arXiv subjects

Sam Staton

Publications and source records attributed to Sam Staton.

At least 19 recordsLinked to original sources

arXiv2026

Cambria: Resource Abstraction for Parametrized Algebraic Effects and Handlers

The algebraic effects and handlers paradigm separates the concerns of the interface and implementation of computational effects in programming languages. We present Cambria, a language that extends this framework to the parametrized setting. Effect signatures may use abstract parameter types that are instantiated by the handler along with the operation implementations. Parameters abstract over resources, such as memory locations or thread IDs, permitting algebraic effects to encode dynamic allocation. They are first-class in the type system but erased at runtime, requiring no coercions or type-directed reduction. We prove parametricity via a step-indexed logical relation, formalizing the abstraction guarantee provided by parametrized handlers. We also establish type safety and classify the annotations needed for completeness of the type inference algorithm. We demonstrate Cambria's practicality with a working implementation and provide examples including local state, P\'olya's urn, and concurrent thread management. The last is a parametrized effect whose abstract thread IDs are shared between concurrent computations, going beyond standard instances. Cambria is the first calculus with user-defined resource-allocating effects that guarantees, via parametricity, that client code cannot depend on how a handler represents its resources.

cs.PL↗

arXiv2026

Hippogriff: a semantic approach to uniting core and modules

In this paper we introduce Hippogriff, a language with a module system that unifies syntax between the core level and the module level. Hippogriff's type theory is dependent, with modularity features enabled via a universe of small types, but Hippogriff still supports general recursion without making typechecking nonterminating. This paper contains two halves. In the first half, we describe Hippogriff and its implementation. In the second half, we build categorical semantics for our use of dependent types that justify the use of general recursion at the value level. Specifically, we use an extension of the second-order generalized algebraic theory framework to include a synthetic phase distinction, and this allows us to make a mathematical connection between dependent type theories and split-context type theories (like System F). Included as supplements are a Haskell implementation of Hippogriff and an appendix describing the full type theory of Hippogriff using a second-order generalized algebraic theory with phase distinction.

cs.PL↗

arXiv2026

LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programs

Hamiltonian Monte Carlo (HMC) is a successful generic inference method in probabilistic programming, but in its ordinary formulation it needs gradients and finite-dimensional parameter spaces. In Haskell, lazy evaluation lets probabilistic programs express stochastic processes and other non-parametric Bayesian models over implicit infinite-dimensional spaces. This paper develops new formulations of gradient-based HMC for this infinite-dimensional setting, via lazy evaluation. For automatic differentiation, we provide an analysis based on a new notion of "piecewise analytic under cylindrical analytic partition" (PACAP), to show that even if a program is infinite-dimensional and defined lazily, the gradient of the likelihood function is finitely supported. For the Monte Carlo method itself, we develop several HMC variants and a No-U-Turn Sampler that operate over the infinite-dimensional parameter space but are still productive because of lazy evaluation. Experiments cover Gaussian mixture clustering, random walks, and piecewise-constant regression with Poisson-process changepoints.

stat.ML↗

arXiv2026

Imprecise Probabilistic Programming, Precisely: Credal Sets via Graded Monads, BDDs, and Semiring-Parametric Inference (Functional Pearl)

Imprecise probability generalizes standard probability theory by replacing a single distribution with a convex set of possible distributions. We show that this generalization requires no change to the standard BDD compilation and weighted model counting pipeline used by discrete probabilistic languages. An imprecise coin flip is simply a BDD variable whose weight is left free rather than fixed. We introduce Imp, a Haskell-embedded DSL for imprecise probabilistic programming. A graded monad, indexed by finite sets of named sources of epistemic uncertainty, restores the commutativity that the standard convex powerset monad lacks, and GHC's type system enforces this at compile time. Weighted model counting is parametric in the semiring, so the same compiled BDD supports exact, differentiable, and interval-bounded inference.

cs.PL↗

arXiv2026

Clock systems for stochastic and non-deterministic categorical systems theories

One of the characteristic features of categorical systems theory is that the behavior of systems can be characterized by certain morphisms into them. In other words, behaviors form a representable covariant functor to Set. And more generally, in the compositional setting, behaviors form a representable double functor to Span. Clock systems are convenient because behavior functors represented by clock systems are automatically well-behaved. It was previously not known whether stochastic and non-deterministic systems theories have clock systems. In this paper, we show that indeed they do have clock systems. Moreover, the clock systems for non-deterministic systems point to generalized notions of behavior for non-linear time.

math.CT↗

arXiv2026

Likelihood Hacking in Probabilistic Program Synthesis

When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likelihood reward by producing programs whose data distribution fails to normalise instead of fitting the data better. We call this failure likelihood hacking (LH). We formalise LH in a core probabilistic programming language (PPL) and give sufficient syntactic conditions for its prevention, proving that a safe language fragment $\mathcal{L}_{\text{safe}}$ satisfying these conditions cannot produce likelihood-hacking programs. Empirically, we show that GRPO-trained models generating PyMC code discover LH exploits within the first few training steps, driving violation rates well above the untrained-model baseline. We implement $\mathcal{L}_{\text{safe}}$'s conditions as $\texttt{SafeStan}$, a LH-resistant modification of Stan, and show empirically that it suppresses LH under optimisation pressure. These results show that language-level safety constraints are both theoretically grounded and effective in practice for automated Bayesian model discovery.

cs.LG↗

arXiv2026

An Equational Axiomatization of Dynamic Threads via Algebraic Effects: Presheaves on Finite Relations, Labelled Posets, and Parameterized Algebraic Theories

We use the theory of algebraic effects to give a complete equational axiomatization for dynamic threads. Our method is based on parameterized algebraic theories, which give a concrete syntax for strong monads on functor categories, and are a convenient framework for names and binding. Our programs are built from the key primitives `fork' and `wait'. `Fork' creates a child thread and passes its name (thread ID) to the parent thread. `Wait' allows us to wait for given child threads to finish. We provide a parameterized algebraic theory built from fork and wait, together with basic atomic actions and laws such as associativity of `fork'. Our equational axiomatization is complete in two senses. First, for closed expressions, it completely captures equality of labelled posets (pomsets), an established model of concurrency: model complete. Second, any two open expressions are provably equal if they are equal under all closing substitutions: syntactically complete. The benefit of algebraic effects is that the semantic analysis can focus on the algebraic operations of fork and wait. We then extend the analysis to a simple concurrent programming language by giving operational and denotational semantics. The denotational semantics is built using the methods of parameterized algebraic theories and we show that it is sound, adequate, and fully abstract at first order for labelled-poset observations.

cs.PL↗

arXiv2025

A causal Markov category with Kolmogorov products

In Fritz & Rischel, Infinite products and zero-one laws in categorical probability, the problem was posed of finding an interesting Markov category which is causal and has all (small) Kolmogorov products (there Problem 6.7). Here we give an example where the deterministic subcategory is the category of Stone spaces (i.e. the dual of the category of Boolean algebras) and the kernels correspond to a restricted class of Kleisli arrows for the Radon monad. We look at this from two perspectives. First via pro-completions and Stone spaces directly. Second via duality with Boolean and algebras and effect algebras.

math.CT↗

arXiv2025

The Relative Monadic Metalanguage

Relative monads provide a controlled view of computation. We generalise the monadic metalanguage to a relative setting and give a complete semantics with strong relative monads. Adopting this perspective, we generalise two existing program calculi from the literature. We provide a linear-non-linear language for graded monads, LNL-RMM, along with a semantic proof that it is a conservative extension of the graded monadic metalanguage. Additionally, we provide a complete semantics for the arrow calculus, showing it is a restricted relative monadic metalanguage. This motivates the introduction of ARMM, a computational lambda calculus-style language for arrows that conservatively extends the arrow calculus.

cs.PL↗

arXiv2025

Modular abstract syntax trees (MAST): substitution tensors with second-class sorts

We adapt Fiore, Plotkin, and Turi's treatment of abstract syntax with binding, substitution, and holes to account for languages with second-class sorts. These situations include programming calculi such as the Call-by-Value lambda-calculus (CBV) and Levy's Call-by-Push-Value (CBPV). Prohibiting second-class sorts from appearing in variable contexts changes the characterisation of the abstract syntax from monoids in monoidal categories to actions in actegories. We reproduce much of the development through bicategorical arguments. We apply the resulting theory by proving substitution lemmata for varieties of CBV.

cs.PL↗

arXiv2025

Deriving Program Logics from Distributive Monoidal Categories

We derive multiple program logics - including correctness, incorrectness, and relational Hoare logic - from the axioms of imperative categories: uniformly traced distributive copy-discard categories. Rules of program logics follow from the axioms of imperative categories. The algebra of guarded commands derived by the categorical structure generalises guarded Kleene algebras with tests.

cs.LO↗

arXiv2025

Uncertainty-Aware Step-wise Verification with Generative Reward Models

Complex multi-step reasoning tasks, such as solving mathematical problems, remain challenging for large language models (LLMs). While outcome supervision is commonly used, process supervision via process reward models (PRMs) provides intermediate rewards to verify step-wise correctness in solution traces. However, as proxies for human judgement, PRMs suffer from reliability issues, including susceptibility to reward hacking. In this work, we propose leveraging uncertainty quantification (UQ) to enhance the reliability of step-wise verification with generative reward models for mathematical reasoning tasks. We introduce CoT Entropy, a novel UQ method that outperforms existing approaches in quantifying a PRM's uncertainty in step-wise verification. Our results demonstrate that incorporating uncertainty estimates improves the robustness of judge-LM PRMs, leading to more reliable verification.

cs.CL↗

arXiv2024

Compositional imprecise probability

Imprecise probability is concerned with uncertainty about which probability distributions to use. It has applications in robust statistics and machine learning. We look at programming language models for imprecise probability. Our desiderata are that we would like our model to support all kinds of composition, categorical and monoidal; in other words, guided by dataflow diagrams. Another equivalent perspective is that we would like a model of synthetic probability in the sense of Markov categories. Imprecise probability can be modelled in various ways, with the leading monad-based approach using convex sets of probability distributions. This model is not fully compositional because the monad involved is not commutative, meaning it does not have a proper monoidal structure. In this work, we provide a new fully compositional account. The key idea is to name the non-deterministic choices. To manage the renamings and disjointness of names, we use graded monads. We show that the resulting compositional model is maximal and relate it with the earlier monadic approach, proving that we obtain tighter bounds on the uncertainty.

cs.PL↗

arXiv2024

Scoped Effects as Parameterized Algebraic Theories

Notions of computation can be modelled by monads. Algebraic effects offer a characterization of monads in terms of algebraic operations and equational axioms, where operations are basic programming features, such as reading or updating the state, and axioms specify observably equivalent expressions. However, many useful programming features depend on additional mechanisms such as delimited scopes or dynamically allocated resources. Such mechanisms can be supported via extensions to algebraic effects including scoped effects and parameterized algebraic theories. We present a fresh perspective on scoped effects by translation into a variation of parameterized algebraic theories. The translation enables a new approach to equational reasoning for scoped effects and gives rise to an alternative characterization of monads in terms of generators and equations involving both scoped and algebraic operations. We demonstrate the power of our fresh perspective by way of equational characterizations of several known models of scoped effects.

cs.PL↗

arXiv2023

Probabilistic Programming with Exact Conditions

We spell out the paradigm of exact conditioning as an intuitive and powerful way of conditioning on observations in probabilistic programs. This is contrasted with likelihood-based scoring known from languages such as Stan. We study exact conditioning in the cases of discrete and Gaussian probability, presenting prototypical languages for each case and giving semantics to them. We make use of categorical probability (namely Markov and CD categories) to give a general account of exact conditioning which avoids limits and measure theory, instead focusing on restructuring dataflow and program equations. The correspondence between such categories and a class of programming languages is made precise by defining the internal language of a CD category.

cs.PL↗

arXiv2023

Probabilistic programming interfaces for random graphs: Markov categories, graphons, and nominal sets

We study semantic models of probabilistic programming languages over graphs, and establish a connection to graphons from graph theory and combinatorics. We show that every well-behaved equational theory for our graph probabilistic programming language corresponds to a graphon, and conversely, every graphon arises in this way. We provide three constructions for showing that every graphon arises from an equational theory. The first is an abstract construction, using Markov categories and monoidal indeterminates. The second and third are more concrete. The second is in terms of traditional measure theoretic probability, which covers 'black-and-white' graphons. The third is in terms of probability monads on the nominal sets of Gabbay and Pitts. Specifically, we use a variation of nominal sets induced by the theory of graphs, which covers Erd\H{o}s-R\'enyi graphons. In this way, we build new models of graph probabilistic programming from graphons.

cs.PL↗

arXiv2023

Denotational semantics for languages for inference: semirings, monads, and tensors

Computational effects are commonly modelled by monads, but often a monad can be presented by an algebraic theory of operations and equations. This talk is about monads and algebraic theories for languages for inference, and their connections to semirings and tensors. A basic class of examples of algebraic theories comes from considering the theory of modules for a semiring, e.g. the theory of unnormalized distributions, where the semiring is that of the non-negative real numbers. We propose that an interesting perspective is given by studying theories via semirings, and to this end explore several examples of subtheories of module theories, mostly relating to probability. Our main contribution concerns the commutative combination of effects, as studied by Hyland, Plotkin and Power: we observe that while the semiring tensor does not in general determine the tensor of subtheories of module theories, it still does in several fundamental probabilistic examples.

cs.LO↗

arXiv2023

Proceedings of the Sixth International Conference on Applied Category Theory 2023

The Sixth International Conference on Applied Category Theory took place at the University of Maryland, 31 July -- 4 August 2023. This conference follows the previous meetings at Leiden (2018), Oxford (2019), MIT (2020, fully online), Cambridge (2021) and Glasgow (2022). The conference comprised contributed talks, a poster session, an industry showcase session, and a session where junior researchers who had attended the Adjoint School presented the results of their research at the school. Information regarding the conference may be found at (https://act2023.github.io/). The contributions to ACT2023 ranged from pure to applied and included contributions in a wide range of disciplines in science and engineering. Submission to ACT 2023 had three tracks: extended abstracts, software demonstrations, and proceedings. Only papers submitted to the proceedings track were considered for publication in this volume.

cs.LO↗