I built an "Artificial Hippocampus" using Vector Symbolic Architectures (VSA) to fix LLM hallucinations — and we just solved the "Capacity Collapse" problem.

Hey everyone,

We all know LLMs struggle with precise, multi-hop logical recall. Even frontier models like Grok fail simple role-binding tests (e.g., if you feed them "Alice trusts Bob" vs "Bob trusts Alice" in a messy context, they often hallucinate the object/subject).

To solve this, I’ve been building Astrum Verum — an open-source Cognitive Memory Architecture based on Vector Symbolic Architectures (VSA / Hyperdimensional Computing) mapped onto perfect root lattices ($D_4$ and $E_8$).

The TL;DR of how it works: Instead of relying on LLM attention over a massive context window, we bind concepts using VSA algebra: Fact = sign(Subject ⊗ Alice + Relation ⊗ trusts + Object ⊗ Bob) This gives us mathematically perfect role-binding. You can query the memory with a probe like Subject ⊗ Alice + Relation ⊗ trusts and accurately "unbind" Bob with $O(D)$ complexity.

Solving the "Capacity Collapse" Myth: If you've heard of VSA, you've probably heard of its biggest flaw: if you add hundreds of facts into a single global superposition vector, the noise eventually drowns out the signal (Capacity Collapse). We bypassed this by not storing a global vector. Instead, Astrum Verum stores facts as an array of independent vectors and uses fast nearest-neighbor search to find relevant facts.

But this introduced a new problem that we just fixed today: When you search across thousands of facts, the VSA noise variance causes something I call "Decoded Clarity Hallucinations". Basically, an absolutely irrelevant fact might randomly produce a "clean" decode (high cosine similarity) purely due to variance, outscoring the true fact!

We mathematically solved this by introducing a Match Quality Metric (match_quality = global_relevance * clean_up_score) and an Early Exit optimization. Because we know the expected math of a 3-term bipolar VSA bundle (the true signal sits exactly at a cosine of 0.5, which is $50\sigma$ away from the noise mean), we can guarantee mathematical certainty and stop the search early.

This gave us a ~5x speedup on clean-ups with 0% hallucinations on role-sensitive queries.

If you are interested in cognitive architectures, memory structures beyond simple RAG, or high-dimensional computing, I’d love your feedback!

🐙 GitHub Repo: https://github.com/vitaliyfedotovpro-art/astrum-verum
📄 Why VSA Works (Our Math & Benchmarks): https://github.com/vitaliyfedotovpro-art/astrum-verum/blob/main/WHY_VSA_WORKS.md

Would love to hear what the community thinks about using VSA instead of standard Vector DBs for agentic memory!