# ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark

arXiv: https://arxiv.org/abs/2501.05031 · HTML: https://mlsanity.com/paper/2501.05031/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 35 of its 42 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 30 of 35)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs](https://mlsanity.com/paper/2406.07476/) (2024)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models](https://mlsanity.com/paper/2310.14566/) (2023)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [ScanQA: 3D Question Answering for Spatial Scene Understanding](https://mlsanity.com/paper/2112.10482/) (2021)
- [SQA3D: Situated Question Answering in 3D Scenes](https://mlsanity.com/paper/2210.07474/) (2022)
- [MovieQA: Understanding Stories in Movies through Question-Answering](https://mlsanity.com/paper/1512.02902/) (2015)
- [MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding](https://mlsanity.com/paper/2406.14515/) (2024)
- [Building and better understanding vision-language models: insights and future directions](https://mlsanity.com/paper/2408.12637/) (2024)
- [Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input](https://mlsanity.com/paper/2408.15542/) (2024)
- [RoboVQA: Multimodal Long-Horizon Reasoning for Robotics](https://mlsanity.com/paper/2311.00899/) (2023)
- [Explore until Confident: Efficient Exploration for Embodied Question Answering](https://mlsanity.com/paper/2403.15941/) (2024)
- [OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models](https://mlsanity.com/paper/2402.10670/) (2024)
- [Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites](https://mlsanity.com/paper/2312.01701/) (2023)
- [KnowIT VQA: Answering Knowledge-Based Questions about Videos](https://mlsanity.com/paper/1910.10706/) (2019)
- [Grounded Multi-Hop VideoQA in Long-Form Egocentric Videos](https://mlsanity.com/paper/2408.14469/) (2024)
- [MarioQA: Answering Questions by Watching Gameplay Videos](https://mlsanity.com/paper/1612.01669/) (2016)

## Built on by (most cited first, 19 of 19)

- [Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges](https://mlsanity.com/paper/2505.04769/) (2025)
- [UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces](https://mlsanity.com/paper/2503.06157/) (2025)
- [Embodied AI: From LLMs to World Models](https://mlsanity.com/paper/2509.20021/) (2025)
- [EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?](https://mlsanity.com/paper/2506.05287/) (2025)
- [CLiViS: Unleashing Cognitive Map through Linguistic-Visual Synergy for Embodied Visual Reasoning](https://mlsanity.com/paper/2506.17629/) (2025)
- [StreamEQA: Towards Streaming Video Understanding for Embodied Scenarios](https://mlsanity.com/paper/2512.04451/) (2025)
- [RynnEC: Bringing MLLMs into Embodied World](https://mlsanity.com/paper/2508.14160/) (2025)
- [Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration](https://mlsanity.com/paper/2505.11191/) (2025)
- [Vision to Geometry: 3D Spatial Memory for Sequential Embodied MLLM Reasoning and Exploration](https://mlsanity.com/paper/2512.02458/) (2025)
- [LifeEval: A Multimodal Benchmark for Assistive AI in Egocentric Daily Life Tasks](https://mlsanity.com/paper/2603.00490/) (2026)
- [Expand Your SCOPE: Semantic Cognition over Potential-Based Exploration for Embodied Visual Navigation](https://mlsanity.com/paper/2511.08935/) (2025)
- [Interaction-aware Representation Modeling with Co-occurrence Consistency for Egocentric Hand-Object Parsing](https://mlsanity.com/paper/2602.20597/) (2026)
- [GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents](https://mlsanity.com/paper/2603.24329/) (2026)
- [How Good are Foundation Models in Step-by-Step Embodied Reasoning?](https://mlsanity.com/paper/2509.15293/) (2025)
- [EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning](https://mlsanity.com/paper/2603.09731/) (2026)
- [Evaluating point-light biological motion in multimodal large language models](https://mlsanity.com/paper/2509.23517/) (2025)
- [Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents](https://mlsanity.com/paper/2511.18685/) (2025)
- [MetaSpace: Metamorphic Testing for Spatial Cognition in Embodied Agents](https://mlsanity.com/paper/2608.07533/) (2026)
- [Ego2World: Compiling Egocentric Cooking Videos into Executable Worlds for Belief-State Planning](https://mlsanity.com/paper/2610.02715/) (2026)

## Abstract (arXiv)

The enhancement of generalization in robots by large vision-language models (LVLMs) is increasingly evident. Therefore, the embodied cognitive abilities of LVLMs based on egocentric videos are of great interest. However, current datasets for embodied video question answering lack comprehensive and systematic evaluation frameworks. Critical embodied cognitive issues, such as robotic self-cognition, dynamic scene perception, and hallucination, are rarely addressed. To tackle these challenges, we propose ECBench, a high-quality benchmark designed to systematically evaluate the embodied cognitive abilities of LVLMs. ECBench features a diverse range of scene video sources, open and varied question formats, and 30 dimensions of embodied cognition. To ensure quality, balance, and high visual dependence, ECBench uses class-independent meticulous human annotation and multi-round question screening strategies. Additionally, we introduce ECEval, a comprehensive evaluation system that ensures the fairness and rationality of the indicators. Utilizing ECBench, we conduct extensive evaluations of proprietary, open-source, and task-specific LVLMs. ECBench is pivotal in advancing the embodied cognitive capabilities of LVLMs, laying a solid foundation for developing reliable core models for embodied agents. All data and code are available at https://github.com/Rh-Dang/ECBench.