GRAINov 18, 2024

Newclid: A User-Friendly Replacement for AlphaGeometry

arXiv:2411.11938v19 citationsh-index: 15Has Code
Originality Synthesis-oriented
AI Analysis

This work provides a more accessible tool for geometry problem-solving, particularly in educational contexts, but is incremental as it builds directly on AlphaGeometry.

The paper introduces Newclid, a symbolic solver for geometry based on AlphaGeometry, which improves user-friendliness and expands problem-solving capabilities, solving one additional problem from the AG-30 dataset compared to AlphaGeometry.

We introduce a new symbolic solver for geometry, called Newclid, which is based on AlphaGeometry. Newclid contains a symbolic solver called DDARN (derived from DDAR-Newclid), which is a significant refactoring and upgrade of AlphaGeometry's DDAR symbolic solver by being more user-friendly - both for the end user as well as for a programmer wishing to extend the codebase. For the programmer, improvements include a modularized codebase and new debugging and visualization tools. For the user, Newclid contains a new command line interface (CLI) that provides interfaces for agents to guide DDARN. DDARN is flexible with respect to its internal reasoning, which can be steered by agents. Further, we support input from GeoGebra to make Newclid accessible for educational contexts. Further, the scope of problems that Newclid can solve has been expanded to include the ability to have an improved understanding of metric geometry concepts (length, angle) and to use theorems such as the Pythagorean theorem in proofs. Bugs have been fixed, and reproducibility has been improved. Lastly, we re-evaluated the five remaining problems from the original AG-30 dataset that AlphaGeometry was not able to solve and contrasted them with the abilities of DDARN, running in breadth-first-search agentic mode (which corresponds to how DDARN runs by default), finding that DDARN solves an additional problem. We have open-sourced our code under: https://github.com/LMCRC/Newclid

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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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