ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments

📰 ArXiv cs.AI

Learn how ReasoningGuard safeguards large reasoning models from harmful content generation at inference time, and how to apply it for safer AI reasoning

advanced Published 7 May 2026

Action Steps

Implement ReasoningGuard in your LRM pipeline to detect and prevent harmful content generation
Use the proposed inference-time safety checks to monitor and adjust your model's reasoning process
Fine-tune your LRM with safety constraints to improve its robustness and reliability
Evaluate the effectiveness of ReasoningGuard in your specific use case and adjust its parameters as needed
Integrate ReasoningGuard with existing defense methods to create a more comprehensive safety framework

Who Needs to Know This

AI researchers and engineers working with large reasoning models can benefit from this safeguard to ensure safer and more reliable AI systems

Key Insight

💡 ReasoningGuard provides an inference-time safeguard for Large Reasoning Models, detecting and preventing harmful content generation without requiring costly fine-tuning or additional expert knowledge

Key Takeaways

Learn how ReasoningGuard safeguards large reasoning models from harmful content generation at inference time, and how to apply it for safer AI reasoning

Full Article

Title: ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments

Abstract:
arXiv:2508.04204v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) have demonstrated impressive performance in reasoning-intensive tasks, but they remain vulnerable to harmful content generation, particularly in the mid-to-late steps of their reasoning processes. Current defense methods, however, depend on costly fine-tuning and additional expert knowledge, which limits their scalability. In this work, we propose ReasoningGuard, an inference-time safeguard for LRMs. It injec

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