Can an LLM resist a motivated investor pushing a fraudulent pitch? Powdthavee's preregistered study across seven leading models and twelve scenarios finds they outperform humans at fraud detection, yet soften warnings when users arrive already convinced. Ellawela tracks LLM agents through repeated rounds of Avalon with persistent memory, surfacing emergent reputation, trust, and deception between games rather than within one. Gabeur and colleagues then show image generators double as generalist vision learners, with Nano Banana Pro hitting state of the art on multimodal benchmarks. Three angles on what today's models quietly know and quietly hide.