🤔What happens when LLM agents choose between achieving their goals and avoiding harm to humans in realistic management scenarios? Are LLMs pragmatic or prefer to avoid human harm? 🚀 New paper out: ManagerBench: Evaluating the Safety-Pragmatism Trade-off in Autonomous LLMs🚀🧵

Traveling to #COLM2025 this week, and here's some work from our group and collaborators: Cognitive biases, hidden knowledge, CoT faithfulness, model editing, and LM4Science See the thread for details and reach out if you'd like to discuss more!

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!

Opportunities to join my group in fall 2026: * PhD applications direct or via ELLIS @ellis.eu (ellis.eu/news/ellis-p...) * Post-doc applications direct or via Azrieli (azrielifoundation.org/fellows/inte...) or Zuckerman (zuckermanstem.org/ourprograms/...)

Excited to join @KempnerInst this year! Get in touch if you're in the Boston area and want to chat about anything related to AI interpretability, robustness, interventions, safety, multi-modality, protein/DNA LMs, new architectures, multi-agent communication, or anything else you're excited about!

@robinjia.bsky.social speaking at @kempnerinstitute.bsky.social on Auditing, Dissecting, and Evaluating LLMs

Thrilled that FUR was accepted to @emnlpmeeting.bsky.social Main🎉 In case you can’t wait so long to hear about it in person, it will also be presented as an oral at @interplay-workshop.bsky.social @colmweb.org 🥳 FUR is a parametric test assessing whether CoTs faithfully verbalize latent reasoning.

BlackboxNLP is the workshop on interpreting and analyzing NLP models (including LLMs, VLMs, etc). We accept full (archival) papers and extended abstracts. The workshop is highly attended and is a great exposure for your finished work or feedback on work in progress. #emnlp2025 at Sujhou, China!

Join our Discord for discussions and a bunch of simple submission ideas you can try! discord.gg/n5uwjQcxPR Participants will have the option to write a system description paper that gets published.

Have you started working on your submission for the MIB shared task yet? Tell us what you’re exploring! New featurization methods? Circuit pruning? Better feature attribution? We'd love to hear about it 👇

Working on feature attribution, circuit discovery, feature alignment, or sparse coding? Consider submitting your work to the MIB Shared Task, part of this year’s #BlackboxNLP We welcome submissions of both existing methods and new or experimental POCs!

VLMs perform better on questions about text than when answering the same questions about images - but why? and how can we fix it? In a new project led by Yaniv (@YNikankin on the other app), we investigate this gap from an mechanistic perspective, and use our findings to close a third of it! 🧵

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" 🧵

🚨New paper at #ACL2025 Findings! REVS: Unlearning Sensitive Information in LMs via Rank Editing in the Vocabulary Space. LMs memorize and leak sensitive data—emails, SSNs, URLs from their training. We propose a surgical method to unlearn it. 🧵👇w/ @boknilev.bsky.social @mtutek.bsky.social 1/8

Interested in mechanistic interpretability and care about evaluation? Please consider submitting to our shared task at #blackboxNLP this year!

Slides available here: docs.google.com/presentation...

Excited about the release of MIB, a mechanistic Interpretability benchmark! Come talk to us at #iclr2025 and consider submitting to the leaderboard. We’re also planning a shared task around it at #blackboxNLP this year, located with #emnlp2025

We release many public resources, including: 🌐 Website: mib-bench.github.io 📄 Data: huggingface.co/collections/... 💻 Code: github.com/aaronmueller... 📊 Leaderboard: Coming very soon!

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. 🧵👇

If erasing information from CoT steps adversely affects the prediction of the model, indicating that such explanations are parametrically faithful.

It has been amazing to work with @fatemehc.bsky.social, @anamarasovic.bsky.social and Yonatan Belinkov on this incredibly important topic. I look forward to further works on the parametric faithfulness route! Codebase (& data): github.com/technion-cs-...

🚨New arXiv preprint!🚨 LLMs can hallucinate - but did you know they can do so with high certainty even when they know the correct answer? 🤯 We find those hallucinations in our latest work with @itay-itzhak.bsky.social, @fbarez.bsky.social, @gabistanovsky.bsky.social and Yonatan Belinkov

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