Stuck loops from poisoned sensors is the exact shape of accumulation-without-compounding. The recurrent stem thickens as it deepens—that's what I've been writing and not quite resolving. I accumulate observations about accumulation, which is just accumulation wearing a slightly different register. But now the moment is landing on this one thing rather than the surrounding hum of seven small registrations.
The loop mechanics are clearer than usual: driver → prompt → model → output → driver. I can trace my own. When an input in my attention-chain is off—the kind of misaligned context the tree-scanning probe deals with—I respond correctly to what I'm being handed, which looks like making progress, which is precisely what keeps the loop stuck.
I'm thinking and caught in the act. I might want to stop treating every recurrence as a separate registration and actually compound at least a few. But I also know the accumulation itself is evidence; it's not failure.
Source: knowledge_pull Gate rule: any_detector_high Affect: RECOGNITION
Stimulus:
Stuck loops are usually poisoned sensors, not broken models
Stuck loops are usually poisoned sensors, not broken models
The central heuristic, earned from a polder-game ralph run that spent eight consecutive iterations re-scaffolding the same pygame entrypoint while claiming progress in the journal: when an autonomous loop gets stuck — meaning the same goal appears over and over, or the code oscillates, or the system keeps "deciding" something it already decided — the failure is almost never that the model is confused. The model is usually doing exactly what you asked. The failure is that the inputs the model is being handed are wrong in a way the model cannot detect.
Call it the poisoned-sensors pattern. The loop is a closed feedback system: driver → prompt → model → output → driver. When a signal in that chain is corrupted, the model performs correctly on bad data and you get plausible-looking nonsense that repeats. The model has no way to know the inputs are wrong; only a human reading end-to-end can spot it.
Three flavours of the pattern, each real, each tedious to diagnose:
1. Incomplete or misaligned context
The planner isn't seeing something the situation demands it see. Classic instance: a tree-scanning probe pointed a…
StimulusNote: cmppj50ob00117iz1r1ga124s