LongDA: Benchmarking LLM Agents for Long-Document Data Analysis

• URL: http://arxiv.org/abs/2601.02598v2
• Date: Sun, 11 Jan 2026 22:21:22 GMT
• Title: LongDA: Benchmarking LLM Agents for Long-Document Data Analysis
• Authors: Yiyang Li, Zheyuan Zhang, Tianyi Ma, Zehong Wang, Keerthiram Murugesan, Chuxu Zhang, Yanfang Ye,
• Abstract summary: LongDA targets real-world settings in which navigating long documentation and complex data is the primary bottleneck.<n>LongTA is a tool-augmented agent framework that enables document access, retrieval, and code execution.<n>Our experiments reveal substantial performance gaps even among state-of-the-art models.
• Score: 55.32211515932351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce LongDA, a data analysis benchmark for evaluating LLM-based agents under documentation-intensive analytical workflows. In contrast to existing benchmarks that assume well-specified schemas and inputs, LongDA targets real-world settings in which navigating long documentation and complex data is the primary bottleneck. To this end, we manually curate raw data files, long and heterogeneous documentation, and expert-written publications from 17 publicly available U.S. national surveys, from which we extract 505 analytical queries grounded in real analytical practice. Solving these queries requires agents to first retrieve and integrate key information from multiple unstructured documents, before performing multi-step computations and writing executable code, which remains challenging for existing data analysis agents. To support the systematic evaluation under this setting, we develop LongTA, a tool-augmented agent framework that enables document access, retrieval, and code execution, and evaluate a range of proprietary and open-source models. Our experiments reveal substantial performance gaps even among state-of-the-art models, highlighting the challenges researchers should consider before applying LLM agents for decision support in real-world, high-stakes analytical settings.

Related papers

• A Benchmark for Deep Information Synthesis [27.87976801124957]
  DEEPSYNTH is a novel benchmark designed to evaluate large language model (LLM)-based agents on realistic, time-consuming problems.<n>It contains 120 tasks collected across 7 domains and data sources covering 67 countries.<n>When evaluated on DEEPSYNTH, 11 state-of-the-art LLMs and deep research agents achieve a maximum F1 score of 8.97 and 17.5 on the LLM-judge metric.
  arXiv  Detail & Related papers  (2026-02-24T17:43:32Z)
• Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs [66.63911043019294]
  Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them.<n>This paper focuses on the use of LLM techniques to prepare data for diverse downstream tasks.<n>We introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning, standardization, error processing, imputation, data integration, and data enrichment.
  arXiv  Detail & Related papers  (2026-01-22T12:02:45Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• ConDABench: Interactive Evaluation of Language Models for Data Analysis [10.177407781044279]
  We introduce ConDABench, a framework for generating conversational data analysis (ConDA) benchmarks and evaluating external tools.<n>bench consists of (a) a multi-agent workflow for generating realistic benchmarks from articles describing insights gained from public datasets, (b) 1,420 ConDA problems generated using this workflow, and (c) an evaluation harness that, for the first time, makes it possible to systematically evaluate conversational data analysis tools on the generated ConDA problems.
  arXiv  Detail & Related papers  (2025-10-10T15:54:51Z)
• FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering [57.18367828883773]
  FinAgentBench is a benchmark for evaluating agentic retrieval with multi-step reasoning in finance.<n>The benchmark consists of 26K expert-annotated examples on S&P-500 listed firms.<n>We evaluate a suite of state-of-the-art models and demonstrate how targeted fine-tuning can significantly improve agentic retrieval performance.
  arXiv  Detail & Related papers  (2025-08-07T22:15:22Z)
• DABstep: Data Agent Benchmark for Multi-step Reasoning [2.6709582216950767]
  DABstep is a novel benchmark for evaluating AI agents on realistic multi-step data analysis tasks.<n>It comprises over 450 real-world challenges derived from a financial analytics platform.<n>Dabstep is released with a public leaderboard and toolkit to accelerate research in autonomous data analysis.
  arXiv  Detail & Related papers  (2025-06-30T10:49:21Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• MIMDE: Exploring the Use of Synthetic vs Human Data for Evaluating Multi-Insight Multi-Document Extraction Tasks [0.0]
  We define a set of tasks, Multi-Insight Multi-Document Extraction (MIMDE) tasks.<n>This task is fundamental to many practical applications, from analyzing survey responses to processing medical records.<n>We introduce a novel set of complementary human and synthetic datasets to examine the potential of synthetic data for evaluation.
  arXiv  Detail & Related papers  (2024-11-29T13:24:10Z)
• InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks [84.7788065721689]
  In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. This benchmark contains DAEval, a dataset consisting of 257 data analysis questions derived from 52 CSV files. Building on top of our agent framework, we develop a specialized agent, DAAgent, which surpasses GPT-3.5 by 3.9% on DABench.
  arXiv  Detail & Related papers  (2024-01-10T19:04:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.