# Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge

arXiv: https://arxiv.org/abs/2504.12734 · HTML: https://mlsanity.com/paper/2504.12734/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 8 of 8)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL](https://mlsanity.com/paper/2410.01943/) (2024)
- [StructLM: Towards Building Generalist Models for Structured Knowledge Grounding](https://mlsanity.com/paper/2402.16671/) (2024)
- [TrustUQA: A Trustful Framework for Unified Structured Data Question Answering](https://mlsanity.com/paper/2406.18916/) (2024)

## Built on by (most cited first, 4 of 4)

- [Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving](https://mlsanity.com/paper/2507.06229/) (2025)
- [Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://mlsanity.com/paper/2510.09671/) (2025)
- [Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis](https://mlsanity.com/paper/2508.10967/) (2025)
- [Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?](https://mlsanity.com/paper/2609.07629/) (2026)

## Abstract (arXiv)

Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions (NLQs) by using structured sources such as tables, databases, and knowledge graphs in a unified way. Existing USKR methods either rely on employing task-specific strategies or custom-defined representations, which struggle to leverage the knowledge transfer between different SKR tasks or align with the prior of LLMs, thereby limiting their performance. This paper proposes a novel USKR framework named textscPandora, which takes advantage of textscPython's textscPandas API to construct a unified knowledge representation for alignment with LLM pre-training. It employs an LLM to generate textual reasoning steps and executable Python code for each question. Demonstrations are drawn from a memory of training examples that cover various SKR tasks, facilitating knowledge transfer. Extensive experiments on four benchmarks involving three SKR tasks demonstrate that textscPandora outperforms existing unified frameworks and competes effectively with task-specific methods.