arXiv (All)
open access
Achieving Olympiad-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning
Large language model (LLM) agents exhibit strong mathematical problem-solving abilities and can even solve International Mathematical Olympiad (IMO) level problems with the assistance of formal proof systems. However, due to weak heuristics for auxiliary constructions, AI for geometry problem solvin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Creating Solid Foundation Through Mathematical Word Problem Solving: A Transformative Approach Leading to Good Performance#436176
- Problem-Based Leraning Model to Improve Students’ Mathematical Problem-Solving Skills and Learning Interest: A Development Study#1090829
- Mathematics Graduate Training in the Age of AI#1035101
- Canva and Geogebra-Assisted Problem Based Learning Worksheet to Improve Students’ Mathematical Problem Solving Ability and Learning Interest on Senior High School#264911
- Function-Structured Reinforcement Learning with Executable Verifiers for Mathematical Reasoning#1157000
Record · ID 927198
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive