three years ago, DiT replaced the legacy unet with a transformer-based denoising backbone. we knew the bulky VAEs would be the next to go -- we just waited until we could do it right. today, we introduce Representation Autoencoders (RAE). >> Retire VAEs. Use RAEs. 👇(1/n)

Thanks for bringing this to my attention. I honestly wasn’t aware of the situation until the recent posts started going viral. I would never encourage my students to do anything like this—if I were serving as an Area Chair, any paper with this kind of prompt would be desk-rejected right away. That said, for any problematic submission, co-authors all share the responsibility, no excuse here. And this has been a good reminder for me, as a PI, to not just check the final PDF but also look through the full submission files. I wasn’t aware of this kind of need before. Let me take a moment to share what we found after doing a full internal review this past week--everything’s backed up by logs and screenshots, available if needed. 1. Background In November 2024, a researcher @jonLorraine9 tweeted this: xitter.cf/jonLorraine9/status/18…. That was the first time I saw this kind of idea, and I think it was also when people realized that LLM prompts could be embedded in papers. Note that such injection only works if the reviewer uploads the PDF to an LLM directly. At that time, one thing we all agree is that LLMs should NOT be used for reviewing. It’s a real threat to the integrity of the process. That’s why conferences like CVPR and NeurIPS have now explicitly and strictly banned LLM reviewing (e.g., “LLMs are NOT allowed to be used for writing the reviews nor the meta-reviews at any step.”). If you've published at AI conferences, you probably know how frustrating it is to receive a review that was clearly written by an AI. It’s nearly impossible to respond to, and often just as hard to definitively prove that an LLM wrote it. While the original post might have been made partly as a joke, we all felt that trying to “fight fire with fire” isn’t the right defense--it raises more ethical issues than it solves. A better path is to address these concerns through official conference policies, not through individual hacks that can backfire. 2. What happened in our case The student author—who was visiting our group briefly from Japan—took that tweet a bit too literally and used the idea in an EMNLP submission. They copied the format exactly, not realizing it was partly a joke and could come across as manipulative or misleading. They also didn’t fully grasp how this might impact public trust in science or the integrity of peer review. On top of that, they included the same thing in the arXiv version without thinking twice. I missed it too—partly because this goes beyond the usual checks I have in place to catch anything ethically questionable as a coauthor. 3. Next steps The student has since updated the paper and reached out to ARR for formal guidance. We'll follow whatever steps they recommend. 4. Bigger picture This has been a teaching moment for me. Students under pressure don’t always think through all the ethical implications—especially in newer areas like this. My job is to guide them through these gray zones, not just react to their mistakes. Rather than punishment, what’s really needed is better education around these issues. I was upset with the student at first too. But after thinking it through, I don’t think the students should be punished beyond having the paper rejected. I’ve told them clearly this can’t happen in the future, and we’re also planning additional training around AI ethics and responsible research practices (which to me is more about having some common sense). I’ll be honest—it’s been not a good feeling being at the center of this kind of public shaming. These conversations should be thoughtful and constructive, not about singling people out. And honestly, the students feel the pressure even more. I've actually been keeping up with the public conversations around this, and in a recent poll, 45.4% of people said they think this kind of thing is actually okay. Sure, it’s just a poll and there could be bias—but it still says something about the nature of this problem. xitter.cf/gabriberton/status/194… The real issue here is the current system—it creates space for things like this to happen. And this isn’t traditional academic misconduct like faking data; it’s something newer, and it calls for a deeper, more nuanced conversation about how research ethics are evolving in the age of AI. In that sense, I don’t feel too bad—I feel confident I could explain the context honestly to any ethics board. And to circle back to the original post’s question—this whole situation really highlights why we need to rethink how the game is played in academia. That’s really the main point I was trying to make in my talk. I’m going to continue doing my best to help students learn how to do solid research. (This post was written by me, with help from ChatGPT-4o on editing.)

metaquery is now open-source — with both the data and code available.