PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables

Researchers introduce PoTable, a novel AI framework that enhances Large Language Models' ability to reason about tabular data through systematic, stage-oriented planning before execution. The approach mimics professional data analyst workflows by breaking complex table reasoning into distinct analytical stages with clear objectives, demonstrating improved accuracy and explainability across benchmark datasets.

PoTable addresses a fundamental limitation in current LLM-based table reasoning systems: while existing approaches achieve step-by-step thinking, they often lack systematic organization, leading to incomplete logic chains and unreliable outputs in complex scenarios. The research recognizes that autonomous exploration without structured guidance frequently produces disorganized reasoning and missed analytical steps. PoTable's innovation lies in its plan-then-execute mechanism, which first maps out an operation chain aligned with specific stage objectives before implementation, then executes through code generation with real-time feedback. This mirrors how human data analysts approach problems methodically rather than exploratively.

The framework's significance extends beyond academic research into practical LLM deployment. As organizations increasingly rely on AI systems for data analysis and business intelligence, the ability to produce verifiable, step-wise commented code with high accuracy becomes critical for enterprise adoption. The systematic staging approach provides explainability—a major concern for regulated industries and high-stakes decision-making environments. Testing on WikiTQ and TabFact benchmarks demonstrates the method's effectiveness across diverse table reasoning tasks.

For the AI development community, PoTable's success validates the importance of structural thinking in prompt engineering and multi-stage reasoning architectures. The approach could influence how future LLM systems are designed for data analysis tasks, particularly in financial services, healthcare, and research where audit trails and reasoning transparency are essential. Developers and researchers working on enterprise AI applications will likely incorporate similar planning-before-execution patterns. The public code availability accelerates potential adoption and refinement by the broader AI community.

• →PoTable introduces systematic stage-oriented planning to improve LLM table reasoning accuracy and reliability.
• →The plan-then-execute mechanism generates fully executable, commented code that mirrors professional data analyst workflows.
• →Framework addresses critical gaps in explainability and auditability for enterprise AI applications.
• →Testing validates effectiveness on benchmark datasets, suggesting broader applicability to data analysis tasks.
• →Publicly available code enables community refinement and adoption of the structured reasoning approach.