A Specification's Realm: Characterizing the Knowledge Required for Executing a Given Algorithm Specification

2025-10-21

AI Summary

This paper addresses the challenges of prior knowledge deficiency and fidelity assessment in executing algorithm specifications—expressed in natural language or pseudocode. To this end, it proposes the “Algorithm Specification Domain” framework, which systematically characterizes the requisite knowledge体系, comprising four structured components: syntactic and semantic rules, domain-specific entities, causal constraints, and operational instructions. Methodologically, it introduces the first integration of large language models with reusable documentation techniques to automatically extract and formalize implicit knowledge, yielding a lightweight, persistable, and cross-system-applicable domain model. Key contributions include: (1) a rigorous definition of “execution fidelity” (distinct from correctness), establishing a novel evaluation paradigm for specifications lacking canonical interpretations; (2) partial automation of knowledge modeling and extraction; and (3) enabling implementation-agnostic algorithm execution and mechanized verification, thereby advancing methodological unification and practical deployment of algorithm specifications.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Search and Optimization: Algorithm Configuration
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

An algorithm specification in natural language or pseudocode is expected to be clear and explicit enough to enable mechanical execution. In this position paper we contribute an initial characterization of the knowledge that an executing agent, human or machine, should possess in order to be able to carry out the instructions of a given algorithm specification as a stand-alone entity, independent of any system implementation. We argue that, for that algorithm specification, such prerequisite knowledge, whether unique or shared with other specifications, can be summarized in a document of practical size. We term this document the realm of the algorithm specification. The generation of such a realm is itself a systematic analytical process, significant parts of which can be automated with the help of large language models and the reuse of existing documents. The algorithm-specification's realm would consist of specification language syntax and semantics, domain knowledge restricted to the referenced entities, inter-entity relationships, relevant underlying cause-and-effect rules, and detailed instructions and means for carrying out certain operations. Such characterization of the realm can contribute to methodological implementation of the algorithm specification in diverse systems and to its formalization for mechanical verification. The paper also touches upon the question of assessing execution faithfulness, which is distinct from correctness: in the absence of a reference interpretation of natural language or pseudocode specification with a given vocabulary, how can we determine if an observed agent's execution indeed complies with the input specification.

Problem

• Characterizing prerequisite knowledge for executing algorithm specifications independently
• Defining a practical document summarizing unique and shared specification knowledge
• Assessing execution faithfulness to specifications without reference interpretations

Innovation

• Automated realm generation using large language models
• Systematic analysis of prerequisite knowledge for algorithms
• Documenting syntax, semantics and domain relationships