# ConEQsA: Concurrent and Asynchronous Embodied Questions Scheduling and Answering

arXiv: https://arxiv.org/abs/2509.11663 · HTML: https://mlsanity.com/paper/2509.11663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 17 of 17)

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- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Neural Map: Structured Memory for Deep Reinforcement Learning](https://mlsanity.com/paper/1702.08360/) (2017)
- [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)
- [Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models](https://mlsanity.com/paper/2502.03607/) (2025)
- [Knowledge-based Embodied Question Answering](https://mlsanity.com/paper/2109.07872/) (2021)
- [Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering](https://mlsanity.com/paper/2507.12846/) (2025)
- [Memory-Centric Embodied Question Answering](https://mlsanity.com/paper/2505.13948/) (2025)
- [AViLA: Asynchronous Vision-Language Agent for Streaming Multimodal Data Interaction](https://mlsanity.com/paper/2506.18472/) (2025)
- [Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination](https://mlsanity.com/paper/2508.14635/) (2025)
- [Extended Set-based Tasks for Multi-task Execution and Prioritization](https://mlsanity.com/paper/2310.16189/) (2023)
- [Informable Multi-Objective and Multi-Directional RRT* System for Robot Path Planning](https://mlsanity.com/paper/2205.14853/) (2022)

## Abstract (arXiv)

This paper formulates the Embodied Questions Answering (EQsA) problem, introduces a corresponding benchmark, and proposes an agentic system to tackle the problem. Classical Embodied Question Answering (EQA) is typically formulated as answering one single question by actively exploring a 3D environment. Real deployments, however, often demand handling multiple questions that may arrive asynchronously and carry different urgencies. We formalize this setting as Embodied Questions Answering (EQsA) and present ConEQsA, an agentic framework for concurrent, urgency-aware scheduling and answering. ConEQsA leverages shared group memory to reduce redundant exploration, and a priority-planning method to dynamically schedule questions. To evaluate the EQsA setting fairly, we contribute the Concurrent Asynchronous Embodied Questions (CAEQs) benchmark containing 40 indoor scenes and five questions per scene (200 in total), featuring asynchronous follow-up questions and human-annotated urgency labels. We further propose metrics for EQsA performance: Direct Answer Rate (DAR), and Normalized Urgency-Weighted Latency (NUWL), which serve as a fair evaluation protocol for EQsA. Empirical evaluations demonstrate that ConEQsA consistently outperforms strong sequential baselines, and show that urgency-aware, concurrent scheduling is key to making embodied agents responsive and efficient under realistic, multi-question workloads. Code is available on https://anonymous.4open.science/r/ConEQsA.