# MMArch: Benchmarking Multimodal Reasoning Grounded in Architectural Evidence

arXiv: https://arxiv.org/abs/2608.09281 · HTML: https://mlsanity.com/paper/2608.09281/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [Chart understanding and anomaly detection](https://mlsanity.com/mllm/chart-understanding-and-anomaly-detection/)

## Builds on (most cited first, 4 of 4)

- [FigureQA: An Annotated Figure Dataset for Visual Reasoning](https://mlsanity.com/paper/1710.07300/) (2017)
- [MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding](https://mlsanity.com/paper/2407.04903/) (2024)
- [AECV-Bench: Benchmarking Multimodal Models on Architectural and Engineering Drawings Understanding](https://mlsanity.com/paper/2601.04819/) (2026)
- [AEC-Bench: A Multimodal Benchmark for Agentic Systems in Architecture, Engineering, and Construction](https://mlsanity.com/paper/2603.29199/) (2026)

## Built on by (most cited first, 1 of 1)

- [ARCH-B: Architectural Representation, Comprehension and Hierarchy Benchmark](https://mlsanity.com/paper/2609.34047/) (2026)

## Abstract (arXiv)

Multimodal large language models (MLLMs) perform strongly on engineering imagery, yet existing benchmarks mostly test drawing recognition, information extraction, or compliance checking, leaving open whether models can combine distributed visual evidence with engineering principles to reach a conclusion. We introduce MMArch, a benchmark for architecture and civil engineering spanning ten subdomains and built entirely from figures in peer-reviewed papers. Its 1,212 short-answer items are produced by a decoupled planner--writer pipeline and validated through automated screening, a blind adversarial audit, and expert review, so that answering requires perceiving the relevant evidence, identifying the governing principle, and applying it, not exploiting textual or single-figure shortcuts. Evaluating 18 open-weight and proprietary MLLMs against a domain-expert panel, we find a wide gap: the strongest open-source model attains about 30% and the best proprietary system 52%, while human experts reach 95%, more than forty points ahead. Our error analysis shows that failures concentrate in applying principles and combining evidence across figures rather than in locating it, pointing to substantial headroom for future research. Code and data are available at https://dcx-swjtu.github.io/MMArch/.