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

• URL: http://arxiv.org/abs/2506.20949v1
• Date: Thu, 26 Jun 2025 02:28:58 GMT
• Title: Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation
• Authors: Chenkai Sun, Denghui Zhang, ChengXiang Zhai, Heng Ji,
• Abstract summary: We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems.<n>We also introduce a dataset of 100 indirect harm scenarios, testing models' ability to foresee adverse, non-obvious outcomes from seemingly harmless user prompts.
• Score: 69.63626052852153
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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