Here is a promise that looks obviously true:
fn add_twice(x: Float) -> Float
ensures result == x + 0.2
{
return x + 0.1 + 0.1
}
Adding a tenth twice is the same as adding two tenths. Every algebra teacher you have ever had agrees. Most program verifiers agree too.
They are wrong, and so is the promise.
Velaris — a language I have been building — refuses to prove it, and hands back the number that breaks it:
error[E700] promise cannot be kept: 'add_twice' ensures result == x + 0.2
proven without running the program:
x = -1.207290298954004637010939404717646539211273193359375
gives result = -1.007290298954004459375255464692600071430206298828125
That is not a rounding display artifact. Those are two different doubles, and the program really does produce the second one.
Why the lie is tempting
To prove things about numbers, a verifier translates your code into formulas for a solver. The question is which theory to translate into.
The comfortable choice is the theory of real numbers. Reals are associative, commutative, infinitely precise, and solvers are fast at them. Translate Float to Real and everything works beautifully: proofs come back in milliseconds, and x + 0.1 + 0.1 == x + 0.2 is trivially true.
It is also a statement about a machine that does not exist.
Your processor implements IEEE-754 binary64. In that world 0.1 is not one tenth — it is the nearest double to one tenth, which is 0.1000000000000000055511151231257827021181583404541015625. Every operation rounds to the nearest representable value. Addition is not associative. Adding a tenth twice takes two rounding steps; adding two tenths takes one. For many values of x those disagree in the last bit, and one bit is all it takes for == to be false.
So a verifier that models floats as reals will happily certify code that fails on the machine it is compiled for. The proof is valid. The theorem is about the wrong object.
What honesty costs
Z3 has a floating-point theory that implements IEEE-754 exactly: rounding modes, subnormals, infinities, NaN, signed zero. Velaris translates Float into that theory rather than into Real.
The bill arrives immediately.
It is slow. The FP theory is decided by bit-blasting — expanding 64-bit values into circuits of individual bits and handing the result to a SAT solver. The refutation above takes about fifteen seconds. Integer proofs in the same compiler finish in milliseconds.
Fewer things are provable. Plenty of true-in-the-reals facts are simply false in IEEE-754, and plenty of true-in-IEEE facts are too expensive to establish. A verifier that pretends floats are reals has a much better success rate on paper. It is winning a game nobody should want to play.
Equality gets strange, correctly. Velaris compares floats with fpEQ, not structural equality, which means NaN is not equal to itself and positive zero equals negative zero. Both are IEEE behaviour, and both surprise people. Using structural equality would have been faster and easier to explain, and would have quietly produced false results at the edges.
There is a bonus that only shows up once you are honest: comparison constraints start doing real work. A precondition like requires x >= 0.0 silently rules out NaN, because NaN fails every comparison. The prover knows that, so a promise you could not otherwise establish sometimes becomes provable for free.
The rule underneath
Velaris has one commitment it will not trade away: it never claims something is proven unless the claim is literally true.
That single rule decided the float design by itself. If you model floats as reals, "proven" starts meaning "proven about an idealised machine that does not exist," and the word has quietly been devalued. Everything after that is negotiation.
The same rule shows up elsewhere in the compiler. Division and modulo are not native-compiled, because a native fdiv by zero yields infinity while the language promises a clean error, and two execution modes that disagree are worse than one slow mode. A premise the solver cannot translate aborts the whole proof rather than being silently dropped, because proving with dropped premises manufactures false counterexamples. Anything unprovable degrades to a runtime check rather than being waved through.
None of these make the demo look better. All of them are the reason the demo can be believed.
Try it
The compiler runs in your browser, no install: the Velaris playground
Paste the function from the top of this post and watch it refuse. Then change ensures result == x + 0.2 to something IEEE actually guarantees — say ensures result >= x — and watch it go through.
Or install it:
pip install velaris-lang
If you can make Velaris say "proven" about something that is false at runtime, that is a soundness bug, and this project treats those as security reports.
github.com/gowrishankar-infra/velaris-lang
Velaris is a language where a function's signature declares its types, the effects it may perform, whether it can fail, and promises checked by Z3 before the program runs. Built with heavy AI assistance across 80+ releases; the design decisions were mine and the commit history is the honest record.
