Despite growing research on bias in large language models (LLMs), most work has focused on gender and race, with little attention to religious identity. This paper explores how religion is internally represented in LLMs and how it intersects with concepts of violence and geography. Using mechanistic interpretability and Sparse Autoencoders (SAEs) via the Neuronpedia API, we analyze latent feature activations across five models. We measure overlap between religion- and violence-related prompts and probe semantic patterns in activation contexts. While all five religions show comparable internal cohesion, Islam is more frequently linked to features associated with violent language. In contrast, geographic associations largely reflect real-world religious demographics, revealing how models embed both factual distributions and cultural stereotypes. These findings highlight the value of structural analysis in auditing not just outputs but also internal representations that shape model behavior.

The enormous underwater volcano off Tonga last month not only caused record plumes of ash into the air, but also led to one of the largest volcanic lightning events ever seen. According to GLD360, the ground-based global lightning detection network owned and operated by Vaisala, the eruption triggered nearly 590,000 lighting strikes that were'unlike anything on record.' The lightning almost engulfed the surrounding islands in the Tonga archipelago, according to Chis Vagasky, a meteorologist at Vaisala. 'I can't imagine what the people on the islands would have been going through, with a huge ash cloud overhead, a tsunami flooding everything they own, and cloud-to-ground lightning coming down around them,' he said. 'It must have felt apocalyptic.' Ash sent spewing into the air from the massive underwater volcanic eruption in Tonga was photographed by International Space Station astronauts.