Context-Only CCSLM sets the data input to a constant vector, such as (x=all 1's), so that all computational variation is controlled by context.


y=Lₙ(Cₙ)... L₂(C₂)L₁(C₁)1

The finite set of context states visited during training becomes the primary organizing structure. Each context selects specialized linear mappings, making context effectively a computational address.

This is particularly interesting for robotics and other stateful systems where useful extrinsic context—task, mode, gait phase, contact state, environment, time, etc.—may exist without any sensible analog data vector to supply. It also provides a clean experimental setting for studying context topology, state transitions, specialization, capacity, and computational structure largely independently of conventional input-driven learning.

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