arXiv subjects

Martin Suda

Publications and source records attributed to Martin Suda.

At least 19 recordsLinked to original sources

arXiv2026

Reintroducing the Second Player in EPR

In this work we investigate the computational complexity of the satisfiability problem of sub-fragments of the Bernays-Schoenfinkel class of first-order logic, also known as EPR (Effectively Propositional). While Bernays-Schoenfinkel is NEXPTIME-complete, we already can obtain fragments that are PSPACE-complete by restricting our clauses to DET-HORN or KROM. However such restrictions yield very different formulas to the canonical PSPACE-complete language of Quantified Boolean Formulas (QBF). This is despite Bernays-Schoenfinkel having a natural connection to an extension of QBF known as Dependency QBF. Our main contribution is the definition of a PSPACE-complete sub-fragment of Bernays-Schoenfinkel that extends from a translation of QBF, retains a similar two-player game evaluation for its semantics and can be restricted in various ways to obtain other complete problems, particularly those at different levels in the polynomial hierarchy. We use this definition to identify problems in the TPTP library that fall into this fragment and their level in the polynomial hierarchy.

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arXiv2025

Hammering Higher Order Set Theory

We use automated theorem provers to significantly shorten a formal development in higher order set theory. The development includes many standard theorems such as the fundamental theorem of arithmetic and irrationality of square root of two. Higher order automated theorem provers are particularly useful here, since the underlying framework of higher order set theory coincides with the classical extensional higher order logic of (most) higher order automated theorem provers, so no significant translation or encoding is required. Additionally, many subgoals are first order and so first order automated provers often suffice. We compare the performance of different provers on the subgoals generated from the development. We also discuss possibilities for proof reconstruction, i.e., obtaining formal proof terms when an automated theorem prover claims to have proven the subgoal.

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arXiv2025

The Vampire Diary

During the past decade of continuous development, the theorem prover Vampire has become an automated solver for the combined theories of commonly-used data structures. Vampire now supports arithmetic, induction, and higher-order logic. These advances have been made to meet the demands of software verification, enabling Vampire to effectively complement SAT/SMT solvers and aid proof assistants. We explain how best to use Vampire in practice and review the main changes Vampire has undergone since its last tool presentation, focusing on the engineering principles and design choices we made during this process.

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arXiv2025

On neutron holography, neutron interferometry and neutron orbital angular momentum

A neutron Laue crystal interferometer has been reported by Saranac et al . to demonstrate neutron holography of a spiral phase plate. Using its two coherent beams as the object and reference beams, the resulting interference pattern was interpreted as a hologram. This interference pattern was then reported to reconstruct neutron beams with various intrinsic orbital angular momenta. There are serious doubts about the method for generating neutron orbital angular momentum with a crystal interferometer. Due to the extremely different lateral coherence lengths in the interferometer, one should expect the pattern described as a hologram to be asymmetric. In addition, a neutron crystal interferometer always produces a Moire image where the beams are combined in the final crystal, that appears as a one-dimensional enlargement of the interference pattern. We present computer simulations showing that the images presented as holograms can be computed as conventional interference patterns assuming the phase shifts of ordinary neutrons passing through objects located in one or the other beam path of the interferometer. Additionally, we discuss the complications of using a crystal interferometer for holography, while raising the question of whether the intrinsic orbital angular momentum states of neutrons are necessary or sufficient to explain the recorded images.

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arXiv2025

Case Study: Verified Vampire Proofs in the LambdaPi-calculus Modulo

The Vampire automated theorem prover is extended to output machine-checkable proofs in the Dedukti concrete syntax for the LambdaPi-calculus modulo. This significantly reduces the trusted computing base, and in principle eases proof reconstruction in other proof-checking systems. Existing theory is adapted to deal with Vampire's internal logic and inference system. Implementation experience is reported, encouraging adoption of verified proofs in other automated systems.

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arXiv2025

Efficient Neural Clause-Selection Reinforcement

Clause selection is arguably the most important choice point in saturation-based theorem proving. Framing it as a reinforcement learning (RL) task is a way to challenge the human-designed heuristics of state-of-the-art provers and to instead automatically evolve -- just from prover experiences -- their potentially optimal replacement. In this work, we present a neural network architecture for scoring clauses for clause selection that is powerful yet efficient to evaluate. Following RL principles to make design decisions, we integrate the network into the Vampire theorem prover and train it from successful proof attempts. An experiment on the diverse TPTP benchmark finds the neurally guided prover improve over a baseline strategy, from which it initially learns -- in terms of the number of in-training-unseen problems solved under a practically relevant, short CPU instruction limit -- by 20%.

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arXiv2024

A Higher-Order Vampire (Short Paper)

The support for higher-order reasoning in the Vampire theorem prover has recently been completely reworked. This rework consists of new theoretical ideas, a new implementation, and a dedicated strategy schedule. The theoretical ideas are still under development, so we discuss them at a high level in this paper. We also describe the implementation of the calculus in the Vampire theorem prover, the strategy schedule construction and several empirical performance statistics.

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arXiv2024

Regularization in Spider-Style Strategy Discovery and Schedule Construction

To achieve the best performance, automatic theorem provers often rely on schedules of diverse proving strategies to be tried out (either sequentially or in parallel) on a given problem. In this paper, we report on a large-scale experiment with discovering strategies for the Vampire prover, targeting the FOF fragment of the TPTP library and constructing a schedule for it, based on the ideas of Andrei Voronkov's system Spider. We examine the process from various angles, discuss the difficulty (or ease) of obtaining a strong Vampire schedule for the CASC competition, and establish how well a schedule can be expected to generalize to unseen problems and what factors influence this property.

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arXiv2024

Learning Guided Automated Reasoning: A Brief Survey

Automated theorem provers and formal proof assistants are general reasoning systems that are in theory capable of proving arbitrarily hard theorems, thus solving arbitrary problems reducible to mathematics and logical reasoning. In practice, such systems however face large combinatorial explosion, and therefore include many heuristics and choice points that considerably influence their performance. This is an opportunity for trained machine learning predictors, which can guide the work of such reasoning systems. Conversely, deductive search supported by the notion of logically valid proof allows one to train machine learning systems on large reasoning corpora. Such bodies of proof are usually correct by construction and when combined with more and more precise trained guidance they can be boostrapped into very large corpora, with increasingly long reasoning chains and possibly novel proof ideas. In this paper we provide an overview of several automated reasoning and theorem proving domains and the learning and AI methods that have been so far developed for them. These include premise selection, proof guidance in several settings, AI systems and feedback loops iterating between reasoning and learning, and symbolic classification problems.

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arXiv2024

Planning Domain Model Acquisition from State Traces without Action Parameters

Existing planning action domain model acquisition approaches consider different types of state traces from which they learn. The differences in state traces refer to the level of observability of state changes (from full to none) and whether the observations have some noise (the state changes might be inaccurately logged). However, to the best of our knowledge, all the existing approaches consider state traces in which each state change corresponds to an action specified by its name and all its parameters (all objects that are relevant to the action). Furthermore, the names and types of all the parameters of the actions to be learned are given. These assumptions are too strong. In this paper, we propose a method that learns action schema from state traces with fully observable state changes but without the parameters of actions responsible for the state changes (only action names are part of the state traces). Although we can easily deduce the number (and names) of the actions that will be in the learned domain model, we still need to deduce the number and types of the parameters of each action alongside its precondition and effects. We show that this task is at least as hard as graph isomorphism. However, our experimental evaluation on a large collection of IPC benchmarks shows that our approach is still practical as the number of required parameters is usually small. Compared to the state-of-the-art learning tools SAM and Extended SAM our new algorithm is able to provide better results in multiple domains in terms of learning action models more similar to reference models, even though it uses less information and has fewer restrictions on the input traces.

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arXiv2023

On the neutron orbital angular momentum: Has it actually been demonstrated?

In a Nature LETTER [1], a control of neutron angular momentum was apparently demonstrated using a neutron crystal interferometer. In the meantime, a number of highly interesting articles have been published dealing with the n-OAM and neutron interferometry, citing [1]. We show that the interpretation of [1] having detected a n-OAM is incorrect for several serious reasons and point out inconsistencies. Based only on the theory of dynamical neutron diffraction, we calculate all the interferograms and show perfect agreement with those given in [1]. There was no need to assume an additional n-OAM because no additional effect was observed that could not be explained by standard theory.

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arXiv2023

MizAR 60 for Mizar 50

As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60\% of the Mizar theorems in the hammer setting. We also automatically prove 75\% of the Mizar theorems when the automated provers are helped by using only the premises used in the human-written Mizar proofs. We describe the methods and large-scale experiments leading to these results. This includes in particular the E and Vampire provers, their ENIGMA and Deepire learning modifications, a number of learning-based premise selection methods, and the incremental loop that interleaves growing a corpus of millions of ATP proofs with training increasingly strong AI/TP systems on them. We also present a selection of Mizar problems that were proved automatically.

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arXiv2021

Spectra of Neutron Wave Functions in Earth's Gravitational Field

The time evolution of a quantum wave packet in the linear gravity potential is known as Quantum Bouncing Ball. The qBounce collaboration recently observed such a system by dropping wave packets of ultracold neutrons by a height of roughly 30 microns. In this article, space and momentum spectra as well as Wigner functions of the neutron wave functions in the gravitational field of the Earth are analyzed. We investigate the quantum states in the "preparation region", into which they transition after exiting a narrow double-mirror system and where we would expect to observe free fall and bounces in classical physics. For this, we start from the stationary solutions and eigenvalues of the Schr\"odinger equation in terms of Airy functions and their zeros. Subsequently, we examine space and momentum distributions as well as Wigner functions in phase space for pure and mixed quantum states. The eventual influence of Yukawa-like forces for small distances of several micrometers from the mirror is included through first order perturbation calculations. Those allow us to study the resulting modifications of space and momentum distributions, and phase space functions.

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arXiv2021

Improving ENIGMA-Style Clause Selection While Learning From History

We re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a recursive neural network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41% improvement on a relevant subset of SMT-LIB in a real time evaluation.

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arXiv2021

Vampire With a Brain Is a Good ITP Hammer

Vampire has been for a long time the strongest first-order automatic theorem prover, widely used for hammer-style proof automation in ITPs such as Mizar, Isabelle, HOL, and Coq. In this work, we considerably improve the performance of Vampire in hammering over the full Mizar library by enhancing its saturation procedure with efficient neural guidance. In particular, we employ a recently proposed recursive neural network classifying the generated clauses based only on their derivation history. Compared to previous neural methods based on considering the logical content of the clauses, our architecture makes evaluating a single clause much less time consuming. The resulting system shows good learning capability and improves on the state-of-the-art performance on the Mizar library, while proving many theorems that the related ENIGMA system could not prove in a similar hammering evaluation.

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arXiv2020

ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (system description)

We describe an implementation of gradient boosting and neural guidance of saturation-style automated theorem provers that does not depend on consistent symbol names across problems. For the gradient-boosting guidance, we manually create abstracted features by considering arity-based encodings of formulas. For the neural guidance, we use symbol-independent graph neural networks (GNNs) and their embedding of the terms and clauses. The two methods are efficiently implemented in the E prover and its ENIGMA learning-guided framework. To provide competitive real-time performance of the GNNs, we have developed a new context-based approach to evaluation of generated clauses in E. Clauses are evaluated jointly in larger batches and with respect to a large number of already selected clauses (context) by the GNN that estimates their collectively most useful subset in several rounds of message passing. This means that approximative inference rounds done by the GNN are efficiently interleaved with precise symbolic inference rounds done inside E. The methods are evaluated on the MPTP large-theory benchmark and shown to achieve comparable real-time performance to state-of-the-art symbol-based methods. The methods also show high complementarity, solving a large number of hard Mizar problems.

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arXiv2020

Layered Clause Selection for Theory Reasoning

Explicit theory axioms are added by a saturation-based theorem prover as one of the techniques for supporting theory reasoning. While simple and effective, adding theory axioms can also pollute the search space with many irrelevant consequences. As a result, the prover often gets lost in parts of the search space where the chance to find a proof is low. In this paper we describe a new strategy for controlling the amount of reasoning with explicit theory axioms. The strategy refines a recently proposed two-layer-queue clause selection and combines it with a heuristical measure of the amount of theory reasoning in the derivation of a clause. We implemented the new strategy in the automatic theorem prover Vampire and present an evaluation showing that our work dramatically improves the state-of-the-art clause-selection strategy in the presence of theory axioms.

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arXiv2019

Proceedings of the Second International Workshop on Automated Reasoning: Challenges, Applications, Directions, Exemplary Achievements

These are the post-proceedings of the second ARCADE workshop, which took place on the 26th August 2019 in Natal, Brazil, colocated with CADE-27. ARCADE stands for Automated Reasoning: Challenges, Applications, Directions, Exemplary achievements. The goal of this workshop was to bring together key people from various sub-communities of automated reasoning--such as SAT/SMT, resolution, tableaux, theory-specific calculi (e.g. for description logic, arithmetic, set theory), interactive theorem proving---to discuss the present, past, and future of the field.

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