• A new FHI technical report on “Quasilinguistic Neural Representations” was shared, proposing a framework for interpreting and steering the internal “language-like” states that emerge in large language models.  
• The paper argues these representations can be probed and edited to achieve controllable, interpretable behavior without fine-tuning, offering a path toward safer and more steerable AI systems.  
• An arXiv preprint titled “Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language” was also circulated, describing a method to chain together multiple foundation models (vision, language, audio) so they critique and refine each other’s outputs in a zero-shot setting.  
• Discussion highlighted that both papers point toward a future where AI capabilities are extended not by scaling data alone, but by orchestrating existing models into higher-level reasoning systems—an approach that could be replicated by open-source toolkits.  
• A brief aside noted ongoing university bureaucracy blocking a student rocketry club from using its own funds, illustrating recurring institutional friction against grassroots, high-risk engineering projects.