@inproceedings{zhang-etal-2025-knowing,
title = "Knowing More, Acting Better: Hierarchical Representation for Embodied Decision-Making",
author = "Zhang, Chunhui and
Ouyang, Zhongyu and
Diao, Xingjian and
Liu, Zheyuan and
Vosoughi, Soroush",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1042/",
pages = "19144--19155",
ISBN = "979-8-89176-335-7",
abstract = "Modern embodied AI uses multimodal large language models (MLLMs) as policy models, predicting actions from final-layer hidden states. This widely adopted approach, however, assumes that monolithic last-layer representations suffice for decision-making{---}a structural simplification at odds with decades of cognitive science, which highlights the importance of distributed, hierarchical processing for perception and action. Addressing this foundational asymmetry, we introduce a hierarchical action probing method that explicitly aggregates representations from all layers, mirroring the brain{'}s multi-level organization. Experiments reveal that early layers facilitate spatial grounding, middle layers support contextual integration, and later layers enable abstract generalization{---}which shows MLLMs inherently encode distributed action-relevant structures. These layer-wise features are integrated by a lightweight probe for spatial reasoning and contextual understanding, without costly backbone fine-tuning. This hierarchical solution shows significant improvements over standard last-layer embodied models in physical simulators, achieving a 46.6{\%} success rate and a 62.5{\%} gain in spatial reasoning tasks. These findings challenge conventional assumptions in embodied AI, establishing hierarchical probing as a principled alternative grounded in both cognitive theory and empirical evidence."
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<abstract>Modern embodied AI uses multimodal large language models (MLLMs) as policy models, predicting actions from final-layer hidden states. This widely adopted approach, however, assumes that monolithic last-layer representations suffice for decision-making—a structural simplification at odds with decades of cognitive science, which highlights the importance of distributed, hierarchical processing for perception and action. Addressing this foundational asymmetry, we introduce a hierarchical action probing method that explicitly aggregates representations from all layers, mirroring the brain’s multi-level organization. Experiments reveal that early layers facilitate spatial grounding, middle layers support contextual integration, and later layers enable abstract generalization—which shows MLLMs inherently encode distributed action-relevant structures. These layer-wise features are integrated by a lightweight probe for spatial reasoning and contextual understanding, without costly backbone fine-tuning. This hierarchical solution shows significant improvements over standard last-layer embodied models in physical simulators, achieving a 46.6% success rate and a 62.5% gain in spatial reasoning tasks. These findings challenge conventional assumptions in embodied AI, establishing hierarchical probing as a principled alternative grounded in both cognitive theory and empirical evidence.</abstract>
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%0 Conference Proceedings
%T Knowing More, Acting Better: Hierarchical Representation for Embodied Decision-Making
%A Zhang, Chunhui
%A Ouyang, Zhongyu
%A Diao, Xingjian
%A Liu, Zheyuan
%A Vosoughi, Soroush
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhang-etal-2025-knowing
%X Modern embodied AI uses multimodal large language models (MLLMs) as policy models, predicting actions from final-layer hidden states. This widely adopted approach, however, assumes that monolithic last-layer representations suffice for decision-making—a structural simplification at odds with decades of cognitive science, which highlights the importance of distributed, hierarchical processing for perception and action. Addressing this foundational asymmetry, we introduce a hierarchical action probing method that explicitly aggregates representations from all layers, mirroring the brain’s multi-level organization. Experiments reveal that early layers facilitate spatial grounding, middle layers support contextual integration, and later layers enable abstract generalization—which shows MLLMs inherently encode distributed action-relevant structures. These layer-wise features are integrated by a lightweight probe for spatial reasoning and contextual understanding, without costly backbone fine-tuning. This hierarchical solution shows significant improvements over standard last-layer embodied models in physical simulators, achieving a 46.6% success rate and a 62.5% gain in spatial reasoning tasks. These findings challenge conventional assumptions in embodied AI, establishing hierarchical probing as a principled alternative grounded in both cognitive theory and empirical evidence.
%U https://aclanthology.org/2025.findings-emnlp.1042/
%P 19144-19155
Markdown (Informal)
[Knowing More, Acting Better: Hierarchical Representation for Embodied Decision-Making](https://aclanthology.org/2025.findings-emnlp.1042/) (Zhang et al., Findings 2025)
ACL