TabGuard: Agentic LLM Orchestration for Adaptive Tabular Anomaly Detection via Dynamic Validator Selection and Generation

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

TabGuard is an agentic framework designed for adaptive tabular anomaly detection, presented by Srihari Unnikrishnan and Minghua Ma at SURGeLLM 2026. It addresses a fundamental trilemma in existing methods, which struggle with heterogeneous columns, domain-specific rules, and enterprise-scale processing. Traditional rule-based systems demand extensive manual configuration, statistical methods miss semantic errors, and direct LLM-based approaches incur prohibitive per-cell inference costs. TabGuard resolves this by employing semantic routing through LLM function calling. It analyzes a small sample of each column, making O(m) LLM calls for m columns, to dynamically select the optimal validation strategy. This includes routing to a regex-based validator for syntactic patterns, a code-generation validator for domain-specific rules like Luhn checksums, or an embedding-based validator for distributional outliers. This architecture efficiently decouples cognitive reasoning from scalable programmatic execution, allowing deployment on large enterprise datasets without per-cell inference.

Key takeaway

For AI Engineers implementing anomaly detection on diverse tabular datasets, you should consider TabGuard's agentic LLM orchestration to overcome the limitations of traditional methods. This framework allows you to semantically route validation tasks, reducing expensive per-cell LLM inference while maintaining accuracy across syntactic, domain-specific, and distributional anomalies. Evaluate integrating dynamic validator selection to efficiently process large-scale data without extensive manual rule configuration.

Key insights

TabGuard uses agentic LLM orchestration to dynamically select optimal validation strategies for tabular anomaly detection, decoupling reasoning from execution.

Principles

• Semantic routing improves anomaly detection.
• Decouple LLM reasoning from execution.
• Dynamic validation adapts to data types.

Method

TabGuard analyzes column samples via LLM function calling to route to regex, code-generation (for domain rules like Luhn checksums), or embedding-based validators, avoiding per-cell LLM inference.

In practice

• Apply regex for syntactic patterns.
• Generate code for domain-specific rules.
• Use embeddings for distributional outliers.

Topics

• Tabular Anomaly Detection
• Agentic LLMs
• LLM Function Calling
• Semantic Routing
• Data Validation
• Enterprise Data Processing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.