Hacker NewsAt first, selfish strategies dominated. But when agents were given memory — the ability to remember who helped them — cooperation suddenly became stable under resource scarcity.
That experiment stayed in the back of my mind for years.
Recently I started rebuilding the idea from scratch as a larger system:
BiomeSyn
Instead of evaluating AI on static tasks, the goal is to explore long-horizon adaptive environments where agents must:
• gather resources • survive environmental pressure • compete with other agents • adapt over many generations
The system is deterministic, so experiments can be reproduced across seeds — which makes it possible to treat it as a benchmark for adaptive agents.
The bigger question I’m interested in:
> What happens when intelligence is evaluated inside a world that keeps evolving?
Many current benchmarks measure short-episode performance. But real adaptive systems must operate in open-ended environments.
BiomeSyn is still an early research sandbox, but I’m curious whether environments like this could become useful for studying:
• evolutionary computation • long-horizon RL agents • multi-agent ecosystems • adaptive AI systems
Would be interested to hear thoughts from people working on agents, simulation platforms, or large-scale AI systems.
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