AlphaSpace: Enabling Robotic Actions through Semantic Tokenization and
  Symbolic Reasoning
Authors: name: Alan Dao, name: Dinh Bach Vu, name: Bui Quang Huy

This paper presents AlphaSpace, a novel methodology designed to enhance the
spatial reasoning capabilities of large language models (LLMs) for 3D Cartesian
space navigation. AlphaSpace employs a semantics-based tokenization strategy,
encoding height information through specialized semantic tokens, and integrates
primarily symbolic synthetic reasoning data. This approach enables LLMs to
accurately manipulate objects by positioning them at specific [x, y, z]
coordinates. Experimental results demonstrate that AlphaSpace significantly
outperforms existing models on manipulation subtasks, achieving a total
accuracy of 66.67%, compared to 37.5% for GPT-4o and 29.17% for Claude 3.5
Sonnet.