Zhe Wang

Learning the generation order in multimodal tasks can boost performance by over 4%—a game changer for DLMs.

Bridging the gap between synthetic and real-world motion prediction, this framework achieves superior performance by leveraging objectness priors to refine motion labels.

ATCCaps reveals that effective call-sign recognition in ATC communications hinges not just on quantity, but on the quality and accuracy of audio-text supervision.

Pipette transforms wet-lab robotics by boosting training efficiency and accessibility, achieving up to 74.7% success rates with just 30 demonstrations per task.

Learning insertion order in sequence generation can drastically improve modeling quality and generalization, challenging the dominance of fixed-canvas methods.

Frontier models can't build playable games in one shot, but a closed-loop system using GUI agents to playtest and refine code achieves a 66.8% success rate, proving that game generation needs to be a conversation, not a translation.

Pathologists using AI assistance can boost diagnostic accuracy by 8% and slash diagnostic time by 20% in lung pathology, according to a prospective, multi-center RCT.

Current video generation benchmarks miss the forest for the trees: EvalVerse actually measures cinematic quality, not just prompt adherence.

Forget wrestling with finicky text prompts or tedious manual camera paths: ShotVerse lets you generate cinematic multi-shot videos from text, thanks to its clever "Plan-then-Control" framework and a dataset of aligned camera trajectories.