Steering Under Compression: Dose-Response, Capability Cost, and Failure Asymmetry in Quantized LLMs

Inference-time activation steering enables behavioral control of large language models without parameter modification, while post-training quantization reduces memory and compute costs for deployment. Despite their growing convergence in practice, the interaction between these two techniques remains