# Miller-Index-Based Latent Crystallographic Fracture Plane Reasoning and generation with Vision-Language Models

arXiv: https://arxiv.org/abs/2605.20416 · HTML: https://mlsanity.com/paper/2605.20416/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 19 May 2026.

## Placed in

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

## Builds on (most cited first, 8 of 8)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action](https://mlsanity.com/paper/2303.11381/) (2023)
- [Robust Checkpoint Selection for Multimodal LLMs via Agentic Evaluation and Stability-Aware Ranking](https://mlsanity.com/paper/2605.18852/) (2026)
- [Multilingual OCR-Aware Fine-Tuning and Prompt-Guided Chain-of-Thought Reasoning for Multimodal Large Language Models](https://mlsanity.com/paper/2605.16409/) (2026)
- [Grounded Post-Training with Hard Examples for Reducing Hallucination in Multimodal Large Language Models](https://mlsanity.com/paper/2605.16411/) (2026)
- [Modeling 3D geometry using 1D laser distance measurements with application to cylinder for visualization and evaluating surface quality](https://mlsanity.com/paper/2110.14833/) (2021)

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

- [Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning](https://mlsanity.com/paper/2606.14765/) (2026)

## Abstract (arXiv)

We study whether multimodal large language models (MLLMs) can leverage crystallographic plane indices (Miller indices) as a structured latent representation for reasoning about fracture geometry. We formulate Miller indices z = (h,k,l) as a latent variable governing idealized planar fracture and evaluate two complementary capabilities: (i) latent inference, where the model maps visual observations to plane hypotheses under physically valid conditions, and (ii) latent applicability assessment, where the model determines whether such a representation is meaningful for a given fracture image. Through extensive experiments spanning synthetic data, controlled 2D--3D geometric pairs, and real-world fracture images across multiple material classes -- including ceramics, glass, metals, and concrete -- we show that MLLMs can reliably perform latent inference in idealized settings and, critically, can reject the latent representation when the underlying physics does not support it. As an exploratory extension, we further examine AI-generated fracture sequences and observe qualitatively plausible brittle-fracture progression behaviors, suggesting that multimodal generative models may encode partial implicit physical priors related to material failure dynamics. These results suggest that MLLMs can act as physics-aware reasoning systems conditioned on structured latent priors, provided that the domain of validity is explicitly modeled.