HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex
  Diagrams in Coding Tasks
Authors: name: Fengji Zhang, name: Linquan Wu, name: Huiyu Bai, name: Guancheng Lin, name: Xiao Li, name: Xiao Yu, name: Yue Wang, name: Bei Chen, name: Jacky Keung

Understanding and reasoning over diagrams is a fundamental aspect of human
intelligence. While Large Multimodal Models (LMMs) have demonstrated impressive
capabilities across various tasks, existing benchmarks lack comprehensive
evaluation of their diagram interpretation and reasoning abilities,
particularly in coding contexts. We present HumanEval-V, a rigorous benchmark
of human-annotated coding tasks that spans six task types and evaluates diverse
visual reasoning capabilities. Each task features carefully crafted diagrams
paired with function signatures and test cases, employing novel code generation
tasks to thoroughly assess models' diagram comprehension. Through extensive
experiments with 22 LMMs, we find that even top-performing models achieve
modest success rates, with Claude 3.5 Sonnet reaching only 36.8% pass@1,
highlighting substantial room for improvement. Our analysis reveals that
current LMMs struggle with spatial transformations, topological relationships,
and dynamic patterns that humans find intuitive. These findings provide
valuable insights for advancing LMMs' visual reasoning abilities. We have
open-sourced our code and benchmark at
https://github.com/HumanEval-V/HumanEval-V-Benchmark.