DeepSeek-R1 Accuracy-Latency Trade-offs in Memory-Constrained Multimodal HumanEval-V Benchmarks

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the trade-off between accuracy and inference latency in DeepSeek-R1 versus baseline multimodal models on HumanEval-V when evaluated under memory-constrained environments. As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.4/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the trade-off between accuracy and inference latency in DeepSeek-R1 versus baseline multimodal models on HumanEval-V when evaluated under memory-constrained environments?

Autonomous literature synthesis. Automated review score: 8.4/10. Full text and citation available at Assignee Research.