Contextual Control without Memory Growth in a Context-Switching Task
Context-dependent sequential decision making is commonly addressed either by providing context explicitly as an input or by increasing recurrent memory so that contextual information can be represented internally. We study a third alternative: realizing contextual dependence by intervening on a shared recurrent latent state, without enlarging recurrent dimensionality. To this end, we introduce an intervention-based recurrent architecture in which a recurrent core first constructs a shared pre-intervention latent state, and context then acts through an additive, context-indexed operator. We evaluate this idea on a context-switching sequential decision task under partial observability. We c...
| Authors | Song-Ju Kim |
|---|---|
| Primary category | cs.AI |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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