We assume that if we generate an embedding and store it, the "memory" is stable. But I found that f32 distance calculations (the backbone of FAISS, Chroma, etc.) act as a "Forking Path."
If you run the exact same insertion sequence on an x86 server (AVX-512) and an ARM MacBook (NEON), the memory states diverge at the bit level. It’s not just "floating point noise" it’s a deterministic drift caused by FMA (Fused Multiply-Add) instruction differences.
I wrote a script to inspect the raw bits of a sentence-transformers vector across my M3 Max and a Xeon instance. Semantic similarity was 0.9999, but the raw storage was different
For a regulated AI agent (Finance/Healthcare), this is a nightmare. It means your audit trail is technically hallucinating depending on which server processed the query. You cannot have "Write Once, Run Anywhere" index portability.
The Fix (Going no_std) I got so frustrated that I bypassed the standard libraries and wrote a custom kernel (Valori) in Rust using Q16.16 Fixed-Point Arithmetic. By strictly enforcing integer associativity, I got 100% bit-identical snapshots across x86, ARM, and WASM.
Recall Loss: Negligible (99.8% Recall@10 vs standard f32).
Performance: < 500µs latency (comparable to unoptimized f32).
The Ask / Paper I’ve written a formal preprint analyzing this "Forking Path" problem and the Q16.16 proofs. I am currently trying to submit it to arXiv (Distributed Computing / cs.DC) but I'm stuck in the endorsement queue.
If you want to tear apart my Rust code: https://github.com/varshith-Git/Valori-Kernel
If you are an arXiv endorser for cs.DC (or cs.DB) and want to see the draft, I’d love to send it to you.
Am I the only one worried about building "reliable" agents on such shaky numerical foundations?
