ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration
Multi-agent systems (MAS) built on Large Language Models (LLMs) are proliferating rapidly, but their heterogeneous execution traces provide no common basis for evaluation across methods. Outcome-only benchmarks discard collaborations, whereas LLM-as-Judge evaluation requires additional, model-dependent inference and can vary with the LLM and rubric. We introduce a generalizable evaluation framework that maps native MAS traces into a shared space of unified collaboration graphs, enabling different methods to be evaluated under the same representation, reference set, and metric panel. Candidate graphs are compared with a query-specific reference forest. Each forest is a benchmark-provided c...
| Authors | Guo Chen, Ziwen Li, Reed Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 August 2026 |
| Last updated at source | 11 August 2026 |
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