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 architec
A hierarchical VLA framework is proposed to address the limitations of Markovian models by maintaining task history through explicit language memory and event-triggered updates.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
HeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 28 checks on 3 different days. Its best position was #20 of 30, reached on Nov 7, 01:28 UTC. It is #28 right now, slipping from #27 to #28 over the last 6 checks.
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Yes. As of Nov 8, 17:28 UTC it is #28, slipping from #27 to #28 over the last 6 checks.
#20 on Hugging Face · Daily papers, on Nov 7, 01:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (28 checks) since.