# Seeing Isn't Orienting: A Cognitively Informed Hierarchical Benchmark for Object Orientation in MLLMs

arXiv: https://arxiv.org/abs/2505.21649 · HTML: https://mlsanity.com/paper/2505.21649/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2025.

## Built on by (most cited first, 3 of 3)

- [MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook](https://mlsanity.com/paper/2509.14142/) (2025)
- [WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents](https://mlsanity.com/paper/2605.20306/) (2026)
- [Autoregressive Mosaics: Probing 2D Spatial Reasoning in Text-Only Language Models](https://mlsanity.com/paper/2608.30751/) (2026)

## Abstract (arXiv)

Humans develop object orientation understanding progressively, from recognizing which way an object faces to reasoning about orientations across multiple objects. Yet existing vision-language benchmarks largely conflate orientation with broader spatial reasoning. We introduce Discriminative Orientation Reasoning Intelligence (DORI), a cognition-informed hierarchical benchmark that establishes object orientation as the primary evaluation target. DORI decomposes orientation into four dimensions, each evaluated at coarse (categorical) and granular (metric) levels, yielding 33,656 multiple-choice questions over 13,652 real-world and synthetic images from 14 sources. Its design isolates orientation from confounding factors through object isolation, standardized reference frames, and structured prompts. Evaluating 26 state-of-the-art vision-language models reveals a consistent limitation: models strong on general spatial benchmarks remain near-random on object-centric orientation reasoning. The best model achieves only 64.2% on coarse and 42.9% on granular judgments, with the largest drops on compound rotations and inter-object reference frame shifts. Large coarse-to-granular performance gaps further indicate reliance on categorical heuristics rather than geometric reasoning. These results establish object orientation understanding as a fundamental open challenge for multimodal systems. Dataset: https://huggingface.co/datasets/appledora/DORI-Benchmark