AI RESEARCH
LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
arXiv CS.LG
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ArXi:2510.03904v2 Announce Type: replace Existing anomaly detection (AD) methods for tabular data usually rely on some assumptions about anomaly patterns, leading to inconsistent performance in real-world scenarios. While Large Language Models (LLMs) show remarkable reasoning capabilities, their direct application to tabular AD is impeded by fundamental challenges, including difficulties in processing heterogeneous data and significant privacy risks. To address these limitations, we propose LLM-DAS, a novel framework that repositions the LLM from a ``data processor'' to an ``algorithmist.