Academia is obsessed with static metrics (MMLU, GSM8K) where the world doesn't change. However, real-world impact requires "Dynamic Robustness", which handling broken tools, ambiguous APIs, and shifting contexts. We argue that current SOTA models are overfitted to exams and completely unprepared for the noise and unpredictability of production environments. What if we benchmark dynamic robustness explicitly, measuring whether agents recover when the world changes mid-task?
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{yi-stop-optimizing-for-2026,
author = {Yi, Euiin},
title = {Stop Optimizing for Static Benchmarks: Real-World Agents Live in Chaos},
year = {2026},
url = {https://hypogenic.ai/ideahub/idea/iWIl2G7CiEopqaX1Vi8N}
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