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Spatial coding agents can be distilled into standalone MLLMs, achieving superior spatial reasoning and generalization without external tools.
This work proposes QUALS, a large-scale time series corpus equilibrium framework that significantly enhances data efficiency, enabling existing models to achieve superior performance using only a small fraction of the original training data.
