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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2604.17810 |
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| _version_ | 1866918455283810304 |
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| author | Li, Chengyang Wang, Shuai Ye, Kejiang Yuan, Weijie Zhou, Boyu Wu, Yik-Chung Xu, Chengzhong Arslan, Huseyin |
| author_facet | Li, Chengyang Wang, Shuai Ye, Kejiang Yuan, Weijie Zhou, Boyu Wu, Yik-Chung Xu, Chengzhong Arslan, Huseyin |
| contents | This paper considers multi-agent embodied question answering (MA-EQA), which aims to query robot teams on what they have seen over a long horizon. In contrast to existing edge resource management methods that emphasize sensing, communication, or computation performance metrics, MA-EQA emphasizes the memory qualities. To cope with this paradigm shift, we propose a quality of memory (QoM) model based on generative adversarial exam (GAE), which leverages forward simulation to assess memory retrieval and uses the resulting exam scores to compute QoM values. Then we propose memory centric power allocation (MCPA), which maximizes the QoM function under communication resource constraints. Through asymptotic analysis, it is found that the transmit powers are proportional to the GAE error probability, thus prioritizing towards high-QoM robots. Extensive experiments demonstrate that MCPA achieves significant improvements over extensive benchmarks in terms of diverse metrics in various scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17810 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Memory Centric Power Allocation for Multi-Agent Embodied Question Answering Li, Chengyang Wang, Shuai Ye, Kejiang Yuan, Weijie Zhou, Boyu Wu, Yik-Chung Xu, Chengzhong Arslan, Huseyin Robotics Information Theory This paper considers multi-agent embodied question answering (MA-EQA), which aims to query robot teams on what they have seen over a long horizon. In contrast to existing edge resource management methods that emphasize sensing, communication, or computation performance metrics, MA-EQA emphasizes the memory qualities. To cope with this paradigm shift, we propose a quality of memory (QoM) model based on generative adversarial exam (GAE), which leverages forward simulation to assess memory retrieval and uses the resulting exam scores to compute QoM values. Then we propose memory centric power allocation (MCPA), which maximizes the QoM function under communication resource constraints. Through asymptotic analysis, it is found that the transmit powers are proportional to the GAE error probability, thus prioritizing towards high-QoM robots. Extensive experiments demonstrate that MCPA achieves significant improvements over extensive benchmarks in terms of diverse metrics in various scenarios. |
| title | Memory Centric Power Allocation for Multi-Agent Embodied Question Answering |
| topic | Robotics Information Theory |
| url | https://arxiv.org/abs/2604.17810 |