DataSeer: A Manager-Centric Collaborative Multi-Agent Framework with Multi-Branch Reasoning for Automated Insight Discovery
Year: 2026
Type: conference-paper
Abstract: The increasing complexity of data drives the demand for automated insight discovery.While LLMs and agent technologies have advanced data analysis, existing methods struggle with maintaining contextual coherence, achieving sufficient coverage (due to single-path exploration), and adapting rigid planning to dynamic data discovery.We propose DataSeer, a collaborative multi-agent framework for automated insight discovery.Our first contribution is a Manager-Centric Collaborative Framework, where the Manager ensures cross-episode contextual coherence through a dual-layer memory system with compression, consolidation, and retrieval, alongside dynamic prompt editing, coordinating the overall process between the Planner and Executor.Second, we optimize the planning and execution components: the Planner employs multi-role discussion for adaptive sub-goal generation and plan refinement; the Executor is endowed with tactical autonomy for exploratory execution and incorporates realtime multi-dimensional self-assessment to guarantee insight quality.Third, we design Multi-Branch Reasoning that executes multiple discovery trajectories and synthesizes outcomes through LLM-based aggregation, improving coverage and reducing single-path stochasticity.Experiments on InsightBench and InsightEval show that DataSeer outperforms baselines, achieving improvements of 18.7% and 12.1% in insight-level scores, and 11.6% and 10.3% in summary-level scores, respectively.
Authors: Suchen Liu, Yuanfeng Song, Jun Gao, Xing Chen
Institutions: Peking University, ByteDance (China)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G7464771943
OpenAlex ID: W7166808576