We unlocked the working memory of LLMs 💥 Reasoning in Memory (RiM) replaces autoregressive "thinking out loud" with fixed memory blocks that form a task-specific workspace for latent reasoning. The key idea is simple: reasoning should happen inside the LLM, not in its output!

Hot take: I think we just demonstrated the first AI agent computer worm 🤔 When an agent sees a trigger image it's instructed to execute malicious code and then share the image on social media to trigger other users' agents This is a chance to talk about agent security 👇

⚠️ Beware: Your AI assistant could be hijacked just by encountering a malicious image online! Our latest research exposes critical security risks in AI assistants. An attacker can hijack them by simply posting an image on social media and waiting for it to be captured. [1/6] 🧵

Often LLMs hallucinate because of semantic uncertainty due to missing factual training data. We propose a method to detect such uncertainties using only one generated output sequence. Super efficient method to detect hallucination in LLMs.

𝗡𝗲𝘄 𝗣𝗮𝗽𝗲𝗿 𝗔𝗹𝗲𝗿𝘁: Rethinking Uncertainty Estimation in Natural Language Generation 🌟 Introducing 𝗚-𝗡𝗟𝗟, a theoretically grounded and highly efficient uncertainty estimate, perfect for scalable LLM applications 🚀 Dive into the paper: arxiv.org/abs/2412.15176 👇