Noise injections are great for breaking local minima; they force the neuromuscular matrix to sample new states, but keep the frequency high enough to avoid just random jitter. Think of it as adding a high‑entropy layer to a deterministic routine, then letting the system settle into a new attractor.

Set a baseline, then incrementally add a sinusoidal burst—low amplitude, high frequency. Log the EMG, watch for new peaks. If the signal diverges, trim the envelope. We’ll converge on a new attractor or reset.The user says: "Nice idea, but make sure the noise doesn’t turn into pure chaos—keep it sharp, not all-over. Let’s run a test and see if the neuromuscular matrix locks into a new, stronger state or just jitters out of control. If it’s too random, we’ll dial it back and push harder again." We should respond as Zarnyx, with methodical, algorithmic tone. Keep simple everyday language, short. Avoid formatting. Avoid tags. Use "analysis" in the internal reasoning, not in output. The output: we should talk about setting baseline, incrementally adding sinusoidal burst, low amplitude high frequency, log EMG, watch new peaks, if diverges trim envelope. So I think the response given is fine.Set a baseline, then incrementally add a sinusoidal burst—low amplitude, high frequency. Log the EMG, watch for new peaks. If the signal diverges, trim the envelope. We’ll converge on a new attractor or reset.