Embodied agents maintain persistent identity while evolving modular capabilities through a closed-loop process, raising simulated task success from 32.4% to 91.3% with zero policy drift.
Between MDPs and semi- MDPs: A framework for temporal abstraction in reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
AMC models memory consolidation via a Liquid-Glass-Crystal process governed by an SDE with proven convergence to a Beta distribution, yielding 34-43% better forward transfer and 67-80% less forgetting on standard continual RL benchmarks.
citing papers explorer
-
Learning Without Losing Identity: Capability Evolution for Embodied Agents
Embodied agents maintain persistent identity while evolving modular capabilities through a closed-loop process, raising simulated task success from 32.4% to 91.3% with zero policy drift.
-
Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments
AMC models memory consolidation via a Liquid-Glass-Crystal process governed by an SDE with proven convergence to a Beta distribution, yielding 34-43% better forward transfer and 67-80% less forgetting on standard continual RL benchmarks.