Banger paper from Microsoft and colleagues.

We talk about human taste being important in this AI era.

But for recursive self-improvement, an agent's taste also matters.

The big question is: How often do frontier agents pick the better direction at a decision point in a long task?

It's apparently just under 60% of the time, according to Taste-Bench.

In this benchmark, each question shows a point in a trajectory where several directions are open, and one leads to a better outcome.

The forks are mined automatically from parallel attempts and detours in engineering and research runs.

The best model answers 59.7% correctly.

Forks whose deciding evidence appears later in the trajectory are much harder, and a larger reasoning budget does not raise accuracy.

The authors then distill a teacher's judgment of the outcome into a student model, which improves end-to-end success on held-out SWE-bench Pro tasks.

Paper: https://arxiv.org/abs/2609.25804

Chat with Paper: https://academy.dair.ai/papers/the-tasteful-agent-measuring-and-improving-taste-in-long-horizon-tasks-2609.25804