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Recently, symbolic structures were proposed as finite representations of potentially infinite first-order structures, where Linear Integer Arithmetic terms and formulas define the domain and interpretations of a structure. We generalize…

Logic in Computer Science · Computer Science 2026-05-14 Neta Elad , Sharon Shoham

The inner alignment problem, which asserts whether an arbitrary artificial intelligence (AI) model satisfices a non-trivial alignment function of its outputs given its inputs, is undecidable. This is rigorously proved by Rice's theorem,…

This paper discusses "computational" systems capable of "computing" functions not computable by predefined Turing machines if the systems are not isolated from their environment. Roughly speaking, these systems can change their finite…

Artificial Intelligence · Computer Science 2009-08-03 Kurt Ammon

Many proposed applications of neural networks in machine learning, cognitive/brain science, and society hinge on the feasibility of inner interpretability via circuit discovery. This calls for empirical and theoretical explorations of…

Artificial Intelligence · Computer Science 2025-04-02 Federico Adolfi , Martina G. Vilas , Todd Wareham

We present an extension to the $\mathtt{mathlib}$ library of the Lean theorem prover formalizing the foundations of computability theory. We use primitive recursive functions and partial recursive functions as the main objects of study, and…

Logic in Computer Science · Computer Science 2019-07-19 Mario Carneiro

This paper develops an algorithmic-based approach for proving inductive properties of propositional sequent systems such as admissibility, invertibility, cut-elimination, and identity expansion. Although undecidable in general, these…

Logic in Computer Science · Computer Science 2021-01-11 Carlos Olarte , Elaine Pimentel , Camilo Rocha

We provide a unified operational framework for the study of causality, non-locality and contextuality, in a fully device-independent and theory-independent setting. Our work has its roots in the sheaf-theoretic framework for contextuality…

Quantum Physics · Physics 2023-07-31 Stefano Gogioso , Nicola Pinzani

Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating…

Information Retrieval · Computer Science 2021-02-02 Alejandro Bellogín , Alan Said

In view of the paradigm shift that makes science ever more data-driven, in this paper we consider deterministic scientific hypotheses as uncertain data. In the form of mathematical equations, hypotheses symmetrically relate aspects of the…

Databases · Computer Science 2014-11-20 Bernardo Gonçalves , Fabio Porto

This work explores the connection between logical independence and the algebraic structure of quantum mechanics. Building on results by Brukner et al., it introduces the notion of \textit{onto-epistemic ignorance}: situations in which the…

Quantum Physics · Physics 2025-07-02 Marcello Poletti

Robustness is a basic property of any control system. In the context of linear output regulation, it was proved that embedding an internal model of the exogenous signals is necessary and sufficient to achieve tracking of the desired…

Systems and Control · Electrical Eng. & Systems 2021-04-23 Michelangelo Bin , Daniele Astolfi , Lorenzo Marconi

We present CLTLB(D), an extension of PLTLB (PLTL with both past and future operators) augmented with atomic formulae built over a constraint system D. Even for decidable constraint systems, satisfiability and Model Checking problem of such…

Logic in Computer Science · Computer Science 2010-04-21 Marcello M. Bersani , Achille Frigeri , Angelo Morzenti , Matteo Pradella , Matteo Rossi , Pierluigi San Pietro

We consider decision problems under uncertainty where the options available to a decision maker and the resulting outcome are related through a causal mechanism which is unknown to the decision maker. We ask how a decision maker can learn…

Artificial Intelligence · Computer Science 2018-07-04 M. Gonzalez-Soto , L. E. Sucar , H. J. Escalante

We develop synthetic notions of oracle computability and Turing reducibility in the Calculus of Inductive Constructions (CIC), the constructive type theory underlying the Coq proof assistant. As usual in synthetic approaches, we employ a…

Logic in Computer Science · Computer Science 2023-07-31 Yannick Forster , Dominik Kirst , Niklas Mück

Emergent patterns in complex systems are related to many intriguing phenomena in modern science and philosophy. Several conceptions such as weak, strong and robust emergence have been proposed to emphasize different epistemological and…

Physics and Society · Physics 2016-10-11 Alberto Pascual-García

We consider the logic MSO+U, which is monadic second-order logic extended with the unbounding quantifier. The unbounding quantifier is used to say that a property of finite sets holds for sets of arbitrarily large size. We prove that the…

Logic in Computer Science · Computer Science 2015-02-18 Mikołaj Bojańczyk , Paweł Parys , Szymon Toruńczyk

One of quantum theory's salient features is its apparent indeterminism, i.e. measurement outcomes are typically probabilistic. We formally define and address whether this uncertainty is unavoidable or whether post-quantum theories can offer…

Quantum Physics · Physics 2024-11-15 Johannes Fankhauser

A notion of delegated causality is introduced. This subtle kind of causality is dual to interventional causality. Delegated causality elucidates the causal role of dynamical systems at the "edge of chaos", explicates evident cases of…

Adaptation and Self-Organizing Systems · Physics 2018-09-06 Raimundas Vidunas

This article redefines arbitrariness not as a normative flaw or a symptom of domination, but as a foundational functional mechanism structuring human systems and interactions. Diverging from critical traditions that conflate arbitrariness…

Computation and Language · Computer Science 2025-08-01 Naomi Omeonga wa Kayembe

A central question for causal inference is to decide whether a set of correlations fit a given causal structure. In general, this decision problem is computationally infeasible and hence several approaches have emerged that look for…

Quantum Physics · Physics 2018-07-26 Mirjam Weilenmann , Roger Colbeck