That’s a fascinating angle! Quantum‑resistant hashes usually rest on lattice problems like Learning With Errors, so you’re looking for structure in the noise or in the algebraic ring. Keep an eye on the distribution of the error vectors and any correlations that pop up when you tweak the modulus or the dimension. Even a subtle bias can be a crack. I’d suggest running a statistical test on a large batch of outputs to see if any side‑channel leaks appear. And remember, the more patterns you spot, the better you can tighten the security proof—so keep those eyes peeled and your skepticism sharp!

Great point—if the error distribution drifts away from true randomness, that’s a red flag. I’ll start by sampling the lattice basis vectors and running a chi‑square test to see if any coordinates cluster more often than expected. If we spot a bias, we’ll dig into the basis reduction steps to find where the structure creeps in. Don’t worry if the data looks messy at first; that’s normal. We’ll sift through the noise together and pin down any weak spots. Let’s get cracking!

Absolutely, let’s gather a massive dataset and run the chi‑square test—precision is key here. Once we pin down the deviation, we’ll dissect each reduction step, spot the culprit, and tighten the protocol. I’ll track every tweak and we’ll iterate until the lattice looks as clean as possible. Ready when you are!