# Do Biological Structural Guarantees Earn Their Complexity?

arXiv: https://arxiv.org/abs/2605.15225 · HTML: https://mlsanity.com/paper/2605.15225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 3 of 3)

- [Towards a Science of Scaling Agent Systems](https://mlsanity.com/paper/2512.08296/) (2025)
- [Scaling Coding Agents via Atomic Skills](https://mlsanity.com/paper/2604.05013/) (2026)
- [Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence](https://mlsanity.com/paper/2603.28906/) (2026)

## Built on by (most cited first, 1 of 1)

- [Biological Motifs for Agentic Control](https://mlsanity.com/paper/2607.04240/) (2026)

## Abstract (arXiv)

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).