# YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection

Source: [YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection](<https://www.sota2.com/research/paper/691bfe426750be83fe5cf009>)

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

Data updated: 2026-01-24T05:00:14.093Z

Authors: Ori Meiraz, Sharon Shalev, Avishai Weizman

Published: 2025-11-17T13:11:11.000Z

## Abstract

This paper presents a novel Mixture-of-Experts framework for object detection, incorporating adaptive routing among multiple YOLOv9-T experts to enable dynamic feature specialization and achieve higher mean Average Precision (mAP) and Average Recall (AR) compared to a single YOLOv9-T model.

## 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/2511.13344>)

[PDF](<https://arxiv.org/pdf/2511.13344.pdf>)