Despite impressive capabilities, large language models have yet to produce a genuine breakthrough. The puzzle is why.

A reason may be that they lack some fundamental aspects of human thought: they are frozen, unable to learn from experience, and they have no “default mode” for background processing, a source of spontaneous human insight.

To solve this, I propose a day-dreaming loop (DDL): a background process that continuously samples pairs of concepts from memory. A generator model explores non-obvious links between them, and a critic model filters the results for genuinely valuable ideas. These discoveries are fed back into the system’s memory, creating a compounding feedback loop where new ideas themselves become seeds for future combinations.