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Many large language models (LLMs) use reasoning to generate responses but do not reveal their full reasoning traces (a.k.a. chains of thought), instead outputting only final answers and brief reasoning summaries. To demonstrate that hiding…

Cryptography and Security · Computer Science 2026-05-14 Tingwei Zhang , John X. Morris , Vitaly Shmatikov

In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such systems, i.e., solving the associated inverse problems, there is no unique solution. This causes fundamental…

Machine Learning · Computer Science 2020-03-23 Kshitij Tayal , Chieh-Hsin Lai , Vipin Kumar , Ju Sun

Intuitionistic logic programming provides the notion of embedded implication in rule bodies, which can be used to reason about a current database modified by the antecedent. This can be applied to a system that translates SQL to Datalog to…

Programming Languages · Computer Science 2016-09-23 Fernando Sáenz-Pérez

We describe a system to prove properties of programs. The key feature of this approach is a method to automatically synthesize inductive invariants of the loops contained in the program. The method is generic, i.e., it applies to a large…

Logic in Computer Science · Computer Science 2019-06-27 Mnacho Echenim , Nicolas Peltier , Yanis Sellami

The pseudoinverse of a matrix, a generalized notion of the inverse, is of fundamental importance in linear algebra and, thereby, in many different fields. Despite its proven existence, an algorithmic approach is typically necessary to…

Numerical Analysis · Mathematics 2026-01-21 Holger Boche , Adalbert Fono , Gitta Kutyniok

We advocate a declarative approach to proving properties of logic programs. Total correctness can be separated into correctness, completeness and clean termination; the latter includes non-floundering. Only clean termination depends on the…

Logic in Computer Science · Computer Science 2011-10-25 W. Drabent , M. Milkowska

To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of ICLP 2015 Recent advances in knowledge compilation introduced techniques to compile \emph{positive} logic programs into propositional logic, essentially exploiting…

Logic in Computer Science · Computer Science 2020-02-19 Bart Bogaerts , Guy Van den Broeck

Probabilistic programs provide an expressive representation language for generative models. Given a probabilistic program, we are interested in the task of posterior inference: estimating a latent variable given a set of observed variables.…

Machine Learning · Computer Science 2022-09-01 Mike Wu , Noah Goodman

This paper describes a general framework for automatic termination analysis of logic programs, where we understand by ``termination'' the finitenes s of the LD-tree constructed for the program and a given query. A general property of…

Programming Languages · Computer Science 2020-06-11 Nachum Dershowitz , Naomi Lindenstrauss , Yehoshua Sagiv , Alexander Serebrenik

This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems…

Artificial Intelligence · Computer Science 2023-08-29 Peter Baumgartner , Elena Tartaglia

Induction in saturation-based first-order theorem proving is a new exciting direction in the automation of inductive reasoning. In this paper we survey our work on integrating induction directly into the saturation-based proof search…

Logic in Computer Science · Computer Science 2024-03-01 Márton Hajdu , Petra Hozzová , Laura Kovács , Giles Reger , Andrei Voronkov

Explaining and reasoning about processes which underlie observed black-box phenomena enables the discovery of causal mechanisms, derivation of suitable abstract representations and the formulation of more robust predictions. We propose to…

Artificial Intelligence · Computer Science 2017-08-02 Svetlin Penkov , Subramanian Ramamoorthy

Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?"…

Machine Learning · Computer Science 2019-05-28 Patrick Schwab , Lorenz Linhardt , Walter Karlen

Modern language models (LMs) can learn to perform new tasks in different ways: in instruction following, the target task is described explicitly in natural language; in few-shot prompting, the task is specified implicitly with a small…

Computation and Language · Computer Science 2024-08-30 Emmy Liu , Graham Neubig , Jacob Andreas

Every definite logic program has as its meaning a least Herbrand model with respect to the program-independent ordering "set-inclusion". In the case of normal logic programs there do not exist least models in general. However, according to…

Logic in Computer Science · Computer Science 2011-09-01 Rainer Lüdecke

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in…

Computation and Language · Computer Science 2013-10-21 Andreas Wotzlaw , Ravi Coote

We consider the problem of diagnosis where a set of simple observations are used to infer a potentially complex hidden hypothesis. Finding the optimal subset of observations is intractable in general, thus we focus on the problem of active…

Artificial Intelligence · Computer Science 2017-07-12 Yewen Pu , Leslie P Kaelbling , Armando Solar-Lezama

An inductive inference system for proving validity of formulas in the initial algebra $T_{\mathcal{E}}$ of an order-sorted equational theory $\mathcal{E}$ is presented. It has 20 inference rules, but only 9 of them require user interaction;…

Logic in Computer Science · Computer Science 2024-05-07 Jose Meseguer

Inductive reasoning enables humans to infer abstract rules from limited examples and apply them to novel situations. In this work, we compare an LLM-based hypothesis search framework with direct program generation approaches on few-shot…

Artificial Intelligence · Computer Science 2025-09-03 Aishni Parab , Hongjing Lu , Ying Nian Wu , Sumit Gulwani

The \emph{International Obfuscated C Code Contest} was a programming contest for the most creatively obfuscated yet succinct C code. By \emph{contrast}, an interest herein is in programs which are, \emph{in a sense}, \emph{easily} seen to…

Logic · Mathematics 2019-03-14 John Case , Michael Ralston
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