TabMATE: A Multi-Agent Framework for Tabular Anomaly Detection

In tabular anomaly detection (AD), different anomaly mechanisms favor different detection strategies, making the complementary strengths of heterogeneous detectors valuable for reliable detection. However, this expertise can be lost when detector outputs are uniformly aggregated or reweighted toward consensus, particularly when useful specialists disagree with the majority. We introduce TabMATE, a $Tab$ular $M$ulti-$A$gent framework that organizes heterogeneous detection specialists into a coordinated $T$eam of $E$xperts through a shared score interface. The framework integrates large language model(LLM) agents as detectors spanning statistical, geometric, isolation-based, and foundation-model approaches, together with a learnable coordination layer that determines their contributions to the final prediction. Alongside the framework, we provide a theoretical analysis of the information limits of label-free coordination and a systematic evaluation on 45 ADBench datasets and four controlled anomaly types. Results show that three confirmed anomalies improve detection over uniform aggregation and the tested consensus-based coordination rules, with learned weights reflecting detector specialization across anomaly types. Further comparisons with alternative coordinators and standalone foundation models clarify the framework’s practical scope, showing that stronger coordination does not necessarily yield a stronger predictor than an individual model under matched label budgets. TabMATE provides a modular foundation for integrating complementary detection expertise and systematically studying how specialist diversity and coordination shape multi-agent tabular AD.