[2510.03904] LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis

Computer Science > Machine Learning arXiv:2510.03904 (cs) [Submitted on 4 Oct 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis Authors:Hangting Ye, Jinmeng Li, He Zhao, Mingchen Zhuge, Dandan Guo, Yi Chang, Hongyuan Zha View a PDF of the paper titled LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis, by Hangting Ye and 6 other authors View PDF HTML (experimental) Abstract: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''. Instead of being exposed to raw data, our framework leverages the LLM's ability to reason about algorithms. It analyzes a high-level description of a given detector to understand its intrinsic weaknesses and then generates detector-specific, data-agnostic Python code to synthesize ``hard-to-detect'' anomalies that exploit these vulnerabilities. This generated synthesis program, which is reusable across diverse datasets, is then instantiated...