arXiv subjects

Kevin Batz

Publications and source records attributed to Kevin Batz.

At least 19 recordsLinked to original sources

arXiv2026

Type-Directed Discretization of Probabilistic Programs (Extended Version)

We study exact discretization as a semantics-preserving transformation for recursive, higher-order probabilistic programs with continuous distributions. We target programs where continuous values are compared against finitely many constants, so exact inference reduces to a discrete problem. Our central technical contribution is a non-local, type-directed analysis that infers where continuous values can be partitioned into finitely many observationally relevant regions, then rewrites sampling and comparison behavior over those regions. We call this transformation Slice. Because this construction is global and type-directed, correctness requires reasoning beyond the local syntax: we formalize the transformation and prove soundness for boolean queries using a coupling-style logical relations argument over operational semantics. As an application, transformed programs can be executed by discrete engines such as Dice, Roulette, and Storm. Our empirical evaluation shows two complementary strengths of Slice when paired with discrete backends: it enables exact inference for challenging continuous programs that lie beyond the reach of previous exact systems, and, on benchmarks where direct comparison is possible, it is competitive with state-of-the-art exact inference systems for continuous programs.

cs.PL↗

arXiv2026

Multiobjective Preexpectation Reasoning for Probabilistic Programs

Probabilistic programs with nondeterminism model planning problems in which a strategy resolves the nondeterminism to optimize an expected outcome. We study the multiobjective setting, optimizing several outcomes at once along a Pareto front, and provide a deductive, program-level account of strategy synthesis. Its core is a multiobjective preexpectation transformer mapping a tuple of postexpectations to the set of simultaneously achievable values, an element of the convex Hoare powerdomain. It conservatively extends weakest preexpectations and lifts standard loop rules. We develop rules to synthesize witnessing strategies as mixed determinizations that randomize over non-probabilistic determinizations. We prove the transformer and synthesis rules sound against an operational MDP semantics, without requiring a finite state space: our approach can be seen as a symbolic approach - at program level - for multiobjective optimization over infinite MDPs. We demonstrate our machinery using various case studies.

cs.PL↗

arXiv2026

A Fast Quantitative Analyzer for NetKAT

When designing a network, engineers must navigate trade-offs (e.g., one topology offers more aggregate bandwidth, another lower latency or better resilience) that demand reasoning about quantitative properties. We present a fast analyzer for quantitative network properties based on weighted NetKAT (wNetKAT), a domain-specific language that provides a semantic foundation for quantitative reasoning by modeling network behavior using weights drawn from a semiring. At the core of our development is the design of a symbolic data structure -- weighted symbolic packet programs (wSPPs) -- that compactly represent the semantics of weighted policies, for which a direct implementation would be intractable. We show how to compute all policy constructs symbolically; unsurprisingly, the crux is Kleene star, for which we design a tailored algorithm. We further develop trace-carrying Pareto semirings, which compute multi-objective frontiers together with the network paths that realize them. We formalize the development in Lean and provide an optimized Rust implementation. Being parametric on a semiring, our implementation covers both classical and quantitative analyses: we show that it is competitive with KATch, a heavily optimized Boolean-reachability verifier, and orders of magnitude faster than McNetKAT and Storm on probabilistic analyses. A case study comparing Fat-tree and Jellyfish data-center topologies shows the framework supports multi-objective design-time analysis.

cs.PL↗

arXiv2026

Scalable Probabilistic Program Verification via Typed Extended Decision Diagrams

Weakest pre-expectations are the probabilistic program analogue to weakest preconditions in classical programs. Deductive verification approaches aim to establish bounds on these quantitative expectations. Their automation has been successful in analysing a variety of discrete probabilistic programs. Key routines in that automation require reasoning about (partially unrolled) loops, however, the logical representation of weakest pre-expectations on such unrollings often explodes. In this paper, we develop typed extended decision diagrams (TEDDs), inspired by various extensions to binary decision diagrams. We demonstrate computing WPs represented as TEDDs, SMT-based pruning to further shrink their representation, and we lift some proof rules to operate on TEDDs. Finally, we demonstrate that TEDDs boost the scalability of deductive probabilistic program verification by orders of magnitudes over the state of the art.

cs.PL↗

arXiv2026

Caesar: A Deductive Verifier for Probabilistic Programs

Caesar is a deductive verifier for probabilistic programs. At its core lies HeyVL, a quantitative intermediate verification language based on the real-valued logic HeyLo. HeyVL allows users to express a probabilistic program, its specifications, and proof rules in a programming-language style, so that new proof rules can be easily integrated into the verifier. Caesar translates HeyVL programs into verification conditions, which are then checked using the Z3 SMT solver. It also includes a backend based on probabilistic model checking for a subset of HeyVL. We report on the results of five years of development of Caesar, highlighting its main features and architecture. In particular, we describe recent improvements such as additional proof rules, a model-checking backend, and better diagnostics.

cs.PL↗

arXiv2026

SMT-Based Active Learning of Weighted Automata

We present an SMT-based active learning algorithm for nondeterministic weighted automata (WFAs) as a practical and robust alternative to Hankel/L*-style methods. Our algorithm is parametric in a given semiring and, if it terminates, guaranteed to produce minimal WFAs. We prove partial correctness and provide a sufficient termination condition, which in particular implies termination for all finite semirings. Our extensive experimental evaluation shows that our algorithm is capable of learning numerous minimal WFAs over both finite and infinite semirings, vastly outperforms a naive baseline, and is competitive with a state-of-the-art algorithm while producing significantly smaller automata and requiring less interaction with the teacher.

cs.FL↗

arXiv2026

The Algebra of Iterative Constructions

Fixed points are a recurring theme in computer science and are often constructed as limits of suitably seeded fixed point iterations. We present the algebra of iterative constructions (AIC) -- a purely algebraic approach to reasoning about fixed point iterations of continuous endomaps on complete lattices. AIC allows derivations of constructive fixed point theorems via equational logic and avoids explicit computations with indices. For example, $$F \,\Diamond\, F^{*} \bot = \Diamond\, F^{*} \bot$$ states in AIC that $\sup_n F^n (\bot)$ -- a construction known from the Kleene fixed point theorem -- is a fixed point of $F$. We demonstrate the applicability of AIC by providing algebraic proofs of several well- and less-well-known fixed point theorems: Among others, we prove the Tarski-Kantorovich principle -- a generalization of the Kleene fixed point theorem -- as well as a fixed point-theoretic generalization of $k$-induction -- a technique used in software verification. We moreover present a novel fixed point theorem. Under suitable continuity conditions, it obtains fixed points as lattice-theoretic limit inferiors and limit superiors of iterating an endomap on an arbitrary seed element. We have mechanized our algebra in Isabelle/HOL. Isabelle's sledgehammer tool is able to find proofs of the above fixed point theorems fully automatically. Finally, we investigate the completeness of our axiomatization of AIC. We prove that our finite set of finitary axioms is (a) sound but incomplete for standard models of AIC (sequences of elements from a complete lattice) and that (b) a different finite set of infinitary axioms is complete. We also prove that infinitary axioms are unavoidable: there exists no complete axiomatization of standard models given by finitely many finitary axioms.

cs.LO↗

arXiv2026

Weighted NetKAT: A Programming Language For Quantitative Network Verification

We introduce weighted NetKAT, a domain-specific language for modeling and verifying quantitative network properties. The language is parametric on a semiring, enabling the treatment of a wide range of quantities in a uniform way. We provide a denotational semantics and an equivalent operational semantics, the latter based on a novel model of weighted NetKAT automata (WNKA) capturing the stateful behavior of our language. With WNKA, we obtain a class of generic decision procedures for reasoning about quantitative safety and reachability in a fully automatic way, even in the presence of possibly unbounded iteration. We demonstrate the applicability of our framework in a case study using Internet2's Abilene network as the underlying topology.

cs.PL↗

arXiv2026

Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops (Extended Version)

A fundamental computational task in probabilistic programming is to infer a program's output (posterior) distribution from a given initial (prior) distribution. This problem is challenging, especially for expressive languages that feature loops or unbounded recursion. While most of the existing literature focuses on statistical approximation, in this paper we address the problem of mathematically exact inference. To achieve this for programs with loops, we rely on a relatively underexplored type of probabilistic loop invariant, which is linked to a loop's so-called occupation measure. The occupation measure associates program states with their expected number of visits, given the initial distribution. Based on this, we derive the notion of an occupation invariant. Such invariants are essentially dual to probabilistic martingales, the predominant technique for formal probabilistic loop analysis in the literature. A key feature of occupation invariants is that they can take the initial distribution into account and often yield a proof of positive almost sure termination as a by-product. Finally, we present an automatic, template-based invariant synthesis approach for occupation invariants by encoding them as generating functions. The approach is implemented and evaluated on a set of benchmarks.

cs.PL↗

arXiv2025

Verifying Sampling Algorithms via Distributional Invariants

This paper presents a Hoare-like veri cation framework for discrete probabilistic programs that we apply to two non-trivial sampling algorithms: Lumbroso's Fast Dice Roller and Saad et al.'s Fast Loaded Dice Roller. These algorithms have previously resisted formal veri cation due to their probabilistic nature, intricate loop structure, and parametric input. Our approach complements existing proof rules based on inductive distributional invariants, enabling us to verify both total and partial correctness of the two algorithms.

cs.LO↗

arXiv2025

Foundations for Deductive Verification of Continuous Probabilistic Programs: From Lebesgue to Riemann and Back

We lay out novel foundations for the computer-aided verification of guaranteed bounds on expected outcomes of imperative probabilistic programs featuring (i) general loops, (ii) continuous distributions, and (iii) conditioning. To handle loops we rely on user-provided quantitative invariants, as is well established. However, in the realm of continuous distributions, invariant verification becomes extremely challenging due to the presence of integrals in expectation-based program semantics. Our key idea is to soundly under- or over-approximate these integrals via Riemann sums. We show that this approach enables the SMT-based invariant verification for programs with a fairly general control flow structure. On the theoretical side, we prove convergence of our Riemann approximations, and establish coRE-completeness of the central verification problems. On the practical side, we show that our approach enables to use existing automated verifiers targeting discrete probabilistic programs for the verification of programs involving continuous sampling. Towards this end, we implement our approach in the recent quantitative verification infrastructure Caesar by encoding Riemann sums in its intermediate verification language. We present several promising case studies.

cs.LO↗

arXiv2025

Quantifier Elimination and Craig Interpolation, Quantitatively

Quantifier elimination (QE) and Craig interpolation (CI) are central to various state-of-the-art automated approaches to hardware and software verification. They are rooted in the Boolean setting and are successful for, e.g., first-order theories such as linear rational arithmetic. What about their applicability in the quantitative setting where formulae evaluate to numbers and quantitative supremum/infimum quantifiers are the natural counterparts of Boolean quantifiers? Applications include establishing quantitative properties of programs, such as bounds on expected outcomes of probabilistic programs featuring nondeterminism, and analyzing the flow of information through programs. In this paper, we present, to the best of our knowledge, the first QE algorithm for possibly unbounded, $\infty$- or $-\infty$-valued, or discontinuous piecewise linear quantities. They are the quantitative counterpart to linear rational arithmetic, and they are a popular quantitative assertion language for probabilistic program verification. We provide rigorous soundness proofs as well as upper space complexity bounds. Moreover, we present two applications of our QE algorithm. First, our algorithm yields a quantitative CI theorem: given arbitrary piecewise linear quantities $f$ and $g$ with $f \models g$, both the strongest and the weakest Craig interpolant of $f$ and $g$ are quantifier-free and effectively constructible. Second, we apply our QE algorithm to compute minimal and maximal expected outcomes of loop-free probabilistic programs featuring unbounded nondeterminism.

cs.LO↗

arXiv2024

J-P: MDP. FP. PP.: Characterizing Total Expected Rewards in Markov Decision Processes as Least Fixed Points with an Application to Operational Semantics of Probabilistic Programs (Technical Report)

Markov decision processes (MDPs) with rewards are a widespread and well-studied model for systems that make both probabilistic and nondeterministic choices. A fundamental result about MDPs is that their minimal and maximal expected rewards satisfy Bellmann's optimality equations. For various classes of MDPs - notably finite-state MDPs, positive bounded models, and negative models - expected rewards are known to be the least solution of those equations. However, these classes of MDPs are too restrictive for probabilistic program verification. In particular, they assume that all rewards are finite. This is already not the case for the expected runtime of a simple probabilisitic program modeling a 1-dimensional random walk. In this paper, we develop a generalized least fixed point characterization of expected rewards in MDPs without those restrictions. Furthermore, we demonstrate how said characterization can be leveraged to prove weakest-preexpectation-style calculi sound with respect to an operational MDP model.

cs.LO↗

arXiv2023

Programmatic Strategy Synthesis: Resolving Nondeterminism in Probabilistic Programs

We consider imperative programs that involve both randomization and pure nondeterminism. The central question is how to find a strategy resolving the pure nondeterminism such that the so-obtained determinized program satisfies a given quantitative specification, i.e., bounds on expected outcomes such as the expected final value of a program variable or the probability to terminate in a given set of states. We show how memoryless and deterministic (MD) strategies can be obtained in a semi-automatic fashion using deductive verification techniques. For loop-free programs, the MD strategies resulting from our weakest precondition-style framework are correct by construction. This extends to loopy programs, provided the loops are equipped with suitable loop invariants - just like in program verification. We show how our technique relates to the well-studied problem of obtaining strategies in countably infinite Markov decision processes with reachability-reward objectives. Finally, we apply our technique to several case studies.

cs.LO↗

arXiv2023

A Deductive Verification Infrastructure for Probabilistic Programs (Extended Version)

This paper presents a quantitative program verification infrastructure for discrete probabilistic programs. Our infrastructure can be viewed as the probabilistic analogue of Boogie: its central components are an intermediate verification language (IVL) together with a real-valued logic. Our IVL provides a programming-language-style for expressing verification conditions whose validity implies the correctness of a program under investigation. As our focus is on verifying quantitative properties such as bounds on expected outcomes, expected run-times, or termination probabilities, off-the-shelf IVLs based on Boolean first-order logic do not suffice. Instead, a paradigm shift from the standard Boolean to a real-valued domain is required. Our IVL features quantitative generalizations of standard verification constructs such as assume- and assert-statements. Verification conditions are generated by a weakest-precondition-style semantics, based on our real-valued logic. We show that our verification infrastructure supports natural encodings of numerous verification techniques from the literature. With our SMT-based implementation, we automatically verify a variety of benchmarks. To the best of our knowledge, this establishes the first deductive verification infrastructure for expectation-based reasoning about probabilistic programs.

cs.PL↗

arXiv2022

A Calculus for Amortized Expected Runtimes

We develop a weakest-precondition-style calculus \`a la Dijkstra for reasoning about amortized expected runtimes of randomized algorithms with access to dynamic memory - the $\textsf{aert}$ calculus. Our calculus is truly quantitative, i.e. instead of Boolean valued predicates, it manipulates real-valued functions. En route to the $\textsf{aert}$ calculus, we study the $\textsf{ert}$ calculus for reasoning about expected runtimes of Kaminski et al. [2018] extended by capabilities for handling dynamic memory, thus enabling compositional and local reasoning about randomized data structures. This extension employs runtime separation logic, which has been foreshadowed by Matheja [2020] and then implemented in Isabelle/HOL by Haslbeck [2021]. In addition to Haslbeck's results, we further prove soundness of the so-extended $\textsf{ert}$ calculus with respect to an operational Markov decision process model featuring countably-branching nondeterminism, provide intuitive explanations, and provide proof rules enabling separation logic-style verification for upper bounds on expected runtimes. Finally, we build the so-called potential method for amortized analysis into the $\textsf{ert}$ calculus, thus obtaining the $\textsf{aert}$ calculus. Since one needs to be able to handle changes in potential which can be negative, the $\textsf{aert}$ calculus needs to be capable of handling signed random variables. A particularly pleasing feature of our solution is that, unlike e.g. Kozen [1985], we obtain a loop rule for our signed random variables, and furthermore, unlike e.g. Kaminski and Katoen [2017], the $\textsf{aert}$ calculus makes do without the need for involved technical machinery keeping track of the integrability of the random variables. Finally, we present case studies, including a formal analysis of a randomized delete-insert-find-any set data structure [Brodal et al. 1996].

cs.LO↗

arXiv2022

Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants

Essential tasks for the verification of probabilistic programs include bounding expected outcomes and proving termination in finite expected runtime. We contribute a simple yet effective inductive synthesis approach for proving such quantitative reachability properties by generating inductive invariants on source-code level. Our implementation shows promise: It finds invariants for (in)finite-state programs, can beat state-of-the-art probabilistic model checkers, and is competitive with modern tools dedicated to invariant synthesis and expected runtime reasoning.

cs.LO↗

arXiv2022

Weighted Programming

We study weighted programming, a programming paradigm for specifying mathematical models. More specifically, the weighted programs we investigate are like usual imperative programs with two additional features: (1) nondeterministic branching and (2) weighting execution traces. Weights can be numbers but also other objects like words from an alphabet, polynomials, formal power series, or cardinal numbers. We argue that weighted programming as a paradigm can be used to specify mathematical models beyond probability distributions (as is done in probabilistic programming). We develop weakest-precondition- and weakest-liberal-precondition-style calculi \`{a} la Dijkstra for reasoning about mathematical models specified by weighted programs. We present several case studies. For instance, we use weighted programming to model the ski rental problem - an optimization problem. We model not only the optimization problem itself, but also the best deterministic online algorithm for solving this problem as weighted programs. By means of weakest-precondition-style reasoning, we can determine the competitive ratio of the online algorithm on source code level.

cs.PL↗