在上下文切换任务中无需记忆增长的上下文控制
Contextual Control without Memory Growth in a Context-Switching Task
AI总结：
本文提出一种无需增加记忆维度的上下文控制方法，通过干预共享的递归潜在状态，在上下文切换任务中实现了有效的决策制定。
摘要：
上下文依赖的序列决策通常通过显式提供上下文作为输入或增加递归记忆来实现。我们研究了一种第三种方法：通过干预共享的递归潜在状态来实现上下文依赖，而无需扩大递归维度。为此，我们引入了一种基于干预的递归架构，其中递归核心首先构建共享的预干预潜在状态，然后上下文通过加法的上下文索引操作符发挥作用。我们评估了该想法在一个部分可观测的上下文切换序列决策任务中。我们比较了三种模型家族：一个带有标签辅助的基线模型，具有直接的上下文访问；一个带有扩大递归状态的基线模型；以及所提出的干预模型，该模型不将上下文直接输入递归核心，也不增加记忆。在主要基准上，干预模型在不增加递归维度的情况下表现优异。我们还使用条件互信息（I(C;O | S）作为定理驱动的操作探针，评估模型在固定潜在状态下的上下文依赖性。对于任务相关的阶段1结果，干预模型显示出正的条件上下文信息。这些结果表明，在这种设置中，对共享递归状态的干预为上下文控制提供了一种可行的替代方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
