I've published a paper on a fundamental problem in AI systems that I believe has significant implications for our work: "Valori: A Deterministic Memory Substrate for AI Systems." [https://arxiv.org/pdf/2512.22280]

THE PROBLEM :

AI systems have unreliable memory.

When you run the same AI agent twice with the same input, it can give you different answers not because the AI is "thinking differently," but because the underlying memory system is non-deterministic.

In simple terms, the math used in building current vector databases (FAISS, Qdrant, Milvus, Pinecone, etc.) is floating-point arithmetic, which behaves differently across CPUs (x86, ARM, Mac, Windows). This is a universal problem acknowledged by industry leaders, including the FAISS team: https://github.com/facebookresearch/faiss/issues/4739

THE SOLUTION (What I Built) :

Valori: A deterministic memory substrate for AI systems.

In simple terms, I replaced floating-point math with fixed-point arithmetic (Q16.16) and built the memory engine using Rust (no_std).

Traditional systems store state (the answer). Valori stores events (how we got the answer).

This means you can:

Replay any decision exactly, Debug across different machines, Audit with mathematical certainty, Prove compliance to regulators

USE CASES (Where This Matters) Simply put, wherever we need bit-level replay, Valori can be applied:

Compliance & Audit (Financial Services), Incident Investigation (SRE/DevOps), Multi-Agent Systems (Autonomous Operations), Quantitative Finance (Hedge Funds), AI Safety & Alignment (Research), Healthcare AI (Medical Decisions)

I'd be happy to discuss this further and explore potential applications. Open to answer any doubts, questions, Criticism and Feedback