Problem
Research questions and friction points this paper is trying to address.
Large Language Models
Non-Determinism
Software Development
Process Reliability
Deterministic Envelope
LLM Non-Determinism in Deterministic Processes: A Controlled Variation Space for Constraining the Propagation of Non-Determinism
🤖 AI Summary
This study addresses the fundamental conflict between the non-deterministic nature of large language models (LLMs) and deterministic software engineering processes, which undermines state reliability and traceability. To resolve this, we introduce a novel "deterministic envelope" mechanism that constrains LLM processing within a controlled variation space. This process-oriented architecture permits intermediate reasoning variations while strictly governing authoritative state transitions and result propagation through predefined property verification and exit condition checks. We demonstrate the successful application of this approach in regulated software development scenarios. By establishing a design paradigm wherein LLM outputs must undergo verifiable review before being promoted to authoritative states, this work effectively bridges the gap between generative AI capabilities and the rigorous requirements of deterministic engineering practices.
📝 Abstract
Large Language Models are increasingly used in software development processes that rely on defined states, rules, and approvals. This creates a tension between the flexibility of LLMs and the need for reliable and traceable process states. We introduce Controlled Non-Determinism, a process-oriented approach that allows variation without letting it directly determine the authoritative process state. We place LLM-based processing within a controlled variation space that is bounded by a Deterministic Envelope. Before execution, we define the process-relevant properties of the result and the exit conditions. We also record the task configuration. These elements remain fixed during execution. At the end of the variation space, an exit check determines whether the result satisfies the predefined conditions and may become the next authoritative state. Different runs may produce different admissible results while the transition to an authoritative state remains controlled. We illustrate the approach with regulated software development and derive design guidelines for LLM-based changes whose results can be checked before they become part of the authoritative software state.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Controlled Non-Determinism
Deterministic Envelope
Controlled Variation Space
Large Language Models
Exit Check
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