type-inference-engine

Hindley-Milner type inference works by generating type constraints from functional language terms and solving them via unification to deduce principal types automatically. It uses occurs check during unification and generalizes polymorphic types to support let-polymorphism.

To add type inference to a new functional programming language, generate type constraints from your language's expressions and pass them to the inference engine. The engine solves these constraints using unification and handles let-polymorphism generalization to infer types.

Yes, you can infer types for lambda calculus expressions with polymorphism. The engine implements let-polymorphism, correctly generalizing polymorphic types during inference and instantiating them as needed to determine the principal type of an expression.

The best way to implement unification with occurs check for type systems is using an established Hindley-Milner engine that handles constraint solving natively. This prevents infinite recursive types during unification while correctly resolving polymorphic type variables.

No, you do not need to manually annotate types when using ML-style type systems. The Hindley-Milner algorithm automatically infers principal types by solving generated constraints, reducing the manual annotation burden on developers and catching type errors early.

Type inference can fail on recursive polymorphic functions if unification encounters an infinite type, which occurs check rejects to prevent unsound types. Constraint solving in Hindley-Milner requires explicit annotations for recursive definitions to resolve correctly.