让AI自动处理表格分析，还能学习用户习惯并透明展示每一步操作。
TabClaw: An Interactive and Self-Evolving Agent for Spreadsheet Manipulation and Table Reasoning
- 通过自然语言指令理解任务，自动生成可编辑的分析计划。
- 在多表对比中实现并行推理，结果标注共识与不确定性。
- 能记录用户习惯，持续优化技能，适合重复性数据分析者使用。
电子表格和表格是结构化数据分析的常用形式，但有效分析仍需大量手动操作和领域知识。尽管大语言模型（LLM）代理可自动化部分流程，但普遍存在中间决策不透明、依赖隐含假设、难以处理多表对比、且无法根据用户偏好调整等问题。本文提出 TabClaw，一个开源的交互式AI代理，用于表格操作与推理。用户上传CSV或Excel文件并输入自然语言请求，TabClaw会澄清模糊意图，展示可编辑的执行计划，流式输出类似ReAct的工具使用分析循环，调度专业代理进行并行多表推理，并以明确的共识与不确定标记合成结果。除一次性分析外，TabClaw还记录完成的工作流，提取持久用户记忆，从重复工具使用模式中提炼可复用技能，支持技能包导入，并通过负面反馈升级技能。在表格操作与推理基准测试中，TabClaw提升了可执行任务完成率与推理性能，同时保持用户工作流的可检查性。本研究展示了如何将电子表格转化为可追溯的分析流程，并逐步个性化适应常见数据分析任务。代码已开源。
原文摘要 · Abstract (English)
Spreadsheets and tables are widely used representations for structured data analysis, but effective analysis still requires substantial manual effort and domain expertise. Recent large language model (LLM) agents can automate parts of this process, but they often provide limited transparency into intermediate decisions, rely on implicit assumptions, struggle with multi-table comparison, and repeat similar workflows without adapting to a user's preferences. This paper presents TabClaw, an open-source interactive AI agent for spreadsheet manipulation and table reasoning. Users upload CSV or Excel files and issue natural-language requests; TabClaw clarifies ambiguous intent, exposes an editable execution plan, streams a ReAct-style tool-using analysis loop, dispatches specialist agents for parallel multi-table reasoning, and synthesizes findings with explicit consensus and uncertainty markers. Beyond one-off analysis, TabClaw records completed workflows, extracts persistent user memory, distills reusable skills from repeated tool-use patterns, supports package-style skill import, and upgrades skills from negative feedback. Experiments on spreadsheet manipulation and table reasoning benchmarks show that TabClaw improves executable task completion and reasoning performance while preserving an inspectable user workflow. This paper shows how TabClaw turns spreadsheets and tables into inspectable analytical workflows while gradually personalizing itself to recurring data-analysis tasks. Our code is available.
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