I’ve come to think that verification is often misunderstood.
It’s treated like:
But in practice, verification should be a guarantee about future consistency:
That the result will still hold when challenged later.
If a system can’t confidently make that guarantee yet, should it ever say “verified”?
Interested in how others define verification in their own systems — especially where trust and dispute resolution matter.
Hello all 
I came across this post and felt it was important to add my own perspective, because this is exactly where I faltered with my framework.
I’ve struggled to get meaningful peer feedback, and in hindsight part of that is on me. I overloaded some of my earlier wording and over-claimed in places — especially around the word “verified”. That created friction, skepticism, and ultimately made it harder for others to engage with the actual engineering. That’s on me.
I agree with this framing, and I’ll be blunt about my side of it: I used “verified” too loosely. I treated it more like a success label than a commitment to future consistency, and that’s not a standard that holds up under challenge.
Going forward, I’m tightening both the language and the bar:
• When I say verified, I mean reproducible behaviour under stated assumptions (same seed, same inputs, same thresholds, same outputs).
• When I mean real-world correctness, I’ll call it validation, and I won’t claim it unless it’s backed by external data, comparisons, and stress testing.
One thing I’ve also had to recalibrate is how I view criticism. Rigorous scrutiny isn’t a failure of the work — it’s a necessary step in making it stronger. If a system can’t survive pressure, edge cases, or hostile questioning, then it isn’t ready to claim anything meaningful yet.
To reduce disputes and remove “trust me” dynamics, I’m restructuring my agent work so it’s inspectable by design:
• explicit uncertainty proxy
• explicit τ_eff / decision-timing mechanism
• explicit gating rule
• baseline vs RFT side-by-side
• exported logs and plots so anyone can challenge the behaviour directly
If the system breaks when assumptions change, that’s not something I want to hide — that’s exactly what I want visible. That feedback is part of the ladder, not a setback.
I appreciate this post because it draws a clean line between confidence and guarantee, and that distinction matters if we want systems people can trust over time.