2026/07/22 by Qunhui Zhang
#cs.PL
Type inference for dynamically typed languages must reconcile four qualitatively different sources of evidence: assigned values, explicit declarations, contextual requirements, and structural operations. Existing approaches often combine them into one constraint set, causing spurious conflicts or requiring annotations. We present Generic Constraints Projection (GCP), a zero-annotation inference framework that stores these sources in four monotone slots on a stable definition-time template and evaluates each call in a fresh projection session, preventing cross-call contamination while specializing return types. GCP uses Outline Equational Matching, an open structural preorder, and a future-this projection rule that preserves concrete receiver types across fluent chains and subtype extensions. On the success-state fragment of a bounded type domain, we prove monotonicity, local and global fixed-point convergence, conditional projection soundness, termination, multi-module convergence, and order independence. For an immutable core language, we also prove big-step evaluation existence, type preservation, runtime receiver retention, and projection-evaluation coherence. We instantiate GCP in Outline for typed ontology worlds and in a Python annotation-recovery pipeline. On 513 manually adapted, fact-paired Outline ports of TypeEvalPy cases, GCP obtains 513/513 exact matches, compared with 485/513 for the published Codestral Q&A baseline on the same fact IDs (two-sided exact McNemar p = 7.45e-9). This is a carrier-port evaluation in TypeEvalPy's closed-world Python vocabulary, not a run on unmodified Python sources.