# Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning

arXiv: https://arxiv.org/abs/2608.29278 · HTML: https://mlsanity.com/paper/2608.29278/

## 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 12 of its 38 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 12 of 12)

- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Qwen3.5-Omni Technical Report](https://mlsanity.com/paper/2604.15804/) (2026)
- [Ola: Pushing the Frontiers of Omni-Modal Language Model](https://mlsanity.com/paper/2502.04328/) (2025)
- [OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM](https://mlsanity.com/paper/2510.15870/) (2025)
- [Baichuan-Omni Technical Report](https://mlsanity.com/paper/2410.08565/) (2024)
- [Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift](https://mlsanity.com/paper/2212.08044/) (2022)
- [Benchmarking Large Multimodal Models against Common Corruptions](https://mlsanity.com/paper/2401.11943/) (2024)
- [Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes](https://mlsanity.com/paper/2510.19400/) (2025)
- [How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction](https://mlsanity.com/paper/2601.08626/) (2026)
- [QuantEval: A Benchmark for Financial Quantitative Tasks in Large Language Models](https://mlsanity.com/paper/2601.08689/) (2026)
- [SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering](https://mlsanity.com/paper/2606.00593/) (2026)

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

- [SAVOR: Self-Aware Visual Grounding via Confidence-Calibrated Reinforcement Learning for Multimodal Hallucination Mitigation](https://mlsanity.com/paper/2609.16601/) (2026)

## Abstract (arXiv)

Omni-modal large language models are increasingly evaluated on clean text--vision--audio inputs, where every channel is present, synchronized, and readily interpretable. Such scores are often taken as evidence of robust cross-modal fusion, but clean evaluation cannot tell whether success depends on stable cross-modal structure or on cues sufficient only in intact inputs. To address this gap, we define a modality fault line: a boundary at which model behavior becomes unstable when a modality remains present and human-interpretable, but its internal evidence structure is perturbed. We introduce SCEval (Structure-Corruption Evaluation) a diagnostic evaluation protocol that keeps the question, answer space, and modality channels fixed while applying controlled structural corruptions to text, vision, and audio individually and jointly. Built from 273 human-verified tri-modal examples from Social-IQ, OmniBench, and VALOR, SCEval evaluates 15 proprietary and open-source omni-modal systems. The results show that structural corruption lowers clean accuracy, text--vision damage forms the most stable shared fault line, and multi-modal degradation is non-additive rather than a simple function of the number of corrupted modalities. Clean omni-modal accuracy therefore does not establish that a model will remain reliable when cross-modal evidence becomes structurally unreliable.