🚨 NEMI decisions are out! Be sure to check your spam folder for the decision email, as a few have ended up there. Looking forward to seeing you at NEMI! 🎉

Tired of writing NeurIPS rebuttals? Take a break by registering to attend the New England Mechanistic Interpretability workshop (deadline today)!

New England Mechanistic Interpretability Workshop@nemiworkshop.bsky.social · last wk.

The NEMI workshop registration deadline is today, July 27th! Last chance to sign up to meet New England’s mech interp community, including Rhett the Terrier, who’s using J-Space to learn what LLMs think when he begs them for treats. Link in thread 👇

The NEMI crew are entering the boat parade at Sail Boston while we wait for the mech interp abstracts to sail in on August 1

NEMI is exactly one month away! Get your abstracts in by August 1st

Micah Benson@micahben.bsky.social · 2mo ago

🧠🤖 The 2026 New England Mechanistic Interpretability (NEMI) Workshop will be Aug. 14 at Boston University! Help spread the word and join the New England mech interp community! Registration and submission info in thread:👇

If you'll be at ACL or ICML this year, come check out the work from our group and collaborators - summary 🧵 below. Lots to like for those into {mechanistic, developmental, pragmatic} interpretability! I'll be at ACL; say hi!

The New England Mechanistic Interpretability (NEMI) workshop is coming to BU on Aug. 14! Join us for talks, a panel, food, and plenty of opportunities to connect with the many great researchers in the area. Register and help spread the word!

Micah Benson@micahben.bsky.social · 2mo ago

🧠🤖 The 2026 New England Mechanistic Interpretability (NEMI) Workshop will be Aug. 14 at Boston University! Help spread the word and join the New England mech interp community! Registration and submission info in thread:👇

✨ it's coming ✨ NEMI 2026 will be lit. It will also be the new BU interp supergroup's debut ball. Come meet us!

Interpretability provides a toolset for understanding how and why LMs behave in certain ways. This survey proposes a perspective on interpretability research grounded in causal mediation analysis: doi.org/10.1162/COLI... #NLProc #CLJournal @jannikbrinkmann.bsky.social @amuuueller.bsky.social

I truly believe the rapid advances in the mech interp subfield have something real to offer AI ethics researchers: A chance to look beyond the HOW of evals to the WHY, a first pass at a technical solution when we see the opportunity, a new avenue for showing failures that prove models are not gods

Representation steering is now a common way to mitigate LLM shortcuts. How much legitimate knowledge does this tend to remove? Turns out that these methods can be surprisingly precise! But also: no single steering operation will fix all shortcuts. Led by @shanzzyy.bsky.social!

Zhengyang Shan@shanzzyy.bsky.social · 7mo ago

Can steering remove LLM shortcuts without breaking legitimate LLM capabilities? In our @eaclmeeting.bsky.social paper, we show that conceptual bias is separable from concept detection; this means inference-time debiasing is possible with minimal capability loss.

New book! I have written a book, called Syntax: A cognitive approach, published by MIT Press. This is open access; MIT Press will post a link soon, but until then, the book is available on my website: tedlab.mit.edu/tedlab_websi...

I also want to mention that the lang x computation research community at BU is growing in an exciting direction, especially with new faculty like @amuuueller.bsky.social, @anthonyyacovone.bsky.social, @nsaphra.bsky.social, & @profsophie.bsky.social! Also, Boston is quite nice :)

In LLMs, concepts aren’t static: they evolve through time and have rich temporal dependencies. We introduce Temporal Feature Analysis (TFA) to separate what's inferred from context vs. novel information. A big effort led by @ekdeepl.bsky.social, @sumedh-hindupur.bsky.social, @canrager.bsky.social!

Can@canrager.bsky.social · 9mo ago

Humans and LLMs think fast and slow. Do SAEs recover slow concepts in LLMs? Not really. Our Temporal Feature Analyzer discovers contextual features in LLMs, that detect event boundaries, parse complex grammar, and represent ICL patterns.

✨ The schedule for our INTERPLAY workshop at COLM is live! ✨ 🗓️ October 10th, Room 518C 🔹 Invited talks from @sarah-nlp.bsky.social John Hewitt @amuuueller.bsky.social @kmahowald.bsky.social 🔹 Paper presentations and posters 🔹 Closing roundtable discussion. Join us in Montréal! @colmweb.org

What's the right unit of analysis for understanding LLM internals? We explore in our mech interp survey (a major update from our 2024 ms). We’ve added more recent work and more immediately actionable directions for future work. Now published in Computational Linguistics!

In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:

If you're at #ICML2025, chat with me, @sarah-nlp.bsky.social, Atticus, and others at our poster 11am - 1:30pm at East #1205! We're establishing a 𝗠echanistic 𝗜nterpretability 𝗕enchmark. We're planning to keep this a living benchmark; come by and share your ideas/hot takes!

The new "Lookback" paper from @nikhil07prakash.bsky.social‬ contains a surprising insight... 70b/405b LLMs use double pointers, akin to C programmers' double (**) pointers. They show up when the LLM is "knowing what Sally knows Ann knows", i.e., Theory of Mind. bsky.app/profile/nik...

@nikhil07prakash.bsky.social

How do language models track mental states of each character in a story, often referred to as Theory of Mind? We reverse-engineered how LLaMA-3-70B-Instruct handles a belief-tracking task and found something surprising: it uses mechanisms strikingly similar to pointer variables in C programming!

bsky.app

SAEs have been found to massively underperform supervised methods for steering neural networks. In new work led by @danaarad.bsky.social, we find that this problem largely disappears if you select the right features!

Dana Arad@danaarad.bsky.social · last yr.

Tried steering with SAEs and found that not all features behave as expected? Check out our new preprint - "SAEs Are Good for Steering - If You Select the Right Features" 🧵

Tried steering with SAEs and found that not all features behave as expected? Check out our new preprint - "SAEs Are Good for Steering - If You Select the Right Features" 🧵

Couldn’t be happier to have co-authored this will a stellar team, including: Michael Hu, @amuuueller.bsky.social, @alexwarstadt.bsky.social, @lchoshen.bsky.social, Chengxu Zhuang, @adinawilliams.bsky.social, Ryan Cotterell, @tallinzen.bsky.social

Lots of progress in mech interp (MI) lately! But how can we measure when new mech interp methods yield real improvements over prior work? We propose 😎 𝗠𝗜𝗕: a 𝗠echanistic 𝗜nterpretability 𝗕enchmark!

Lots of work coming soon to @iclr-conf.bsky.social and @naaclmeeting.bsky.social in April/May! Come chat with us about new methods for interpreting and editing LLMs, multilingual concept representations, sentence processing mechanisms, and arithmetic reasoning. 🧵

It’s common to assume one task = one static circuit. But this ignores that the important computations depend on position! We propose a way to find 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻-𝗮𝘄𝗮𝗿𝗲 circuits. (Highlight: using LLMs to help us create multi-token causal abstractions!)

Tal Haklay@talhaklay.bsky.social · last yr.

1/13 LLM circuits tell us where the computation happens inside the model—but the computation varies by token position, a key detail often ignored! We propose a method to automatically find position-aware circuits, improving faithfulness while keeping circuits compact. 🧵👇

What can mechanistic interpretability do for computational psycholinguists? @michaelwhanna.bsky.social and I took a stab at this question! We investigate garden path sentence processing in LMs at the feature (circuit) level.

Michael Hanna@michaelwhanna.bsky.social · 2y ago

Sentences are partially understood before they're fully read. How do LMs incrementally interpret their inputs? In a new paper, @amuuueller.bsky.social and I use mech interp tools to study how LMs process structurally ambiguous sentences. We show LMs rely on both syntactic & spurious features! 1/10

Today we had the pleasure to host @michaelwhanna.bsky.social from @amsterdamnlp.bsky.social for a seminar on his work w/ @amuuueller.bsky.social to interpret incremental sentence processing in language models. Thank you for joining us Michael!

The babies are now in their beds, but what a year it was! Highlighting some findings of BabyLM Architectures & Training objective matter a lot (and got the highest scores) alphaxiv.org/pdf/2410.24159 🤖

How do language models organize concepts and their properties? Do they use taxonomies to infer new properties, or infer based on concept similarities? Apparently, both! 🌟 New paper with my fantastic collaborators @amuuueller.bsky.social and @kanishka.bsky.social