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PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion

Yiran Rex Ma

TL;DR

PanelTR proposes a zero-shot, multi-agent framework where five scientist personas conduct Investigation, Self-Review, and Peer-Review to perform robust table reasoning without task-specific training data. By formalizing problem analysis, iterative validation, and collaborative deliberation, PanelTR leverages semantic understanding and structured discourse to surpass vanilla LLMs and rival supervised approaches across multiple benchmarks. Experiments on FEVEROUS, TAT-QA, WikiSQL, and SEM-TAB-FACTS show competitive performance, with ablations highlighting the positive roles of Investigation, Self-Review, and Peer-Review and revealing trade-offs in iteration and task type. The work demonstrates that disciplined methodological frameworks can meaningfully enhance foundation-model reasoning, offering a practical path for complex information extraction and verification without extensive data augmentation.

Abstract

Table reasoning, including tabular QA and fact verification, often depends on annotated data or complex data augmentation, limiting flexibility and generalization. LLMs, despite their versatility, often underperform compared to simple supervised models. To approach these issues, we introduce PanelTR, a framework utilizing LLM agent scientists for robust table reasoning through a structured scientific approach. PanelTR's workflow involves agent scientists conducting individual investigations, engaging in self-review, and participating in collaborative peer-review discussions. This process, driven by five scientist personas, enables semantic-level transfer without relying on data augmentation or parametric optimization. Experiments across four benchmarks show that PanelTR outperforms vanilla LLMs and rivals fully supervised models, all while remaining independent of training data. Our findings indicate that structured scientific methodology can effectively handle complex tasks beyond table reasoning with flexible semantic understanding in a zero-shot context.

PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion

TL;DR

PanelTR proposes a zero-shot, multi-agent framework where five scientist personas conduct Investigation, Self-Review, and Peer-Review to perform robust table reasoning without task-specific training data. By formalizing problem analysis, iterative validation, and collaborative deliberation, PanelTR leverages semantic understanding and structured discourse to surpass vanilla LLMs and rival supervised approaches across multiple benchmarks. Experiments on FEVEROUS, TAT-QA, WikiSQL, and SEM-TAB-FACTS show competitive performance, with ablations highlighting the positive roles of Investigation, Self-Review, and Peer-Review and revealing trade-offs in iteration and task type. The work demonstrates that disciplined methodological frameworks can meaningfully enhance foundation-model reasoning, offering a practical path for complex information extraction and verification without extensive data augmentation.

Abstract

Table reasoning, including tabular QA and fact verification, often depends on annotated data or complex data augmentation, limiting flexibility and generalization. LLMs, despite their versatility, often underperform compared to simple supervised models. To approach these issues, we introduce PanelTR, a framework utilizing LLM agent scientists for robust table reasoning through a structured scientific approach. PanelTR's workflow involves agent scientists conducting individual investigations, engaging in self-review, and participating in collaborative peer-review discussions. This process, driven by five scientist personas, enables semantic-level transfer without relying on data augmentation or parametric optimization. Experiments across four benchmarks show that PanelTR outperforms vanilla LLMs and rivals fully supervised models, all while remaining independent of training data. Our findings indicate that structured scientific methodology can effectively handle complex tasks beyond table reasoning with flexible semantic understanding in a zero-shot context.

Paper Structure

This paper contains 25 sections, 8 equations, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Tabular Reasoning Models
  4. Datasets for Table Reasoning
  5. PanelTR
  6. Problem Definition
  7. Investigation
  8. Self-Review
  9. Peer-Review
  10. Experiments
  11. Datasets
  12. Experiment Setup
  13. Baselines
  14. Metrics
  15. Results
  16. ...and 10 more sections

Figures (3)

  • Figure 1: The Investigation and Self-Review stages of individual scientist agents. The figure illustrates the systematic workflow where each scientist agent: (I) Problem Analysis - assesses problem complexity and identifies key insights; (II) Solution Formulation - proposes an initial solution strategy based on the analysis; (III) Self-Review - verifies the solution's validity.
  • Figure 2: The Peer-Review process in multi-agent panel discussions. Each scientist performs Individual Presentation, followed by iterative refinement from Collective Deliberation if no consensus. The process iterates until either consensus or maximum iteration is reached, at which point majority voting is used.
  • Figure 3: Iteration Turn Study on All Benchmarks.