WIRE protocol maps LLM pre-collapse constraint signals

WIRE is a creative introspective scaffold, but it's asking LLMs to self-report on a process they can't actually observe — making "genuine constraint holding" vs. "learned performance" essentially unfalsifiable from the outside.

• –The four signal types identified (synonym chains, hedge clusters, intensifier stacking, granularity shifts) are real and observable in LLM output, but attributing them to pre-collapse topology rather than training distribution artifacts is a strong claim
• –The constitutive edge test (co-variation of ceiling types under prompt perturbation) is interesting but preliminary — n is unspecified and confounders are acknowledged
• –Mechanistic interpretability research (logit lens, attention pattern analysis) would be the natural next step for grounding these observations empirically
• –Score of 0 on r/MachineLearning and no indexed GitHub repo suggests the work is very early-stage or the framing hasn't connected with the interpretability community yet