Latent Fact-Checking: Detecting Misinformation through Activation Engineering
The proliferation of misinformation online has driven demand for scalable detection systems. While most existing approaches rely on surface-level linguistic features or external knowledge retrieval, we examine truthfulness as a geometric property of a language model's representation space. We introduce a misinformation detection framework grounded in activation engineering, which leverages the latent geometry of transformer models. Our approach elicits a misinformation direction in the residual stream by contrasting activations from paired truthful and false statements, following the difference-in-means principle of Contrastive Activation Addition (CAA). At inference time, the last-token ...
| Authors | Pedro T. Barcelos, Otávio Parraga, Marcelo M. Delucis et al. |
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| Primary category | cs.LG |
| Published | 5 August 2026 |
| Last updated at source | 3 September 2026 |
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