Hi HN,

I am one of the authors from Sakana AI and MIT. We just released this paper where we hooked up LLMs to the classic 1984 programming game Core War. For those who haven't played it, Core War involves writing assembly programs in a language called Redcode that battle for control of a virtual computer's memory. You win by crashing the opponent's process while keeping yours running. It is a Turing-complete environment where code and data share the same address space, which leads to some very chaotic self-modifying code dynamics.

We did not just ask the model to write winning code from scratch. Instead, we treated the LLM as a mutation operator within a quality-diversity algorithm called MAP-Elites. The system runs an adversarial evolutionary loop where new warriors are continually evolved to defeat the champions of all previous rounds. We call this Digital Red Queen because it mimics the biological hypothesis that species must continually adapt just to survive against changing competitors.

The most interesting result for us was observing convergent evolution. We ran independent experiments starting from completely different random seeds, yet the populations consistently gravitated toward similar behavioral phenotypes, specifically regarding memory coverage and thread spawning. It mirrors how biological species independently evolve similar traits like eyes to solve similar problems. We also found that this training loop produced generalist warriors that were robust even against human-written strategies they had never encountered during training.

We think Core War is an under-utilized sandbox for studying these kinds of adversarial dynamics. It lets us simulate how automated systems might eventually compete for computational resources in the real world, but in a totally isolated environment. The simulation code and the prompts we used are open source on GitHub.

Other info other than the blog link:

Paper (website): https://pub.sakana.ai/drq/

Arxiv: https://arxiv.org/abs/2601.03335

Code: https://github.com/SakanaAI/drq

> adversarial evolutionary loop where new warriors are continually evolved to defeat the champions of all previous rounds.

Interesting. So you're including past generation champions in the "fights"? That would intuitively model a different kind of evolution than just "current factors"-driven evolution.

> We also found that this training loop produced generalist warriors that were robust even against human-written strategies they had never encountered during training.

Nice. Curious, did you do any ablations for the "all previous champions" vs. "current gen champions"?

Very interesting paper, thank you. It makes me wonder what other game substrates could form the basis for adversarial/evolutionary strategy optimization for LLMs, and whether these observations replicate across games.

Since LLMs are text based, a text-based game might be interesting. Something like Nomic?

Or a "meme warfare" game where each agent tries to prompt-inject its adversaries into saying a forbidden codeword, and can modify its own system prompt to attempt to prevent that from happening to itself.