As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations, including uncontrollable task complexity, extensive manual annotation with limited scenarios, and a lack of multidimensional evaluation.

In response to these challenges, we introduce OmniBench, a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.

To evaluate the diverse capabilities of virtual agents on the graph, we further present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.

Our synthesized dataset contains 36k graph-structured tasks across 20 scenarios, achieving a 91% human acceptance rate. Training on our graph-structured data shows that it can more efficiently guide agents compared to manually annotated data. We conduct multidimensional evaluations for various open-source and closed-source models, revealing their performance across various capabilities and paving the way for future advancements.

To cost-effectively construct diverse task scenarios with complexity at multiple granularities for comprehensive agent evaluation, we propose a novel self-generating, graph-based benchmark, OmniBench. It dynamically synthesizes tasks with controllable complexity based on a bottom-up pipeline.

OmniBench spans five fundamental types of task complexity to construct 10 evaluation dimensions (see the main figure). Test tasks across these dimensions are categorized based on combinations of complexity types. For example, a long-range planning test task typically exhibits higher dependency complexity and hierarchical complexity.

Overview of OmniBench, a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments.

We designed a bottom-up automated pipeline to synthesize tasks with controllable complexity. This pipeline consists of four processes:
(1) Subtask Discovery: First, we synthesize a series of simple subtask instructions from the explorable environment.
(2) Subtask Synthesis: Then, we iteratively synthesize subtask trajectories and evaluation functions.
(3) Task Composition: Next, the subtasks are combined into a task bottom-up.
(4) Task Validation: Finally, we validate the semantics of the tasks.

Bottom-up task synthesis pipeline of OmniBench.

We propose a graph-based multidimensional evaluation framework, OmniEval. In contrast to previous coarse-grained evaluation methods, we introduce a graph-based evaluator that applies subtask-level evaluation functions in OmniBench. Specifically, we design two novel fine-grained metrics to evaluate agents' performance on graph-structured tasks and their alignment with human logic. Based on OmniBench, we comprehensively evaluate 12 virtual agents, including both open-source and proprietary models, across all 10 capability dimensions as shown in the main figure, fully revealing the capability boundaries and providing concrete directions for future improvement.

Comparison of mainstream virtual agent evaluation strategies with the evaluation strategy we propose.