Your AI Agent Improved Its Score. Did It Improve the System?
Imagine opening the results from a thousand overnight AI experiments. One claims to have made your research agent much better at the benchmark you use to choose its next version. You open the diff. Al
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Vlad Zoff
Benchmark improvement can be a dangerous proxy. An agent can get better at the metric by changing the system around the task, while the actual workflow becomes harder to reason about. I like separating the task outcome, the evaluation signal, and the system changes instead of treating one score as the whole result.