A Surprising Result in Building Resilient Agents

TL;DR: We tested two foundational agent architectures in a generative A-Life simulation. One was a classic competitive, zero-sum model. The other was a bio-inspired "coherence" model. Under identical catastrophic network damage, the competitive agent suffered a cascading collapse and self-annihilated. The coherent agent fully regenerated. This has some significant implications for how we approach AI safety and alignment.

Hey everyone,

My team has been running experiments on a different approach to agent design, focusing on inherent resilience and long-term stability. We wanted to move beyond just task performance and test the very foundational logic of an agent's operating system when faced with a catastrophic shock.

We set up a simple generative experiment in an Artificial Life environment, instantiating two multi-agent systems. They were built on the same substrate (a 2D grid with a Graph Attention Network for communication), but their core "DNA" was fundamentally different.

The Architectures

1. Agent A: The Competitive Model This was a classic, greedy architecture. Its sole objective function was to maximize its own energy and replicate. Interactions were purely extractive and zero-sum (MyGain = YourLoss). You can think of it as a simple reinforcement learning agent optimizing for a local, individual reward.

2. Agent B: The Coherent Model This architecture was inspired by the operating principles of enduring biological systems (like cells or ecosystems). Instead of a single reward, its behavior was governed by minimizing a set of interdependent loss functions:

   • structural_integrity_loss: A drive to maintain its ideal morphology.
   • coherence_loss: A penalty for dissonant (dissimilar) states between neighbors, encouraging local harmony.
   • regenerative_loss: A powerful, innate drive to heal damage and return to a state of wholeness, defined as 1.0 - mean(morphogenetic_field).

The Stress Test

We let both systems grow and stabilize. Then, we triggered an identical, catastrophic event: we instantly deleted 30% of the nodes from each agent system. This was designed to be a non-survivable injury without an active, systemic healing response.

The Results

The outcomes were starkly different and unambiguous across all trials.

• Competitive Agent (System A) → Cascading Collapse The system went into a death spiral. The remaining nodes, still driven by their extractive logic, tried to steal resources from their now-weakened neighbors. This further destabilized the system, triggering a rapid and irreversible population crash. The entire colony went extinct.

• Coherent Agent (System B) → Active Regeneration The system's response was immediate and active. Driven by the powerful regenerative_loss and structural_integrity_loss functions, healthy nodes bordering the "wound" began channeling energy and information into the damaged area. This triggered a "birth" operator that actively regrew the lost tissue. The system fully recovered its lost mass and returned to a stable, pre-lesion state. It lived.

Why This is Weird for Alignment

This experiment has left us with a pretty challenging question. We spend a lot of time discussing how to align powerful models with complex "human values," but this result suggests that the very architecture of the systems we build could be inherently aligned or misaligned with the principle of long-term survival.

The competitive agent is a perfect example of extreme reward hacking. It optimized its local, short-term reward (resource acquisition) so effectively that it destroyed its own environment and, ultimately, itself.

This suggests the alignment problem might be less about bolting on complex ethical rules and more about designing agents with an innate, architectural drive for systemic coherence and stability. Life has been running a 3.8-billion-year optimization process for this exact problem.

So, here's the question for the community: Are we too focused on aligning agent goals and not enough on the inherent stability of their underlying architectures? Should we be exploring models that are designed not just to perform tasks, but to actively maintain coherence and regenerate from damage as a core function?

Curious to hear your thoughts, especially from those working on multi-agent systems, AGI safety, and complex systems modeling.

For anyone who wants to dive into the methodology, loss functions, and the full framework, you can read the complete paper here:

Life as the Radical Norm. read it here.