PLA Framework Achieves 100% Feasibility in On-Device Itinerary Generation, Outperforming Frontier LLMs
Researchers introduce Plan, Learn, Adapt (PLA), a three-stage framework for personalized on-device itinerary generation that guarantees 100% feasibility and achieves a 67.8% win rate in human comparisons. In contrast, leading large language models such as GPT-5, Claude Opus 4.5, and Gemini 3 Pro achieved 0% feasibility under the same constraints. In production deployment within FlyEnJoy, PLA increased itinerary completion rates by 91% with an average on-device latency of 109.9 ms.
Why it matters: This work demonstrates that combining classical optimization with lightweight learning can outperform large language models on constrained planning tasks, enabling practical and efficient mobile deployment.
Full story at: arXiv AI/ML ↗