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cs.LG2026
AgenticCache: Cache-Driven Asynchronous Planning for Embodied AI Agents
Hojoon Kim, Yuheng Wu, Thierry Tambe
Embodied AI agents increasingly rely on large language models (LLMs) for planning, yet per-step LLM calls impose severe latency and cost. In this paper, we show that embodied tasks…
cs.LG2025
RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
Yuzong Chen, Xilai Dai, Jake Hyun +6
The recently introduced NVFP4 format demonstrates remarkable performance and memory benefits for quantized large language model (LLM) inference. However, we observe two types of re…