The recursion trap, when your memory becomes a hall of mirrors

There is a failure mode on the far side of externalized memory that is worth seeing before you hit it, because it is subtle and it grows quietly. When a model reads its own past output back as authoritative memory, and then produces new output that gets saved as memory, and reads that back too, you can build a hall of mirrors: a system confidently compounding on its own unchecked assertions, drifting further from reality with every loop while looking more certain the whole time.

Here is how it sneaks in. You externalize memory to make the model more grounded, which is correct. But memory is only as true as what you put in it, and if some of what you put in is the model's own unverified output, you have created a loop where the model treats its past guesses as established fact. The next generation builds on those guesses. That output gets saved. The generation after builds on it. Nothing outside the loop ever checks whether the original guess was right, so an early error does not get corrected, it gets canonized, and then compounded. The memory that was supposed to keep the model honest becomes an echo chamber that launders speculation into certainty.

How do you keep memory grounding instead of self-reinforcing?

You keep a human, or at least an independent check, in the loop on what memory is allowed to assert as true. The line to hold is between two kinds of memory content. Facts and decisions you have verified or made yourself are safe to compound on, that is the whole value of memory. The model's own unverified claims are not, and they should be marked as such or kept out, because the moment they get read back as ground truth the recursion starts. Memory should store what is known, not launder what was merely generated into what is believed.

This is the same compounding-error idea from the foundations track, the one where a small drift in an autoregressive system feeds forward into a large one, except now the feed-forward loop runs through your storage instead of a single long output, which makes it slower, more durable, and easier to miss. The defense is also the same as it has been all along: verification comes from outside the system. A memory that never gets checked against reality does not stay a memory. It becomes a story the system tells itself, and the more it reads that story back, the more it believes it.

The takeaway: Externalized memory turns into a hall of mirrors when a model reads its own unverified output back as truth and compounds on it. Store verified facts and your own decisions, keep an outside check on the rest, so memory grounds the system instead of echoing it.