# Spine-local Type Inference

arXiv: https://arxiv.org/abs/1805.10383 · HTML: https://mlsanity.com/paper/1805.10383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 1 of 1)

- [Just Type It in Isabelle! AI Agents Drafting, Mechanizing, and Generalizing from Human Hints](https://mlsanity.com/paper/2604.15713/) (2026)

## Abstract (arXiv)

We present spine-local type inference, a partial type inference system for inferring omitted type annotations for System F terms based on local type inference. Local type inference relies on bidirectional inference rules to propagate type information into and out of adjacent nodes of the AST and restricts type-argument inference to occur only within a single node. Spine-local inference relaxes the restriction on type-argument inference by allowing it to occur only within an {application spine and improves upon it by using contextual type-argument inference. As our goal is to explore the design space of local type inference, we show that, relative to other variants, spine-local type inference enables desirable features such as first-class curried applications, partial type applications, and the ability to infer types for some terms not otherwise possible. Our approach enjoys usual properties of a bidirectional system of having a specification for our inference algorithm and predictable requirements for typing annotations, and in particular maintains some the advantages of local type inference such as a relatively simple implementation and a tendency to produce good-quality error messages when type inference fails.