Omnidirectional type inference for ML: principality any way
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914522615250944 |
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| author | O'Brien, Alistair Rémy, Didier Scherer, Gabriel |
| author_facet | O'Brien, Alistair Rémy, Didier Scherer, Gabriel |
| contents | The Damas-Hindley-Milner (ML) type system owes its success to principality, the property that every well-typed expression has a unique most general type. This makes inference predictable and efficient. Unfortunately, many extensions of ML (GADTs, higher-rank polymorphism, and static overloading) endanger princpality by introducing _fragile_ constructs that resist principal inference. Existing approaches recover principality through directional inference algorithms, which propagate _known_ type information in a fixed (or static) order (e.g. as in bidirectional typing) to disambiguate such constructs. However, the rigidity of a static inference order often causes otherwise well-typed programs to be rejected.
We propose _omnidirectional_ type inference, where type information flows in a dynamic order. Typing constraints may be solved in any order, suspending when progress requires known type information and resuming once it becomes available, using _suspended match constraints_. This approach is straightforward for simply typed systems, but extending it to ML is challenging due to let-generalization. Existing ML inference algorithms type let-bindings (let x = e1 in e2) in a fixed order: type e1, generalize its type, and then type e2. To overcome this, we introduce _incremental instantiation_, allowing partially solved type schemes containing suspended constraints to be instantiated, with a mechanism to incrementally update instances as the scheme is refined.
Omnidirectionality provides a general framework for restoring principality in the presence of fragile features. We demonstrate its versatility on two fundamentally different features of OCaml: static overloading of record labels and datatype constructors and semi-explicit first-class polymorphism. In both cases, we obtain a principal type inference algorithm that is more expressive than OCaml's current typechecker. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_10343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Omnidirectional type inference for ML: principality any way O'Brien, Alistair Rémy, Didier Scherer, Gabriel Programming Languages The Damas-Hindley-Milner (ML) type system owes its success to principality, the property that every well-typed expression has a unique most general type. This makes inference predictable and efficient. Unfortunately, many extensions of ML (GADTs, higher-rank polymorphism, and static overloading) endanger princpality by introducing _fragile_ constructs that resist principal inference. Existing approaches recover principality through directional inference algorithms, which propagate _known_ type information in a fixed (or static) order (e.g. as in bidirectional typing) to disambiguate such constructs. However, the rigidity of a static inference order often causes otherwise well-typed programs to be rejected. We propose _omnidirectional_ type inference, where type information flows in a dynamic order. Typing constraints may be solved in any order, suspending when progress requires known type information and resuming once it becomes available, using _suspended match constraints_. This approach is straightforward for simply typed systems, but extending it to ML is challenging due to let-generalization. Existing ML inference algorithms type let-bindings (let x = e1 in e2) in a fixed order: type e1, generalize its type, and then type e2. To overcome this, we introduce _incremental instantiation_, allowing partially solved type schemes containing suspended constraints to be instantiated, with a mechanism to incrementally update instances as the scheme is refined. Omnidirectionality provides a general framework for restoring principality in the presence of fragile features. We demonstrate its versatility on two fundamentally different features of OCaml: static overloading of record labels and datatype constructors and semi-explicit first-class polymorphism. In both cases, we obtain a principal type inference algorithm that is more expressive than OCaml's current typechecker. |
| title | Omnidirectional type inference for ML: principality any way |
| topic | Programming Languages |
| url | https://arxiv.org/abs/2511.10343 |