CAST Framework Enhances LLM Stability for Text Analysis

Researchers have introduced CAST (Consistency via Algorithmic Prompting and Stable Thinking), a framework designed to improve output stability in large language models (LLMs) for text analysis of tabular data. Text analysis relies on summarization for corpus-level theme extraction and tagging for row-level labeling, but LLMs often fail to meet the high stability standards required in data analytics. CAST addresses this by constraining the model's latent reasoning path through two components: Algorithmic Prompting, which imposes a procedural scaffold over valid reasoning transitions, and Thinking-before-Speaking, which enforces explicit intermediate commitments before final generation. To measure progress, the team also introduced CAST-S and CAST-T, stability metrics for bulleted summarization and tagging, respectively. The paper is available on arXiv under ID 2602.15861.