NeurIPS 2025 Paper Awards (The Best Papers of 2025)

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NeurIPS 2025 Paper Awards (The Best Papers of 2025)

AI, But Simple Issue #90

The rapid progress of AI in recent years has raised many questions and challenges. In 2025, researchers responded with breakthroughs that pushed the boundaries of deep learning, machine learning, and other related fields.

As deep learning advances into 2026, these developments continue to shape its trajectory. In this issue, we’ll take a look at a selection of highly impactful NeurIPS 2025 papers, highlighting the insights and innovations that are defining the “cutting-edge” of AI research.

A Better Real-Time Object Detection Algorithm (YOLOv10)

The YOLO family has long dominated real-time object detection, among other image tasks, but every version up to now has shared a small issue: they all rely on Non-Maximum Suppression (NMS) after inference to filter out duplicate detections.

NMS adds latency and introduces sensitivity to hyperparameters that are hard to tune in production. YOLOv10 sets out to fix this and does so convincingly.

The centerpiece of the paper is a training strategy called consistent dual assignments.

The idea is that during training, the model runs two object detection heads in parallel.

One head uses the standard one-to-many label assignment, which provides rich supervision and is responsible for most of YOLO's historical accuracy.

The other head uses one-to-one assignment, which naturally produces a single prediction per object and therefore needs no NMS.

At inference time, you simply discard the first head and use the second. But how could this work? The authors have a clever approach.

The two heads are actually aligned using a shared matching metric, minimizing the "supervision gap" between the branches. As a result, the one-to-one head learns from the strength of the one-to-many head without drifting.

The math behind this matching is grounded in a Wasserstein-distance analysis of the classification targets.

Above, the PAN is the Path Aggregation Network, used from YOLOv4 to v10.

On the architecture side, the authors don't just make one or two tweaks but do a systematic audit of the entire YOLO pipeline.