Towards million-token context windows: a topology-preserving framework for adaptive transformer sparsification

Area of Science:

• Computer Science
• Network Analysis
• Artificial Intelligence

Background:

• Transformer self-attention and large network analyses face computational bottlenecks due to all-to-all interaction evaluation.
• Current solutions involve hardware distribution or recurrent operator substitution, often sacrificing associative recall for efficiency.

Purpose of the Study:

• Introduce Reduced Interaction Sampling (RIS), a novel stochastic sparsification framework.
• To enable efficient analysis of large-scale networks and long-context transformers without significant loss of structural accuracy or performance.

Main Methods:

• RIS computes a fraction of pairwise interactions, leveraging topological redundancy in real-world networks.
• A partition-based variant, RIS-Structural, enhances hub identification in sparse networks.
• Evaluated on com-LiveJournal graphs for network analysis and TinyLlama-1.1B for transformer attention.

Main Results:

• RIS preserves degree centrality rank on large graphs using only 10% of edges.
• RIS-Structural identifies twice as many hubs as sliding-window methods under heavy sparsity.
• In transformer tests, RIS achieves significantly greater geometric reach (21k tokens) compared to Longformer and BigBird, maintaining hub recall with minimal edge budget.

Conclusions:

• RIS offers a mathematically robust method for scaling context architectures without structural collapse.
• Stochastic sparsification effectively separates structural accuracy from computational cost.
• RIS demonstrates superior performance in both network analysis and long-context transformer applications.