Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation

Authors: Chenkai Sun, Denghui Zhang, ChengXiang Zhai, Heng Ji

Published: 2025-06-26

arXiv ID: 2506.20949v1

Added to Library: 2025-06-27 04:00 UTC

Safety

📄 Abstract

Given the growing influence of language model-based agents on high-stakes societal decisions, from public policy to healthcare, ensuring their beneficial impact requires understanding the far-reaching implications of their suggestions. We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems on a macroscopic scale over time, enabling more robust alignment. To assess the long-term safety awareness of language models, we also introduce a dataset of 100 indirect harm scenarios, testing models' ability to foresee adverse, non-obvious outcomes from seemingly harmless user prompts. Our approach achieves not only over 20% improvement on the new dataset but also an average win rate exceeding 70% against strong baselines on existing safety benchmarks (AdvBench, SafeRLHF, WildGuardMix), suggesting a promising direction for safer agents.

🔍 Key Points

Introduction of a proactive alignment framework that simulates societal impacts of language model decisions over a long-term horizon, enhancing safety in output generation.
Development of a novel dataset containing 100 indirect harm scenarios to evaluate models' ability to foresee adverse, non-obvious outcomes from their responses.
Empirical results show a significant performance improvement (over 20% on new dataset) and a win rate exceeding 70% against strong baselines on existing safety benchmarks.
Utilization of world modeling and event trajectory search techniques to forecast social consequences based on model outputs, leading to enhanced model realignment training.
Establishment of a unique approach to tackle the challenge of aligning AI for long-term risk awareness rather than relying solely on reactive feedback.

💡 Why This Paper Matters

This paper is relevant and important as it addresses the growing concerns surrounding the safety and alignment of language models in high-stakes decision-making contexts. By introducing proactive measures to simulate and anticipate the long-term societal impacts of AI-generated content, the authors contribute significantly to the development of more responsible and reliable AI systems. Their results not only show promising empirical gains but also set a foundation for further research in risk-aware AI design, ultimately aiming for safer and more ethically sound AI interventions in society.

🎯 Why It's Interesting for AI Security Researchers

This paper would be of interest to AI security researchers as it emphasizes the need for aligning AI systems with societal values and long-term safety considerations. By focusing on indirect harm and proactive alignment, the study introduces crucial methodologies that could inform the development of more resilient AI systems capable of avoiding potential hazards. Furthermore, the framework and findings presented in this paper may provide insights into addressing issues of AI misuse and fostering a deeper understanding of AI’s societal implications, both key concerns in AI security.

Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation

📄 Abstract

🔍 Key Points

💡 Why This Paper Matters

🎯 Why It's Interesting for AI Security Researchers

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