Do Biological Structural Guarantees Earn Their Complexity?
For AI researchers using bio-inspired frameworks, this work questions the necessity of their complexity, but the negative result is incremental.
The paper tests whether biologically-inspired structural guarantees in AI agents outperform simpler alternatives across three benchmarks with 10M+ data points, finding no consistent advantage.
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).