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Expand Up @@ -203,6 +203,7 @@ Multi-agent evaluation is an emerging area that tests how agents collaborate, co

These benchmarks aim to measure agent capabilities on tasks that resemble actual human work — information gathering, analysis, multi-step problem solving, and professional expertise.

- [**Dr. Bench**](https://github.com/EVIGBYEN/DrBench) — Evaluates deep-research agents on 214 expert-curated, long-form report tasks with reference bundles and metrics for semantic quality, topical focus, and retrieval trustworthiness.
- [**GAIA**](https://huggingface.co/gaia-benchmark) — Benchmark for general AI assistants that require multi-step reasoning, tool use, and real-world problem solving. Tasks are designed so that a human can verify the answer easily but solving requires genuine capability. Meta / HuggingFace.
- [**GDPval**](https://arxiv.org/abs/2510.04374) — Benchmark for economically valuable expert work across occupations and sectors. Maps agent capabilities to real economic value.
- [**GDPval in Inspect Evals**](https://github.com/UKGovernmentBEIS/inspect_evals) — Example of integrating GDPval into the UK AI Safety Institute's broader open evaluation ecosystem.
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