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University of Electronic Science and Technology of China
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Agents using the Experience Memory Graph can recover from failures in a single execution, eliminating the need for costly trial-and-error loops.
Diffusion models can now predict driver attention with state-of-the-art accuracy by incorporating LLM-enhanced semantic reasoning.
VecFormer slashes the computational cost of graph transformers while boosting out-of-distribution generalization by operating attention on quantized "graph tokens" instead of individual nodes.
