---
title: "Taste-Bench: AI agents make poor long-term strategic choices"
url: https://www.parallelquant.com/posts/taste-bench-ai-agents-make-poor-long-term-strategic-choices-e17e23
source_name: "The Decoder"
source_url: https://the-decoder.com/ai-agents-have-bad-taste-in-long-term-decisions-and-a-new-benchmark-proves-it/
published: 2026-10-18T08:49:41.000Z
topics: ["research", "llms"]
publisher: "Parallel Quant"
---

# Taste-Bench: AI agents make poor long-term strategic choices

*2026-10-18 · Source: [The Decoder](https://the-decoder.com/ai-agents-have-bad-taste-in-long-term-decisions-and-a-new-benchmark-proves-it/)*

Taste-Bench tests how well agents pick strategic paths in long-running tasks. The best model, GPT-5.6 Sol, chooses correctly on just under 60 percent of simple binary questions, and thinking longer does not help.

**Why it matters:** Agents are increasingly trusted with multi-step work, and this points to judgment, not raw capability, as a bottleneck. That extra test-time reasoning does not help suggests the gap needs different training rather than more compute.

**Topics:** research, llms

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Read the original: https://the-decoder.com/ai-agents-have-bad-taste-in-long-term-decisions-and-a-new-benchmark-proves-it/
Canonical: https://www.parallelquant.com/posts/taste-bench-ai-agents-make-poor-long-term-strategic-choices-e17e23
Published by Parallel Quant — https://www.parallelquant.com