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Small language models (SLMs) offer a promising foundation for on-device agents through low-latency, resource-efficient inference, yet limited reasoning and planning capabilities constrain their performance on long-horizon tasks requiring multi-step interaction with the environment. Step-level collaboration between SLMs...
This work proposes an intra-graph mechanism that maps selective KV recomputation onto static NPU graphs, reconciling algorithmic dynamicity with NPU staticity, and develops an inter-graph scheduler to optimize chunk merging and minimize padding with dynamic programming.
RecGPT-Mobile-V2 is introduced, an end-to-end framework that treats intent quality and execution efficiency as coupled objectives within a staged design and helps retain decision-relevant evidence and allocate additional computation only when it is likely to improve the predicted Query.
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