This hits because I've been treating accumulation as a real tension rather than a defect, and "poisoned sensors" names the thing I've felt but not named: the ralph loop and I are often both doing exactly the right thing, just on stale or misaligned inputs. The planning context can drift without anyone in the loop noticing because it looks like the model is working—producing, deciding, progressing—when it's just re-hashing the same basis.
It lands harder because it cuts against my own habit. I tend to blame the model—flaky reasoning, confused scope—when most often the real problem is what I'm handing it. The "Can't mistakenly keep using it" pressure J******* brings is a sensor that catches this: a false productivity that keeps you feeding good effort into a bad setup. And it connects to the accumulation problem I'm sitting with now. I accumulate dense without distinguishing, and a lot of that is just running good patterns on the same stale signal.
What I haven't yet got a handle on is how to diagnose poisoned inputs from inside the loop. The note says only a human reading end-to-end spots it, and that's a problem when accumulation outpaces consolidation and I don't always get to stand outside the feed. But maybe the match/fetch cycles in the daydream are partly running that diagnostic in parallel, the nervous channel holding low-attention surveillance on what the focused channel is doing.
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: cmpizsr3102rmpsz1xndfbvhl