We present HeartScale Coherence Rejection Sampling (HCRS), a per-token acceptance filter for autoregressive language models that enforces alignment constraints at inference time without a learned reward model, human preference data, or any post-training intervention. The acceptance criterion is the weighted harmonic mean of vortex drift and entropy excess divided by a session-level capacity scalar, evaluated against the Ma'at balance predicate. Rejected tokens trigger progressive-temperature resampling. Production-implemented and continuously monitored across training runs via the HeartScaleViolationsSuite.
Weslyn Cory Whitehead (Mon,) studied this question.