DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoning
- URL: http://arxiv.org/abs/2511.14299v2
- Date: Mon, 24 Nov 2025 08:37:38 GMT
- Title: DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoning
- Authors: Xiaochuan Liu, Yuanfeng Song, Xiaoming Yin, Xing Chen,
- Abstract summary: DataSage is a novel multi-agent framework that incorporates external knowledge retrieval to enrich the analytical context.<n>Extensive experiments on InsightBench demonstrate that DataSage consistently outperforms existing data insight agents across all difficulty levels.
- Score: 10.04895420035484
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In today's data-driven era, fully automated end-to-end data analytics, particularly insight discovery, is critical for discovering actionable insights that assist organizations in making effective decisions. With the rapid advancement of large language models (LLMs), LLM-driven agents have emerged as a promising paradigm for automating data analysis and insight discovery. However, existing data insight agents remain limited in several key aspects, often failing to deliver satisfactory results due to: (1) insufficient utilization of domain knowledge, (2) shallow analytical depth, and (3) error-prone code generation during insight generation. To address these issues, we propose DataSage, a novel multi-agent framework that incorporates three innovative features including external knowledge retrieval to enrich the analytical context, a multi-role debating mechanism to simulate diverse analytical perspectives and deepen analytical depth, and multi-path reasoning to improve the accuracy of the generated code and insights. Extensive experiments on InsightBench demonstrate that DataSage consistently outperforms existing data insight agents across all difficulty levels, offering an effective solution for automated data insight discovery.
Related papers
- InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agents [31.43134407708759]
We develop a data-curation pipeline to construct a new dataset named InsightEval.<n>We highlight prevailing challenges in automated insight discovery and raise some key findings to guide future research.
arXiv Detail & Related papers (2025-11-28T05:19:24Z) - CoDA: Agentic Systems for Collaborative Data Visualization [57.270599188947294]
Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations.<n>Existing approaches, including simple single- or multi-agent systems, often oversimplify the task.<n>We introduce CoDA, a multi-agent system that employs specialized LLM agents for metadata analysis, task planning, code generation, and self-reflection.
arXiv Detail & Related papers (2025-10-03T17:30:16Z) - Scaling Generalist Data-Analytic Agents [95.05161133349242]
DataMind is a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents.<n>DataMind tackles three key challenges in building open-source data-analytic agents.
arXiv Detail & Related papers (2025-09-29T17:23:08Z) - Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study [55.09905978813599]
Large Language Models (LLMs) hold promise in automating data analysis tasks.<n>Yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios.<n>In this work, we investigate strategies to enhance the data analysis capabilities of open-source LLMs.
arXiv Detail & Related papers (2025-06-24T17:04:23Z) - AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science [31.08682306627942]
We introduce AssistedDS, a benchmark designed to evaluate how large language models handle domain knowledge.<n>We assess state-of-the-art LLMs on their ability to discern and apply beneficial versus harmful domain knowledge.<n>Our results demonstrate a substantial gap in current models' ability to critically evaluate and leverage expert knowledge.
arXiv Detail & Related papers (2025-05-25T05:50:21Z) - MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model [1.33134751838052]
This paper introduces the Multidimensional Data Storytelling Framework (MDSF) based on large language models for automated insight generation and context-aware storytelling.<n>The framework incorporates advanced preprocessing techniques, augmented analysis algorithms, and a unique scoring mechanism to identify and prioritize actionable insights.
arXiv Detail & Related papers (2025-01-02T02:35:38Z) - An Information Criterion for Controlled Disentanglement of Multimodal Data [39.601584166020274]
Multimodal representation learning seeks to relate and decompose information inherent in multiple modalities.<n>Disentangled Self-Supervised Learning (DisentangledSSL) is a novel self-supervised approach for learning disentangled representations.
arXiv Detail & Related papers (2024-10-31T14:57:31Z) - DISCOVER: A Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of Human Behaviour [6.716560115378451]
We introduce a modular, flexible, yet user-friendly software framework specifically developed to streamline computational-driven data exploration for human behavior analysis.
Our primary objective is to democratize access to advanced computational methodologies, thereby enabling researchers across disciplines to engage in detailed behavioral analysis without the need for extensive technical proficiency.
arXiv Detail & Related papers (2024-07-18T11:28:52Z) - NeedleBench: Evaluating LLM Retrieval and Reasoning Across Varying Information Densities [51.07379913779232]
NeedleBench is a framework for assessing retrieval and reasoning performance in long-context tasks.<n>It embeds key data points at varying depths to rigorously test model capabilities.<n>Our experiments reveal that reasoning models like Deep-R1 and OpenAI's o3 struggle with continuous retrieval and reasoning in information-dense scenarios.
arXiv Detail & Related papers (2024-07-16T17:59:06Z) - InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation [79.09622602860703]
We introduce InsightBench, a benchmark dataset with three key features.<n>It consists of 100 datasets representing diverse business use cases such as finance and incident management.<n>Unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics.
arXiv Detail & Related papers (2024-07-08T22:06:09Z) - DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery.
Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering.
Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
arXiv Detail & Related papers (2024-07-01T18:58:22Z) - Benchmarking Data Science Agents [11.582116078653968]
Large Language Models (LLMs) have emerged as promising aids as data science agents, assisting humans in data analysis and processing.
Yet their practical efficacy remains constrained by the varied demands of real-world applications and complicated analytical process.
We introduce DSEval -- a novel evaluation paradigm, as well as a series of innovative benchmarks tailored for assessing the performance of these agents.
arXiv Detail & Related papers (2024-02-27T03:03:06Z) - Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis [4.539569292151314]
Large Language Models (LLMs) enable human-bot collaboration in Software Engineering (SE)<n>This study is to design and develop an LLM-based multi-agent system that synergizes human decision support with AI to automate various qualitative data analysis approaches.
arXiv Detail & Related papers (2024-02-02T13:10:46Z)
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.