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  • βœ‡cs.AI, q-bio.NC updates on arXiv.org
  • Contextual Control without Memory Growth in a Context-Switching Task Song-Ju Kim
    arXiv:2604.03479v1 Announce Type: new Abstract: 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
     

Contextual Control without Memory Growth in a Context-Switching Task

arXiv:2604.03479v1 Announce Type: new Abstract: 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 compare three model families: a label-assisted baseline with direct context access, a memory baseline with enlarged recurrent state, and the proposed intervention model, which uses no direct context input to the recurrent core and no memory growth. On the main benchmark, the intervention model performs strongly without additional recurrent dimensions. We also evaluate the models using the conditional mutual information (I(C;O | S)) as a theorem-motivated operational probe of contextual dependence at fixed latent state. For task-relevant phase-1 outcomes, the intervention model exhibits positive conditional contextual information. Together, these results suggest that intervention on a shared recurrent state provides a viable alternative to recurrent memory growth for contextual control in this setting.

Contextuality from Single-State Ontological Models: An Information-Theoretic No-Go Theorem

24 February 2026 at 13:00
arXiv:2602.16716v2 Announce Type: replace Abstract: Contextuality is a central feature of quantum theory, traditionally understood as the impossibility of reproducing quantum measurement statistics using noncontextual ontological models. We consider classical ontological models constrained to reuse a single ontic state space across multiple interventions. We prove an information-theoretic no-go theorem showing that such models must incur an irreducible contextual information cost: contextual dependence cannot be fully mediated through the ontic state alone and requires additional contextual information beyond it. We provide a constructive example illustrating this obstruction and show that it arises solely from the requirement of ontic state reuse within a classical probability space. We further explain how quantum theory avoids this obstruction by relaxing the assumption that all measurement statistics arise from a single underlying classical ontic variable. These results identify contextuality as a fundamental information-theoretic constraint on classical ontological models and clarify its origin as a limitation on classical representations.
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