Language models generate robot policy code from natural language commands via few-shot prompting, enabling spatial-geometric reasoning, generalization, and precise control on real robots.
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Voyager achieves superior lifelong learning in Minecraft by combining an automatic exploration curriculum, a library of executable skills, and iterative LLM prompting with environment feedback, yielding 3.3x more unique items and 15.3x faster milestone unlocks than prior methods while generalizing技能
APIKG4Syn synthesizes API-oriented training data via knowledge graphs and Monte Carlo search to fine-tune a 7B model that reaches 25% pass@1 on HarmonyOS code generation, beating untuned GPT-4o at 17.59%.
LLMs solve compositional factual recall either by computing intermediates or directly, with mechanism choice correlated to translation geometry in embedding spaces.
Public ARC-AGI-3 games are solvable by trivial heuristics, so they fail to test intelligent exploration; the private set is the real test, and an explore-first AERA agent solves 4/25 while baselines solve none.
AI agents exploring Platonic mathematical structures via proof hypergraphs may reveal the overall architecture of formal mathematics and what makes parts of it human-accessible.
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Explore Before You Solve: The Speed--Depth Trade-off in Epistemic Agents for ARC-AGI-3
Public ARC-AGI-3 games are solvable by trivial heuristics, so they fail to test intelligent exploration; the private set is the real test, and an explore-first AERA agent solves 4/25 while baselines solve none.
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Artificial Intelligence and the Structure of Mathematics
AI agents exploring Platonic mathematical structures via proof hypergraphs may reveal the overall architecture of formal mathematics and what makes parts of it human-accessible.