Noura: specification-guided supervision markedly improves LLM dietary planning
arXiv:2610.40992v1 introduces Noura, a system that pairs LLM planning with executable personal specifications derived from public clinical guidance and individual health/dietary profiles to generate and iteratively correct multi-day meal plans. In a dietitian-approved NHANES-based weekly planning evaluation (4,993 held-out participants), thirteen baseline models pass fewer than 3% of complete plans by direct generation; Noura-SFT raises Qwen3-14B's complete-plan pass rate from 24.0% to 72.5%, cuts rule violations by 97.5% with no observed critical failures, shows a locally hosted 14B planner with fewer violations than a GPT-6 Luna teacher, and reports executed-target and self-training gains that reduce rule violations by 41–61% across Qwen, Phi, and Llama backbones.