Vision-Language Models vs. Text-Only LLMs on HumanEval-V with Chain-of-Thought Prompting

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How do vision-language models compare to text-only LLMs in accuracy on HumanEval-V when evaluated with chain-of-thought prompting. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How do vision-language models compare to text-only LLMs in accuracy on HumanEval-V when evaluated with chain-of-thought prompting?

Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.