Zero-Shot Anomaly Detection in High-Dimensional ISAC Data: Code-Generated Pipelines vs. Pre-Trained Multimodal Models

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the inference efficiency and throughput comparison of code-generated anomaly detection pipelines versus pre-trained multimodal models for high-dimensional ISAC data streams. Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning. In this paper, we study the problem of using LLMs to detect tabular anomalies and show that pre-trained LLMs are zero-shot batch-level anomaly detectors. 8 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the inference efficiency and throughput comparison of code-generated anomaly detection pipelines versus pre-trained multimodal models for high-dimensional ISAC data streams?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance