What Are Types? What is Type Theory?

There isn’t a good answer.

Type Theory is what a type theorist does. Useless answer but most honest. What is physics vs chemistry? There are things firmly in one subject (colliders, tylenol), but others in grey areas (nuclear, atomic spectra). Depends on what the person considers themselves, what journals they submit to.

Some goes for type theory. What is proof theory? Computer Science? Logic? It’s somewhere in all of there.

-what is a type paulson

Type Checking

https://discuss.ocaml.org/t/writing-type-inference-algorithms-in-ocaml/8191 PL zoo grow your own type system inferno a library for unification in type systems hindley miler in applicative style How OCaml type checker works – or what polymorphism and garbage collection have in common The Simple Essence of Algebraic Subtyping: Principal Type Inference with Subtyping Made Easy

Bidirectional Typing

Bidirectional Type Checking - jetbrains

mckinna mcbride gundry. tye inferencei n context https://adam.gundry.co.uk/pub/type-inference-in-context/#:~:text=We%20consider%20the%20problems%20of,for%20implementing%20type%20inference%20algorithms.

Complete and easy Complete and Easy Bidirectional Typechecking for Higher-Rank Polymorphism. Dunfield Krishnaswami https://arxiv.org/pdf/1306.6032.pdf

FreezeML https://www.pure.ed.ac.uk/ws/portalfiles/portal/142279783/FreezeML_EMRICH_DOA21022020_AFV.pdf

Nice simple tutorial http://davidchristiansen.dk/tutorials/bidirectional.pdf

pfenning lecture notes https://www.cs.cmu.edu/~fp/courses/15312-f04/handouts/15-bidirectional.pdf

Hindley Milner



Hindley-Milner Elaboration in Applicative Style - Pottier https://gitlab.inria.fr/fpottier/inferno

type inference as generation and then solving of constraints

types are simple types + a var constructor type schema are forall wrappers around a type let allows you to generalize a type with vars into a schema.


env - e : t - C

take in env and e, output t and C. Set of contraints. DCG might be nice for the constraints.

% type(+, +, - , -). % constants type(E, int(X), int, []). type(E, bool(X), bool, []). type(E, var(X), Y, []) :- lookup(X,E,Y). type(E,ite(E1,E2,E3), tvar(T), C1 ++C2 ++ C3 ++ C) :- type(E, E1,T1,C1), type(E, E2,T2,C2)


https://en.wikipedia.org/wiki/Lambda_cube is a way to classify different subsystems

Typed Expressions

Kind of it’s nice to avoid all the lambda calculus stuff. Consider compound boolean and nat expressions.

#use "topfind";;
#require "ppx_jane";;
type ty = Bool | Nat
type term =
    True | False | If of term * term * term | Zero 
    | Succ of term | Pred of term | IsZero of term [@@deriving sexp, equal, compare]
let term_of_string s = Sexplib.Sexp.of_string s |> term_of_sexp
let ex = term_of_string "(if true true false)"
let () = Sexplib.Sexp.pp_hum Format.std_formatter (sexp_of_term ex)
; very goofy since I have no evaluable forms
(declare-datatype Term
  ((True) (False) (Zero))

(declare-datatype Type
  ((Bool) (Nat))

(define-fun-rec hastype ((t Term) (ty Type)) Bool
  (match t (
    (True (= ty Bool))
    (False (= ty Bool))
    (Zero (= ty Nat))

(declare-const ty1 Type)
(assert (hastype True ty1))

(define-fun value ((x Term)) Bool

(define-fun eval ((x Term) (y Term)) Bool

(define-fun progress () Bool
  (forall ((x Term) (ty Type))
    (=> (hastype x ty)
        (or (exists ((y Term)) (eval x y))
            (value x))

(define-fun preservation () Bool
  (forall ((x Term) (y Term) (ty Type))
    (=> (and (hastype x ty) (eval x y))
        (hastype y ty)

(echo "progress")
(assert-not progress)

(echo "preservation")
(assert-not preservation)

Is normalization of typed expressions easy? We don’t need logical relations?

Simply Typed lambda Calculus (STLC)

typechecking stlc does not have the binding issues you might expect of a lambda calc

System T

“Quantifier free theory of functionals of finite type”

System F


polymorphic lambda calculus

Lambda2 Terms can depend on types. Big Lambda /
(al types can dpeend on types)

THE SYSTEM F OF VARIABLE TYPES, FIFTEEN YEARS LATER - Girard System F is developed on as a intuitionistic second order arithmetic like how system T is first order.

Data types are definable. Strongly normalizaing

System F as dependent types. Type variables could be runtime data, just a special class of them. In that case, the polymorphic signature of a function is a dependent type.

Reynolds towards a theory of type structure From the get go, considering the implementation of integers across two machines is brought up. A relational intepretation. The “inner” and “outer” view, one which sees representation and one that doesn’t

Types abstraction and parametric polymorphism

Polymorphism is not set theoretic https://inria.hal.science/inria-00076261/document http://h2.jaguarpaw.co.uk/posts/polymorphism-is-not-set-theoretic/

Uniform representation

Polymorphism is set theoretic constructively - Pitts

Theorems for Free - Wadler

from typing import Tuple
def interp(typ):
  if typ == bool:
    {True, False}
  if typ == Tuple[A,B]:
    { (a,b)  for a in interp(A) for b in interp(B)}
  if typ == Callable[A,B]:

# we can't finitely list our universe. Since it needs to include all iterated arrow. Eh. I don't see why really.
Universe = {bool : {True, False}}
def Universe(S): # check if in universe
def Universe(S): # infinite stream.  
  yield {True, False}
# Or maybe produce Z3 expressions. Hmmm.

Typ = Sort("Type")

def interp(ctx, ty):
  if ty == bool:
    ForAll([x,x1], interp(x,x1) == (x == x1))
  if ty == int:
  if ty == TVar:

  if ty == Tuple[a,b]:
  if ty == Callable[a,b]:
    ForAll([x,x1], interp(f,f1) == ForAll([x,x1], interp(x,x1) == interp(f(x), f1(x1))))
  if ty == Generic(X,T):
    ForAll([x,x1], )

# I can't enumerate ? forall X, 


Pure Type Systems Sorts, axioms

System U

Lambda Cube


  • stlc
  • stlc+type operators. Lambdas at the type level. Does any term have type $\lambda x. x -> x$? I don’t think so. Could add constants like List but again, no term has this type.
  • system F. $\lambda 2$ Adds Big Lambda at at term and forall at type level. Polymorphic types. $\forall x. x -> x$
  • $F\omega$ $\lambda \omega$
  • $lambda P$

Other interesting named systems don’t fit in the cube though.

“computational strength” LF has same computayional strength as stlc. what does that mean?

F Omega

first class type constructors.


Calculus of constructions

Equi vs iso recursive types

Strong Normalization for the Calculus of constructions - Casinghino


Whole different ball game (?) Luo’s thesis - An Extended Calculus of Constructions Proof on normalization of CIC and its consistency

Martin Lof

A distinctin can be drawn


$\lambda P$ $\lambda \Pi$ http://www.cs.cmu.edu/~aldrich/sasylf/ Twelf http://twelf.org/wiki/Main_Page https://www.cs.cmu.edu/~fp/papers/cade99.pdf

Abella Beluga

There is a claim that LF (which is a corner of the lambda cube?) has enough power to do lots of good stuff for progamming language metahtoery bu not so much that your life sucks. A Framework for Defining Logics harper honsell plotkin 93 judgements as types

https://en.wikipedia.org/wiki/Logical_framework logical framework can refer to a general notion of a system you encode your logic into or a specific type theory lambda pi system https://en.wikipedia.org/wiki/Dependent_type#First_order_dependent_type_theory

LFSC smtlib3 is also an LF?


dedukti - see term rewriting

https://arxiv.org/abs/2311.07185 Dedukti: a Logical Framework based on the λΠ-Calculus Modulo Theory https://deducteam.github.io/ https://github.com/Deducteam/lambdapi is the assistant? VS code extension

echo "
(; https://deducteam.github.io/tutorial.html 
Doesn't seem valid
#NAME foo.


(; declaring new types. 'static' ;)
A  : Type.
Nat: Type.
Z  : Nat.
S  : Nat -> Nat.

(; def and just stating existence are different
Notice the defnition of plus is open.
def 1 := S Z.
def 2 := S 1.

def plus: Nat -> Nat -> Nat.
[m]   plus  Z    m --> m
[n,m] plus (S n) m --> S (plus n m).

(; also fine? superfluous thugh. ;)
[n,m] plus n (S m) --> S (plus n m).

Bool : Type.
true : Bool.
false : Bool.

def equal : Nat -> Nat -> Bool.
[] equal Z Z --> true
[n,m] equal (S n) (S m) --> equal n m
[] equal (S _) Z --> false
[]  equal Z (S _) --> false.
(; avoiding intermediate periods makes it kind of a block of declarations.  ;)

IsTrue : Bool -> Type.
tt : IsTrue true.

#CHECK tt : IsTrue (equal Z 1).
#ASSERT tt : IsTrue (equal Z Z).
#EVAL (equal Z Z).

def test1 : IsTrue (equal (plus 1 1) 2) := tt.

(; partial functions are fine? They just get stuck. ;)
def pred : Nat -> Nat.
[ n : Nat ] pred (S n) --> n.

Listn : Nat -> Type.
nil   : Listn Z.
cons  : n: Nat -> A -> Listn n -> Listn (S n).

(; Hmm. but.. I need to take in n?
def length : n : Nat -> Listn n -> Nat.
[] length _ nil --> Z.
[n] length _ (cons _ _ tl) --> length tl 

(; smart constructors. Huh. So because there is a normalizaton, we're chill.
   WHat makes this a 'constructor'. Nothing really.
Int : Type.
def Diff : Nat -> Nat -> Int.
[m,n] Diff (S m) (S n) --> Diff m n.

def abs : Int -> Nat.
[n] abs (Diff Z n) --> n.
[n] abs (Diff n Z) --> n.

def append: n: Nat -> Listn n -> m: Nat -> Listn m -> Listn (plus n m).
[l2] append _ nil _ l2 --> l2
  append _ (cons n a l1) m l2 --> cons (plus n m) a (append n l1 m l2).

#INFER nil.
#PRINT \"hello world\".
(;  partial equality. Neeeeeat. ;)
def A_eq : A -> A -> Bool.
[a] A_eq a a --> true.  

  (; Thi _does_ extend the power of equal, because stuck terms are also equal now. like pred Z ;)
[ n : Nat ] equal n n --> true.
  " > /tmp/append.dk
dk check  /tmp/append.dk #-v 

confluence checking. It tries to? Or maybe lambdapi does? “Correct by cnfluence” dk check --confluence=CMD add new rules, confluence checking demonstrates that the new rules are “consistent” wth the old one? https://termination-portal.org/wiki/TPDB needs to support tpdb format. http://cl-informatik.uibk.ac.at/software/csi/ho/ http://cops.uibk.ac.at/?q=hrs confluence problem database

dk meta rewrites a dedukti file. Could I write an egraph as a file and then rewrite it?

https://github.com/Deducteam/Dedukti/tree/master/examples examples

https://github.com/Deducteam/Dedukti/blob/master/README.md manual

#CHECK t1 == t2.
#CHECKNOT t1 == t2.

commands beautify, check, dep, meta, top a toplevel, prune

kontroli is rust version https://arxiv.org/pdf/2102.08766.pdf Safe, Fast, Concurrent Proof Checking for the lambda-Pi Calculus Modulo Rewriting


Supposedly is kind of a shit show.

Intersection Types


Recursive Types

iso recursive vs. equirecursive

Refinement Types

Refinement Types: A tutorial Explicit Refinment Types “Instrinsic semantics niterprets typing relationd rathert than terms

  • FStar
  • Liquid Haskell

  • PVS?
:- initialization(main,main).
:- op(900, xfx, user:(::)).
tt :: unit.

main(_) :- tt :: Ty, writeln(Ty). 

Quotient Types

Cody has alluded to there being accidental stumbling blocks into inconsistency here

Dependent Types

See also

  • Coq
  • lean

Sometimes we get hung up on that types in some systems are compile time (static) and values are run-time (dynamic). Certainly dynamic typed languages put the types at runtime. Some statically typed languages persist the type information somewhere at runtime so you can reflect to it if need be, It is a little more subtle that you can pull values back into compile time. Really, programs themselves are compile time things that are talking about abstract runtime values. The variable x is kind-of sort-of a value (I am using terminology in a highly loose non-technical colloquial sense here).

Dependent types let you mention abstract runtime variables and concrete values in the types. It’s sort of a natural box in the compile-runtime, type-value grid.

calculus of constructions

Two Level Type Theory

two level type theory staging


Data Types

What makes “datatypes” different from the things above? They aren’t. My organization is all fucked.

Schema for allowable definitions.

Introduction Elimination Computation Rule

Inductive Types

nlab https://leanprover.github.io/theorem_proving_in_lean/inductive_types.html

Curious restrictions on their form. positivity restrictions

Parameters vs indices


parameters stay fixed / opaque in all definitions. indices can be

Inductive Vec a : nat -> Type :=
 | VNil : Vec a 0
 | VCons : forall n, a -> Vec a n -> Vec a (S n)

n is index. a is argument

Equi vs Iso recursive types - https://www.cs.cornell.edu/courses/cs4110/2012fa/lectures/lecture27.pdf Does equality hold on the nose in recursion (equi) or is there some unpacking to do (iso) Coinductive Types

Induction Recursion

https://ncatlab.org/nlab/show/inductive-recursive+type induction recursion induction induction

Syntax and Semantics

Type theory takes syntax more seriously than I ever realize. It’s like drawing a house. Do you draw a box with a triangle over it, or do you really look.


These are something you can write on paper that represents “I know this”. This is where your mind meets the paper in some sense.


  • Has type $\Gamma \turnstile t : T$
  • Is well formed
  • Is Type $\Gamma \turnstile
  • evaluates to (big step/small step) $(1 + 3) \downarrow 4$ or $(1+3) \rightarrow 4$
  • Hoare logic step {true}x := 3{x > 0}
  • Sequent. Under the assumption A,B hold, A holds. $A,B - A$
  • Focused Sequent. Kind of fishy that this has intuitive meaning
  • Type synthesizes, Type Checks - In bidirictional typing discipline, a type being synthesizable/inferrable from the term vs a type being checkable given both type and term are distinct judgements.

Inference Rules

These are the things drawn with horizontal lines. They take you from


Concrete computer encodings of judgements and inference rules

  • Coq inductive types
  • Prolog programs (Twelf is related I think)
  • Ocaml/Haskell/python functions. There are many different modes you might pick. You can choose
  • Tactics

Computation Rules

I don’t know what this means really. Whatever it means, probably beta reduction (applying lambda functions) is included.

The central idea I think is that if you’re building a theorem prover, it needs to understand how to do something. The hypothesis is that it is useful and flexible for the theorem prover to understand well behaved operations of lambda calculus. Lambda calculus has it’s origin as a study of pure logic. It hopefully is capable of expressing anything that belongs to our intuitive notion of “algorithm” (again this was what it was invented for).

So we might want to build in knowing how to add numbers or do sines or cosines or something into our theorem prover, but we can usefully encode these concepts into the master concepts of lambda calculus.

I am ultimately not convinced or unconvinced this hypothesis holds and wonder if the mainstream of interactive theorem proving isn’t a little too attached to the idea.

Conversion vs Reduction vs Expansion

Coq may have it’s own idiosyncratic naming for different kinds of reduction rules. https://coq.inria.fr/refman/language/core/conversion.html

  • (fun x => x ) q ~> q beta conversion
  • (fun x => x) ~> (fun z => z) alpha conversion, aka dummy index rewriting
  • match () with () => z ~> z match conversion
  • let x := 3 in x ~> 3 zeta reduction
  • if Definition foo := 3. then foo ~> 3 delta reduction/unfolding of definitions
  • f ~> (fun x => f x), eta conversion. expanding ou
  • x ~> match x with tt => x end Is this right? This is eta for unit

I think eta conversion is the most confusing and puzzling out of all of them. Sometimes eta may refer to only eta expanding functions, but other times it may be referring to the relationship of a data type with it’s match statement.

In some sense functions are very special datatypes. They are not defined via an Inductive definition. But they are also can be thought of as being kind of similar to these data types. The “constructor” of a function is a lambda binder, and the “eliminator” is an application. We can encode data types into functions using church like encodings. match can be encoded by positing a special function that has a computation rule like match.

Pattern Matching

There is a choice whether to consider pattern matching primitive or not Eliminators dependent pattern matching

Conor Mcbride thesis

a few constructions on constructors Pattern Matching Without K


Many type systems talk about lambda calculus, so the metatheory is intertwined with the rewrite theory of lambda calc.

Mechanized Metatheory

Decidability of type checking

type checking is the question of whether given all the pieces going into the judgement under line of the inference rule, is it decidable to construct a typing tree that produces such a thing. Even this seemingly simple question can be undecidable in some type systems, because higher up in the typing tree you may ask questions that are very difficult. In the presence of nontermination, you probably are encountering a variant of the halting problem. Undecidability of type checking is typically considered to be an undesriable property, but people are willing to entertain the notion.

Princiapl Types

Type inference supposes I give the context and term, but leave out the type, can I construct both the typing tree and the type. Principle types are asking whether there is a unique most general type that can be inferred.

What are principal typings and what are they good for?

“Uniqueness of types” may hold in some system.



Well typed terms can take a step under the eval relation or are values.

A kind of liveness property? Eh. Really it feels kind of “coinductive” to me.

Preservation, Subject Reduction

under evaluation, terms remain well typed.

This can mean evaluation does not cause types to change. These are slightly different statements that may not be equivalent in all systems

Preservation is very reminiscent of verifying that types are an invariant, or that the typing relation is an invariant set.




Altenkirch the4sis https://www.cs.nott.ac.uk/%7Epsztxa/publ/phd93.pdf Constructions, inductive types and strong normalizaTION

normalization by evaluation

type term = Lam of string * term
 | Var of string
 | App of term * term

type typ = Arr of typ * typ
  | Unit

type value = Arr of value Value.Map.t | UnitV

let eval : term -> typ -> value


The same thing as normalization? https://cs.stackexchange.com/questions/112893/what-does-canonicity-property-mean-in-type-theory

Computation rules



Soundness is whatever the type system claims to show about evaluation, it shows. Sometimes this is progress + preservation

Head Normal Form



What are possible definitions of “value”?

  • a subset of terms
  • a separate entity akin to terms
  • semantic objects

Some of the distinction and confusion depends on how much you rely on your host meta system.



Reducible Expression

Curry vs Church style

Extrinsic vs Instrinsic types

Large Elimination

pattern matching on a value to construct a value of Type https://cstheory.stackexchange.com/questions/40339/what-exactly-is-large-elimination

bool_to_type : bool -> Type := fun b =>
     match b with
         | true => Unit
         | false => Empty

impredicative polymorphism + exlcuded middle + large elimination can only choose 2

Excluded Middle

Axiom classic : forall P:Prop, P \/ ~ P.

Sorts & Universes


  • Prop vs Type vs Set - Prop is impredicative. Prop and Set are deleted during extraction? Which make them interestingly different from a metaprogramming perspective.

Prop SProp Set SProp is Prop with proof irrelevance

Prop is erased at extraction. Prop doesn’t have large elimination. But it is impredicative and can support excluded middle.

Inductive Bool : Prop := T : Bool | F : Bool.

Definition two_elements_Bool (H : T = F) : False :=
  match H in (_ = y0) return (match y0 with
                                | T => True
                                | F => False
                              end) with
    | eq_refl => I
(* Error: *)
(* Incorrect elimination of "y0" in the inductive type "Bool": *)
(* the return type has sort "Type" while it should be "Prop". *)
(* Elimination of an inductive object of sort Prop *)
(* is not allowed on a predicate in sort Type *)
(* because proofs can be eliminated only to build proofs. *)

[why does coq have prop(https://cstheory.stackexchange.com/questions/21836/why-does-coq-have-prop)]

Is there a difference between Sorts and Universes? https://github.com/jdolson/hott-notes/blob/pdfs/pdfs/notes_week5.pdf Universes are

Algerbaic universes - If the levels of type are a DAG, they can be ordered. So one doesn’t eagerly pick integer levels for Type. Instead you maintain contraints like max{l1,l2} etc.

Impredicative vs Predicative

This is describing allowing some kind of form of self reference I think.

Impredicative types allow quantification over themselves. Prop

Proof Irrelevance

Propositional extensionality implies proof irrelevance https://coq.inria.fr/library/Coq.Logic.ProofIrrelevance.html Axiom proof_irrelevance : forall (P:Prop) (p1 p2:P), p1 = p2.

https://github.com/coq/ceps/pull/32 RFC: Disentangling erasure and proof irrelevance

It feels like proof irrelevance and erasure are related concepts, but like almost everything in type theory there is probably a time and place and level of sophistication to consider them as distinct

Irrelevance, Polymorphism, and Erasure in Type Theory

Berardi’s paradox which says that in the calculus of constructions, excluded middle (EM) and axiom of choice (AC) imply proof irrelevance (PI).

Inconsistent Combinations

See counterexamples in type theory


Extensionality is a notion that applies to more than just function and equality types. https://cstheory.stackexchange.com/questions/17636/%CE%B7-conversion-vs-extensionality-in-extensions-of-lambda-calculus

forall x, f x = g x -> f = g forall A B : Prop, A <-> B -> A = B forall x, x = match x with () -> ()

Consider fun x -> x = fun x -> tt Is this provable?

Some of these are provable

alpha beta eta iota

Computational Content

Heterogenous vs Homogenous equality

Eta equivalence - The dumb expansion? Observational Equivalence

Reduction vs Equivalence


carette definitional equality

Extensional vs Intensional

Extensional equality collapses definitional and propositional equality.

Adding a rule

Gamma |- t : Id_Type A B
Gamma |- A = B

Notice that this is a transition from a typing judgement to a definitional equality judgement. Very different fellas.

Observational Type Theory

observational type theory type theory has an equality type and refl. Extentional type theoru adds reflection, intentional adds eliminators

pavel summarizing some different choices

Judgemental/Definitional / Propositional

https://coq.inria.fr/refman/language/core/conversion.html#conversion-rules https://coq.inria.fr/refman/proofs/writing-proofs/equality.html

Another way of talking about judgementally equal is if the proof of Prop equality is just eq_refl. Why is this? Well, type checkeing more or less must look for an inferrance rule with eq_refl : a = b below the horizontal line. This rule breaks apart this problem into a definitional. Definitional equality is so in the air we breath, it may not even be noted explicitly. Any nonlinear occurence of pattern variables in the inferrence rule must imply it.

Tests. Example Definition mytest : tt = tt := eq_refl. See if coq accepts it https://coq.vercel.app/scratchpad.html

  • tt = tt
  • unit = unit
  • 1 + 1 = 2
  • x : nat |- x + 0 = x
  • x : nat |- 0 + x = x
  • (fun x => x) = (fun x => x)
  • (fun x => x) = (fun x => tt)
  • (match tt with tt => tt end) = tt
  • x : unit |- tt = x
  • x : unit |- match x with () => () end = ()
  • x : unit |- match x with () => () end = x
  • x : unit |- match x with () => () end = match x with _ => () end
  • f |- f = (fun x => f x) eta
  • (fun x => x) tt = tt beta


Implies extensionality Example unit' = unit Isomorphisms


The homotopy type theory book

Implementing Patch Theories in Homotopy Type Theory https://www.cs.ru.nl/bachelors-theses/2023/Dick_Blankvoort___1056960___Implementing_Patch_Theories_in_Homotopy_Type_Theory.pdf

https://www.youtube.com/watch?v=Pu16iodESTU&list=PL245PKGUDdcN9-El9D7DRefwX4c9feiYq&t=594s&ab_channel=JacobNeumann Youtube videos



https://epit2020cnrs.inria.fr/ spring school Type/Space A type Elekemntn/Point t:A type and point may depend on other points x:A1, x2:A2 |- B(x1,,x2) type

R^n depends on n. n : N |- R^n type [a,b] depends on a,b, and a <= b a:R, b:R, p :(a<= b) |- [a,b] Equality judgemental equal A === B equal types s ===_A t equal points

Type theory as formal system Gam |- A type t A A === B s ===_A t

rules of inference

Base space A a:A |- B(x) fiber at x. ttoal space - dpeendent sum

Basic constructions

dependent sum


The most straightforward computational attack on homotopy is usuing a trinagulated mesh that I think is fairly intuitive to computer people

Computerizing something involes:

  • how to you even represent the thing you want to talk about
  • howw do you actually compute answers to interesting questions. I divide these into differewnt classes 1. Just recognize something, i.e.e proof checking or 2. Calculate or find something, i.e. actual numerical computations or proof finding or what have you. 1. Is often an interesting thing to start with since it is easier even if what you really want is 2.

Paths are lists of adjacent vertices.

Using haskell gadts for example, we can encode all the paths and then use the types to enforce that the path is actually connected.

Another persepctive on this is to encopde the proerties of the mesh in logic, like first order logic. Z3 is a way to make this conrete

From the perspective of systems like Coq or agda, these two pictures become the same thing. There systems have the notion of inductive predicates that boil down to defining a datatype like that in Haskell.

It sucks to work with this stuff though.

A second straightforward attack to model homotopy would be to directly model functions from the interval R -> R. Reals are floats.

We might try to directly model these things in functions and use functional programming def homo1():

def composepath(p1,p2):
  assert(p1(1) == p2(0) )
  def res(t):
    if t < 0.5:
      return p1(2 * t)
      return p2(2*t-1)
  return res

Skeletonizing this path with an open cover. 1 open cover = [0,1) and (0,1]

Or we might try to take a more symbolic approach, like using sympy or some custom dsl. The difference here is tha6t in many languages, lambdas are basically totally opaque. There are advantages and disadvenmtages to this rule. It does not have to be so. Languages with strong metaprogramming paradigms (and I include lisps, Julia, and coq here) let you poke around in the guts to varying degrees.

Reals as floats may be objectionable to your sensibiliuties. Then perhaps a preferable approach may be to use exact reals, numbers that retain the capacity to be computed to arbitrary precision upon request.

Maybe there is more connection to my path thing than I realezied

Higher inductive types are where it is actually at I guess.

https://www.youtube.com/watch?v=A6hXn6QCu0k emily reihl - inifnity categroeis for undergrads, but really it’s about homotopy type thoery

11/2020 https://github.com/HoTT-Intro/Agda I screwed up my emacs agda by having a rotten tuareg in it. I think https://github.com/martinescardo/HoTT-UF-Agda-Lecture-Notes

https://pure.itu.dk/portal/files/84649948/icfp19main_p164_p.pdf - ICFP cubical agda, a depdently typed programming language with higher inductive types. Interestingly, from Mertberg’s demo, I feel like the emphasis was not on cubical itself. More interested in standard HoTT they just want it to be better

HoTT - quotients supposedly? Fractions? Integers vs naturals. I guess that’s the zeroth. Or maybe True / False modulo True = False? Anders Morberg - cubical agda https://vimeo.com/459020971

data _===_ {A : Set} (x : A) : A -> Set where
  refl : x == x


replcace inductive === with paths

{-# OPTIONS -- cubical #-}
open import Cubival.Foundations.Prelude

-- I , npoitns i0, i1
apply0 : (A; Type ) -> (p : I -> A) -> A
apply0 A p = p i0

refl' : {A : Type} (x : A) -> x \== x -- PathP (\ _ -> A) x x
refl' x = \i -> x

-- x \== x  h means PathP (\ _ -> A) x y
-- path over path. depednent paths

cong' : {A B : Type} (f : A -> B) {x y : A} -> x == y -> f x == f y
cong' f p = \ i -> f ( p i )

funext'  : {A B : Type} (f g : A -> B) {x y : A} -> ( p: x : A ->  f x == g y ) -> f == g
funext p i = \x -> p x i

transport' : {A B : Type} -> A == B -> A -> B
trasnport' p x = transp (\i -> p i) i0 x

-- another primitive called hcomp

ua' :  {A B : Type} -> A ~ B -> A == B
ua' e = ua e

isToEquiv' : {A B :type} -> Iso A B -> A ~ B
isToEquiv' e =  isoToEquiv e

isoToPath : Iso A B -> A == B
isoToPath e = ua (isoToEquiv e)

data Bool : Type where
   false : Bool
   true : Bool

not : Bool -> Bool
not false = true
not true = false

notPath : Bool === Bool
notPath = isoToPath' (iso  not not rem rem)
  rem : (x : Bool) -> not (not x) == x
  rem false = refl
  rem true = refl

transport notPath true -- false

sucPath : Int === Int
sucPath = (isoToPath' (iso sucInt predInt sucPred redSuc)

transport ( sucPath . sucPath) (pos 4)

-- multisets

data FMSet (A : Type) : Type where
   [] : FMSet A
   _::_ : A -> FMSet A -> FMSet A
   comm : ( x y : A) (xs : FMSet A) ->
         x :: y :: xs == y :: x :: xs
    trunc : isSet (FMSet A) -- UIP

_++_ : {A : Type} -> FMSet A -> FMSet A -> FMSet A
[] ++ ys = ys
(x :: xs) ++ ys = x :: (xs ++ ys)
(comm x y xs i) ++ ys = comm x y (xs ++ ys) i

-- can prove all kinds of things : xs ++ [] == xs ...


unitr-++ : {A : Type} (xs : FMSet) -> xs ++ [] == xs
unitr++ [] = refl
unitr++ (x :: xs) =

SIP structure idenity principle
Cubical.Algerba.Monoid.Base -- reflection

queues, matrices. Huh. representiation independence transportiung proofs.

https://arxiv.org/abs/1701.04391 - de moura selsam. good explanation of equality type John Major equality - conor mcbride Doing raw recursor proofs. Can I do it? I feel like it’s kind of straightforward. Begin able to write out exactly where I want the equality applied via a lambda. Could Axiomatize equality rather than use Inductive definition. The pattern match of a HIT must desguar into recursors?

Types are spaces. values are points dependent types are fibrations identity types are paths

Other weirdness: Observational type theory

Describing a concrete simplex as a gadt. How do you make a correct by construction data structure of a path? A list of vertices. Fine. But is there a path between each of them? Ok, list of edges. Do those edges meet?


univalence let’s you turn isomorphisms into equality and get isomorphisms out of equalities.

HIT - Higher inductive types. Instead of always needing the data constructor to end with the type itself A , now it can end with =_A also. You can do this using

recursors. Patrern matching is cool,. but it can be desgared into something else. A function that takes a continuation for each case of the type.

focus on inclusion and projection maps of the basic simplex

Cubical Sets.

of of the basic interval.

Simpilical Sets.

By using the basic simplex with its inclusion maps as sort of a index into your complex, you can grab all n-simplices at once. This is a Functor relation.


Great intro


You can use a postulate methodology where you slam out all the pieces of your type as axioms

Dan Licata explaining how to use hiding to implement HIT. You export an interface. This let’s you postulate less


Homotopy group of circle is Z



You always need streicher K and rewriting turned on. What do these do?



Axiom K is Uniqeness of Identity Proofs. Default pattern matching allows this. Modified pattern matching doesn’t. The recursor form doesn’t seem to have this problem

    uip : {A : Set} -> {a : A} -> (p q : a ≡ a) -> p ≡ q
    uip refl refl = refl


Kind of hard to navigate. Like the std-lib but harder.

Useful start in core. Check out univalence, check out the types folder. Look at the normal types like Bool, Nat, List. Now check out Circle, Torus,

Interesting lectures using cubicaltt


Logical Relations

Tait proved strong normalization using them 1967.

Parametricity is supposedly an example also

Chapter (crary) in Advanced Topics in Types by Pierce People keep referencing Mitchell 1996

Logical relations as a generalization of homomorphism - Stump podcast referencing a paper


andrej notes on realizability

Cody’s suggested book TOC Extensional vs Intensional models? Chapter 1: The type theory and its variants:

  1. The CIC with universes
  2. Recursors vs guarded fixpoints
  3. Predicative variant
  4. Untyped conversion
  5. Some additional conversion rules
  6. Extensionality principles
  7. Anti-extensionality principles
  8. Coinduction Chapter 2: Theorems about syntax
  9. Substitution, weakening
  10. Well-sortedness
  11. Progress and preservation
  12. Uniqueness of types(?)
  13. Properties of normal forms Chapter 3: Extensional models
  14. The not-so-simple set theoretic interpretation
  15. Models of sets in types
  16. The effective topos interpretation
  17. The groupoid interpretation
  18. Consistency implications Chapter 4: Intensional models
  19. Realizability interpretations
  20. The Lambda-set model
  21. The normalization proof
  22. Consistency implications Chapter 5: Inconsistent combinations of rules
  23. Failures of subject reduction
  24. Failures of termination alone
  25. Impredicative inconsistencies
  26. Guard condition and inductive type issues
  27. Other universe issues


A collection of programming languages and type systems.

Gradual Typing

Intersection types


ALgebraic subtyping https://lptk.github.io/programming/2020/03/26/demystifying-mlsub.html mlsub dolan thesis

union types

Bidirectional Typing Old

Notes on Bidirectional Typing and the Meaning of Horizontal Lines

type var = string
type typ = Unit | Arr of typ * typ
type expr = Var of var | Lam of var * expr |
     App of (expr * expr) | TT | Annot of (expr * typ)
type typ_ctx = (var * typ) list

let rec type_check gamma e t = match e,t with
  (* | Var v, t -> (List.assoc v gamma) = t *)
  | TT , Unit -> true
  | Lam(x,e), Arr(a1,a2) -> type_check ((x,a1) :: gamma) e a2
  | _,  _  -> let t' = type_infer gamma e in
              t = t'
and type_infer gamma e = match e with
  | Var v -> List.assoc v gamma
  | Annot(e, t) -> let c = type_check gamma e t in
                  if c then t else failwith "Bad annotation"
  | App(f, x) -> let Arr (a,b) = type_infer gamma f in
                let c = type_check gamma x a in
                if c then b else failwith "App typecheck failed"
  | _ -> failwith "can't infer"

(* #use bidi.ml;;
type_check [] TT Unit;;
type_infer [] (Annot (Lam ("x", App(Var "x", TT)), Arr(Arr(Unit,Unit),Unit)));;
type_infer [] (Annot (Lam ("x", TT), Arr(Unit,Unit)));;

% It works but it is not well moded

type(G, var(X), A) :- member( X - A  , G ). % member is a little wrong.
type(G, ann( E, T), T) :- type(G, ann(E,T), T).
type(G, tt, unit).
type(G, lam(X,E), arr(A1,A2)) :- type([X - A1 | G], E, A2 ).
type(G, app(E1,E2), B) :- type(G, E1, arr(A,B)), type(G, E2, A).

%?- type([], lam(x,lam(y,var(y))), T).
%T = arr(_6662, arr(_6680, _6680)) 

Require Import String.
Inductive type :=
    | unit
    | arr : type -> type -> type
Inductive expr :=
    | var : string -> expr
    | lam : string -> expr -> expr
    | ap : expr -> expr -> expr
    | tt : expr
    | ann : expr -> type -> expr.

Fixpoint type_eq a b :=
     match a,b with
     | unit, unit => true
     | (arr x y), (arr z w) => andb (type_eq x z) ( type_eq y w)
     | _, _ => false

Definition ctx := list (string * type).
Require Import List.
Import ListNotations.

Search app.

Fixpoint lookup k l := match l with 
| (t,v) :: ls => if String.eqb k t then v else lookup k ls 
| [] => unit (* chris mocked me for this *)
(** adequacy *)
Inductive hastype (gamma : ctx) : expr -> type -> Type :=
   | Var : forall x A, lookup x gamma = A -> hastype gamma (var x) A
   | UnitI : hastype gamma tt unit
   | ArrI :  forall e x A1 A2, hastype ((x, A1) :: gamma) e A2 -> 
             hastype gamma (lam x e) (arr A1 A2)
   | ArrE :  forall A B e2 e1, 
             hastype gamma e1 (arr A B) ->
             hastype gamma e2 A ->
             hastype gamma (ap e1 e2) B
    | Anno : forall a b e, 
        hastype gamma e a -> 
        type_eq a b = true -> 
        hastype gamma (ann e a) b

Inductive inferstype (g : ctx) : expr -> type -> Type :=
| Varinf : forall x A, lookup x g = A -> inferstype g (var x) A
| ArrEInf :  forall A B e2 e1, 
            inferstype g e1 (arr A B) ->
            checkstype g e2 A ->
            inferstype g (ap e1 e2) B
| AnoInf : forall a e, checkstype g e a -> 
           inferstype g (ann e a) a

with checkstype (g : ctx) : expr -> type -> Type :=
| UnitIc : checkstype g tt unit
| ArrIc :  forall e x A1 A2, checkstype ((x, A1) :: g) e A2 -> 
          checkstype g (lam x e) (arr A1 A2)
| Subc : forall A e B, inferstype g e A -> type_eq A B = true -> checkstype g e B

Goal checkstype [] tt unit. eauto using checkstype. Qed.
Goal hastype [] tt unit. eauto using hastype. Qed.
Goal {t & hastype [] tt t}. eauto using hastype. Qed.
Goal checkstype [] (lam "x" tt) (arr unit unit).
eauto using checkstype. Qed.
Goal hastype [] (lam "x" tt) (arr unit unit).
eauto using hastype. Qed.
Goal hastype [] (ap (lam "x" tt) tt) unit.
eauto using hastype. Qed.
Goal inferstype [] (ap 
                   (lam "x" tt)
                   tt) unit.
                   eauto using inferstype, checkstype. Abort.

Goal inferstype [] (ap 
                   (ann (lam "x" tt) (arr unit unit)) 
                   tt) unit.
                   eauto using inferstype, checkstype. Qed.
(* eapply ArrEInf. 
- eapply AnoInf.
apply ArrIc. apply UnitIc.
- apply UnitIc.
Qed. *)  

(* Decision procedures. 

Fixpoint inferstype (g:ctx) (e : expr) : option {t : type | inferstype g e t} :=
Fixpoint checkstype (g:ctx) (e : expr) (t : type) : option (checkstype g e t) :=

sound : checkstype -> hastype
complete : hastype -> checkstype \/ inferstype

From elpi Require Import elpi.

I picked bidirectional typechecking for reading group this week. A lovely survey paper.

Bidirectional typing is a methodology for taking piles of typing rules and making them more algorithmic. This gets into my strike zone, as an outsider to type theory and logic, the notion of inference rules bends me in knots. I don’t know what they mean. I do have some notion of computer programs having meaning. C, python, C++, ocaml, even Coq mean something to me because they create artifacts that exist and live outside of myself. They run. Their execution becomes fact regardless if I understand them or not. Executable programs become a part of the physical world. I do not feel the same about mathematics or computer science written down in papers. It is static and only moves in the minds of people. I do not feel compelled to consider what is written in a paper as fact, a fait accompli, if I do not personally understand it. I don’t think many people are trying to willfully deceive me in the academic literature, but there is always the possibility and that of self deception.

So for me, a pile of cryptic symbols with horizontal lines is unacceptable and contains almost no meaning. It begins to have meaning when I know how to translate the concepts into an executable programming language.

These concepts also being to have meaning if I personally know how to manipulate them with paper and pencil.

To put symbolic mathematical stuff in a computer, a standard methodology is to consider expressions as trees. We then want to manipulate those trees in programs or produce new trees.

Something that gives me conniptions is that implication \(\rimpl\), turnstile $\vdash$, And horizontal bars are all some kind of notion of logic implication, albeit with the subtlest conceptual and mechanical distinctions. Somehow it the interplay of pushing things around between these three similar things encapsulates a notion of logical deduction. Super bizarre.

B is mangetic field, Gamma is a context

What are types?

Checking is easy? Inference is hard?

Is this always true? Checking of application is hard f x : b because it needs to invent an inner type.

We can make checking always go through with intense annotations.

infertype below the line is good. It is producing information infertype above the line is bad. It needs a information producing process to output

It almost feels like a constraint satisfaction problem to me. Perhaps the more flexible notion of a prolog search with pruning.

We want to pick an ordering and infer/check for the rules such that they are well moded. We want as many infers below lines and checks above lines as possible. These may be weighted by some notion of painfulness of annotation. Freqeuncy of usage * annotation size perhaps. To require a synethsizing derivation is bad. To produce a synthesizing derivation is good.

The Pfenning recipe is a heuristic

Well moded : premise known.

The rules aren’t independent of each other though? How does that hjapen? Yes. The rules need to match up to each other. Maybe really the cost is every annotation. which happens when there is a form that infers up top but checks below the line.

Of course, a language designer is not constrained to a fixed system. Adding extra forms,, or interesintg new contexts or ways of thinkgin about things.

elimination forms are often in inference because there is an invented type

type-directed synthesis of programs is proof search.

  1. principal judgement
  2. elimination synethesize, introduction check. Elimination is usually above and introuducion usually below. This is counterintuitive, we have to step backward to step forward. WWe don’t want synthesis above and check below.

A pile of introductions should just check? Carry the top level information through

subtyping :< often has input mode. It’s easy to tell if things are subtypes than to come up with a maximal type persay

We don’t want to dump information

The simply typed lambda calculus is kind of a bad example. It is so simple that really the nonbidirectional version does a pretty dang good job in raw prolog.

Inference rules: What is their computational reading/encoding? The notation is extremely vague if this is your desire.

  1. The expression below the line defines a relation.

Relations may be represented different ways R a b c Am I mixing types and expressions

  1. [(a, b, c)] - explicit list (possibly infinite)
  2. a -> b -> c -> bool (decidable or partial depending on language) indicator function
  3. Halfsies. a -> [(b,c)], a -> b -> [c], in this sense a -> b -> c -> bool is really a -> b -> c -> [()]
  4. type Rel a b c = a -> b -> c -> Prop – a formula
  5. explicit data type. data Rel = True False And (Rel) (Rel) Or
  6. R(A,B,C). – prolog
  7. C C++, Java, Python? Do they give any new ideas?

Ok but what about Rules. Different but related question.

  1. Prolog clause. Problems
  2. Coq Inductive data type
  3. Cases in recursive function definitions
  4. a pile of switch cases or whatever
  5. Tactics - th below -> ( [ th above ], [Pf above] -> Pf below )
  6. LCF Recognizers - typ rule = [Pf up top] -> theore below -> Pf below - sometimes yuou need to give the shape of the thing you want below. Even all the above parts and the label of the rule might be insufficient. It is possible for this function to be partial / undecidable. Proof terms are a deep DSL recording these shallow moves.
  7. You could make an argument for an inference rule being a relation of relations? A meta lifting.
  8. rules with single things up top = executions of abstract machines

Inference rules are relations on syntactic forms?

This notion of modes is essentially the “Halfsies” notion. Bidirectional typechecking defines the relation not between

A typing judgement is a relation between contexts, expressions, and types. Expressions have types. Values have types. These are distinct notions. Value as a meta notion vs Value as a subclass of expressions.

(tenv, expr, typ ) -> true (tenv, expr) -> Maybe typ

type var = string type expr = Var of var | Lam of var expr | App of (expr expr) | TT | Annot of (expr typ) type typ = Unit | Arr of typ typ type typ_ctx = (var * typ) list

type_check gamma = function | Var v, | TT , Unit -> true | e, t -> let t’ = type_infer gamma e in t = t’ and type_infer gamma = function | Var v -> lookup gamma v | Annot e t -> let c = type_check gamma e t in if c then t else failwith “Bad annotation” | App f x -> let Arr (a,b) = infer_type gamma f in let c = type_check gamma x a in if c then b else fail | _ -> failwith

Does it matter that we should return errors? I guess it does.

type_judge(Gamma, e, type). type_judge(Gamma, tt, unit). type_judge(Gamma, lam(V,Body) , arr(A,B)) :- type_judge([ V -> A | Gamma], Body , B ). type_judge(Gamma, app(F,X) , ) :-

He mentioned thaty a computational interpretation of inference rules requirtes modes Symntax directed rules can be considered as checkers, since we can pattern match on the syntax and know what to do

What interesting problem can I use? Well, interleaved moded predicates is interesting.

Mode correctness - not a notion of bidirectional type checking.

This is an interesting idea. Take a prolog program that is ill formed. Consider all possible modal forms. In recursiove calls, consider all possible modes to recrusviely call. Consider all orderings of right side preds too. This is all mechanical enough it could be considered mode synthesis. Perhaps + some test cases

rel(A,B) :- rel(A,C), rel(C,B).

rel+-(-A,+B) :- rel–, rel–

Trim all non well moded

Maybe I hsould use mercury? Or swipl has a mode chekcing thing probably? https://www.swi-prolog.org/pldoc/man?section=modes

The fool.

It has something to do with focusing? Hmm The review paper says explicitly it does not.

https://twitter.com/rob_rix/status/1379966266723680256?s=20 rob rix dicssuion polarity sterling says look at sequent. negative have noninvertible left rules, positive has noninvertible right Substructural logic (like linear) What about bunched logic like seperation? Does that add any clairty? What is the polarity of seperating conjunction?

https://github.com/jonsterling/dreamtt “pedagogical” binrectional dependent tpye implentation

https://astampoulis.github.io/blog/taksim-modes-makam/ - huh makam has modes inspired by the bidi survey





Positive and negative types


The two G and D categories in Harrop/Horn formula

Positivityv checking

Covaraince and contravaraince

hmm. Harper seems to suggest a positive v neagtive product type is a lazy vs strict distinction https://ncatlab.org/nlab/show/product+type -

https://ncatlab.org/nlab/show/sum+type - need multiple sequents for

Linear logic.

Positive is “charactersize “ by introductions. Plays nice with eager evaluation Negative is “characterized” by eliminations. Plays “nice” with lazy evaluation

Introduction rules, elimination rules AND computation rules?

Proof search vs Preoof normalization semantics.

focussing and prolog

hmm harper actually has a chapter on classical logic dynamics

Bidrectional typing Type synthesis can be interleaved with type checking

Session types to understand linear logic vs memory management Types are things that can be attached to terms. Terms are expressions Process calculi are some kind of terms. https://homepages.inf.ed.ac.uk/wadler/papers/propositions-as-sessions/propositions-as-sessions.pdf

tactics are perhaps another operational interpetation of inference rules https://hackage.haskell.org/package/refinery https://reasonablypolymorphic.com/blog/towards-tactics/ https://www.youtube.com/watch?v=S0HvfXq3454&ab_channel=Z%C3%BCrichFriendsofHaskell


https://www.danielgratzer.com/courses/type-theory-s-2024/lecture-notes.pdf Principles of Dependent Type Theory - Angiuli, Gratzer

http://strictlypositive.org/thesis.pdf mcbride’s thesis. pattern matching


smalltt a demo for high performance type theory elaboration. Really good readme too elboration seminar learn type theory simple observationsl type theory

regular propositions are basically the “constructive-content-free” parts of propositions

https://github.com/coq/coq/issues/9458 future of impredicative set

introductory type theory resources https://www.haskellforall.com/2022/05/introductory-resources-to-type-theory.html

Amusingly, by mixing the type level and value level, do we end up with a situation akin to dynamic types where we often need to carry types a runtime? Or is that mistaken?

Type Theory in Color internalizes parametricity results

Programming = proving? The Curry-Howard correspondence today Xavier Leroy - so freakin good


Resources for Teaching with Formal Methods mostly type thoery based