The IsalProgram Programming Language

• URL: http://arxiv.org/abs/2605.17008v1
• Date: Sat, 16 May 2026 14:08:32 GMT
• Title: The IsalProgram Programming Language
• Abstract summary: We introduce IsalProgram, a novel assembly-like programming language.<n>It is a regular language in the sense of formal language theory.<n>Every finite string over the instruction alphabet is a syntactically valid program.
• Score: 2.893558866535708
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce IsalProgram (Instruction Set and Language for Programming), a novel assembly-like programming language with three distinctive theoretical properties: (1) it is a regular language in the sense of formal language theory, meaning its programs are accepted by a finite automaton; (2) every finite string over the instruction alphabet is a syntactically valid program; and (3) it makes no explicit use of memory addresses or variable names, absolute or relative. Programs are finite sequences of tokens drawn from a fixed instruction set, and are executed on a virtual machine whose sole data structure is a circular doubly linked list (CDLL) navigated by three data pointers, with control flow governed by two code pointers. We give a complete formal definition of the language and its virtual machine, prove its regularity, and demonstrate its expressive power. We further discuss IsalProgram's potential advantages as a target language for neural program synthesis, the amenability of its program space to metric-based exploration via the Levenshtein edit distance, and directions for analyzing computability and complexity within this framework.

Related papers

• A Literate Programming Environment for Human and Machine Agents [0.0]
  This paper introduces an environment for constructing literate programs in concert with language-aware machine agents.<n>It includes a grammar for executable program essays, a name-graph which relates prose, names and executable artifacts, and a binding mechanism for existing languages and testing toolsets.
  arXiv  Detail & Related papers  (2026-08-25T14:54:24Z)
• NL2Scratch: An Executable Benchmark and Evaluation for Block-Based Programming [62.89531126732269]
  We introduce NL2Scratch, an executable benchmark for natural-language-to-Scratch generation.<n>We construct a semantically validated pool of 23,594 examples, and a slot-balanced 800 diagnostic benchmark.<n>Experiments across instruction-tuned and fine-tuned LLMs reveal a notable gap between lexical similarity and semantic alignment.
  arXiv  Detail & Related papers  (2026-06-20T14:22:59Z)
• PLSemanticsBench: Large Language Models As Programming Language Interpreters [31.611330217819713]
  As large language models (LLMs) excel at code reasoning, a natural question arises: can an LLM execute programs (i.e., act as an interpreter) purely based on a programming language's formal semantics?<n>We study this question using the imperative language IMP, formalized via small-step operational semantics (SOS) and rewriting-based operational semantics (K-semantics)<n>We introduce three evaluation sets-Human-Written, LLM-Translated, and Fuzzer- Generated-whose difficulty is controlled by code-complexity metrics spanning the size, control-flow, and data-flow axes.
  arXiv  Detail & Related papers  (2025-10-03T18:23:26Z)
• Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages [21.18996339478024]
  We introduce emphsynthetic programming elicitation and compilation (SPEAC) SPEAC produces syntactically correct programs more frequently and without sacrificing semantic correctness. We empirically evaluate the performance of SPEAC in a case study for the UCLID5 formal verification language.
  arXiv  Detail & Related papers  (2024-06-05T22:16:19Z)
• Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning [84.12154024070024]
  We propose natural language embedded programs (NLEP) as a unifying framework for addressing math/symbolic reasoning, natural language understanding, and instruction following tasks. Our approach prompts a language model to generate full Python programs that define functions over data structures which contain natural language representations of structured knowledge. A Python interpreter then executes the generated code and prints the output.
  arXiv  Detail & Related papers  (2023-09-19T17:54:21Z)
• Benchmarking Language Models for Code Syntax Understanding [79.11525961219591]
  Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding. In this work, we perform the first thorough benchmarking of the state-of-the-art pre-trained models for identifying the syntactic structures of programs. Our findings point out key limitations of existing pre-training methods for programming languages, and suggest the importance of modeling code syntactic structures.
  arXiv  Detail & Related papers  (2022-10-26T04:47:18Z)
• QParallel: Explicit Parallelism for Programming Quantum Computers [62.10004571940546]
  We present a language extension for parallel quantum programming. QParallel removes ambiguities concerning parallelism in current quantum programming languages. We introduce a tool that guides programmers in the placement of parallel regions by identifying the subroutines that profit most from parallelization.
  arXiv  Detail & Related papers  (2022-10-07T16:35:16Z)
• Synchromesh: Reliable code generation from pre-trained language models [38.15391794443022]
  We propose Synchromesh: a framework for substantially improving the reliability of pre-trained models for code generation. First, it retrieves few-shot examples from a training bank using Target Similarity Tuning (TST), a novel method for semantic example selection. Then, Synchromesh feeds the examples to a pre-trained language model and samples programs using Constrained Semantic Decoding (CSD), a general framework for constraining the output to a set of valid programs in the target language.
  arXiv  Detail & Related papers  (2022-01-26T22:57:44Z)
• Leveraging Language to Learn Program Abstractions and Search Heuristics [66.28391181268645]
  We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization.
  arXiv  Detail & Related papers  (2021-06-18T15:08:47Z)
• Representing Partial Programs with Blended Abstract Semantics [62.20775388513027]
  We introduce a technique for representing partially written programs in a program synthesis engine. We learn an approximate execution model implemented as a modular neural network. We show that these hybrid neuro-symbolic representations enable execution-guided synthesizers to use more powerful language constructs.
  arXiv  Detail & Related papers  (2020-12-23T20:40:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.