Changelog¶

All notable changes to the quivers library are documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]¶

[0.2.0] - 2026-05-06¶

Changed¶

• Every record-shaped value type (AST nodes, FinSet, ProductSet, CoproductSet, ContinuousSpace variants, Category variants, RuleSystem) is now a didactic.api.Model. Recursive sums are dx.TaggedUnion roots discriminated by a kind: Literal[...] field. JSON round-trips via model_dump_json / model_validate_json are available on every value type.
• Resolution from TypeExpr / SpaceExpr AST trees to runtime SetObject / ContinuousSpace values is expressed as a dx.Lens family in quivers.dsl.resolution.
• Variadic constructors ProductSet(A, B, C) and CoproductSet(A, B, C) are replaced by keyword form ProductSet(components=(A, B, C)) and CoproductSet(components=(A, B, C)). The flattening converter preserves the previous flattening behaviour.
• Continuous spaces (Euclidean, Simplex, PositiveReals, ProductSpace) expose public name and dim fields (no longer private with property accessors).
• Minimum supported Python is now 3.14.

Added¶

• A tree-sitter grammar for the QVR DSL at grammars/qvr/, registered with panproto's panproto-grammars-all distribution.
• quivers.dsl.parser delegates parsing to panproto via the qvr tree-sitter grammar.
• quivers.dsl.program_theory defines QVR_PROGRAM_PROTOCOL and extract_program_schema, lifting every compiled program to a panproto Schema for use with panproto schema diff, panproto lens generate, and related tooling.
• A Denotational Semantics documentation section giving a formal, compositional semantics for the DSL across the discrete, stochastic, and continuous strata, plus an adequacy theorem connecting the compiler implementation to the denotation.
• RuleSystem carries cross-field axioms (__axioms__) ensuring binary_weights/unary_weights lengths match binary_rules/unary_rules when supplied.
• .github/workflows/release.yml builds an sdist + wheel on tag push and publishes to PyPI via the FACTSlab/quivers OIDC trusted publisher.
• Pull request template under .github/PULL_REQUEST_TEMPLATE.md and issue templates under .github/ISSUE_TEMPLATE/.

Removed¶

• quivers.dsl.lexer and quivers.dsl.tokens: the hand-written lexer is replaced by panproto's tree-sitter–integrated lexing.
• LexError: lexical errors now surface as ParseError.

[0.1.0] - 2026-03-26¶

Added¶

Core Categorical Algebra¶

• Fundamental category types and morphisms
• Object declarations and morphism composition
• Support for latent and observed morphisms
• Basic categorical operations and abstractions

Stochastic Morphisms¶

• Stochastic morphism declarations and semantics
• Integration with probability theory
• Support for morphism composition in stochastic settings

Continuous Distributions (30+ Families)¶

• Normal distribution and variants (LogitNormal, TruncatedNormal)
• Beta, Dirichlet for probability simplices
• Exponential family: Exponential, Gamma, Chi2
• Heavy-tailed: Cauchy, StudentT, Pareto
• Bounded: Uniform, Kumaraswamy
• Half-variants: HalfCauchy, HalfNormal
• Transformed: LogNormal, Gumbel, Laplace, Weibull
• Multivariate: MultivariateNormal, LowRankMVN, Wishart
• Bernoulli variants: Bernoulli, ContinuousBernoulli, RelaxedBernoulli
• Advanced: RelaxedOneHotCategorical, FisherSnedecor
• Normalized flows: Flow
• Categorical and discrete approximations

Monadic Programs¶

• Draw statements for sampling from morphisms
• Observe statements for conditioning and likelihood
• Return statements with optional labeled outputs
• Variable binding and destructuring in patterns
• Program parameters and composition

QVR DSL¶

• Complete lexer with token recognition for all language constructs
• Recursive descent parser with full grammar support
• Abstract syntax tree (AST) node definitions
• Program block execution with proper scoping
• Let bindings for expression computation
• Built-in let functions: sigmoid, exp, log, abs, softplus
• Comment support (#)
• Type expressions: products (*), coproducts (+)
• Expression operators: composition (>>), tensor product (@), marginalization
• Indentation-aware program body parsing
• Specialized handling for draw/observe arguments

Variational Inference Layer¶

• Inference interface for probabilistic programs
• Support for approximate posterior computation
• Integration with continuous distribution families