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!

if you think about it, how crazy is it that we've found nonlinear functions that are better than us at doing stuff

my AC @NeurIPSConf can see the future. before the rebuttal period had even started, the AC posted a meta-review stating that we did not submit a response -- so I guess the final justification was made a bit early. but since it was already generated, why not ignore our rebuttal and just use it again, right? peak peer review.

Isn’t the ultimate question whether AI can come up with the next NS-like problem, or really any genuinely new and meaningful problem? To be clear, current progress is undeniably remarkable. But it’s also somewhat biased by how we measure it. We optimize models incredibly hard to solve math problems because we have a clear objective, then are surprised when they become very good at exactly those tasks. That does not necessarily generalize to all tasks. My bet is that AI, if developed with care, makes humans dramatically more capable rather than replacing humans altogether.

LLMs can only think out loud: every thought is a token, every token a forward pass. We trained LLMs to reason in a latent working memory instead. No reasoning trace, TTFT matches direct answering. See it for yourself in my new interactive blog post: 👉 aichberger.github.io/blog/re…

Thrilled to see my work from @OATML_Oxford @UniofOxford featured in @sciam! Huge thanks to my collaborators and mentors for all the support ✨