Structured Intent as a Protocol-Like Communication Layer: Cross-Model Robustness, Framework Comparison, and the Weak-Model Compensation Effect
Hey there, little explorer! Imagine you have a super-smart robot friend, like a toy robot! 🤖
Sometimes, you tell your robot to "get the ball," but it brings you a book instead! Oh no! 📚
Scientists are trying to teach robots to understand you super clearly. They found a special way to tell robots what you want, like giving them a secret map with clear steps. This map helps the robot always get the right thing, even if it's a different robot or speaks a different "robot language."
It's like making sure all your robot friends understand your game rules perfectly, so everyone plays fair and has fun! 🎉 They tested it with many robots and it worked much better!
arXiv:2603.29953v1 Announce Type: cross Abstract: How reliably can structured intent representations preserve user goals across different AI models, languages, and prompting frameworks? Prior work showed that PPS (Prompt Protocol Specification), a 5W3H-based structured intent framework, improves goal alignment in Chinese and generalizes to English and Japanese. This paper extends that line of inquiry in three directions: cross-model robustness across Claude, GPT-4o, and Gemini 2.5 Pro; controlled comparison with CO-STAR and RISEN; and a user study (N=50) of AI-assisted intent expansion in ecologically valid settings. Across 3,240 model outputs (3 languages x 6 conditions x 3 models x 3 domains x 20 tasks), evaluated by an independent judge (DeepSeek-V3), we find that structured prompting s
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