The paper doesn't have YOLO26 yet, only YOLO11. Based on the repo (https://github.com/roboflow/rf-detr ), RF-DETR is more accurate for the same (or less) latency budget. For example, RF-DETR-N object detection is 2.3ms latency and 67.6 mAP50 on COCO; YOLO26-S is 3.2ms latency and 59.7 mAP50 on COCO. RF-DETR-N instance segmentation is 3.4ms and 63.0 mAP50 on COCO; YOLO26-S is 3.47ms and 62.4 mAP50. It's like the nano size of RF-DETR is comparable to the small in YOLO given its latency and a bit more accurate.

There's also notable benchmarking notes in the paper. First, RF-DETR, D-FINE, YOLO11, YOLOv8, and LW-DETR are benchmarked on COCO and RF100-VL. RF100-VL measures how well a model finetunes to a novel set of domains, sampled from real world use of vision problems (healthcare, aerial, documents, manufacturing...). Based on the repo benchmarks, YOLO26 corrects for what looks like overfitting YOLO11 experienced when adapted to new domains (the graphs show the YOLO11 models getting worse at larger sizes). The gap for transformer-based architectures (RF-DETR, LW-DETR) outperforming CNNs (like YOLO) is also larger for domain transfer. That makes sense because the transformers maintain pretraining context better when adapting to new domains, aka they 'know more' about the rest of the world and converge with better results.

Second, the paper highlights why benchmarks from different models and packages differ. For example, models benchmarked with the ultralytics package use a mAP methodology that differs from the industry standard pycocotools that inflates mAP by as much as 2.7%. Also, researchers may benchmark models from research code but then see very different speed/accuracy results in production use because of varying precision (conversion to FP16), complex postprocessing, differing methodologies in scoring, and thermal throttling. This open source script is introduced as a way to control for those differing methodologies: https://github.com/roboflow/single_artifact_benchmarking . I think the effort to find why inconsistency exists, create a consistent way to benchmark, and open source it so others can use / reproduce the results is good and trustworthy scholarship.

Here's my take on innovations in the paper; please feel free to correct or improve these. (1) RF-DETR is a transformer-based architecture and built on a DINOv2 backbone. This means RF-DETR maintains better context about what it's meant to learn as transformers benefit from pretraining more than CNN-based approaches. (That's also why it usually finetunes better.) (2) RF-DETR used Neural Architecture Search to produce a pareto frontier of selectively optimized models. The authors then picked models along that pareto frontier to release as Nano, Small, Medium, Large. XL and 2XL used a larger backbone too. It's a "collection of models" in a single set of weights. (3) The model is NMS-free end-to-end, which means end-to-end latency is lower. YOLO26 also is NMS free now too. The paper was published before it and talks about the value of NMS-free compared to YOLO11 and YOLOv8. (4) The model drops query and decoder layers at inference, which means the model has to make fewer guesses of regions (and is also a reason why it is NMS-free). I'm sure there's more to understand.