- Updated: August 27, 2026
- 2 min read
Reward‑Optimized Probe‑and‑Respond (RO‑PnR): A Decision Framework for Multi‑Turn Health Misinformation Intervention
Reward‑Optimized Probe‑and‑Respond (RO‑PnR): A Decision Framework for Multi‑Turn Health Misinformation Intervention
Abstract: Correcting health misinformation in dialogue requires more than a factual rebuttal. Users differ in knowledge, belief, and information needs, making the timing of clarifying questions crucial. The RO‑PnR framework learns when probing is worth its cost, balancing expected gain against interaction cost. Experiments on three health‑misinformation datasets show RO‑PnR achieves the highest cost‑adjusted utility with 30 % fewer turns than always‑probe baselines.
Why RO‑PnR Matters
- Personalised interaction: Adapts to user health literacy and belief commitment.
- Cost‑effective: Reduces unnecessary dialogue turns while maximising correction impact.
- Scalable: Works with multiple base models and datasets.
Key Components
- Probe vs. Respond Decision: At each turn the model chooses to ask a clarifying question (probe) or deliver the final correction (respond).
- Turn‑level Reward: Weighs expected informational gain against the interaction cost of an extra turn.
- User Latent State: Models health literacy and belief commitment to estimate probing value.
Experimental Results
Across three health‑misinformation datasets (e.g., vaccine myths, dietary claims, COVID‑19 facts) and three base language models, RO‑PnR consistently outperforms:
- Always‑Probe baselines (30 % fewer turns).
- Always‑Respond baselines (higher utility).
Implications for AI‑Driven Health Communication
The framework can be integrated into chat‑bots, virtual assistants, and patient‑education platforms to provide more nuanced, efficient, and trustworthy interventions.
Learn More
Read the full paper on arXiv and explore related resources on our site:
For inquiries, contact the UBOS research team.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.