Subjective Depth and Timescale Transformers: Learning Where and When to Compute

This article, sourced from ArXiv, likely presents a novel approach to Transformer architectures. The title suggests a focus on optimizing computation within Transformers, potentially by dynamically adjusting the depth of processing and the timescale of operations. The terms "subjective depth" and "timescale" imply a learned, adaptive mechanism rather than a fixed configuration. The research likely explores methods to improve efficiency and performance in large language models (LLMs).