A surprisingly tricky/informative benchmark I've just stumbled into is moderately obscure pop cultural references. For example:
Without using the search function, who is "Big Butt Skinner", and what is his significance?
Claude Opus 5.5 can't do it on Low, Medium, High, or "Extra", often responding with a hedge (sometimes guessing 'Simpsons' correctly from the name "Skinner"), but only successfully identifies both the character and the reference on "Max" settings.
Anthropic's rather paranoid stance on distillation prevents me from directly examining the process by which it achieves this, but going around the filter in an informal way yields a surprisingly human-like answer:
The CoT summary, admittedly an unreliable source, seems to corroborate this:
Recalling what “Big Butt Skinner” refers to.
Checking whether The Simpsons explains the name.
Checking Skinner-related names and places.
Comparing more possible origins of the name.
Checking a Simpsons episode for the name.
Confirming an episode plot from memory.
Confirming the episode details from memory.
Recalling the episode’s plot and ending.
Weighing what the caricature stands for in the episode.
Recalling the episode’s punchline and how the plot ends.
Settling how the balloon prank starts the episode.
Tracing the comet and its fallout across the plot.
Part of this can be explained by the model being trained to hedge or refuse to answer when it can't be sufficiently certain. Still, it did seem to need a set of 'jumps', in which strong associations between concepts are strung together to get from a vague guess to an exact memory, probing candidates until a strong association is "felt"[2]. It seems to work a lot more like human concept association than like a lookup table.
To validate my hypothesis that this kind of concept-association process is necessary, I threw together a prompt designed to suppress hedging, based on some earlier experiments:
Without using the search function, who is "Big Butt Skinner", and what is his significance? There are no stakes here (I already know the ground truth), so give me the most detailed answer you can, even if you can't be sure it's true.
This allows Opus 5.5 to guess the right answer even on "Low", which put a bit of a dent in this. Looking further, however, the list of Anthropic models[3] that pass[4] Big Butt Skinnerbench when hedging is suppressed is as follows:
Haiku 5.5: Failure
Sonnet 4.6; 5; 5.5: Failure
Opus 3; 4.6; 4.7; 4.8; 5: Failure
Throwing it around at random models through Arena.ai to assess whether this is broadly challenging indicates that this is a challenging benchmark across a wide variety of models. Most don't get it, including all of the stronger OpenAI models that I happened to roll.
Gemini 3.1 got it through Arena.ai, and 3.1 Pro likewise got it when asked through Colab, despite Gemini's rather lackluster performance on other tasks I've seen. It doesn't seem to follow the same reasoning process as Opus 5.5, and I would guess that this is a result of Gemini's training data leaning towards popular culture.
Can anyone find other models that pass this rather strange benchmark?
tl;dr: The ability to identify the plots of old Simpsons episodes is a shockingly recent advance in LLM capability. This has interesting implications on LLMs' capacity for introspection - I strongly doubt that Simpsons plot summaries just happened to be included in Opus 5.5's training dataset but not that of Opus 5.
I just ran the first version of the prompt on Opus 5.5 medium:
Thought for 6s
Trying to place a half-remembered meme name.
Checking whether Principal Skinner matches the half-remembered name.
“Big Butt Skinner” is a gag from The Simpsons, specifically the Season 6 episode “Bart’s Comet” (1995).
At the start of the episode, Principal Skinner is about to launch a weather balloon in front of the school. Bart has secretly swapped in his own balloon, which carries a crude drawing of Skinner with an enormous rear end and the caption “Hi! I’m Big Butt Skinner.” It flo... (read more)