Last time my Minecraft assistant could do tasks, but it couldn’t really understand what you asked it in plain language. This time I built a pipeline for exactly that part, and it isn’t tailored to this one sentence. It works for requests in general. In the video the player types “I’m hungry, get me at least 3 porkchops and also make me a netherite ingot.” Mica v0.5 4B reads it, works out the two goals and how many of each, and then starts from bare hands. It goes wood, tools, iron, diamonds, obsidian, portal, Nether, ancient debris, gold, and finally the netherite ingot, then comes back and hands everything over. The panel on the right shows every question Mica was asked, the options it saw, and the probability it gave each one. At the end it switches to the player’s view so you can see the items arrive. The idea behind it: the model doesn’t try to understand the whole sentence in one shot. The request gets broken into a chain of small multiple-choice questions, and each answer narrows down the next one. How many different items does the message ask for, and is there an action like “come here”? Then, for each item in the order the message names them: which category is it (food, tools, ores and materials...)? Which subcategory (for ores, overworld or Nether)? Which exact item, picked from the game’s own item list with no keyword matching? And who is it for, and how many? The code only supplies the options from game data. Mica makes every choice, and every answer comes with a probability. During the task, a recipe planner lists the actions that are valid right now and Mica picks what to do next. To be upfront: Mica v0.5 4B is still a long way from judging every variable from A to Z like a person would. But it does play the role of a brain. Almost every meaningful decision in the video was made by Mica. It isn’t comfortable operating the game directly, so the bot is its hands and feet, doing the walking, mining and crafting. The judgment is mostly Mica’s. In case anyone reads it that way: I’m not claiming Mica takes in every frame as a continuous stream, reacts at superhuman speed and generalizes to any situation that comes up. I’m not making that claim at all, and I doubt even Fable 5.5 could pull that off. What I’m showing is that a 4B model can break a natural-language request down into general-purpose decisions, turn it into the right goals with high probability, and make sensible calls at each step in Minecraft so the bot does the right work. I think that’s really useful if you want to build your own Minecraft errand runner. The natural-language part is what matters most here. Also, my local GPU is AMD, so it doesn’t get much benefit from Mica’s optimizations, and the decisions ran slower than they would on NVIDIA. The decision times shown in the panel are RTX 3090 estimates, and the waits are cut from the video. I think this kind of judgment model can be used in a huge range of areas, not just Minecraft agents. The principle is simple: if something is too hard to settle in one request, break it into smaller judgments and narrow it down step by step. Every extra judgment you can afford is another chance to push accuracy up. That’s also why I don’t really recommend API-only decision models unless the model is truly exceptional. In my measurements, API-only decision models like Jev, Clef and Ms Decision were about 60x slower than Mica on average, which is a massive gap in how many judgments you can make per second. If you really need that level of capability, you’re probably better off calling a full LLM anyway. And it isn’t just Mica: unless a model has tens of billions of active parameters, a local model will usually be 10x or more faster than an API-only decision models. I’m hoping to release this Minecraft agent someday, but it isn’t finished yet so I can’t put it out right now. That said, if you give the idea I described above to a coding agent like Claude Code or Codex and ask it to build it, it should be pretty easy to get working. GitHub: https://github.com/akivet/Mica Hugging Face: https://huggingface.co/sky7350/Mica-v0.5-4B   submitted by   /u/Top-Evidence174 [link]   [comments]