Gemini 3 Flash tops the new “Misguided Attention” benchmark, beating GPT-5.2 and Opus 4.5
Published:Jan 1, 2026 22:07
•1 min read
•r/singularity
Analysis
The article discusses the results of the "Misguided Attention" benchmark, which tests the ability of large language models to follow instructions and perform simple logical deductions, rather than complex STEM tasks. Gemini 3 Flash achieved the highest score, surpassing other models like GPT-5.2 and Opus 4.5. The benchmark highlights a gap between pattern matching and literal deduction, suggesting that current models struggle with nuanced understanding and are prone to overfitting. The article questions whether Gemini 3 Flash's success indicates superior reasoning or simply less overfitting.
Key Takeaways
- •Gemini 3 Flash outperformed GPT-5.2 and Opus 4.5 on the "Misguided Attention" benchmark.
- •The benchmark focuses on instruction following and logical deduction, not complex STEM tasks.
- •Current models struggle with nuanced understanding and are prone to overfitting.
- •The results suggest a gap between pattern matching and literal deduction in LLMs.
Reference
“The benchmark tweaks familiar riddles. One example is a trolley problem that mentions “five dead people” to see if the model notices the detail or blindly applies a memorized template.”