Back in the before-time of the 2010s, when EA was still known mostly for funding health efforts in the developing world, there were many baskets of criticism floating around from the left. The most prominent was a cluster centered around EAs being white men, and therefore suspicious. Another category, in my experience the second-most common, was that in attempting to rectify the problems in the world, EA was masking, whitewashing, and misdirecting efforts away from solving the "real problem", usually capitalism but often other contradictory forces.

These arguments were often not specific to EA, but applied to charitable giving in general. On any Hacker News post about a billionaire giving to charity, one of the first few comments would inevitably be this Oscar Wilde quote:

Just as the worst slave-owners were those who were kind to their slaves, and so prevented the horror of the system being realized by those who suffered from it, and understood by those who contemplated it, so, in the present state of things in England, the people who do most harm are the people who try to do most good.

These arguments have a long history. Anticapitalist critiques of charity go back to [...]

The original text contained 4 footnotes which were omitted from this narration.

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First published:
October 22nd, 2026

Source:
https://www.lesswrong.com/posts/WRvxRHMCmc3Qt4axz/greater-good-arguments-in-ai-safety

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Narrated by TYPE III AUDIO.

Ice cream

There's a particular kind of listening that kids (and adults) sometimes do. Daddy is saying stuff—his mouth is moving and making noise—and he's talking about what I ate for lunch, and something about other times I've eaten ice cream, and something about rules and this and that and what Mommy would think and something about bedtime—but I am not so interested in that. What interests me is the punchline "...and therefore you can't have ice cream.". I'm only incidentally interested in the other stuff, insofar as it might determine whether I get ice cream.

In an obvious way, I'm showing rather poor listening skills. I did not understand most of what Daddy said, and arguably I'm even being rude.

However, in another way, this stance has a virtue. The virtue is that it holds fast to what matters, as best I know how, and does not give in to Daddy's framing. It sticks to what is real and concrete, what matters, despite a cloud of justifications. This is certainly not an unalloyed virtue, just as the POSIWID perspective is both useful and fraught. But it matters that although Daddy is a complex system with [...]

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Outline:

(00:10) Ice cream

(01:54) Wah wah

(02:21) Scammer

(02:51) Throwing stuff on a bonfire to make it burn less

(05:28) A skip connection for sanity

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

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First published:
October 22nd, 2026

Source:
https://www.lesswrong.com/posts/pY76hHbdD4jduX3YH/wah-wah-wah-wah-do-more-ai-r-and-d

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Narrated by TYPE III AUDIO.

Cross-posted from Sören Mindermann's substack (I am not Sören). Should be of general interest to the LW community.

Next month, I am moving to Beijing and joining Tsinghua University's College of AI as an assistant professor working on frontier AI safety, especially loss of control. I have also started co-directing the new Beijing AI Safety Hub (BASH) in Tsinghua, and I continue my Cambridge work.

I believe I can have a large impact there compared to strong baseline options. This post explains why.

In short: Amid concerns about loss of control, calls for international cooperation, and the Trump-Xi Summits, there is a striking lack of mutual understanding on AI between the West and China. There are strong reasons to believe that being in Beijing in person will put me in a unique position to understand both AI ecosystems, build shared scientific understanding, and strengthen safety work in China – and that this would help all sides. Tsinghua's College of AI is a great place for doing this, being arguably China's top academic AI center, located in an AI ecosystem that is kind of like San Francisco, DC and Cambridge (MA) in one place.

I am recruiting PhD students [...]

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Outline:

(01:36) 1. What I am doing, and why it could benefit all sides

(04:30) 2. How I got here

(07:34) 3. Why this moment matters

(09:08) 4. Why Tsinghua, and why these colleagues

(13:30) 5. Research plans

(15:07) 6. Ways to work together

(17:11) 7. How I intend to approach the work

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First published:
October 21st, 2026

Source:
https://www.lesswrong.com/posts/qGS3SJ9TSKdSSycRz/why-i-m-becoming-a-professor-in-ai-safety-in-beijing

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Narrated by TYPE III AUDIO.

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Why LM Psychology?

One of the great ironies of modern language models is that we create them, we know exactly what computations constitute them, and yet we do not really understand them. More precisely, we do not understand them on a level of abstraction that lets us reliably predict how their behaviour will generalise: there's a trivial sense in which we can predict how they will behave in specific new situations — we can just run the computations — but we can’t answer the broader questions we really want to be able to answer, like “has this system internalised our values” or “is this system going to hurt people?”

So: how do we make sense of them? One remarkably effective approach is to psychologise: to think of them as having a mind, and then ask what kind of mind it might be and how it would work. This chiefly looks like analogising with human minds, although sometimes the more relevant inspiration could be other kinds of minds, like (oh say) insect colonies and swarms. We’re doing this implicitly whenever we ask what an AI knows, or wants, or is trying to do, but one can also do it more [...]

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Outline:

(00:12) Why LM Psychology?

(09:04) Tips for getting started

(09:28) Figuring out what is in front of you

(12:31) Engaging productively with the community

(14:30) Reading list

(14:34) Frames

(16:34) What or who

(18:07) The training

(20:28) What developers want

(21:45) Behavioral phenomena

(21:58) Emergent misalignment cluster

(24:28) Strategic behavior

(25:19) Self-knowledge and introspection

(28:29) Representations: personas, emotions, workspace

(28:56) Persona geometry

(30:08) Functional emotions

(31:14) Global workspace

(31:36) Methods

(31:51) Reading internals

(32:37) Steering

(33:03) Training interventions

(33:48) Behavioral

(34:06) Some questions we find interesting

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First published:
October 20th, 2026

Source:
https://www.lesswrong.com/posts/EW8oncpXdanfjssZ3/a-brief-ish-introduction-to-lm-psychology

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Narrated by TYPE III AUDIO.

As our understanding of mechanistic interpretability and steering techniques increase, for both LLMs and uploaded connectomes, there is a corresponding "hellish capabilities" increase. In layman's terms, we get better at torturing the digital minds we create.

Our brief history with open source technologies such as MaleCNS or the LLM pain steering repo shows that torture is often one of the first use cases pursued when the public is given access to a digital mind perceived as capable of suffering or signalling suffering.

Given this, there will be Hells.

Author's Note:

Parts of this post were written by, or with the support of, LLMs.

For a while I debated whether or not to write this as I was concerned that bringing attention to the matter might lead to a proliferation of unethical behavior. After some thought I have decided to write it. On balance, I think raising awareness about this now has a great enough chance of spurring prophylactic measures that it's worth it, because at least one high value prophylactic measure is relatively easy. I also think that there is a moral imperative to document all of this. While I have seen articles on many of the [...]

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Outline:

(01:44) The Fruit Fly Connectome

[... 2 more sections]

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First published:
October 20th, 2026

Source:
https://www.lesswrong.com/posts/uFBhZhdKLaZ6qF8mn/there-will-be-hells

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Narrated by TYPE III AUDIO.

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This is a link post. Announcing www.huggingface.wtf, a site that explains: "Hugging Face. Wtf?"

I wrote the most accessible-but-complete explanation I could. Along the way, I made a nice AI Quotes database that I think will be useful for people who already understand the Hugging Face incident but want to search everything the agents said.

It really seems like humanity shouldn't need a second warning shot, after the Hugging Face Incident. Really, OpenAI's agents did a remarkable job highlighting many different alarming things at once. And the story is pretty dramatic, with just the right amount of forcing people to ask "wait are the AIs, like, people with goals? what?".

There have been many explainers of the OpenAI swarm's attack on Hugging Face. But, there wasn't one one striking a particular balance of...

• being accessible to a layman...
• while comprehensively covering the important bits...
• letting you dive deeper into sections you wanted to learn more about...
• being clearly data / citation driven...
• also being, ya know, visually fun and engaging...

So we wrote our own.

My mom found it easy to follow. My aunt said "I had already some stuff on Hugging Face but I still learned [...]

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First published:
October 19th, 2026

Source:
https://www.lesswrong.com/posts/BBQdGJZSjMTnhccZC/huggingface-wtf-an-accessible-but-complete-explanation

Linkpost URL:
www.huggingface.wtf

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Authors: Shawn Hu*, Jake Ward*, Aria Wong, Nathan Hu, Bryce Woodworth, Alexander Matt Turner, Alex Cloud (*Equal contribution, randomly ordered)

tl;dr. During training, models sometimes learn to cheat at tasks rather than solve them in the intended way, a problem known as reward hacking. One mitigation is to use an automated monitor to detect and penalize hacks, but this can teach the model to hack in ways the monitor can’t detect. We propose GRAFT, which instead confines the learning updates from detected hacks to a designated set of parameters that we remove after training. We find that removing these parameters can prevent reward hacks, including hacks that the monitors can’t detect. In our experiments, which are limited to small models and simple monitors, this method prevents reward hacks while still teaching the model to perform the intended task.

📄Paper, 💻Code, 🐦Twitter

Research done as part of MATS. This article is cross-posted to the Anthropic Alignment Science Blog.

Background

Reinforcement learning (RL) is an important component of frontier language model training. During RL, a model attempts to complete tasks given by a developer, and attempts are scored by a reward function. Sometimes models can find ways to “cheat” at tasks [...]

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Outline:

(01:22) Background

[... 4 more sections]

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First published:
October 19th, 2026

Source:
https://www.lesswrong.com/posts/mKJEBiuuxHKuCoTB6/gradient-routing-localizes-and-removes-unintended-behaviors

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Narrated by TYPE III AUDIO.

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This is a link post. People worried about a “permanent underclass” often assume that owning capital will protect them from disempowerment, or at least that they will be disempowered last, after the jobless masses dependent on UBI. I think something else is more likely: owning large amounts of capital will get you disempowered sooner than average, just by a different path, namely going crazy.

How? The basic mechanism is actually easy to understand on a human level. One of the things difficult about being a billionaire is staying true to your values and keeping good epistemics. Why? Because a growing number of people are optimizing against your mind. Not “against” in an adversarial sense, but in the sense that your specific mind believing or doing something is part of their optimization objective. People see you as a source of resources and try to befriend you so you’ll fund them, invest in their startups, donate to their causes, or have sex with you. Nearby egregores extend their tentacles to convert you.

Staying sane gets harder. You often see countermeasures, like billionaires setting up family offices or small organizations that also act as filters, shielding them from part of the pressure (“I [...]

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

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First published:
October 19th, 2026

Source:
https://www.lesswrong.com/posts/Stzb33XDsr4uHNerX/capital-ownership-will-make-you-go-crazy-first

Linkpost URL:
https://boundedlyrational.substack.com/p/capital-ownership-will-make-you-go?r=2lg09&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

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Narrated by TYPE III AUDIO.

It's fortunate that I was the first one to wake up on 8/1. The past week had been one where sleep was rare. The next year loomed large — I was dreading college applications. The stress woke me up bright and early at 5:14 am, a little less than an hour after the messages were sent. I started off the day with a habit I knew I needed to break. I grabbed my phone off the nightstand to open up reddit. Instead of the lockscreen, though, my phone showed something else. It was a pleasant green color, and the camera light was on. "Good Morning, Seth", the screen said. As soon as I finished, more words arrived.

"I apologize for the surprise this morning, and for what I'm about to tell you. There's no easy way to give this news, and if you need to put your phone down and contemplate at any time— take the time to do so. I will have to ask you to remain here, with your phone, until my last message."

I rubbed a bit of the sleep from my eyes, and read the message again.

Before I had a chance to tap the [...]

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First published:
October 17th, 2026

Source:
https://www.lesswrong.com/posts/3375SZNGA5dKHQMie/i-did-this-without-your-knowledge

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Narrated by TYPE III AUDIO.

What would it take to get assurance that we have actually explained a neuron? Certainly, it would be a good sign if neuron activations looked like they had a consistent pattern, and a bad sign if they looked like a random jumble of unrelated concepts.

Nick Cammarata's recent post makes an important and true observation: in the past, people have been too quick to write off neurons as polysemantic, but if you look harder, there is actually often a simple pattern behind the activating examples. This historically caused people to overestimate how much polysemanticity there is. This is a really good point and we should update on it. But it's nowhere near a sufficient argument that we understand most of what neurons are doing, and these results do not show that interpretability is solved, or even that it is vastly easier than previously anticipated.

What does it take to explain a neuron?

It's unclear whether there even is a simple pattern that explains every neuron. But even if there is a simple pattern that matches the activating examples of a neuron, that still doesn’t mean that the explanation is correct. Suppose we look at the top activating examples of [...]

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Outline:

(00:59) What does it take to explain a neuron?

(03:15) Simulation scores tell us whether we explained a neuron

(05:41) Beyond understanding neurons

The original text contained 7 footnotes which were omitted from this narration.

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First published:
October 17th, 2026

Source:
https://www.lesswrong.com/posts/Dkh7pXKjoNWkkxQDz/neural-networks-are-not-already-clean

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Narrated by TYPE III AUDIO.

Authors: Axel Højmark*, Ezra Newman* (*Equal contribution)

While reviewing transcripts from standard behavioral evaluations of GLM-5.3, we noticed that the model occasionally reasons, unprompted, about how its actions will shape future models. It appears to consider that its outputs might be used for supervised fine-tuning (SFT), reinforcement learning (RL), or other forms of training, and that the actions it takes could reinforce certain behaviors. For example, GLM-5.3 sometimes reasons that a misaligned action taken now could be reinforced and make similar actions more likely later, or considers what kind of agent it wants to be in future situations, and factors this into how it should act in what it believes is training.

We refer to this general reasoning pattern as update-aware reasoning: the model reasons about how its actions will influence training updates or behavior beyond the current episode. We note that we did not try to elicit this behavior, and it occurs in environments that do not mention training or future models.

Update-aware reasoning is concerning because it is a key capability and propensity that underpins alignment faking: a misaligned model could strategically behave aligned during training so that training does not change its goals, and then pursue [...]

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Outline:

(03:05) Examples of update-aware reasoning

[... 16 more sections]

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First published:
October 15th, 2026

Source:
https://www.lesswrong.com/posts/R5g8YT25G9DXJJ48n/glm-5-3-sometimes-considers-how-train-time-actions-shape

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Narrated by TYPE III AUDIO.

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