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We Think, Therefore We Align LLMs to Helpful, Harmless and Honest Before They Go Wrong
Gautam Siddharth Kashyap, Mark Dras, Usman Naseem
https://arxiv.org/abs/2509.22510 https:/…

We Think, Therefore We Align LLMs to Helpful, Harmless and Honest Before They Go Wrong
Alignment of Large Language Models (LLMs) along multiple objectives-helpfulness, harmlessness, and honesty (HHH)-is critical for safe and reliable deployment. Prior work has used steering vector-small control signals injected into hidden states-to guide LLM outputs, typically via one-to-one (1-to-1) Transformer decoders. In this setting, optimizing a single alignment objective can inadvertently overwrite representations learned for other objectives, leading to catastrophic forgetting. More rece…