# LookSharp: Attention Entropy Minimization for Test-Time Adaptation

Source: [LookSharp: Attention Entropy Minimization for Test-Time Adaptation](<https://www.sota2.com/research/paper/692538afd6b0dd334292fa4e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-11T05:00:04.178Z

Authors: Yash Mali, Evan Shelhamer

Published: 2025-11-24T09:32:01.000Z

## Abstract

Test-time adaptation (TTA) updates models during inference to reduce error on distribution shifts. While entropy minimization over the output distribution has proven effective as a TTA loss, we study using the intermediate distributions computed by transformers in the attention mechanism. We propose LookSharp, which minimizes the entropy of CLS-to-patch attention in the final layer as a novel TTA objective, encouraging the model to maintain focused attention on shifted data. We demonstrate that attention entropy minimization improves robustness on ImageNet-C. We also show that it is complementary to output entropy minimization and maintains performance on clean data.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.18925>)

[PDF](<https://arxiv.org/pdf/2511.18925.pdf>)