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LSTMem is proposed, an LSTM-inspired online memory that equips each layer of a frozen LLM with two matrix-valued states: a cell state that accumulates history and a hidden state whose readouts correct the backbone's attention.
Enabling robotic agents to perform complex long-horizon tasks has been a long-standing goal in robotics and artificial intelligence (AI). Existing Large Language Models (LLMs)-based symbolic planners auto-generate Planning Domain Definition Language (PDDL) specifications to bridge language and formal reasoning, but the...
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