V7 one-sided health per agent · V10 self-calibrating divergence detector · throttle + savings/quality modes
Tokens saved
+0
Quality Δ
+0.0%
ModeFault
Savings: throttle doomed agents, bank the freed tokens.
—
Pick a setup and press ▶ Run.
Per-agent health (V7) divergence z = —
Run
degrading agent's spend floor (0 = full park · 0.30 = scaffold discount)
σ above healthy gap to fire · lower = earlier/twitchier
Advanced
Swarm — spend & health
boost
throttle
Architect
—
spend 100%
↓ feeds ↓
boost
throttle
Impl-1
—
spend 100%
boost
throttle
Impl-2
—
spend 100%
boost
throttle
Impl-3
—
spend 100%
↓ feeds ↓
boost
throttle
Integrator
—
spend 100%
Run-straight budget
0
Policy spend
0
What it's telling you
Detector = V10 (self-calibrating). Instead of a hand-set threshold, it runs all agents' health trajectories in parallel, learns the normal inter-agent gap from healthy data, and fires when the gap opens past k·σ — divergence z-score. This is why it works on gradual drift, where a fixed threshold is nearly impossible to tune. Re-convergence (gap closing) triggers resume. Monitor = V7 (one-sided, degradation-from-peak): each agent's health drops on degradation but isn't fooled by outperformance, and — unlike V1 — doesn't re-normalize through a sustained drop, so slow faults stay visible. Action = throttle (continuous, to the floor you set), with the freed spend either banked (Savings) or reallocated to recovery (Quality). Lineage: this is Paper 20's V10 detection + scaffold disposition, generalized to a continuous dial and ported to coupled agents. Cut A in-sim — proves the control logic given these dynamics, not that real agents behave this way. V7's exact formula is reconstructed from the published mechanism (degradation-from-peak); pending the CoVer-VLA script.