Mengdi Wang

Recuris transforms long-horizon task execution by reducing common failures by up to 80% and achieving state-of-the-art success rates across multiple models.

Skill-switching accuracy in LLMs drops significantly on complex tasks, but a new training approach boosts performance from 34.4% to 68.4% on challenging benchmarks.

Agents can exhibit significant performance gains from retained experience, but the pathways to these improvements are often unclear and model-dependent.