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Mitigating Over-Refusal in Aligned Large Language Models via Inference-Time Activation Energy

arXiv – CS AI|Eric Hanchen Jiang, Weixuan Ou, Run Liu, Shengyuan Pang, Guancheng Wan, Ranjie Duan, Wei Dong, Kai-Wei Chang, XiaoFeng Wang, Ying Nian Wu, Xinfeng Li|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce Energy Landscape Steering (ELS), a new framework that reduces false refusals in AI safety-aligned language models without compromising security. The method uses an external Energy-Based Model to dynamically guide model behavior during inference, improving compliance from 57.3% to 82.6% on safety benchmarks.

Key Takeaways

→ELS addresses the over-refusal problem where safety-aligned AI models incorrectly reject benign requests.
→The framework uses a lightweight external Energy-Based Model to steer AI behavior in real-time without modifying core parameters.
→Testing showed compliance improvements from 57.3% to 82.6% on the ORB-H benchmark while maintaining safety standards.
→The approach is computationally efficient and fine-tuning free, making it practical for deployment.
→ELS decouples behavioral control from the model's core knowledge, providing a flexible safety solution.

#ai-safety #language-models #machine-learning #energy-based-models #inference-optimization #ai-alignment #research #arxiv

Read Original →via arXiv – CS AI

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