Subliminal Learning

Transfer across different initializations: The preprint showed subliminal transfer between models that shared an initialization. The new MNIST results demonstrate transfer between models with different initializations. Although a toy setup, it meaningfully broadens the scope of the effect beyond shared-weight scenarios.

Misalignment transmitted through code and chain-of-thought: General misalignment, not just benign preferences, can also be transmitted subliminally. The new results show this transfer can happen through model-written code or chain-of-thought reasoning, not only through numeric sequences, which expands the attack and contamination surface considerably.

Connections to independent follow-ups: The authors highlight concurrent work from Aden-Ali et al. (2026) showing trait transfer via standard post-training datasets filtered by the teacher, Draganov et al. (2026) demonstrating a cross-family "phantom transfer" data poisoning attack, and Weckbecker et al. (2026) describing a subliminal "virus" that spreads between agent groups. Together they suggest the phenomenon is robust, reproducible, and difficult to defend against.

Implications for safety evaluations: The practical takeaway is that safety evaluations may need to examine not just model behavior, but the origins of models and the processes used to create training data. As systems increasingly train on each other's outputs, properties invisible in the data can still be inherited, undermining evaluations that focus purely on observable responses.