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Real-world coding tasks are now contamination-resistant and tailored for diverse domains, challenging agents in ways traditional benchmarks cannot.
NegROI achieves superior 3D segmentation with fewer clicks by intelligently refining only the most ambiguous regions while effectively managing background confusion.
SL-S4Wave achieves state-of-the-art performance in arrhythmia detection while requiring significantly fewer labeled examples than traditional methods.
CapRL++ redefines caption quality through utility, enabling models to produce high-fidelity descriptions without the constraints of traditional supervised fine-tuning.
User corrections of AI agents are a goldmine: Echo shows how to automatically transform these noisy interactions into a 10% absolute improvement in code completion acceptance rates.
Decomposing GUI agent trajectories into verifiable milestones and auditing the evidence chain yields a 10% boost in RL training performance, outperforming single-judge reward systems.
