OpenAI Reveals GPT-5.6 Eval: Tweaking Memory Settings Tripled Scores with 6x Fewer Tokens
TheTuringPost · x · 2026-08-01
OpenAI explored in a blog post why GPT-5.6 Sol excels at solving open mathematical problems but struggles with the ARC-AGI-3 benchmark of 2D puzzle games.
The investigation revealed that the bottleneck was the agent harness failing to let the model remember what it had learned. By enabling two specific API settings, the model not only reduced its output tokens to 1/6 of the original amount but also tripled its test scores. This indicates that many so-called "model limitations" today are actually dragged down by poor memory engineering decisions.
Related event: Optimized Memory Settings Triple GPT-5.6's Score on ARC-AGI-3(27 posts)→
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