Contextual Control without Memory Growth in a Context-Switching Task
Summary: 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.
In this article, we introduce an innovative intervention-based recurrent architecture that constructs a shared pre-intervention latent state. Subsequently, context influences this state through an additive, context-indexed operator. Our evaluation focuses on a context-switching sequential decision task under conditions of partial observability, where we compare three model families:
- A label-assisted baseline with direct context access
- A memory baseline with enlarged recurrent state
- The proposed intervention model, which operates without direct context input and avoids memory growth
Our main findings reveal that the intervention model performs robustly on the primary benchmark, maintaining strong performance without additional recurrent dimensions. This indicates the potential effectiveness of our approach in managing contextual control.
Furthermore, we assess the models using the conditional mutual information (I(C;O | S)) as an operational probe for contextual dependence at a fixed latent state. Notably, for task-relevant phase-1 outcomes, the intervention model demonstrates positive conditional contextual information, reinforcing the idea that intervention on a shared recurrent state can serve as a viable alternative to traditional recurrent memory growth methods.
Key Insights and Implications
The results of our research indicate that contextual dependence can be effectively managed without the necessity of expanding memory structures, which often complicates model training and increases computational overhead. The implications of this study are significant for the fields of artificial intelligence and machine learning, particularly in the development of models that require efficient decision-making capabilities in dynamic environments.
- Efficiency: By eliminating the need for increased memory, our approach allows for more efficient model training and deployment.
- Scalability: The intervention model is scalable and can be adapted to various tasks where context-switching is required.
- Robust Performance: The strong performance of the intervention model on benchmarks highlights its potential for practical applications in areas such as robotics, autonomous systems, and real-time decision-making.
In conclusion, this research presents a compelling case for rethinking how contextual control is achieved in sequential decision-making tasks. By leveraging interventions on a shared latent state rather than relying on extensive memory growth, we open new avenues for the design of more efficient and capable AI systems.