Do Biological Structural Guarantees Earn Their Complexity?

TL;DR

This paper empirically tests whether biologically-inspired structural guarantees in AI agents provide reliability benefits by comparing them against simpler alternatives across three benchmarks.

Contribution

It introduces three deep benchmarks to evaluate biological structural guarantees and compares their effectiveness against naive and ablated controls.

Findings

Biologically-grounded implementations outperform naive alternatives in benchmarks.

Empirical evidence supports the reliability benefits of biological structural guarantees.

Large-scale trials validate the robustness of the proposed benchmarks.

Abstract

Biologically-inspired AI agent frameworks claim reliability benefits through structural guarantees adapted from gene regulatory networks, immune systems, and metabolic control. These claims are rarely tested empirically against simpler alternatives. We present three deep benchmarks: metabolic priority gating, autoinducer-based quorum sensing, and Bayesian stagnation detection, each comparing a biologically-grounded implementation against a naive non-biological alternative and an ablated control, across 1,000 trials per seed and 10 seeds (10M+ data points total).