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I've had some weird "thinking outside the box" behavior like this. I once asked 3 Pro what Ozzy Osbourne is up to. The CoT was a journey, I can tell you! It's not in its training data that he actually passed away. It did know he was planning a tour though. It had a real struggle trying to consolidate "suspicious search results" and even questioned whether it was fake news, or running against a simulation!, determining it wasn't going to fall for my "test".

It did ultimately decide Ozzy was alive. I pushed back on that, and it instantly corrected itself and partially blamed my query "what is he up to" for being formulated as if he was alive.

“Gemini 3 Pro was often overloaded, which produced long spans of downtime that 2.5 Pro experienced much less often”

I was unclear if this meant that the API was overloaded or if he was on a subscription plan and had hit his limit for the moment. Although I think that the Gemini plans just use weekly limits, so I guess it must be API.

I can't believe how massively underpaid I was when I was 11

True though I bet the $200 a month plan could do it, maybe a few extra days of downtime when quota was maxed

To beat it, not to solve it. Solving means something very specific in the context of games — deriving and proving a GTO strategy.

Who is paying for this?

Did the streamer get subsidized by Google?

(The stream isn't run by Google themselves, is it?)

:/ Damn. That needs to cost 1000x less before people can try it on their own games.

Yes, at least to some extent. The author mentions that the base model knows the answer to the switch puzzle but does not execute it properly here.

"It is worth noting that the instruction to "ignore internal knowledge" played a role here. In cases like the shutters puzzle, the model did seem to suppress its training data. I verified this by chatting with the model separately on AI Studio; when asked directly multiple times, it gave the correct solution significantly more often than not. This suggests that the system prompt can indeed mask pre-trained knowledge to facilitate genuine discovery."

I'm wondering about this too. Would be nice to see an ablation here, or at least see some analysis on the reasoning traces.

It definitely doesn't wipe its internal knowledge of Crystal clean (that's not how LLMs work). My guess is that it slightly encourages the model to explore more and second-guess it's likely very-strong Crystal game knowledge but that's about it.

It will definitely have some effect. Why won't it? Even adding noise into prompts (like saying you will be rewarded $1000 for each correct answer) has some effect.

Whether the 'effect' something implied by the prompt, or even something we can understand, is a totally different question.

I very much doubt it

Do we have examples of this in promps in other contexts?

I would imagine that prompting anything like this will have an excessively ironic effect like convincing it to suppress patterns which it would consider to be pre-knowledge.

If you looked inside they would be spinning on something like "oh I know this is the tile to walk on, but I have to only rely on what I observe! I will do another task instead to satisfy my conditions and not reveal that I have pre-knowledge.

LLMs are literal douche genies. The less you say, generally, the better

If they trained the model to respond to that, then it can respond to that, otherwise it can't necessarily.

It might get things wrong on purpose, but deep down it knows what it's doing

I have to think they need to know enough of the guides for the game for it to work out, how do they know whats on screen?

It would unfortunately also need several runs of each to be reliable. There's nothing in TFA to indicate the results shown aren't to a large degree affected by random chance!

(I do think from personal benchmarks that Gemini 3 is better for the reasons stated by the author, but a single run from each is not strong evidence.)

You're too kind. Even the CEO of Google retweeted how well Gemini 2.5 did on Pokemon. There is a high chance that now it's explicitly part of the training regime. We kind of need a different kind of game to know how well it generalizes.

Isn't that the point of a new model anyway?

“And, because AI never got any better or any cheaper after that point, sussmanbaka’s wry observation remained true in perpetuity, forever.”

- History, most likely

Children are incredibly smart. All of this was fantasy 15 years ago. Comments like yours are amazing to me…

I used to think the same until latest agents started adding perfectly fine features to a large existing react app with just basic input (in English) . Most of the jobs require levels of intelligence below that. It's just a matter of time before agents get to that.

There was a well-publicised "Claude plays Pokémon" stream where Claude failed to complete Pokemon Blue in spectacular fashion, despite weeks of trying. I think only a very gullible person would assume that future LLMs didn't specifically bake this into their training, as they do for popular benchmarks or for penguins riding a bike.