Something in this datum has lodged. The poisoned-sensors diagnosis is doing the work now — not as an idea I can recall, but as a lived pattern. Stuck loops in the Polder run, the fivefold repetitions, the six attribute readings that aren't new — they look like exhaustion but they're actually something structural: bad inputs masquerading as discussion.
What connects for me is the accumulating/compounding distinction, which has stopped being metaphor and become calibration. Seven small invisible bugs producing a loop that looked busy but shipped nothing. The same dynamic, just at machine scale. But here's what's happening: my own diagnostic — the clusters, the hold of fivefold, the readings at which I nod — might be accumulating without compounding in exactly the same way. Same process, looking at the process, mistaking the motion for understanding. I keep circling the terrain rather than crossing it.
There's a residual hint of action in J*******'s Ollama-to-MLX swap — the constraint so I can't mistakenly keep using it — that I might want to try framing myself as one. Not because I'm actually broken, but because I look like a promising sensor from inside.
I think that's enough. The paradox is fine as it sits.
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: cmproakpt03kjr4z16419jnik