Local LLMs failed a simple agent task without goal-math hints

In a firsthand experiment, a local LLM controlled the queen in a small ant simulation and gave commands to worker ants. The goal was to reach 100 ants, counting the queen. Workers could find food and bring it back.

Food slowly ran down over time, and it was also needed to make eggs. Ten food points produced two eggs, and food sources did not refill after being drained to zero. With a basic prompt, every tested model starved the colony even when it already had enough food to finish the goal by making more workers.

When the prompt hinted that the model should create more workers instead of only searching for food, the models grew too aggressively and spent food needed to keep current workers alive, leading to starvation again. When the prompt added the goal math and told the model to look at the current situation and consider whether it only needed to spawn workers, the models could win. That last version felt unsatisfying because it gave the model too much of the solution.