arXiv subjects

Jurriaan Rot

Publications and source records attributed to Jurriaan Rot.

At least 19 recordsLinked to original sources

arXiv2026

GKAT with Hoare Hypotheses

Guarded Kleene Algebra with Tests (GKAT) is a variant of Kleene algebra which allows for reasoning about simple imperative programs, and which features a decision procedure for program equivalence in nearly linear time. In the current paper, we address the challenge of reasoning under assumptions about these programs. In particular, we develop a form of Hoare hypotheses, which allow modelling basic domain knowledge on pre- and post-conditions of uninterpreted basic programs, and which are well-developed for classical Kleene algebra but not yet for GKAT. We show that the resulting axiomatisation is sound and complete. We then extend Hoare hypotheses to the more general form of word hypotheses. Based on an automata-theoretic approach, we show that equivalence of GKAT under word hypotheses is as efficiently decidable as for plain GKAT.

cs.LO↗

arXiv2026

Correct and Complete Symbolic Execution for Free

Symbolic execution is a powerful technique for program analysis. However, the formal semantics underlying symbolic execution is often developed on an ad-hoc basis and decoupled from the concrete semantics of the programming language. To overcome this issue, we introduce symbolic SOS: a rule format that allows us to simultaneously specify concrete and symbolic operational semantics. We prove that symbolic semantics, when generated from symbolic SOS, is both correct and complete with respect to the corresponding concrete semantics. The approach relies only on an algebraic signature of the source language, and is thus language-independent.

cs.PL↗

arXiv2026

Error-awareness Accelerates Active Automata Learning

Active automata learning (AAL) algorithms can learn a behavioral model of a system from interacting with it. The primary challenge remains scaling to larger models, in particular in the presence of many possible inputs to the system. Modern AAL algorithms fail to scale even if, in every state, most inputs lead to errors. In various challenging problems from the literature, these errors are observable, i.e., they emit a known error output. Motivated by these problems, we study learning these systems more efficiently. Further, we consider various degrees of knowledge about which inputs are non-error producing at which state. For each level of knowledge, we provide a matching adaptation of the state-of-the-art AAL algorithm L# to make the most of this domain knowledge. Our empirical evaluation demonstrates that the methods accelerate learning by orders of magnitude with strong but realistic domain knowledge to a single order of magnitude with limited domain knowledge.

cs.LG↗

arXiv2026

Incremental Fingerprinting in an Open World

Network protocol fingerprinting is used to identify a protocol implementation by analyzing its input-output behavior. Traditionally, fingerprinting operates under a closed-world assumption, where models of all implementations are assumed to be available. However, this assumption is unrealistic in practice. When this assumption does not hold, fingerprinting results in numerous misclassifications without indicating that a model for an implementation is missing. Therefore, we introduce an open-world variant of the fingerprinting problem, where not all models are known in advance. We propose an incremental fingerprinting approach to solve the problem by combining active automata learning with closed-world fingerprinting. Our approach quickly determines whether the implementation under consideration matches an available model using fingerprinting and conformance checking. If no match is found, it learns a new model by exploiting the structure of available models. We prove the correctness of our approach and improvements in asymptotic complexity compared to naive baselines. Moreover, experimental results on a variety of protocols demonstrate a significant reduction in misclassifications and interactions with these black-boxes.

cs.CR↗

arXiv2025

Initial Algebra Correspondence under Reachability Conditions

Suitable reachability conditions can make two different fixed point semantics of a transition system coincide. For instance, the total and partial expected reward semantics on Markov chains (MCs) coincide whenever the MC at hand is almost surely reachable. In this paper, we present a unifying framework for such reachability conditions that ensures the correspondence of two different semantics. Our categorical framework naturally induces an abstract reachability condition via a suitable adjunction, which allows us to prove coincidences of fixed points, and more generally of initial algebras. We demonstrate the generality of our approach by instantiating several examples, including the almost surely reachability condition for MCs, and the unambiguity condition of automata. We further study a canonical construction of our instance for Markov decision processes by pointwise Kan extensions.

cs.LO↗

arXiv2025

Constructing Witnesses for Lower Bounds on Behavioural Distances

Behavioural distances provide a robust alternative to notions of equivalence such as bisimilarity in the context of probabilistic transition systems. They can be defined as least fixed points, whose universal property allows us to exhibit upper bounds on the distance between states, showing them to be at most some distance apart. In this paper, we instead consider the problem of bounding distances from below, showing states to be at least some distance apart. Contrary to upper bounds, it is possible to reason about lower bounds inductively. We exploit this by giving an inductive derivation system for lower bounds on an existing definition of behavioural distance for labelled Markov chains. This is inspired by recent work on apartness as an inductive counterpart to bisimilarity. Proofs in our system will be shown to closely match the behavioural distance by soundness and (approximate) completeness results. We further provide a constructive correspondence between our derivation system and formulas in a modal logic with quantitative semantics. This logic was used in recent work of Rady and van Breugel to construct evidence for lower bounds on behavioural distances. Our constructions provide smaller witnessing formulas in many examples.

cs.LO↗

arXiv2025

Trees in Coalgebra from Generalized Reachability

An automaton is called reachable if every state is reachable from the initial state. This notion has been generalized coalgebraically in two ways: first, via a universal property on pointed coalgebras, namely, that a reachable coalgebra has no proper subcoalgebras; and second, a coalgebra is reachable if it arises as the union of an iterative computation of successor states, starting from the initial state. In the current paper, we present corresponding universal properties and iterative constructions for trees. The universal property captures when a coalgebra is a tree, namely, when it has no proper tree unravellings. The iterative construction unravels an arbitrary coalgebra to a tree. We show that this yields the expected notion of tree for a variety of standard examples. We obtain our characterization of trees by first generalizing the previous theory of reachable coalgebras and of a minimal object in a category, related to projectivity. Surprisingly, both the universal property and the iterative construction for trees arise as instances of this generalized notion of reachability. Our iterative construction works for all analytic set functors.

cs.LO↗

arXiv2025

Learning Automata with Name Allocation

Automata over infinite alphabets have emerged as a convenient computational model for processing structures involving data, such as nonces in cryptographic protocols or data values in XML documents. We introduce active learning methods for bar automata, a species of automata that process finite data words represented as bar strings, which are words with explicit name binding letters. Bar automata have pleasant algorithmic properties. We develop a framework in which every learning algorithm for standard deterministic or nondeterministic finite automata over finite alphabets can be used to learn bar automata, with a query complexity determined by that of the chosen learner. The technical key to our approach is the algorithmic handling of $\alpha$-equivalence of bar strings, which allows bridging the gap between finite and infinite alphabets. The principles underlying our framework are generic and also apply to bar B\"uchi automata and bar tree automata, leading to the first active learning methods for data languages of infinite words and finite trees.

cs.FL↗

arXiv2024

Intrinsically Correct Sorting in Cubical Agda

The paper "Sorting with Bialgebras and Distributive Laws" by Hinze et al. uses the framework of bialgebraic semantics to define sorting algorithms. From distributive laws between functors they construct pairs of sorting algorithms using both folds and unfolds. Pairs of sorting algorithms arising this way include insertion/selection sort and quick/tree sort. We extend this work to define intrinsically correct variants in cubical Agda. Our key idea is to index our data types by multisets, which concisely captures that a sorting algorithm terminates with an ordered permutation of its input list. By lifting bialgebraic semantics to the indexed setting, we obtain the correctness of sorting algorithms purely from the distributive law.

cs.LO↗

arXiv2024

Complete Test Suites for Automata in Monoidal Closed Categories

Conformance testing of automata is about checking the equivalence of a known specification and a black-box implementation. An important notion in conformance testing is that of a complete test suite, which guarantees that if an implementation satisfying certain conditions passes all tests, then it is equivalent to the specification. We introduce a framework for proving completeness of test suites at the general level of automata in monoidal closed categories. Moreover, we provide a generalization of a classical conformance testing technique, the W-method. We demonstrate the applicability of our results by recovering the W-method for deterministic finite automata, Moore machines, and Mealy machines, and by deriving new instances of complete test suites for weighted automata and deterministic nominal automata.

cs.FL↗

arXiv2024

Correspondences between codensity and coupling-based liftings, a practical approach

The Kantorovich distance is a widely used metric between probability distributions. The Kantorovich-Rubinstein duality states that it can be defined in two equivalent ways: as a supremum, based on non-expansive functions into [0, 1], and as an infimum, based on probabilistic couplings. Orthogonally, there are categorical generalisations of both presentations proposed in the literature, in the form of codensity liftings and what we refer to as coupling-based liftings. Both lift endofunctors on the category Set of sets and functions to that of pseudometric spaces, and both are parameterised by modalities from coalgebraic modal logic. A generalisation of the Kantorovich-Rubinstein duality has been more nebulous-it is known not to work in some cases. In this paper we propose a compositional approach for obtaining such generalised dualities for a class of functors, which is closed under coproducts and products. Our approach is based on an explicit construction of modalities and also applies to and extends known cases such as that of the powerset functor.

math.CT↗

arXiv2024

Relating Apartness and Branching Bisimulation Games

Geuvers and Jacobs (LMCS 2021) formulated the notion of apartness relation on state-based systems modelled as coalgebras. In this context apartness is formally dual to bisimilarity, and gives an explicit proof system for showing that certain states are not bisimilar. In the current paper, we relate apartness to another classical element of the theory of behavioural equivalences: that of turn-based two-player games. Studying both strong and branching bisimilarity, we show that winning configurations for the Spoiler player correspond to apartness proofs, for transition systems that are image-finite (in the case of strong bisimilarity) and finite (in the case of branching bisimilarity).

cs.LO↗

arXiv2024

Relational Connectors and Heterogeneous Bisimulations

While behavioural equivalences among systems of the same type, such as Park/Milner bisimilarity of labelled transition systems, are an established notion, a systematic treatment of relationships between systems of different type is currently missing. We provide such a treatment in the framework of universal coalgebra, in which the type of a system (nondeterministic, probabilistic, weighted, game-based etc.) is abstracted as a set functor: We introduce relational connectors among set functors, which induce notions of heterogeneous (bi)simulation among coalgebras of the respective types. We give a number of constructions on relational connectors. In particular, we identify composition and converse operations on relational connectors; we construct corresponding identity relational connectors, showing that the latter generalize the standard Barr extension of weak-pullback-preserving functors; and we introduce a Kantorovich construction in which relational connectors are induced from relations between modalities. For Kantorovich relational connectors, one has a notion of dual-purpose modal logic interpreted over both system types, and we prove a corresponding Hennessy-Milner-type theorem stating that generalized (bi)similarity coincides with theory inclusion on finitely-branching systems. We apply these results to a number of example scenarios involving labelled transition systems with different label alphabets, probabilistic systems, and input/output conformances.

cs.LO↗

arXiv2024

A Unifying Approach to Product Constructions for Quantitative Temporal Inference

Probabilistic programs are a powerful and convenient approach to formalise distributions over system executions. A classical verification problem for probabilistic programs is temporal inference: to compute the likelihood that the execution traces satisfy a given temporal property. This paper presents a general framework for temporal inference, which applies to a rich variety of quantitative models including those that arise in the operational semantics of probabilistic and weighted programs. The key idea underlying our framework is that in a variety of existing approaches, the main construction that enables temporal inference is that of a product between the system of interest and the temporal property. We provide a unifying mathematical definition of product constructions, enabled by the realisation that 1) both systems and temporal properties can be modelled as coalgebras and 2) product constructions are distributive laws in this context. Our categorical framework leads us to our main contribution: a sufficient condition for correctness, which is precisely what enables to use the product construction for temporal inference. We show that our framework can be instantiated to naturally recover a number of disparate approaches from the literature including, e.g., partial expected rewards in Markov reward models, resource-sensitive reachability analysis, and weighted optimization problems. Further, we demonstrate a product of weighted programs and weighted temporal properties as a new instance to show the scalability of our approach.

cs.LO↗

arXiv2024

State Matching and Multiple References in Adaptive Active Automata Learning

Active automata learning (AAL) is a method to infer state machines by interacting with black-box systems. Adaptive AAL aims to reduce the sample complexity of AAL by incorporating domain specific knowledge in the form of (similar) reference models. Such reference models appear naturally when learning multiple versions or variants of a software system. In this paper, we present state matching, which allows flexible use of the structure of these reference models by the learner. State matching is the main ingredient of adaptive L#, a novel framework for adaptive learning, built on top of L#. Our empirical evaluation shows that adaptive L# improves the state of the art by up to two orders of magnitude.

cs.LO↗

arXiv2024

A Categorical Approach to Coalgebraic Fixpoint Logic

We define a framework for incorporating alternation-free fixpoint logics into the dual-adjunction setup for coalgebraic modal logics. We achieve this by using order-enriched categories. We give a least-solution semantics as well as an initial algebra semantics, and prove they are equivalent. We also show how to place the alternation-free coalgebraic $\mu$-calculus in this framework, as well as PDL and a logic with a probabilistic dynamic modality.

cs.LO↗

arXiv2024

Proving Behavioural Apartness

Bisimilarity is a central notion for coalgebras. In recent work, Geuvers and Jacobs suggest to focus on apartness, which they define by dualising coalgebraic bisimulations. This yields the possibility of finite proofs of distinguishability for a wide variety of state-based systems. We propose behavioural apartness, defined by dualising behavioural equivalence rather than bisimulations. A motivating example is the subdistribution functor, where the proof system based on bisimilarity requires an infinite quantification over couplings, whereas behavioural apartness instantiates to a finite rule. In addition, we provide optimised proof rules for behavioural apartness and show their use in several examples.

cs.LO↗

arXiv2024

Composing Codensity Bisimulations

Proving compositionality of behavioral equivalence on state-based systems with respect to algebraic operations is a classical and widely studied problem. We study a categorical formulation of this problem, where operations on state-based systems modeled as coalgebras can be elegantly captured through distributive laws between functors. To prove compositionality, it then suffices to show that this distributive law lifts from sets to relations, giving an explanation of how behavioral equivalence on smaller systems can be combined to obtain behavioral equivalence on the composed system. In this paper, we refine this approach by focusing on so-called codensity lifting of functors, which gives a very generic presentation of various notions of (bi)similarity as well as quantitative notions such as behavioral metrics on probabilistic systems. The key idea is to use codensity liftings both at the level of algebras and coalgebras, using a new generalization of the codensity lifting. The problem of lifting distributive laws then reduces to the abstract problem of constructing distributive laws between codensity liftings, for which we propose a simplified sufficient condition. Our sufficient condition instantiates to concrete proof methods for compositionality of algebraic operations on various types of state-based systems. We instantiate our results to prove compositionality of qualitative and quantitative properties of deterministic automata. We also explore the limits of our approach by including an example of probabilistic systems, where it is unclear whether the sufficient condition holds, and instead we use our setting to give a direct proof of compositionality. ...

cs.LO↗