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This paper introduces Coupled Scaling, a task-conditioned framework that explains how neural scaling laws vary based on the relationship between task structure and the geometric representations accessible by architecture-optimization systems.
MemPrism proposes a task-conditioned relational memory framework that separates persistent experience storage from decision-time working memory, enabling long-horizon agents to dynamically construct relational views for improved performance and reduced token usage.
This paper investigates the signed nature of FFN residual writes in long-context retrieval, finding that FFN writes act as suppressors or amplifiers depending on layer and task, and proposes a gradient-based diagnostic to distinguish these roles.