For me, ChatGPT (the free version, GPT-4o mini I believe?) gets it right, choosing option 2 because the coffee will cool faster due to the larger temperature difference.
Unless there's a gotcha somewhere in your prompt that I'm missing, like what if the temperature of the room is hotter than the coffee, or so cold that the coffee becomes colder than the milk, or something?
I would be suprised if any models get it wrong, since I assume it shows up in training data a bunch?
This is what I got from full-fat 4o. Maybe thinking less helps!
ChatGPT:
Option 1 — Add the cold milk immediately — will result in a lower final temperature after 2 minutes.
Why:
• Heat loss depends on the temperature difference between the coffee and the environment (usually room temperature).
• If you add the milk early, the overall temperature of the coffee-milk mixture is reduced immediately. This lowers the average temperature over the 2 minutes, so less heat is lost to the air.
• If you wait 2 minutes to add the milk, the hotter coffee loses more heat to the environment during those 2 minutes, but when you finally add the milk, it doesn’t cool it as much because the coffee’s already cooler and the temp difference between the milk and the coffee is smaller.
Summary:
• Adding milk early = cooler overall drink after 2 minutes.
• Adding milk late = higher overall temp after 2 minutes, because more heat escapes during the time the coffee is hotter.
Want me to show a simple simulation or visualisation of this?
In my experience LLM's tend to be pretty good at basic logic as long as they understand the domain well enough.
I mean, it even gets it right at first -- "This lowers the average temperature over the 2 minutes, so less heat is lost to the air." -- but then it seems to get conceptually confused about heat loss vs cooling, which is surprising.
Unless there's a gotcha somewhere in your prompt that I'm missing, like what if the temperature of the room is hotter than the coffee, or so cold that the coffee becomes colder than the milk, or something?
I would be suprised if any models get it wrong, since I assume it shows up in training data a bunch?