L2context

L2context is a term used in discussions of hierarchical context modeling in artificial intelligence and computer science. It denotes the contextual information processed at the second level of a two-layer architecture, distinguishing it from L1 context, which operates on more immediate, token-level signals. L2context typically aggregates and abstracts signals coming from the first layer to represent higher-level state such as sentence-level meaning, discourse, task history, or domain-specific cues.

In a neural or probabilistic system, L2context is produced by a higher-level module that consumes L1 representations and applies mechanisms such as attention, recurrence, or memory to retain and organize information over longer spans. Common implementations include transformer-based layers with extended context windows, recurrent modules with memory, and differentiable external memories. The purpose is to improve coherence, resolve ambiguity, and guide downstream predictors with richer, temporally extended information.

Applications of L2context appear in natural language processing tasks that require longer-range dependencies, such as document-level sentiment analysis, coreference resolution, or multi-turn dialogue, as well as in code understanding and multimodal reasoning where cross-sentence information matters. It is often studied in conjunction with L1 context to evaluate how much the second layer contributes to performance.

Challenges for L2context include managing computational cost, preventing context drift, and balancing information between layers. The concept is used across various architectures and is not tied to a single standardized definition, leading to variant terminology in different papers and systems. See also L1 context, hierarchical modeling, and context window.