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points by JBits 4 hours ago | 0 comments
As impressive as this is, I can't help but conclude that the need for agents to write JavaScript programs to act out their actions represents a considerable limitation in current AI reasoning.
[–] NickNaraghi 4 hours ago
On the contrary, having the intelligence write deterministic code that saves time and lowers error rate seems like a winning approach.
[–] JBits 4 hours ago
It demonstrates that current LLMs are completely unable to follow a simple series of logical tasks. I feel the same way about ARC-AGI 3. While it might be the winning approach today, if they need this amount of complexity to do something simple, then it shows that their ability to generalise is clearly limited. Mining ore in Minecraft shouldn't be such a challenge for LLMs and yet apparently it is!
[–] latentsea 3 hours ago
I dunno. Aren't we somewhat similar? Why do we use software?
[–] bigyabai 4 hours ago
It depends on what the tasks are. Text-native work like programming or interacting with a terminal should be a lot more immediate for LLMs than something abstract like Starcraft, DOTA, Minecraft or Mindustry. LLMs certainly know the rules for these games, but processing game state and formulating a plan in the form of a Python script or MCP call is a rough debugging loop.

Minecraft is really the perfect example, because humans get all sorts of unfair advantages. Even a young player has enough spatial reasoning to recognize and remember landmarks, visualize large projects or explore without getting lost. These are simple instincts that LLMs will struggle to match even with huge context sizes and native multimodal input.

[–] Starlevel004 3 hours ago
> Mining ore in Minecraft shouldn't be such a challenge for LLMs and yet apparently it is!

It seems like it wasn't really a challenge at first, but GPT has a major anxiety disorder and immediately retreats at the sign of any adversity (in this case, dying) and tries to be as safe as possible.

[–] peri-cl 4 hours ago
Wonder if you could glue together deep networks such that there's a big reasoning LLM model, and high-speed small network of a different type, and they share a subset of their activations. High bandwidth vector communication. You train them together.

edit to add: Seems this is a well-known idea in ML / robotics already.