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AlphaGeometry2: A Deep Dive into a Gold-Medalist AI Geometry Solver
Artificial Intelligence   Latest   Machine Learning

AlphaGeometry2: A Deep Dive into a Gold-Medalist AI Geometry Solver

Jesus Rodriguez
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February 19, 2025
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Last Updated on February 20, 2025 by Editorial Team

Author(s): Jesus Rodriguez

Originally published on Towards AI.

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DeepMind’s journey toward mathematical AI dominance took a major leap last year when AlphaProof and AlphaGeometry nearly clinched gold at the International Math Olympiad (IMO). Now, with the latest upgrade, AlphaGeometry2 (AG2) has officially surpassed top human competitors in geometry, marking a milestone in AI-driven mathematical reasoning. The general consensus among IMO competitors is that geometry problems are among the toughest in each day of the Olympiad.

AG2 represents a significant advancement in AI-driven mathematical reasoning, particularly in solving Olympiad geometry problems. Building on its predecessor, AlphaGeometry, AG2 surpasses the performance of an average gold medalist in the International Mathematical Olympiad (IMO). This essay provides a technical overview of AG2’s architecture, key improvements, and broader contributions to AI.

so we have an idea of the complexiity of the geometry problems in IMO, look at the example below from the paper:

Image Credit: Google DeepMind
Image Credit: Google DeepMind

Core Architecture: A Neuro-Symbolic Hybrid

AG2 employs a neuro-symbolic approach that combines neural networks and symbolic engines. Its architecture consists of three primary components:

  1. Language Model (LM): Based on the Gemini architecture, the LM interprets problem statements, generates auxiliary constructions, and proposes proof steps.
  2. Symbolic Engine (DDAR): The Deductive Database Arithmetic Reasoning (DDAR) engine verifies proof steps using predefined rules and axioms.
  3. Search Algorithm: The Shared Knowledge Ensemble of Search Trees (SKEST) algorithm runs multiple beam searches in parallel, sharing knowledge to improve the search process.

Key Improvements and Contributions

Expanded Domain Language

AG2 extends the original domain language of AlphaGeometry, enhancing its coverage of IMO geometry problems from 66% to 88%. Key additions include:

  • Find x predicates: acompute and rcompute predicates solve for specific angles or ratios.
  • Linear equations: distmeq, distseq, and angeq predicates express linear equations involving geometric quantities.
  • Locus problems: The * token represents fixed-point placeholders.
  • Diagram checks: sameclock, noverlap, and lessthan predicates ensure valid deductions.
  • Non-constructive problems: Allows points defined by three or more predicates.
Image Credit: Google DeepMind

Stronger and Faster Symbolic Engine

AG2’s DDAR engine incorporates several optimizations:

  • Double points: Handles points with identical coordinates but different names.
  • Faster algorithm: The DDAR2 algorithm uses hard-coded searches and hashing techniques for efficiency.
  • Faster implementation: Core computations use C++, achieving a 300x speed improvement.

Enhanced Language Model

The LM, based on the Gemini architecture, is trained on a diverse dataset of 300 million synthetic theorems. Improvements include:

  • Larger and more complex diagrams
  • Theorems with greater complexity and longer proofs
  • More balanced data distribution across problem types
  • New “locus” type theorems

During inference, AG2 uses top-k sampling with a temperature of 1.0 and k = 32, generating diverse auxiliary construction proposals.

Novel Search Algorithm

SKEST runs multiple beam searches in parallel with different configurations, sharing knowledge through a central database. Search tree variations include:

  • Classic search tree (similar to AG1)
  • Trees predicting multiple auxiliary points
  • Deep, narrow trees
  • Shallow, wide trees

This approach accelerates the search process and improves overall performance.

Image Credit: Google DeepMind

Results and Performance

AG2 achieves an 84% solve rate on IMO geometry problems (2000–2024), surpassing the average gold medalist. It also solves 20 of the 30 hardest IMO shortlist problems.

Image Credit: Google DeepMind
Image Credit: Google DeepMind

Ablation Studies

  • Increasing model size reduces perplexity loss.
  • High temperature and multiple samples are essential for success.
  • Optimal search tree configuration: beam size 128, depth 4, and 32 samples.

Broader Impact and Future Directions

AG2’s success highlights the potential of neuro-symbolic approaches in complex reasoning tasks. Future research may explore:

  • Expanding domain language for more advanced geometric concepts
  • Using reinforcement learning for subproblem decomposition
  • Automating geometry problem-solving with natural language input

Is Multi-modality the Future?

Multi-modal training using Gemini 1.5 (combining text and diagrams) did not improve solve rates, likely due to the complexity of IMO diagrams and limitations in image tokenization. However, combining multi-modal models with other frameworks still enhances overall performance.

Creative Solutions and Human-Like Reasoning

AG2’s solutions often exhibit superhuman creativity, discovering unconventional auxiliary constructions and elegant solutions. Examples include the IMO 2024 P4 problem.

Tokenizers and Domain-Specific Languages

Performance remains consistent across different tokenizers. Translating AG2’s data into natural language yields comparable results, suggesting modern LLM tokenizers are flexible enough for mathematical tasks.

Generating Full Proofs with LMs

While AG2 relies on the symbolic engine for proof verification, its LM can generate partial solutions independently, hinting at future LMs becoming more self-sufficient in mathematical reasoning.

Conclusion

AlphaGeometry2 marks a significant milestone in AI-driven mathematical reasoning. Its neuro-symbolic architecture, enhanced domain language, optimized symbolic engine, and novel search algorithm collectively enable gold-medalist performance in solving IMO geometry problems. As research continues, AI’s capabilities in mathematics and other domains are expected to grow, unlocking new possibilities for both AI and human collaboration.

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