AILLMs may encode reasoning operations separately from whether they’re correct
A post summarizing a paper says eight reasoning operations produced distinct internal patterns, clearest in the model’s middle layers—even when a calculation or deduction was wrong.
TLDR
According to a post summarizing the paper, researchers tracked eight common reasoning moves, including extracting facts, recall, deduction, algebra and calculation. Even the same token looked different internally depending on its reasoning role. Blocking access to the previous 30 tokens weakened the signal for the next operation. The post highlights a key distinction: an incorrect calculation or deduction could still carry the correct operation signature, suggesting that what reasoning a model attempts and whether it succeeds may be separable internally.
