Papers with hierarchical action probing method

1 papers
Knowing More, Acting Better: Hierarchical Representation for Embodied Decision-Making (2025.findings-emnlp)

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Challenge: Modern embodied AI uses multimodal large language models as policy models, predicting actions from final-layer hidden states.
Approach: They propose a hierarchical action probing method that aggregates representations from all layers, mirroring the brain's multi-level organization.
Outcome: Experiments show that hierarchical probing improves on last-layer embodied models and achieves a 46.6% success rate and a 62.5% gain in spatial reasoning tasks.

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