Researchers from Microsoft and City University of Hong Kong have introduced Taste-Bench, a benchmark for testing how well AI agents make long-term decisions. In tests of 14 models, even the top performer barely exceeded coin-flip accuracy when choosing optimal paths without immediate feedback, according to The Decoder’s report.
Microsoft AI Watch analysis
What happened
The benchmark targets a particular weakness: decisions whose consequences arrive later, when there is no immediate feedback to guide the next choice. The researchers also report that training a student model on a teacher’s judgements significantly improved its task success on downstream benchmarks. The report does not give the size of that improvement.
Why it matters
A model can appear capable one step at a time and still make poor choices across a longer sequence. That distinction matters for AI agents expected to plan and act, not merely produce a plausible answer on demand. Taste-Bench offers a way to probe that gap, while the reported student-model result points to one possible route for improving performance.
Our read
The striking result is not that models are imperfect; it is that long-horizon choices remain so difficult even for the leading system tested. The training result is promising, but without the scale of the gains or fuller benchmark detail, it is a lead to investigate rather than a settled recipe. The Decoder’s report sets out the findings.
What to watch
- How the benchmark defines an optimal path and measures accuracy.
- Whether the reported gains from teacher judgements hold across other tasks and models.
- How future systems perform when decisions have delayed consequences.
Discussion spark: For agents making decisions over many steps, should developers prioritise better benchmark scores or evidence that the system succeeds in real-world tasks?
Sources and evidence
- AI agents have bad taste in long-term decisions and a new benchmark proves it (18 October 2026, 08:49 UTC)
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