ResearchResearch paperLarge Language Models · Robotics & Embodied AI · Reasoning & Planning1 source · Oct 8, 2026

GeoReform: Reflective Formalization Evolution for Multimodal Geometry Problem Solving

Multimodal large language models (MLLMs) often struggle to identify and use geometric relations in diagrams.

Recent methods address this challenge by converting geometric entities, relations, and constraints into explicit textual representations for the model to reason over.
However, effective formalization is highly non-trivial: on Geometry3K, structure injection fixes 28 errors but introduces 13 new ones among 200 examples.
To fully exploit the power of formalization, we further propose GeoReform, a reflective formalization evolution framework that treats formalization as an optimizable policy rather than a fixed parser output.
Extensive experiments and analyses across geometry reasoning benchmarks demonstrate that effective formalization is crucial for improving multimodal geometry reasoning.

[1]GeoReform: Reflective Formalization Evolution for Multimodal Geometry Problem Solving
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:39 PM
Multimodal large language models (MLLMs) often struggle to identify and use geometric relations in diagrams.
Recent methods address this challenge by converting geometric entities, relations, and constraints into explicit textual representations for the model to reason over.

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