# Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering

arXiv: https://arxiv.org/abs/2503.11117 · HTML: https://mlsanity.com/paper/2503.11117/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 45 references that are in the map.
- First posted on arXiv 14 Mar 2025.

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

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2311.16922/) (2023)
- [Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models](https://mlsanity.com/paper/2402.07865/) (2024)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [InstructNav: Zero-shot System for Generic Instruction Navigation in Unexplored Environment](https://mlsanity.com/paper/2406.04882/) (2024)
- [IQA: Visual Question Answering in Interactive Environments](https://mlsanity.com/paper/1712.03316/) (2017)
- [Towards Learning a Generalist Model for Embodied Navigation](https://mlsanity.com/paper/2312.02010/) (2023)
- [Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions](https://mlsanity.com/paper/2309.11382/) (2023)
- [Embodied Question Answering in Photorealistic Environments with Point Cloud Perception](https://mlsanity.com/paper/1904.03461/) (2019)
- [Multi-Target Embodied Question Answering](https://mlsanity.com/paper/1904.04686/) (2019)
- [Explore until Confident: Efficient Exploration for Embodied Question Answering](https://mlsanity.com/paper/2403.15941/) (2024)
- [Towards Long-Horizon Vision-Language Navigation: Platform, Benchmark and Method](https://mlsanity.com/paper/2412.09082/) (2024)
- [Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering](https://mlsanity.com/paper/2207.12647/) (2022)
- [GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering](https://mlsanity.com/paper/2412.14480/) (2024)
- [CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space](https://mlsanity.com/paper/2502.12532/) (2025)
- [Knowledge-based Embodied Question Answering](https://mlsanity.com/paper/2109.07872/) (2021)
- [Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models](https://mlsanity.com/paper/2402.15721/) (2024)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models](https://mlsanity.com/paper/2312.04533/) (2023)
- [EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering](https://mlsanity.com/paper/2410.20263/) (2024)
- [VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering](https://mlsanity.com/paper/1908.04950/) (2019)
- [Cross-modal Causal Relation Alignment for Video Question Grounding](https://mlsanity.com/paper/2503.07635/) (2025)
- [NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries](https://mlsanity.com/paper/2412.10726/) (2024)
- [Map-based Modular Approach for Zero-shot Embodied Question Answering](https://mlsanity.com/paper/2405.16559/) (2024)
- [DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering](https://mlsanity.com/paper/2503.03190/) (2025)
- [RT-Grasp: Reasoning Tuning Robotic Grasping via Multi-modal Large Language Model](https://mlsanity.com/paper/2411.05212/) (2024)

## Built on by (most cited first, 30 of 30)

- [ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop](https://mlsanity.com/paper/2605.18746/) (2026)
- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering](https://mlsanity.com/paper/2507.12846/) (2025)
- [Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration](https://mlsanity.com/paper/2601.10744/) (2026)
- [Memory-Centric Embodied Question Answering](https://mlsanity.com/paper/2505.13948/) (2025)
- [Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System](https://mlsanity.com/paper/2606.18112/) (2026)
- [When Robots Should Say "I Don't Know": Benchmarking Abstention in Embodied Question Answering](https://mlsanity.com/paper/2512.04597/) (2025)
- [Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection](https://mlsanity.com/paper/2512.13250/) (2025)
- [Vision to Geometry: 3D Spatial Memory for Sequential Embodied MLLM Reasoning and Exploration](https://mlsanity.com/paper/2512.02458/) (2025)
- [PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments](https://mlsanity.com/paper/2510.21111/) (2025)
- [Multi-Step Reasoning for Embodied Question Answering via Tool Augmentation](https://mlsanity.com/paper/2510.20310/) (2025)
- [HIMM: Human-Inspired Long-Term Memory Modeling for Embodied Exploration and Question Answering](https://mlsanity.com/paper/2602.15513/) (2026)
- [E3VS-Bench: A Benchmark for Viewpoint-Dependent Active Perception in 3D Gaussian Splatting Scenes](https://mlsanity.com/paper/2604.17969/) (2026)
- [World Models as Group Actions](https://mlsanity.com/paper/2605.24578/) (2026)
- [MM-OPERA: Benchmarking Open-ended Association Reasoning for Large Vision-Language Models](https://mlsanity.com/paper/2510.26937/) (2025)
- [Prune-Then-Plan: Step-Level Calibration for Stable Frontier Exploration in Embodied Question Answering](https://mlsanity.com/paper/2511.19768/) (2025)
- [LIME: Learning Intent-aware Camera Motion from Egocentric Video](https://mlsanity.com/paper/2607.02417/) (2026)
- [ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception](https://mlsanity.com/paper/2607.10180/) (2026)
- [SpatialReasoner: Active Perception for Large-Scale 3D Scene Understanding](https://mlsanity.com/paper/2512.03284/) (2025)
- [Memory-Guided View Refinement for Dynamic Human-in-the-loop EQA](https://mlsanity.com/paper/2603.09541/) (2026)
- [RenderMem: Rendering as Spatial Memory Retrieval](https://mlsanity.com/paper/2603.14669/) (2026)
- [PhyScene3D: Physically Consistent Interactive 3D Tabletop Scene Generation](https://mlsanity.com/paper/2606.01649/) (2026)
- [ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering](https://mlsanity.com/paper/2606.14772/) (2026)
- [ObsGraph: Hierarchical Observation Representation for Embodied Reasoning and Exploration](https://mlsanity.com/paper/2606.24068/) (2026)
- [EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence](https://mlsanity.com/paper/2606.24797/) (2026)
- [Scaffolding Foundation Models into Physical-World Agents Pushes the Frontier of Long-Horizon Navigation](https://mlsanity.com/paper/2608.30396/) (2026)
- [Budget-Constrained Embodied Perception: Four Resource Walls and a Pre-Registered Evaluation of Access-Structured Perception on Open Models at less than 31B](https://mlsanity.com/paper/2608.22975/) (2026)
- [Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks](https://mlsanity.com/paper/2609.11018/) (2026)
- [SafeVantage: Vantage-Aware Memory for Reliable Embodied Decisions](https://mlsanity.com/paper/2609.36906/) (2026)
- [MemAct: Streaming Spatial Understanding with Memory and Active Exploration](https://mlsanity.com/paper/2610.17640/) (2026)

## Abstract (arXiv)

Embodied Question Answering (EQA) is a challenging task in embodied intelligence that requires agents to dynamically explore 3D environments, actively gather visual information, and perform multi-step reasoning to answer questions. However, current EQA approaches suffer from critical limitations in exploration efficiency, dataset design, and evaluation metrics. Moreover, existing datasets often introduce biases or prior knowledge, leading to disembodied reasoning, while frontier-based exploration strategies struggle in cluttered environments and fail to ensure fine-grained exploration of task-relevant areas. To address these challenges, we construct the EXPloration-awaRe Embodied queStion anSwering Benchmark (EXPRESS-Bench), the largest dataset designed specifically to evaluate both exploration and reasoning capabilities. EXPRESS-Bench consists of 777 exploration trajectories and 2,044 question-trajectory pairs. To improve exploration efficiency, we propose Fine-EQA, a hybrid exploration model that integrates frontier-based and goal-oriented navigation to guide agents toward task-relevant regions more effectively. Additionally, we introduce a novel evaluation metric, Exploration-Answer Consistency (EAC), which ensures faithful assessment by measuring the alignment between answer grounding and exploration reliability. Extensive experimental comparisons with state-of-the-art EQA models demonstrate the effectiveness of our EXPRESS-Bench in advancing embodied exploration and question reasoning.