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The paper proposes ADNet, an adaptive decomposition network for multi-step traffic forecasting that learns to disentangle heterogeneous traffic dynamics into dominant and residual components via spectral decomposition, achieving superior performance on the TraffiDent dataset.
This paper proposes a method to predict decisions of unfamiliar AI agents in negotiation games by combining tabular features with LLM-based text representations and hidden states from a frozen observer model, outperforming direct prompting approaches.