HeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation

Gyeonghun Kim, Donghoon Kim, Minsoo Choi, Suyun Lee

HeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation: 9 upvotes on Hugging Face Daily Papers, #18 of 32 papers on 2026-11-06. Day-by-day upvote history.

Upvotes over time

It had 7 upvotes by its first day, 7 upvotes by the next day, 8 upvotes by day 3, 9 upvotes by day 7, and has 9 now. It ranked #117 of 207 papers in its week and #177 of 340 in its month. Its code repository has 2 GitHub stars.

Recent advances in Vision-Language-Action (VLA) models have enabled fine manipulation capabilities, yet performing tasks that require working memory remains a key challenge. Most existing VLA architectures operate under a Markovian assumption, mapping only the current observation to an action, which leads to failure in non-Markovian environments. Inspired by the human 'inner monologue', we propose Hierarchical Explicit Language Memory (HeLM), a hierarchical VLA framework that maintains task-relevant history in explicit language through factorized event detection and event-triggered memory updates. We evaluate HeLM across 10 non-Markovian real-world tasks with 56 variants, including five newly formulated inter-episode tasks that require persistent context across task boundaries. HeLM improves intra-episode success by 12.8 percentage points over the strongest baseline and achieves 78% subtask success on compositional generalization tasks, while using up to 4.5x less memory and running 2.5x faster than the keyframe baseline at long horizons.

Paper page on Hugging Face · arXiv

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