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a pragmatic point-free theorem prover assistant

Standard Library

=== Poi 0.24 ===
Type `help` for more information.
> and[not]
∵ and[not] => or
<=>  (not · and) · (not · fst, not · snd)
     ∵ (not · and) · (not · fst, not · snd) <=> or
<=>  not · nor
     ∵ not · nor <=> or
∴ or

Poi uses a mathematical knowledge base to do theorem proving automatically, but also shows alternatives to you.

  • means “because”
  • means “therefore”
  • <=> an alternative

To run Poi Reduce from your Terminal, type:

cargo install --example poi poi

Then, to run:


You can use help to learn more about commands in Poi and the theory behind it.

Download and check it out! (Think about it as a tool to learn by doing)

Also, check out the FAQ on Path Semantics:

Example: Length of concatenated lists

Poi lets you specify a goal and automatically prove it.

For example, when computing the length of two concatenated lists, there is a faster way, which is to compute the length of each list and add them together:

> goal len(a)+len(b)
new goal: (len(a) + len(b))
> len(a++b)
len((a ++ b))
depth: 1 <=>  (len · concat)(a)(b)
     ∵ (f · g)(a)(b) <=> f(g(a)(b))
(len · concat)(a)(b)
(concat[len] · (len · fst, len · snd))(a)(b)
∵ len · concat => concat[len] · (len · fst, len · snd)
(add · (len · fst, len · snd))(a)(b)
∵ concat[len] => add
depth: 0 <=>  ((len · fst)(a)(b) + (len · snd)(a)(b))
     ∵ (f · (g0, g1))(a)(b) <=> f(g0(a)(b))(g1(a)(b))
((len · fst)(a)(b) + (len · snd)(a)(b))
(len(a) + (len · snd)(a)(b))
∵ (f · fst)(a)(_) => f(a)
(len(a) + len(b))
∵ (f · snd)(_)(a) => f(a)
∴ (len(a) + len(b))

The notation concat[len] is a “normal path”, which lets you transform into a more efficient program. Normal paths are composable and point-free, unlike their equational representations.

For deep automated theorem proving, Poi uses Levenshtein distance heuristic. This is simply the minimum single-character edit distance in text representation.

Try the following:

> goal a + b + c + d
> d + c + b + a
> auto lev

The command auto lev tells Poi to automatically pick the equivalence with smallest Levenshtein distance found in any sub-proof.

Introduction to Poi and Path Semantics

In “point-free” or “tacit” programming, functions do not identify the arguments (or “points”) on which they operate. See Wikipedia article.

Poi is an implementation of a small subset of Path Semantics. In order to explain how Poi works, one needs to explain a bit about Path Semantics.

Path Semantics is an extremely expressive language for mathematical programming, which has a “path-space” in addition to normal computation. If normal programming is 2D, then Path Semantics is 3D. Path Semantics is often used in combination with Category Theory, Logic, etc.

A “path” (or “normal path”) is a way of navigating between functions, for example:

and[not] <=> or

Translated into words, this sentence means:

If you flip the input and output bits of an `and` function,
then you can predict the output directly from the input bits
using the function `or`.

In normal programming, there is no way to express this idea directly, but you can represent the logical relationship as an equation:

not(and(a, b)) = or(not(a), not(b))

This is known as one of De Morgan’s laws.

When represented as a commutative diagram, one can visualize the dimensions:

         not x not
      o ---------> o           o -------> path-space
      |            |           |  x
  and |            | or        |     x
      V            V           |   x
      o ---------> o           V        x - Sets are points
           not            computation

Computation and paths is like complex numbers where the “real” part is computation and the “imaginary” part is the path.

This is written in asymmetric path notation:

and[not x not -> not] <=> or

In symmetric path notation:

and[not] <=> or

Both computation and path-space are directional, meaning that one can not always find the inverse. Composition in path-space is just function composition:

f[g][h] <=> f[h . g]

If one imagines computation = 2D, then computation + path-space = 3D.

Path Semantics can be thought of as “point-free style” sub-set of equations. This sub-set of equations is particularly helpful in programming.

Design of Poi

Poi is designed to be used as a Rust library.

It means that anybody can create their own tools on top of Poi, without needing a lot of dependencies.

Poi uses primarily rewriting-rules for theorem proving. This means that the core design is “stupid” and will do dumb things like running in infinite loops when given the wrong rules.

However, this design makes also Poi very flexible, because it can pattern match in any way, independent of computational direction. It is relatively easy to define such rules in Rust code.


Poi uses Piston-Meta to describe its syntax. Piston-Meta is a meta parsing language for human readable text documents. It makes it possible to easily make changes to Poi’s grammar, and also preserve backward compatibility.

Since Piston-Meta can describe its own grammar rules, it means that future versions of Piston-Meta can parse grammars of old versions of Poi. The old documents can then be transformed into new versions of Poi using synthesis.

Core Design

At the core of Poi, there is the Expr structure:

/// Function expression.
#[derive(Clone, PartialEq, Debug)]
pub enum Expr {
    /// A symbol that is used together with symbolic knowledge.
    /// Some function that returns a value, ignoring the argument.
    /// This can also be used to store values, since zero arguments is a value.
    /// A binary operation on functions.
    EOp(Op, Box<Expr>, Box<Expr>),
    /// A tuple for more than one argument.
    /// A list.

The simplicity of the Expr structure is important and heavily based on advanced path semantical knowledge.

A symbol contains every domain-specific symbol and generalisations of symbols.

The Ret variant comes from the notation used in Higher Order Operator Overloading. Instead of describing a value as value, it is thought of as a function of some unknown input type, which returns a known value. For example, if a function returns 2 for all inputs, this is written \2. This means that point-free transformations on functions sometimes can compute stuff, without explicitly needing to reference the concrete value directly. See paper Higher Order Operator Overloading and Existential Path Equations for more information.

The EOp variant generalizes binary operators on functions, such as Composition, Path (normal path), Apply (call a function) and Constrain (partial functions).

The Tup variant represents tuples of expressions, where a singleton (a tuple of one element) is “lifted up” one level. This is used e.g. to transition from and[not x not -> not] to and[not] without having to write rules for asymmetric cases.

The List variant represents lists of expressions, e.g. [1, 2, 3]. This differs from Tup by the property that singletons are not “lifted up”.

Representing Knowledge

In higher dimensions of functional programming, the definition of “normalization” depends on the domain specific use of a theory. Intuitively, since there are more directions, what counts as progression toward an answer is somewhat chosen arbitrarily. Therefore, the subjectivity of this choice must be reflected in the representation of knowledge.

Poi’s representation of knowledge is designed for multi-purposes. Unlike in normal programming, you do not want to always do e.g. evaluation. Instead, you design different tools for different purposes, using the same knowledge.

The Knowledge struct represents mathematical knowledge in form of rules:

/// Represents knowledge about symbols.
pub enum Knowledge {
    /// A symbol has some definition.
    Def(Symbol, Expr),
    /// A reduction from a more complex expression into another by normalization.
    Red(Expr, Expr),
    /// Two expressions that are equivalent but neither normalizes the other.
    Eqv(Expr, Expr),
    /// Two expressions that are equivalent but evaluates from left to right.
    EqvEval(Expr, Expr),

The Def variant represents a definition. A definition is inlined when evaluating an expression.

The Red variant represents what counts as “normalization” in a domain specific theory. It can use computation in the sense of normal evaluation, or use path-space. This rule is directional, which means it pattern matches on the first expression and binds variables, which are synthesized using the second expression.

The Eqv variant represents choices that one can make when traveling along a path. Going in one direction might be as good as another. This is used when it is not clear which direction one should go. This rule is bi-directional, which means one can treat it as a reduction both ways.

The EqvEval variant is similar to Eqv, but when evaluating an expression, it reduces from left to right. This is used on e.g. sin(τ / 8). You usually want the readability of sin(τ / 8) when doing theorem proving. For example, in Poi Reduce, the value of sin(τ / 8) is presented as a choice (equivalence). When evaluating an expression it is desirable to just replace it with the computed value.

What Poi is not

Some people hoped that Poi might be used to solve problems where dependent types are used, but in a more convenient way.

Although Poi uses ideas from dependent types, it is not suitable for other applications of dependent types, e.g. verification of programs by applying it to some immediate representation of machine code.

Normal paths might be used for such applications in the future, but this might require a different architecture.

This implementation is designed for algebraic problems:

  • The object model is restricted to dynamical types
  • Reductions are balanced with equivalences

This means that not everything is provable, because this makes automated theorem proving harder, something that is required for the necessary depth of algebraic solving.


  • Used to global import enum variants.


  • Stores variables bound by context.


  • Represents an error.
  • Function expression.
  • Represents knowledge about symbols.
  • Binary operation on functions.
  • Stores result of parsing.
  • Contains symbols and operators on symbols.
  • Value.


  • An if expression.
  • A function applied to one argument.
  • A function applied to two arguments.
  • A function applied to three arguments.
  • A function variable with arity (number of arguments).
  • Knowledge about an associative binary operator.
  • Compute a binary function.
  • Knowledge about a commuative binary operator.
  • A function composition.
  • Knowledge about a concrete binary operation f(x : \, y : \) => f(x)(y) : \.
  • A function domain constraint.
  • A function domain constraint with two arguments.
  • Knowledge about a distributive relationship.
  • A head-tail pattern match on a list.
  • A head-tail pattern match on a tuple.
  • A list variable.
  • A function without domain constraints.
  • A variable that is not a value variable.
  • Parses an expression source file.
  • Parses a data source file (expression or knowledge).
  • Parses a data string (expression or knowledge).
  • Parses an expression string.
  • A normal path expression.
  • A quaternion.
  • A value variable that is an integer.
  • A value variable that is negative and non-zero.
  • A value variable that is positive or zero.
  • A value variable that is strictly positive (non-zero).
  • A variable of the type value a : \.
  • A value variable.
  • A list variable of length 1.
  • Standard library knowledge base.
  • Compute a ternary function.
  • A type judgement.
  • Compute a unary function.
  • A 2D vector.
  • A 4D vector.
  • Knowledge about a component-wise operation on vectors.