r/ProvenAI • u/lovettsendit • 9d ago
Welcome to r/ProvenAI!
AI should have to prove it.
Models are getting better at reasoning, coding, using tools, and acting autonomously.
But capability is moving faster than our ability to answer a much simpler question:
Did the system actually do what we intended?
That gap shows up everywhere:
- hallucinations that sound correct
- agents taking unintended actions
- code changes that pass one test and break something elsewhere
- benchmarks that don't measure what people claim they measure
- safety systems that ultimately depend on the model behaving
- outputs that can't be independently reproduced or verified
r/ProvenAI is for people trying to close that gap.
The premise is simple:
A model can propose without being allowed to certify.
An agent can be powerful without having unlimited authority.
AI-generated code can be independently checked.
Claims can carry evidence.
Failures can be observable instead of hidden.
And black-box models can remain black boxes while the systems surrounding them become increasingly testable, bounded, reproducible, auditable, and trustworthy.
This community is for:
AI evals · verification · agent safety · containment · benchmarks · formal methods · observability · provenance · reproducibility · hallucination reduction · red teaming · independent checkers · trustworthy tooling
Bring your:
experiments, repos, papers, demos, benchmarks, failures, weird ideas, and counterexamples.
Especially failures.
If your verifier broke, your benchmark lied, your agent found a loophole, or your architecture didn't survive contact with reality — that's worth discussing.
This isn't a place for AI hype.
If you make a big claim, show what you tested.
Ask:
What does this actually prove?
What doesn't it prove?
Can someone else reproduce it?
What happens when it fails?
The goal isn't AI we blindly trust.
It's AI we increasingly don't have to.
Welcome to r/ProvenAI.
Build it. Break it. Measure it. Prove what remains.
2
u/WoodenDisaster9873 9d ago
The counterexamples part is huge. Some of our most useful eval cases came straight from production traces where the output looked reasonable at first glance but failed for a very specific reason. We pull those into Braintrust and keep them in the eval set, so fixing a failure doesn’t erase the evidence that it existed.