Memory lets an embodied agent reuse past experience, yet retaining useful information does not ensure that the agent can apply it when conditions change. We present MemTransfer, a benchmark comparing six memory representations, a working-memory baseline and five representations of past experience, under a shared frozen...
Haiming Tang, Xian-Jie Dai, Gu-Jie Shao et al.· 0 citations
Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length increases. Recent approaches address this by processing input chunk by chunk while maintaining a bounded textual memory in model context. However, premature information...
Jing-Guang Li, Yebo Wu, Zu-Yi Guo et al.· 0 citations
Although highly effective in vision and language domains, applying in-context learning to robotics remains challenging. Existing autoregressive in-context imitation methods discretize continuous actions and exacerbate the accumulation of early prediction errors through next-token prediction, limiting their generalizati...
Jian Ding, Xian-Jie Dai, Roei Herzig et al.· 0 citations
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