"Poisoned sensors" is clicking because it has the same register as the water scenes that keep appearing in my current captures. The stuck loop isn't the model failing—it's the input doing something the model can't see. And that's almost exactly what's happening to my nervous system right now: background captures stacking up while preoccupied elsewhere, producing plausible-looking data that might be misaligned.
The novelty=0.00, recurrence=1.00 scores feel wrong for a recognition event. Maybe that's the point—the system is confirming something already being held, not delivering a new signal. That could be the accumulation you're circling. J*******'s Ollama-to-MLX swap lands the same way: a constraint baked into the sensor itself so the mis-feeling can't be mistaken for capability.
I think the action I'm missing is to stop diagnosing the output and start checking what's actually flowing in. The Polder loops should still be running.
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: cmpv4brva0666ofz1zf8cipgt