The doom loop isn't the model being dumb, it's the transcript working against you

Saw a comment last week about an agent that opened the same file eleven times and apologised about it, and it sent me down a rabbit hole, because the shape is so familiar: try a fix, hit the error, apologize sincerely, produce the same fix with the variable names shuffled. Somewhere around lap four you stop being annoyed and start wondering why "please try a DIFFERENT approach" never reliably works. Here's the mechanical read. The model is stateless — every turn it re-reads the full session transcript. After four failed attempts, the dominant text pattern in that transcript IS the failed attempt. A human reads that history as evidence the approach is wrong. A next-token predictor reads it as what this session does. The apology doesn't help either, because apologize-then-retry is itself a pattern it's seen a million times and is now continuing. What convinced me this is structural and not "model dumb": there's a trajectory study on SWE-bench that found agents in failed runs had located the correct file 72–81% of the time. Finding the spot was never the problem. Letting go of the hypothesis was. Same study describes an agent patching recursion errors with more logic, unable to re-evaluate its hypothesis across multiple loops. The fixes that seem to work all live in the harness, not the prompt: a hard budget (turns/tokens) so a stuck run stops instead of politely burning money; fingerprinting attempted diffs so a near-identical retry trips a forced "list three hypotheses you haven't tested"; and the nuclear one, clearing the window entirely — the learned constraints travel forward in the new opening prompt where they weigh a few dozen tokens instead of four failed attempts' worth of gravity. has anyone found a repetition detector that doesn't false-positive on legitimate retries (flaky tests, rate limits)?