Newclid: A User-Friendly Replacement for AlphaGeometry
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Abstract (arXiv)
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
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2024.
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Citing papers per year: 2024: 2; 2025: 4; 2026: 3.
9 papers.
- 2024Proposing and solving olympiad geometry with guided tree searchcited by 19
- 2025TrustGeoGen: Formal-Verified Data Engine for Trustworthy Multi-modal Geometric Problem Solvingcited by 13
- 2024Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learningcited by 11
- 2025Achieving Olympiad-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learningcited by 7
- 2025GeoBench: Rethinking Multimodal Geometric Problem-Solving via Hierarchical Evaluationcited by 4
- 2026Milestones over Outcome: Unlocking Geometric Reasoning with Sub-Goal Verifiable Rewardcited by 4
- 2025GenesisGeo: Technical Reportcited by 2
- 2026Draw2Think: Harnessing Geometry Reasoning through Constraint Engine Interactioncited by 0
- 2026Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometrycited by 0
- 2026Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometry17 Jun 2026
- 2026Draw2Think: Harnessing Geometry Reasoning through Constraint Engine Interaction20 May 2026
- 2026Milestones over Outcome: Unlocking Geometric Reasoning with Sub-Goal Verifiable Reward8 Jan 2026
- 2025GeoBench: Rethinking Multimodal Geometric Problem-Solving via Hierarchical Evaluation30 Dec 2025
- 2025Achieving Olympiad-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning11 Dec 2025
- 2025GenesisGeo: Technical Report26 Sep 2025
- 2025TrustGeoGen: Formal-Verified Data Engine for Trustworthy Multi-modal Geometric Problem Solving22 Apr 2025
- 2024Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning19 Dec 2024
- 2024Proposing and solving olympiad geometry with guided tree search14 Dec 2024
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- 2026NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry
- 2024FGeo-TP: A Language Model-Enhanced Solver for Geometry Problems
- 2024Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry
- 2023FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving
- 2026MechGeo: Autoformalizing and Proving Euclidean Geometry in Lean 4
- 2025GeoFM: Enhancing Geometric Reasoning of MLLMs via Synthetic Data Generation through Formal Language
- 2026Hilbert-Geo: Solving Solid Geometric Problems by Neural-Symbolic Reasoning
- 2025AutoGPS: Automated Geometry Problem Solving via Multimodal Formalization and Deductive Reasoning