Is there anything interesting to learn from the agent written bots? Or are they just inferior versions of the best bots because the time limit is so short? Is there any indication that they’re trained on the code from the human written bots (like, do they magically “guess” creating functionality that requires experimental feedback or domain experience?) How much are the bots on rails applying the same techniques every time?

I think those kinds of questions would be more interesting than the raw win/loss ratios.

Dan Gant (author of Purple Wave) has written about some of this.

I did a 48 hour test with GPT-6 Astra and Claude Opus 5.5 and we saw some interesting uses of Psionic Storm.

I've open sourced some of GPT-Astra's work (below). It actually did an interesting amount of changes in strategy over time as it's opponents became more difficult.

https://github.com/llmskirmish/starskirmish-hillclimb

none of these games really require a lot of experimental feedback, the goals are well stated and known; experiments may drive optimization if thats the question.

as far as the on rails comment : there's no good way to know the provenance of an LLM output, but the LLM made bots generally look like very small, modular, sometimes functionalized versions of the human equivalents; the difference being that the LLM is calling the funtions often rather than a conditional loop in a main().

a factorio bot I worked on when Astra was first getting previewed was essentially a directory of very small argument-receiving functional and self-supporting programs that had very limited scope, and an agents.md file that directed the LLM to where such capabilities existed to preempt a directory search every compaction/context-swap.

so you'd have things like "python walk.py N 40" for 40 units of northern travel. a watchdog that waited for HP to drop or movement to become inhibited, etc.

My very first experience with LLM driven bot development in the GPT3 days created huge unmaintainmable monoliths that kind of worked due to TDD but were essentially spaghetti. LLMs have been trending away from those paradigms into small self-encapsulated programs. probably because those are less abusive to ctx count and produce better results since they lose/compact less.