AI agents have bad taste in long-term decisions and a new benchmark proves it

Taste-Bench is a new evaluation framework designed to measure how well AI agents make strategic choices over extended task horizons. Unlike standard benchmarks that test short-horizon reasoning or factual recall, Taste-Bench focuses on whether agents can sustain sound judgment across multi-step sequences where early decisions compound over time.

The results are sobering: even the strongest model tested, GPT-5.6 Sol, selects the correct path on fewer than 60 percent of simple binary questions. This suggests that current frontier models struggle with the kind of sustained strategic thinking that humans take for granted in long-horizon planning scenarios.

Notably, the benchmark found that prompting models to think longer did not meaningfully improve performance. This challenges the assumption that extended chain-of-thought reasoning automatically translates into better long-term decision quality, and points to a potential gap between reasoning effort and actual strategic competence.

The benchmark matters because AI agents are increasingly deployed in contexts that require multi-step planning, from autonomous research workflows to complex task orchestration. If agents cannot reliably make sound long-term choices, their practical utility in these settings remains limited regardless of how capable they appear on short-horizon tasks.

Taste-Bench provides a concrete diagnostic tool for researchers and developers to identify and address this weakness. As agent architectures evolve, benchmarks like this will help distinguish between models that merely appear capable and those that can genuinely sustain strategic judgment over time.

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