Lei Yang

Native unified modelling is position as a promising path towards systems that perceive, reason and create within a fully end-to-end framework through SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free and VAE-free architecture.

Agent memory breaks when models are forced to simultaneously interpret, update, and rewrite facts—decoupling maintenance into explicit pairwise relation classification and role-based fusion yields up to a 29.8 percentage point accuracy leap.

Static infrastructure dominates occupancy perception, occupying 97.3% of voxels, while dynamic elements barely register, challenging existing benchmarks and methods.

Unsupervised query correction can achieve superior performance by cleverly encoding phonetic and visual similarities, avoiding the pitfalls of intent drift.

Achieving 98.6% accuracy in 3D Gaussian world modeling could redefine efficiency benchmarks for robotic manipulation tasks.

Models trained with VBVR-Pro not only excel in a controlled task space but also show significant transferability to external benchmarks, revealing critical insights into visual reasoning mechanisms.

AdaptiveEmbed reveals that tailoring representation capacity to individual sample needs can dramatically enhance multimodal retrieval performance.

A single unified model can outperform specialized systems across various computer vision tasks, all without the need for custom architectures.