Structured Intent as a Protocol-Like Communication Layer: Cross-Model Robustness, Framework Comparison, and the Weak-Model Compensation Effect
Summary: arXiv:2603.29953v1 Announce Type: new
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 substantially reduces cross-language score variance relative to unstructured baselines. The strongest structured conditions reduce cross-language sigma from 0.470 to about 0.020. We also observe a weak-model compensation pattern: the lowest-baseline model (Gemini) shows a much larger D-A gain (+1.006) than the strongest model (Claude, +0.217). Under the current evaluation resolution, 5W3H, CO-STAR, and RISEN achieve similarly high goal-alignment scores, suggesting that dimensional decomposition itself is an important active ingredient.
Key Findings and Implications
- Cross-Model Robustness: The study found significant improvements in goal alignment across different AI models when utilizing structured intent representations.
- Controlled Framework Comparison: The research compared structured frameworks such as 5W3H, CO-STAR, and RISEN, revealing that dimensional decomposition plays a crucial role in enhancing performance.
- User Study Insights: The user study indicated that AI-expanded 5W3H prompts can lead to a 60% reduction in interaction rounds and an increase in user satisfaction from 3.16 to 4.04.
Conclusion
The findings from this study strongly support the practical value of structured intent representation as a robust, protocol-like communication layer for human-AI interaction. By utilizing frameworks like PPS and 5W3H, developers and researchers can achieve enhanced goal alignment across various languages and models. This not only minimizes the variance in performance but also improves user experience, making AI systems more effective and user-friendly.
Future Directions
As AI technology continues to evolve, further research is needed to explore additional structured intent frameworks and their potential applications in diverse contexts. Future studies could also investigate the long-term impacts of structured intent representations on user engagement and satisfaction in real-world scenarios.