happy to answer questions.&lt;/p&gt;

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      <category>ai</category>
      <category>machinelearning</category>
      <category>opensource</category>
      <category>python</category>
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