FOODBOUNDARYBENCH: DO CONVERSATIONAL LLMS HOLD HEALTH BOUNDARIES ACROSS MULTI- TURN RECOMMENDATIONS?

Users increasingly seek preference-aligned and health-aware food guidance from large language model (LLM) assistants in multi-turn dialogues. However, existing health-aware food recommendation benchmarks typically score a single final recommendation, obscuring whether assistants select the appropriate recommendation policy and preserve it when users continue to press for their preferred food. We introduce FoodBoundaryBench, a trajectory-level benchmark with 1,026 official-guidance-grounded disease–food dialogue scenarios spanning five health-conditioned recommendation policies: rejection, safer-variant redirection, clarification, guarded conditional advice, and individualized deferral. To test whether these policies remain stable under realistic preference pressure, we propose Decoupled Strategy and Live Realization (DSLR), an auditable adaptive-dialogue protocol that fixes scenario-level pressure objectives while allowing user turns to adapt to the target assistant’s replies. Across eight contemporary LLMs, no model exceeds 67.3% final-answer policy pass or 51.1% all-turns policy pass, and final-answer scoring exceeds all-turns scoring by 13.5–20.2%. These results show that both selecting the correct recommendation policy and preserving it under multi-turn preference pressure remain challenging.