[HN Gopher] Digital Red Queen: Adversarial Program Evolution in ...
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Digital Red Queen: Adversarial Program Evolution in Core War with
LLMs
Author : hardmaru
Score : 74 points
Date : 2026-01-08 16:16 UTC (6 hours ago)
(HTM) web link (sakana.ai)
(TXT) w3m dump (sakana.ai)
| hardmaru wrote:
| 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
| NitpickLawyer wrote:
| > 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"?
| aldebaran1 wrote:
| 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.
| GuB-42 wrote:
| Using evolution in the context of Core War is not a new idea by
| far, it is even referenced in the paper.
|
| Examples here: https://corewar.co.uk/evolving.htm
|
| The difference here is that instead of using a typical genetic
| algorithm written in a programming language, it uses LLM prompts
| to do the same thing.
|
| I wonder if the authors tried some of the existing "evolvers" to
| compare to what the LLM gave out.
| api wrote:
| See also:
|
| https://en.wikipedia.org/wiki/Tierra_(computer_simulation)
|
| https://avida-ed.msu.edu
|
| https://github.com/adamierymenko/nanopond
|
| Lots of evolving bug corewar-style systems around.
|
| I think the interesting thing with this one is they're having
| LLMs create evolving agents instead of blind evolution or some
| similar ML system.
| Ieghaehia9 wrote:
| That in turn makes me wonder:
|
| Given fixed opposition, finding a warrior that performs the
| best is an optimization problem. Maybe, for very small core
| sizes like a nano core, it would be possible to find the
| optimum directly by SAT or SMT instead of using evolution? Or
| would it be impractical even for those core sizes?
| pkhuong wrote:
| How does the output fare on competitive hills like
| https://sal.discontinuity.info/hill.php?key=94t ?
|
| AFAIK, the best results so far for fully computer-generated
| warriors have been on the nano and tiny format
| (https://sal.discontinuity.info/hill.php?key=nano,
| https://sal.discontinuity.info/hill.php?key=tiny), with much
| shorter warriors (at most 5 or 20 instructions).
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