Sparse Token Merger (STM)

• Sparse Token Merger (STM) is a concept suggesting techniques for reducing tokens in models to possibly improve efficiency, though it is not yet well-defined.
• The approach may involve dynamic token selection or merging strategies to optimize computational resources in large language models.
• Understanding STM’s potential requires further research into its methodologies and applications in enhancing cost-aware LLM performance.

Sparse Token Merger (STM) is not described or referenced in "One Head, Many Models: Cross-Attention Routing for Cost-Aware LLM Selection" (Pulishetty et al., 11 Sep 2025). The paper exclusively details the Cross-Attention Routing Adapter (CARA), a predictor-based router for dynamic LLM selection using a single-head cross-attention mechanism. In the absence of relevant material in the provided data about Sparse Token Merger, no encyclopedia article can be produced on this topic with factual fidelity as required.