Graph2Eval: Automatic Multimodal Task Generation for Agents via
  Knowledge Graphs
Authors: name: Yurun Chen; name: Xavier Hu; name: Yuhan Liu; name: Ziqi Wang; name: Zeyi Liao; name: Lin Chen; name: Feng Wei; name: Yuxi Qian; name: Bo Zheng; name: Keting Yin; name: Shengyu Zhang
Published: 2025-10-01 04:37:54

Abstract: As multimodal LLM-driven agents continue to advance in autonomy and
generalization, evaluation based on static datasets can no longer adequately
assess their true capabilities in dynamic environments and diverse tasks.
Existing LLM-based synthetic data methods are largely designed for LLM training
and evaluation, and thus cannot be directly applied to agent tasks that require
tool use and interactive capabilities. While recent studies have explored
automatic agent task generation with LLMs, most efforts remain limited to text
or image analysis, without systematically modeling multi-step interactions in
web environments. To address these challenges, we propose Graph2Eval, a
knowledge graph-based framework that automatically generates both multimodal
document comprehension tasks and web interaction tasks, enabling comprehensive
evaluation of agents' reasoning, collaboration, and interactive capabilities.
In our approach, knowledge graphs constructed from multi-source external data
serve as the task space, where we translate semantic relations into structured
multimodal tasks using subgraph sampling, task templates, and meta-paths. A
multi-stage filtering pipeline based on node reachability, LLM scoring, and
similarity analysis is applied to guarantee the quality and executability of
the generated tasks. Furthermore, Graph2Eval supports end-to-end evaluation of
multiple agent types (Single-Agent, Multi-Agent, Web Agent) and measures
reasoning, collaboration, and interaction capabilities. We instantiate the
framework with Graph2Eval-Bench, a curated dataset of 1,319 tasks spanning
document comprehension and web interaction scenarios. Experiments show that
Graph2Eval efficiently generates tasks that differentiate agent and model
performance, revealing gaps in reasoning, collaboration, and web interaction
across different settings and offering a new perspective for agent evaluation.