arXiv subjects

Masaki Waga

Publications and source records attributed to Masaki Waga.

At least 19 recordsLinked to original sources

arXiv2026

Specification-Guided Path Shortcutting for Efficient Probabilistic Model Checking

Given the safety-critical nature of many embedded systems, their safety assurance is essential. Because such systems are typically stochastic, probabilistic model checking is a particularly important technique. However, there is a well-known scalability issue due to state-space explosion, especially when verifying complex properties. To mitigate this issue, we propose specification-guided path shortcutting for probabilistic systems, focusing on Markov chains (MCs) and $\omega$-regular properties. The key idea is that, when the verified property is fixed, certain sequences of transitions in an MC can be replaced with a single transition without changing the satisfaction probability, and thus, we can reduce the state space of the MC. We implement the proposed path shortcutting and evaluate its contribution to the performance of probabilistic model checking, using Storm as the baseline model checker. The results suggest that our approach often outperforms the baseline, particularly on benchmark instances with complex specifications.

cs.LO↗

arXiv2026

Learning Alternating Real-Time Automata

We present the AL*RTA algorithm for learning alternating real-time automata (ARTAs) using membership and equivalence queries. AL*RTA combines ideas from AL*for learning alternating finite automata and NL*RTA for learning nondeterministic real-time automata. We first define ARTAs and show that alternation improves succinctness, although it does not increase expressive power. We then present AL*RTA and show its termination. Our empirical evaluation suggests that AL*RTA generally learns smaller automata than NL*RTA at the cost of more queries.

cs.FL↗

arXiv2026

SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Corpora

We present SoftMatcha 2, an ultra-fast and flexible search algorithm that enables search over trillion-scale natural language corpora in under 0.3 seconds while allowing semantic variations in the form of substitution, insertion, and deletion. Our approach employs string matching based on suffix arrays that scales well with corpus size, and represents words as vectors, which underpin its semantic flexibility. To mitigate the combinatorial explosion induced by the semantic relaxation of queries, our method is built on two key algorithmic ideas: dynamic corpus-aware pruning and fast exact lookup enabled by a disk-aware design. We theoretically analyze the efficiency of the proposed method, indicating that it can mitigate exponential growth in the search space. Empirically, on FineWeb-Edu (Lozhkov et al., 2024) (1.4T tokens), it attains substantially lower search latency than existing methods: infini-gram (Liu et al., 2024), infini-gram mini (Xu et al., 2025), and SoftMatcha (Deguchi et al., 2025). As a practical application, our method uncovers benchmark contamination in training corpora that existing approaches miss, and it also benefits information retrieval and paraphrase detection. We also provide an online demo of fast, soft search across corpora in seven languages.

cs.CL↗

arXiv2026

A Case Study on Runtime Verification of a Continuous Deployment Process

We report our experience in applying runtime monitoring to a FluxCD-based continuous deployment (CD) process. Our target system consists of GitHub Actions, GitHub Container Registry (GHCR), FluxCD, and an application running on Kubernetes. We monitored its logs using SyMon. In our setting, we regard a deployment update as detected when FluxCD's polling log resolves the latest image tag. Through the case study, we found that FluxCD did not always detect a new image within five minutes after it was pushed to GHCR, whereas it always did so within ten minutes in the collected logs. Moreover, our results show that SyMon is fast enough for near-real-time monitoring in our setting.

cs.SE↗

arXiv2025

A Variety of Request-Response Specifications

We find, motivated by real-world applications, that the well-known request-response specification comes with multiple variations, and that these variations should be distinguished. As the first main contribution, we introduce a classification of those variations into six types, and present it as a decision tree, where a user is led to the type that is suited for their application by answering a couple of questions. Our second main contribution is the formalization of those six types in various formalisms such as temporal logics, grammars, and automata; here, two types out of the six are non-regular specifications and their formalization requires extended formalisms. We also survey tools for monitoring these specifications to cater for practitioners' needs.

cs.FL↗

arXiv2025

Active Learning of Symbolic Mealy Automata

We propose $Λ^*_M$-an active learning algorithm that learns symbolic Mealy automata, which support infinite input alphabets and multiple output characters. Each of these two features has been addressed separately in prior work. Combining these two features poses a challenge in learning the outputs corresponding to potentially infinite sets of input characters at each state. To address this challenge, we introduce the notion of essential input characters, a finite set of input characters that is sufficient for learning the output function of a symbolic Mealy automaton. $Λ^*_M$ maintains an underapproximation of the essential input characters and refines this set during learning. We prove that $Λ^*_M$ terminates under certain assumptions. Moreover, we provide upper and lower bounds for the query complexity. Their similarity suggests the tightness of the bounds. We empirically demonstrate that $Λ^*_M$ is i) efficient regarding the number of queries on practical benchmarks and ii) scalable according to evaluations with randomly generated benchmarks.

cs.FL↗

arXiv2025

Componentwise Automata Learning for System Integration (Extended Version)

Compositional automata learning is attracting attention as an analysis technique for complex black-box systems. It exploits a target system's internal compositional structure to reduce complexity. In this paper, we identify system integration -- the process of building a new system as a composite of potentially third-party and black-box components -- as a new application domain of compositional automata learning. Accordingly, we propose a new problem setting, where the learner has direct access to black-box components. This is in contrast with the usual problem settings of compositional learning, where the target is a legacy black-box system and queries can only be made to the whole system (but not to components). We call our problem componentwise automata learning for distinction. We identify a challenge there called component redundancies: some parts of components may not contribute to system-level behaviors, and learning them incurs unnecessary effort. We introduce a contextual componentwise learning algorithm that systematically removes such redundancies. We experimentally evaluate our proposal and show its practical relevance.

cs.FL↗

arXiv2025

Hyper pattern matching

In runtime verification, pattern matching, which searches for occurrences of a specific pattern within a word, provides more information than a simple violation detection of the monitored property, by locating concrete evidence of the violation. However, witnessing violations of some properties, particularly hyperproperties, requires evidence across multiple input words or different parts of the same word, which goes beyond the scope of conventional pattern matching. We propose here hyper pattern matching, a generalization of pattern matching over a set of words. Properties of interest include robustness and (non-)interference. As a formalism for patterns, we use nondeterministic asynchronous finite automata (NAAs). We first provide a naive algorithm for hyper pattern matching and then devise several heuristics for better efficiency. Although we prove the NP-completeness of the problem, our implementation HypPAu is able to address several case studies scalable in the length, number of words (or logs) and number of dimensions, suggesting the practical relevance of our approach.

cs.FL↗

arXiv2025

Certifying Lyapunov Stability of Black-Box Nonlinear Systems via Counterexample Guided Synthesis (Extended Version)

Finding Lyapunov functions to certify the stability of control systems has been an important topic for verifying safety-critical systems. Most existing methods on finding Lyapunov functions require access to the dynamics of the system. Accurately describing the complete dynamics of a control system however remains highly challenging in practice. Latest trend of using learning-enabled control systems further reduces the transparency. Hence, a method for black-box systems would have much wider applications. Our work stems from the recent idea of sampling and exploiting Lipschitz continuity to approximate the unknown dynamics. Given Lipschitz constants, one can derive a non-statistical upper bounds on approximation errors; hence a strong certification on this approximation can certify the unknown dynamics. We significantly improve this idea by directly approximating the Lie derivative of Lyapunov functions instead of the dynamics. We propose a framework based on the learner-verifier architecture from Counterexample-Guided Inductive Synthesis (CEGIS). Our insight of combining regional verification conditions and counterexample-guided sampling enables a guided search for samples to prove stability region-by-region. Our CEGIS algorithm further ensures termination. Our numerical experiments suggest that it is possible to prove the stability of 2D and 3D systems with a few thousands of samples. Our visualization also reveals the regions where the stability is difficult to prove. In comparison with the existing black-box approach, our approach at the best case requires less than 0.01% of samples.

eess.SY↗

arXiv2025

SoftMatcha: A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches

Researchers and practitioners in natural language processing and computational linguistics frequently observe and analyze the real language usage in large-scale corpora. For that purpose, they often employ off-the-shelf pattern-matching tools, such as grep, and keyword-in-context concordancers, which is widely used in corpus linguistics for gathering examples. Nonetheless, these existing techniques rely on surface-level string matching, and thus they suffer from the major limitation of not being able to handle orthographic variations and paraphrasing -- notable and common phenomena in any natural language. In addition, existing continuous approaches such as dense vector search tend to be overly coarse, often retrieving texts that are unrelated but share similar topics. Given these challenges, we propose a novel algorithm that achieves \emph{soft} (or semantic) yet efficient pattern matching by relaxing a surface-level matching with word embeddings. Our algorithm is highly scalable with respect to the size of the corpus text utilizing inverted indexes. We have prepared an efficient implementation, and we provide an accessible web tool. Our experiments demonstrate that the proposed method (i) can execute searches on billion-scale corpora in less than a second, which is comparable in speed to surface-level string matching and dense vector search; (ii) can extract harmful instances that semantically match queries from a large set of English and Japanese Wikipedia articles; and (iii) can be effectively applied to corpus-linguistic analyses of Latin, a language with highly diverse inflections.

cs.CL↗

arXiv2024

Oblivious Monitoring for Discrete-Time STL via Fully Homomorphic Encryption

When monitoring a cyber-physical system (CPS) from a remote server, keeping the monitored data secret is crucial, particularly when they contain sensitive information, e.g., biological or location data. Recently, Banno et al. (CAV'22) proposed a protocol for online LTL monitoring that keeps data concealed from the server using Fully Homomorphic Encryption (FHE). We build on this protocol to allow arithmetic operations over encrypted values, e.g., to compute a safety measurement combining distance, velocity, and so forth. Overall, our protocol enables oblivious online monitoring of discrete-time real-valued signals against signal temporal logic (STL) formulas. Our protocol combines two FHE schemes, CKKS and TFHE, leveraging their respective strengths. We employ CKKS to evaluate arithmetic predicates in STL formulas while utilizing TFHE to process them using a DFA derived from the STL formula. We conducted case studies on monitoring blood glucose levels and vehicles' behavior against the Responsibility-Sensitive Safety (RSS) rules. Our results suggest the practical relevance of our protocol.

cs.CR↗

arXiv2024

Hyper parametric timed CTL

Hyperproperties enable simultaneous reasoning about multiple execution traces of a system and are useful to reason about non-interference, opacity, robustness, fairness, observational determinism, etc. We introduce hyper parametric timed computation tree logic (HyperPTCTL), extending hyperlogics with timing reasoning and, notably, parameters to express unknown values. We mainly consider its nest-free fragment, where temporal operators cannot be nested. However, we allow extensions that enable counting actions and comparing the duration since the most recent occurrence of specific actions. We show that our nest-free fragment with this extension is sufficiently expressive to encode properties, e.g., opacity, (un)fairness, or robust observational (non-)determinism. We propose semi-algorithms for model checking and synthesis of parametric timed automata (an extension of timed automata with timing parameters) against this nest-free fragment with the extension via reduction to PTCTL model checking and synthesis. While the general model checking (and thus synthesis) problem is undecidable, we show that a large part of our extended (yet nest-free) fragment is decidable, provided the parameters only appear in the property, not in the model. We also exhibit additional decidable fragments where parameters within the model are allowed. We implemented our semi-algorithms on top of the IMITATOR model checker, and performed experiments. Our implementation supports most of the nest-free fragments (beyond the decidable classes). The experimental results highlight our method's practical relevance.

cs.FL↗

arXiv2024

Parametric Timed Pattern Matching

Given a log and a specification, timed pattern matching aims at exhibiting for which start and end dates a specification holds on that log. For example, "a given action is always followed by another action before a given deadline". This problem has strong connections with monitoring real-time systems. We address here timed pattern matching in the presence of an uncertain specification, i.e., that may contain timing parameters (e.g., the deadline can be uncertain or unknown). We want to know for which start and end dates, and for what values of the timing parameters, a property holds. For instance, we look for the minimum or maximum deadline (together with the corresponding start and end dates) for which the property holds. We propose two frameworks for parametric timed pattern matching. The first one is based on parametric timed model checking. In contrast to most parametric timed problems, the solution is effectively computable. The second one is a dedicated method; not only we largely improve the efficiency compared to the first method, but we further propose optimizations with skipping. Our experiment results suggest that our algorithms, especially the second one, are efficient and practically relevant.

cs.FL↗

arXiv2024

Model-bounded monitoring of hybrid systems

Monitoring of hybrid systems attracts both scientific and practical attention. However, monitoring algorithms suffer from the methodological difficulty of only observing sampled discrete-time signals, while real behaviors are continuous-time signals. To mitigate this problem of sampling uncertainties, we introduce a model-bounded monitoring scheme, where we use prior knowledge about the target system to prune interpolation candidates. Technically, we express such prior knowledge by linear hybrid automata (LHAs) -- the LHAs are called bounding models. We introduce a novel notion of monitored language of LHAs, and we reduce the monitoring problem to the membership problem of the monitored language. We present two partial algorithms -- one is via reduction to reachability in LHAs and the other is a direct one using polyhedra -- and show that these methods, and thus the proposed model-bounded monitoring scheme, are efficient and practically relevant.

eess.SY↗

arXiv2024

Learning Weighted Finite Automata over the Max-Plus Semiring and its Termination

Active learning of finite automata has been vigorously pursued for the purposes of analysis and explanation of black-box systems. In this paper, we study an L*-style learning algorithm for weighted automata over the max-plus semiring. The max-plus setting exposes a "consistency" issue in the previously studied semiring-generic extension of L*: we show that it can fail to maintain consistency of tables, and can thus make equivalence queries on obviously wrong hypothesis automata. We present a theoretical fix by a mathematically clean notion of column-closedness. We also present a nontrivial and reasonably broad class of weighted languages over the max-plus semiring in which our algorithm terminates.

cs.FL↗

arXiv2024

Temporal Logic Formalisation of ISO 34502 Critical Scenarios: Modular Construction with the RSS Safety Distance

As the development of autonomous vehicles progresses, efficient safety assurance methods become increasingly necessary. Safety assurance methods such as monitoring and scenario-based testing call for formalisation of driving scenarios. In this paper, we develop a temporal-logic formalisation of an important class of critical scenarios in the ISO standard 34502. We use signal temporal logic (STL) as a logical formalism. Our formalisation has two main features: 1) modular composition of logical formulas for systematic and comprehensive formalisation (following the compositional methodology of ISO 34502); 2) use of the RSS distance for defining danger. We find our formalisation comes with few parameters to tune thanks to the RSS distance. We experimentally evaluated our formalisation; using its results, we discuss the validity of our formalisation and its stability with respect to the choice of some parameter values.

cs.RO↗

arXiv2023

Learning nonlinear hybrid automata from input--output time-series data

Learning an automaton that approximates the behavior of a black-box system is a long-studied problem. Besides its theoretical significance, its application to search-based testing and model understanding is recently recognized. We present an algorithm to learn a nonlinear hybrid automaton (HA) that approximates a black-box hybrid system (HS) from a set of input--output traces generated by the HS. Our method is novel in handling (1) both exogenous and endogenous HS and (2) HA with reset associated with each transition. To our knowledge, ours is the first method that achieves both features. We applied our algorithm to various benchmarks and confirmed its effectiveness.

cs.DC↗

arXiv2023

Probabilistic Black-Box Checking via Active MDP Learning

We introduce a novel methodology for testing stochastic black-box systems, frequently encountered in embedded systems. Our approach enhances the established black-box checking (BBC) technique to address stochastic behavior. Traditional BBC primarily involves iteratively identifying an input that breaches the system's specifications by executing the following three phases: the learning phase to construct an automaton approximating the black box's behavior, the synthesis phase to identify a candidate counterexample from the learned automaton, and the validation phase to validate the obtained candidate counterexample and the learned automaton against the original black-box system. Our method, ProbBBC, refines the conventional BBC approach by (1) employing an active Markov Decision Process (MDP) learning method during the learning phase, (2) incorporating probabilistic model checking in the synthesis phase, and (3) applying statistical hypothesis testing in the validation phase. ProbBBC uniquely integrates these techniques rather than merely substituting each method in the traditional BBC; for instance, the statistical hypothesis testing and the MDP learning procedure exchange information regarding the black-box system's observation with one another. The experiment results suggest that ProbBBC outperforms an existing method, especially for systems with limited observation.

cs.SE↗