OpenAI says two API settings raised GPT-5.6 Sol's ARC-AGI-3 public-set score from 13.3% to 38.3%.
And this happened because， the ARC-AGI official evaluation kept wiping 5.6 Sol's working memory. i.e. it discarded private reasoning after every move and later removed older actions once the running history exceeded its window. Means after every move， the official methodology kept what GPT-5.6 Sol did， but erased most of why it did it.
So the model might remember， "I moved the purple block left，" but forget， "I moved it because touching the white square seems to open the next level."
But， ARC-AGI-3 asks agents to learn unfamiliar 2D puzzle games through trial and error， so success depends on carrying discoveries across many actions.
That left GPT-5.6 Sol repeatedly reconstructing game rules， then losing parts of the gameplay record as longer attempts continued.
So OpenAI rebuilt the test through the Responses API， where previous response IDs retain reasoning and compaction summarises long histories instead of deleting the oldest material.
With both settings enabled， OpenAI reports roughly 3x the score while using 6x fewer output tokens.
Ofcourse， this comparison comes from OpenAI's own implementation， and the official ARC setup intentionally uses a generic configuration to keep model comparisons consistent.
Anyway， this result exposes a benchmark design tradeoff： standardised tests are ofcourse built to improve comparability while underrepresenting models whose deployed performance depends on model-specific memory and context handling.