A small vision window actually helps a Transformer model understand more complex languages.
Engineers usually try to increase the context window of a model so it can see as much data as possible at once. Mathematical proofs now show that limiting attention to local neighborhoods expands the formal class of languages a model can recognize. This counterintuitive constraint prevents the model from getting lost in global noise and forces it to learn more intricate local structures. This discovery challenges the bigger is better philosophy of modern AI architecture. It suggests that specific hardware or software constraints might be a feature rather than a bug for certain reasoning tasks.
Characterizing the Expressivity of Local Attention in Transformers
arXiv · 2605.00768
The transformer is the most popular neural architecture for language modeling. The cornerstone of the transformer is its global attention mechanism, which lets the model aggregate information from all preceding tokens before generating the next token. One common variant of attention is called local attention, which restricts each token to aggregating information from a bounded window of predecessors, reducing the quadratic cost of global attention to linear. Although this restriction is usually