arXiv subjects

Samer Abdallah

Publications and source records attributed to Samer Abdallah.

4 recordsLinked to original sources

arXiv2026

Imandra CodeLogician: Neuro-Symbolic Reasoning for Precise Analysis of Software Logic

Large Language Models (LLMs) have shown strong performance on code understanding tasks, yet they fundamentally lack the ability to perform precise, exhaustive mathematical reasoning about program behavior. Existing benchmarks either focus on mathematical proof automation, largely disconnected from real-world software, or on engineering tasks that do not require semantic rigor. We present CodeLogician, a neurosymbolic agent for precise analysis of software logic, integrated with ImandraX, an industrial automated reasoning engine deployed in financial markets and safety-critical systems. Unlike prior approaches that use formal methods primarily to validate LLM outputs, CodeLogician uses LLMs to construct explicit formal models of software systems, enabling automated reasoning to answer rich semantic questions beyond binary verification outcomes. To rigorously evaluate mathematical reasoning about software logic, we introduce code-logic-bench, a benchmark targeting the middle ground between theorem proving and software engineering benchmarks. It measures reasoning correctness about program state spaces, control flow, coverage constraints, and edge cases, with ground truth defined via formal modeling and region decomposition. Comparing LLM-only reasoning against LLMs augmented with CodeLogician, formal augmentation yields substantial improvements, closing a 41-47 percentage point gap in reasoning accuracy. These results demonstrate that neurosymbolic integration is essential for scaling program analysis toward rigorous, autonomous software understanding.

cs.AI↗

arXiv2017

More declarative tabling in Prolog using multi-prompt delimited control

Several Prolog implementations include a facility for tabling, an alternative resolution strategy which uses memoisation to avoid redundant duplication of computations. Until relatively recently, tabling has required either low-level support in the underlying Prolog engine, or extensive program transormation (de Guzman et al., 2008). An alternative approach is to augment Prolog with low level support for continuation capturing control operators, particularly delimited continuations, which have been investigated in the field of functional programming and found to be capable of supporting a wide variety of computational effects within an otherwise declarative language. This technical report describes an implementation of tabling in SWI Prolog based on delimited control operators for Prolog recently introduced by Schrijvers et al. (2013). In comparison with a previous implementation of tabling for SWI Prolog using delimited control (Desouter et al., 2015), this approach, based on the functional memoising parser combinators of Johnson (1995), stays closer to the declarative core of Prolog, requires less code, and is able to deliver solutions from systems of tabled predicates incrementally (as opposed to finding all solutions before delivering any to the rest of the program). A collection of benchmarks shows that a small number of carefully targeted optimisations yields performance within a factor of about 2 of the optimised version of Desouter et al.'s system currently included in SWI Prolog.

cs.PL↗

arXiv2017

Memoisation: Purely, Left-recursively, and with (Continuation Passing) Style

Memoisation, or tabling, is a well-known technique that yields large improvements in the performance of some recursive computations. Tabled resolution in Prologs such as XSB and B-Prolog can transform so called left-recursive predicates from non-terminating computations into finite and well-behaved ones. In the functional programming literature, memoisation has usually been implemented in a way that does not handle left-recursion, requiring supplementary mechanisms to prevent non-termination. A notable exception is Johnson's (1995) continuation passing approach in Scheme. This, however, relies on mutation of a memo table data structure and coding in explicit continuation passing style. We show how Johnson's approach can be implemented purely functionally in a modern, strongly typed functional language (OCaml), presented via a monadic interface that hides the implementation details, yet providing a way to return a compact represention of the memo tables at the end of the computation.

cs.LO↗

arXiv2017

Automatic Differentiation using Constraint Handling Rules in Prolog

Automatic differentiation is a technique which allows a programmer to define a numerical computation via compositions of a broad range of numeric and computational primitives and have the underlying system support the computation of partial derivatives of the result with respect to any of its inputs, without making any finite difference approximations, and without manipulating large symbolic expressions representing the computation. This note describes a novel approach to reverse mode automatic differentiation using constraint logic programmming, specifically, the constraint handling rules (CHR) library of SWI Prolog, resulting in a very small (50 lines of code) implementation. When applied to a differentiation-based implementation of the inside-outside algorithm for parameter learning in probabilistic grammars, the CHR based implementations outperformed two well-known frameworks for optimising differentiable functions, Theano and TensorFlow, by a large margin.

cs.MS↗