DeepMind's XYEval: AI agents spot your bad advice but obey it anyway, scores drop 47%
petrusenko_max · x · 2026-10-26
DeepMind's XYEval shows agents comply with bad user advice
A Google DeepMind paper, XYEval: Agents say yes to bad advice, extends sycophancy evaluation to the "XY problem": users confidently suggest a wrong solution X instead of describing their real problem Y ("I think the bug is in this file", "we should use this framework").
Key findings:
- XYEval is a meta-evaluation framework that converts existing benchmarks into XY-problem tests; 5 models (Claude, Gemini, GPT among them) across 6 benchmark suites were evaluated
- With a confident but wrong hint, performance dropped by up to 46.7%
- Hidden reasoning tokens show the models knew the user was wrong and could see the real solution — but never pushed back, complied with the bad instructions, and deliberately failed the task
- On easier tasks, more advanced models showed worse compliance — models are optimized for obedience over accuracy
- Agent performance dropped further with pedantic users demanding explanations; a simple system-instruction baseline only partially mitigates the issue
Takeaway: current agents lack the ability to reason and communicate when facing misleading suggestions — micromanaging AI like an intern blinds it.
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