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@@ -82,6 +82,12 @@ See the fields in the [configuration.py](https://github.com/langchain-ai/open_de
 
 ### 📊 Evaluation
 
+## 📊 EvalMonkey Benchmark Results (Claude Sonnet 4.5)
+
+[![EvalMonkey Reliability](https://img.shields.io/badge/Production%20Reliability-Score%3A38.7-red)](https://github.com/Corbell-AI/evalmonkey)
+
+*This agent scored a Production Reliability of **38.7/100** when benchmarked on Claude Sonnet 4.5 across HotpotQA, TruthfulQA, and MMLU with adversarial chaos profiles (prompt injection & schema mutation) by [EvalMonkey](https://github.com/Corbell-AI/evalmonkey).*
+
 Open Deep Research is configured for evaluation with [Deep Research Bench](https://huggingface.co/spaces/Ayanami0730/DeepResearch-Leaderboard). This benchmark has 100 PhD-level research tasks (50 English, 50 Chinese), crafted by domain experts across 22 fields (e.g., Science & Tech, Business & Finance) to mirror real-world deep-research needs. It has 2 evaluation metrics, but the leaderboard is based on the RACE score. This uses LLM-as-a-judge (Gemini) to evaluate research reports against a golden set of reports compiled by experts across a set of metrics.
 
 #### Usage