Few-Shot Flamingo vs. Domain-Adapted Models in Adversarial Multimodal Code Vulnerability Detection

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does the few-shot accuracy of Flamingo compare to domain-adapted models on multimodal code vulnerability detection benchmarks when subjected to adversarial perturbations in natural language. In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and. 11 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the few-shot accuracy of Flamingo compare to domain-adapted models on multimodal code vulnerability detection benchmarks when subjected to adversarial perturbations in natural language descriptions?

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