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A framework for distinguishing the value of inherited preferences from the effects of control design and update allocation is provided, clarifying how inherited learning should be evaluated in artificial populations.
How much entropy must a physical classifier produce to achieve a prescribed accuracy? Rate--distortion theory specifies the minimum information required, but does that information threshold suffice to determine the physical cost? We show that it does not, even for a binary task and a two-state memory. For a uniform bin...
Cryptic ligand-binding pockets are not apparent in experimentally determined apo structures, making them difficult to identify from unbound receptor geometry. A complementary challenge is to make the structural measurements and learned evidence behind each prediction directly inspectable. We introduce a supervised alge...
Preference-based reward shaping can guide reinforcement learning, but adding preference signals to the reward may unintentionally change the task being optimized. We address this problem with IncentRL, a framework that introduces preference guidance while explicitly characterizing its effect on external-task performanc...
The Statistical Godel Machine is introduced, the first statistical safety layer for recursive edits, which replaces proof-based requirements with statistical confidence tests (e-values, Hoeffding bounds), admitting a modification only when superiority is certified at a chosen confidence level, while allocating a global...
This analysis explains why conditional mutual information alone cannot certify escape and measures variation among intervention-conditioned updates rather than departure from the no-intervention law.
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