Pooling Helps, Learned Weighting Hurts In-Context: Decomposing Group Attention

Abstract:Group attention, introduced by the time series forecasting model Chronos-2, attends over the variates of a group at a fixed patch index and serves both multivariate (MV) and in-context learning (ICL) forecasting. Rather than evaluating this cross-variate attention design as a whole, we ask which part of the mechanism earns the benefit and probe its applicability to both MV and ICL regimes. By editing the attention matrix $\alpha$ at inference we separate the two pathways a head comprises: V/O, which projects a weighted summary of the group, and Q/K, which decides the weights. Uniform pooling (V/O without any Q/K weighting) is positive on 18 of our 20 sensor-network configurations, while the learned weighting (Q/K) splits by group type: its contribution is positive or negligible for MV, but materially degrades 8 of the 10 sensor-network ICL configurations, leaving 4 of them worse than univariate inference. By isolating the impact of different layers, we find that uniforming $\alpha$ in the first block alone improves every ICL configuration we test.

Machine Learning (cs.LG)

arXiv:2610.01831 [cs.LG]

(or arXiv:2610.01831v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.01831 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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