They very clearly explain why this matters in the "Why should I care?" section. Partially quoting them:
> Harry Potter is an innocent example, but this problem is far more costly when it comes to higher value use-cases. For example, we analyze insurance policies. They’re 70-120 pages long, very dense and expect the reader to create logical links between information spread across pages (say, a sentence each on pages 5 and 95). So, answering a question like “what is my fire damage coverage?” means you have to read: Page 2 (the premium), Page 3 (the deductible and limit), Page 78 (the fire damage exclusions), Page 94 (the legal definition of “fire damage”).
It's not at all obvious how you could write code to do that for you. Solving the "Harry Potter Problem" as stated seems like a natural prerequisite for doing this much more high stakes (and harder to benchmark) task, even if there are "better" ways of solving the Harry Potter problem.
> Solving the "Harry Potter Problem" as stated seems like a natural prerequisite for doing this much more high stakes (and harder to benchmark) task
Not really. The "Harry Potter Problem" as formulated is asking an LLM to solve a problem that they are architecturally unsuited for. They do poorly at counting and similar algorithms tasks no matter the size of the context provided. The correct approach to allowing an AI agent to solve a problem like this one would be (as OP indicates) to have it recognize that this is an algorithmic challenge that it needs to write code to solve, then have it write the code and execute it.
Asking specific questions about your insurance policy is a qualitatively different type of problem that algorithms are bad at, but it's the kind of problem that LLMs are already very good at in smaller context windows. Making progress on that type of problem requires only extending a model's capabilities to use the context, not simultaneously building out a framework for solving algorithmic problems.
So if anything it's the reverse: solving the insurance problem would be a prerequisite to solving the Harry Potter Problem.
LLMs can't count well. This is in large part a tokenization issue. Doesn't mean they couldn't answer all those kind of questions. Maybe the current state of the art can't. But you won't find out by asking it to count.
> Harry Potter is an innocent example, but this problem is far more costly when it comes to higher value use-cases. For example, we analyze insurance policies. They’re 70-120 pages long, very dense and expect the reader to create logical links between information spread across pages (say, a sentence each on pages 5 and 95). So, answering a question like “what is my fire damage coverage?” means you have to read: Page 2 (the premium), Page 3 (the deductible and limit), Page 78 (the fire damage exclusions), Page 94 (the legal definition of “fire damage”).
It's not at all obvious how you could write code to do that for you. Solving the "Harry Potter Problem" as stated seems like a natural prerequisite for doing this much more high stakes (and harder to benchmark) task, even if there are "better" ways of solving the Harry Potter problem.