LessWrong (Curated & Popular)

Here's a dynamic I’ve seen at least a dozen times:




Alice: Man that article has a very inaccurate/misleading/horrifying headline.

Bob: Did you know, *actually* article writers don't write their own headlines?

…

But what I care about is the misleading headline, not your org chart

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Another example I’ve encountered recently is (anonymizing) when a friend complained about a prosaic safety problem at a major AI company that went unfixed for multiple months. Someone else with background information “usefully” chimed in with a long explanation of organizational limitations and why the team responsible for fixing the problem had limitations on resources like senior employees and compute, and actually not fixing the problem was the correct priority for them etc etc etc.

But what I (and my friend) cared about was the prosaic safety problem not being fixed! And what this says about the company's ability to proactively respond to and fix future problems. We’re complaining about your company overall. Your internal team management was never a serious concern for us to begin with!

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A third example comes from Kelsey Piper.

Kelsey wrote about the (horrifying) recent case where Hantavirus carriers in the recent [...]

The original text contained 1 footnote which was omitted from this narration.

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First published:

May 8th, 2026


Source:

https://www.lesswrong.com/posts/PCsmhN9z65HtC4t5v/bad-problems-don-t-stop-being-bad-because-somebody-s-wrong

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Narrated by TYPE III AUDIO.

Sometimes, a friend who works around here, at an x-risk-themed organisation, will think about leaving their job. They’ll ask a group of people “what should I do instead?”. And everyone will chime in with ideas for other x-risk-themed orgs that they could join. A lot of the conversation will be about who's hiring, what the pay is, what the work-life balance is like, or how qualified the person is for the role.

Sometimes the conversation focuses on what will help with x-risk, and where people are dropping the ball. But often, that's not the focus. In those conversations, people seem mostly worried about where they'll thrive. And I think that's often the correct concern.

Most people aren’t in crunch mode, in super short timelines mode; even if their models would license that, I think they don’t know how to do it without throwing their minds away or Pascal's mugging themselves. And if they're playing a longer time horizon game, the plan can't be to run unsustainably forever. People probably make better plans if they’re honest about their limits.

But, given that they're willing to trade off so much impact for fit, I’m surprised that basically no one mentions [...]

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First published:

May 6th, 2026


Source:

https://www.lesswrong.com/posts/eW7knx6zPSKzFc8iK/x-risk-themed

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Narrated by TYPE III AUDIO.

Abstract

We introduce Natural Language Autoencoders (NLAs), an unsupervised method for generating natural language explanations of LLM activations. An NLA consists of two LLM modules: an activation verbalizer (AV) that maps an activation to a text description and an activation reconstructor (AR) that maps the description back to an activation. We jointly train the AV and AR with reinforcement learning to reconstruct residual stream activations. Although we optimize for activation reconstruction, the resulting NLA explanations read as plausible interpretations of model internals that, according to our quantitative evaluations, grow more informative over training.

We apply NLAs to model auditing. During our pre-deployment audit of Claude Opus 4.6, NLAs helped diagnose safety-relevant behaviors and surfaced unverbalized evaluation awareness—cases where Claude believed, but did not say, that it was being evaluated. We present these audit findings as case studies and corroborate them using independent methods. On an automated auditing benchmark requiring end-to-end investigation of an intentionally-misaligned model, NLA-equipped agents outperform baselines and can succeed even without access to the misaligned model's training data.

NLAs offer a convenient interface for interpretability, with expressive natural language explanations that we can directly read. To support further work, we release training code and trained NLAs [...]

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Outline:

(00:15) Abstract

[... 6 more sections]

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First published:

May 7th, 2026


Source:

https://www.lesswrong.com/posts/oeYesesaxjzMAktCM/natural-language-autoencoders-produce-unsupervised

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Narrated by TYPE III AUDIO.

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Images from the article:

This is a link post. This is the latest work in our Parameter Decomposition agenda. We introduce a new parameter decomposition method, adVersarial Parameter Decomposition (VPD)[1] and decompose the parameters of a small[2] language model with it.

VPD greatly improves on our previous techniques, Stochastic Parameter Decomposition (SPD) and Attribution-based Parameter Decomposition (APD). We think the parameter decomposition approach is now more-or-less ready to be applied at scale to models people care about.







Importantly, we show that we can decompose attention layers, which interp methods like transcoders and SAEs have historically struggled with.





We also build attribution graphs of the model for some prompts using causally important parameter subcomponents as the nodes, and interpret parts of them.

While we made these graphs, we discovered that our adversarial ablation method seemed pretty important for faithfully identifying which nodes in them were causally important for computing the final output. We think this casts some doubt on the faithfulness of subnetworks found by the majority of other subnetwork identification methods in the literature.[3][4] More details and some examples can be found in the paper.

Additionally, as with our previous technique SPD, VPD does not [...]

The original text contained 5 footnotes which were omitted from this narration.

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First published:

May 5th, 2026


Source:

https://www.lesswrong.com/posts/eAQZaiC3PcBhS4HjM/linkpost-interpreting-language-model-parameters


Linkpost URL:
https://www.goodfire.ai/research/interpreting-lm-parameters

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I have two shameful secrets that I probably shouldn't talk about online:

I love my family.
I enjoy my hobbies.
"What an idiot!" you probably think. "Doesn't he realize that at his next job interview, HR will probably use an AI that can match his online writing based on a short sample of written text, and when they ask 'hey AI, is this guy really 100% devoted to his job, and does he spend his entire days and nights thinking about how to make his boss more rich?', the AI will laugh and print: 'beep-boop, negative, mwa-ha-ha-ha'."

And, hey, I get it. If I had a company, and I could choose between two people who are about equally qualified, but for one of them, working hardest for me is the true meaning of his life, while the other one only hopes to collect his salary and then go home and spend the rest of his day with his wife and children, I would also prefer to hire the former.

Which is why so many of us pretend to be the former. Even when we are not. Because we prefer that our families not starve. Thus the job interviews [...]

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First published:

May 4th, 2026


Source:

https://www.lesswrong.com/posts/qRZLEBmNtT6LBuFsE/it-s-nice-of-you-to-worry-about-me-but-i-really-do-have-a

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Narrated by TYPE III AUDIO.