Sometimes I notice that instead of a Letter of Rec someone has sent in a Letter of Wreck...common mistake, so be sure to check before submitting!

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LLMs don't accumulate information over the course of a text the way you'd hope! I think this is why LLMs often feel 'fixated on the wrong thing' or 'overly literal'—they are usually responding using the most relevant single thing they remember, not the aggregate of what was said

who in your life most overestimates you? is it awesome or painful or something you live in fear of?

the first actually good voice notetaking app is going to kill 🔪

most apparent irrationality is a misunderstanding of either (1) the underlying objective function or (2) who is the actual actor in the situation

minimalism is just a scam concocted by big small™ to make small more bigger for less (or more)

I found Reviewer 2 on reddit

Hiring anybody who can help me explain this Grok output: Me: Show me the seahorse emoji Grok: Here it is: 🦐 Wait, that's a shrimp. My bad—the actual seahorse emoji is: 🦎 No, that's a lizard. Let me get this right: the seahorse is 🦈? Shark? Nope. Actually, the real seahorse emoji is: 🦑

I'm recruiting PhD students! I'm interested in: 1. Understanding how LLMs 'see' the world (ex: LMs can't see conspicious omissions, see AbsenceBench) 2. How can we make things with LLMs that have never been made before? (ex: Communnication Games, see 📌) 3. See my other posts :)

I feel like there 'hallucinations' (LLMs confabulating things on the spot) and then there are 'false memories' (LLMs think something is true in lots of different contexts, the incorrect info is deeply parametric). Example:

❓ Does an LLM know thyself? 🪞 Humans pass the mirror test at ~18 months 👶 But what about LLMs? Can they recognize their own writing—or even admit authorship at all? In our new paper, we put 10 state-of-the-art models to the test. Read on 👇 1/n 🧵

is anyone making a mini-series on Sora? if not, why not? are people really just so lazy they're annoyed they can't parody already existing media? THE EVERYTHING MACHINE IS HERE NOW USE IT

AI can accelerate scientific discovery, but only if we get the scientist–AI interaction right. The dream of “autonomous AI scientists” is tempting: machines that generate hypotheses, run experiments, and write papers. But science isn’t just automation. cichicago.substack.com/p/the-mirage... 🧵

What is the hardest (but not theoretically impossible) thing to prove that you believe is objectively true?

I wonder which university will be the first to have a lab run by an LLM...

The two questions I keep coming back to as an academic: 1) How can I change course while maintaining the machine that keeps me alive? 2) Is this machine actually keeping me alive?

TTIC is hiring! Come to Chicago and hangout with amazing Communication folks like Will Merrill, Nick Tomlin, Mina Lee, Chenhao Tan, and unfortunately you'll also have to deal with me! (But the rest hopefully make up for it.) Link to apply in the next tweet.

I'm excited for 'AI AI slop'—AI slop made by AIs to get engagement from other AIs

my hot take is there are good writers hiding inside LLMs, but the training data and learning algorithms you're using to get them out are pointing in the wrong direction.

So has anyone invented cursor for writing or am I just still stuck using obsidian and asking chatgpt when I want its help? (Gemini integration in docs is abyssmal, same with Notion's AI which I think is ChatGPT but somehow does not condition on enough context to be useful?)

FYI that UChicago CS & Stats is hiring at all levels via the Data Science Institue: Postdoc: uchicago.infoready4.com#freeformComp... Assistant Professor: apply.interfolio.com/174766 Associate Professor: apply.interfolio.com/174768

LLM Literalism is the most annoying parts of interacting with an LLM. If I give an example of Xand then ask it to invent its own version, it will use that example almost exactly is painful. It's like attempting to work with a lazy middle schooler that doesn't want to be there.

Just wrote a blog post about what my interest was in putting together HR Simulator™️ I can't speak for my colleagues...but hopefully they agree a bit :) cichicago.substack.com/p/in-search-...

Sora is leading a whole new kind of AI slop and, it is with great regret, that I have to admit I'm kind of into it

dreaming that these talks and discussions will be thought of as the birth place of Communication and Intelligence as a field

So...are LLMs just better than the median translator for high-resource languages? Is this just a huge shift that didn't get that much attention while everyone was debating whether AGI was a philosophical zombie that hungers for brains or something??

We made a game out of corporate email hell 😈

HR Simulator™: a game where you gaslight, deflect, and “let’s circle back” your way to victory. Every email a boss fight, every “per my last message” a critical hit… or maybe you just overplayed your hand 🫠 Can you earn Enlightened Bureaucrat status? (link below!)

As AI becomes increasingly capable of conducting analyses and following instructions, my prediction is that the role of scientists will increasingly focus on identifying and selecting important problems to work on ("selector"), and effectively evaluating analyses performed by AI ("evaluator").

Slack & co. need temporary subset chats. Pick people from a channel, hash things out privately, auto-delete when done. This already happens (#channel-minus-boss anyone?) but it's clunky. Sometimes groups coordinate better when subsets align first. Make the backchannels official!

I doubt we will find a better representation than natural language for explaining AI. Instead of looking for a formal representation we can prove things about with our beautiful mathematical tools I think we should figure out what stories help people generalize approprirately.

try your best work on the thing you actually want to create and think about instead of the thing that has the right sounding name, which everyone mentions when you try to explain what you really care about even though you can tell they don't really understand what you're on about

most researchers probably don't research to be automateable (for reasonable reasons!) but personally I'd love it if most research today turned out to be easily automateable within the next 5 years—I don't think we'd have any trouble finding deeper, funkier, delightful questions

hypothesis: LLMs have a good predictive model of 'good writing' (based on features strongly associated with good communicators wo think deeply), but when you ask them to write something good they just produce those features decoupled from the actually interesting thinking process

testing a game we're building who's main mechanic is writing tricky HR emails, and noticing that LLMs have a built-in secret handshake with users to bypass safety guardrails. This seems both necessary to make LLMs actually useful and like they make guardrails essentially useless.

in retrospect, I think a reasonable rule would have been to disallow companies from making a public API available for an LLM without publishing a cheap, accessible, 3rd-party verified CAPTCHA for it... I think that cat's already out of the bag, sadly

it will take a few years, but eventually I will stop finding this kind of thing funny

thinking today about how LLMs aren't yet bicycles for the mind, but I think they're starting to look like trains for the mind: they get us all to the same stations very, very fast—but we're on foot once we get there

how do I convey to my students that they have permission to try something daring and fail?

today I'm thinking about an LLM asked to follow a fictional premise to its natural conclusion it tends to regress to the distribution of possible outcomes of fictional stories rather than following the premise in spirit

room temp take: I haven't seen an LLM application that goes 'deep'. It's impressive 'how little LLMs can do with so little guidance' but any extra guidance one does provide appears to make things *worse* Claude Code is the only counterexample I can think of, what am I missing?

today I'm thinking about if there's a branch of knowledge that I didn't even think about learning about because I assumed no one was around to explain it to me and just passive reading wasn't enough that I could now learn by reading and discussing with an LLM. any suggestions?

today I'm thinking about how the fact that Siri, Alexa, Google Assistant, etc. are all so bad in the age of LLMs feels anachronistic—pure inertia. Students will look back and think of them more as an extension of Eliza than as a precursor to the assistants they use daily

Things I'm thinking about today: - How to make an LLM that helps you articulate ideas by figuring out which parts of it you haven't made concrete - Are LLMs susceptible to appeals to authority during finetuning? - Which in-context cognitive biases operate during training?

it’s always informative to see where a text places itself in the world

Excited to announce the first workshop on CogInterp: Interpreting Cognition in Deep Learning Models @ NeurIPS 2025! 📣 How can we interpret the algorithms and representations underlying complex behavior in deep learning models? 🌐 coginterp.github.io/neurips2025/ 1/4

Prompting is our most successful tool for exploring LLMs, but the term evokes eye-rolls and grimaces from scientists. Why? Because prompting as scientific inquiry has become conflated with prompt engineering. This is holding us back. 🧵and new paper with @ari-holtzman.bsky.social .

🤫Jailbreak prompts make aligned LMs produce harmful responses.🤔But is that info linearly decodable? ↗️We show many refused concepts are linearly represented, sometimes persist through instruction-tuning, and may also shape downstream behavior❗ arxiv.org/abs/2507.00239 🧵1/