"Gradient Routing Localizes and Removes Unintended Behaviors in RL" by shawnghu, Jake Ward, ariaw, Nathan Hu, Bryce Woodworth, TurnTrout, cloud

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 [...] ---Outline:(01:44) The Fruit Fly Connectome[... 2 more sections]--- First published: October 20th, 2026 Source: https://www.lesswrong.com/posts/uFBhZhdKLaZ6qF8mn/there-will-be-hells --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

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 [...] --- First published: October 19th, 2026 Source: https://www.lesswrong.com/posts/BBQdGJZSjMTnhccZC/huggingface-wtf-an-accessible-but-complete-explanation Linkpost URL:www.huggingface.wtf --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

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 [...] ---Outline:(01:22) Background[... 4 more sections]--- First published: October 19th, 2026 Source: https://www.lesswrong.com/posts/mKJEBiuuxHKuCoTB6/gradient-routing-localizes-and-removes-unintended-behaviors --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

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. --- 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 --- 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 [...] --- First published: October 17th, 2026 Source: https://www.lesswrong.com/posts/3375SZNGA5dKHQMie/i-did-this-without-your-knowledge --- 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 [...] ---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. --- First published: October 17th, 2026 Source: https://www.lesswrong.com/posts/Dkh7pXKjoNWkkxQDz/neural-networks-are-not-already-clean --- 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 [...] ---Outline:(03:05) Examples of update-aware reasoning[... 16 more sections]--- First published: October 15th, 2026 Source: https://www.lesswrong.com/posts/R5g8YT25G9DXJJ48n/glm-5-3-sometimes-considers-how-train-time-actions-shape --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

I think “On Social Reality in China” makes many accurate observations, but I find the comparison fundamentally uneven. There are already many interesting comments there, but they’re too scattered, so I’m starting a separate post to respond in a more systematic and comprehensive way. The most critical blind spot is this: the author draws on his familiarity with Western liberal middle-class intellectual circles, yet primarily understands China through pop culture and the experiences of the previous generation of immigrants. This is hardly fair. China has always had many small, highly self-selected communities of intellectuals, much like LessWrong itself. Since we’re looking for rationalists—regardless of which country they come from—why judge them by their country's pop culture? I’ll respond to the author's four axes one by one, then discuss why rationalist and EA communities are harder to build in China. Briefly, I believe that censorship mechanisms and speech controls affect independent organizations far more than China's broader cultural context does. Under these circumstances, even when people with similar interests and beliefs come together, it's difficult for them to build anything—I’ve seen far too many examples. About me: born in China in the mid-1990s, grew up in a middle-class family in [...] ---Outline:(02:30) Chinese Social Media Is Like Social Media Everywhere(05:20) The Primacy of Social Reality: How Is This Relevant?(08:00) The Dark World Frame: A Product of Its Time(13:27) Chinese Nationalism as Collective Insecurity: A Deeper Look(18:07) Looking for Rationalists in China: Where Should We Start? --- First published: October 15th, 2026 Source: https://www.lesswrong.com/posts/m4fWCPqWkpb39SKi7/a-chinese-perspective-on-on-social-reality-in-china-2 --- Narrated by TYPE III AUDIO.

TL;DR: Increasingly when you interact with frontier models, your prompt doesn't only contain the words on the page, but also a lot of what you've ever publicly written online. This is not intended to be a scary post. And I'm not going to suggest that you self-censor (and honestly, by the time you're reading this, it's kinda too late anyhow). But it is something that's underappreciated and would be good for more people to be aware of and even proactively address with their models. Latent space fidelity has improved Modern transformers which power large language models (LLMs) take a very large amount of information in their training data and condense many different aspects of that data into a coherent structure, and this representation of that compressed knowledge is generally referred to as their "latent space." Latent space is not uniform and varies across models. A small model like Claude Haiku might have worse recall for the details of a late bronze age peace treaty than a model like Claude Fable. In fact, I’ve had a few benchmark questions around unintuitive details in various texts which historically models were unable to recall that have suddenly been able to be [...] ---Outline:(00:49) Latent space fidelity has improved[... 6 more sections]--- First published: October 14th, 2026 Source: https://www.lesswrong.com/posts/STBuKAX9kkMJunErA/your-prompt-plus-everything-you-ve-ever-publicly-written --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

This is an extremely lengthy post by Nick Cammarata. Author's summary on twitter: interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works Roon's quote tweet when nick told me that he had some interp breakthroughs [...] --- First published: October 15th, 2026 Source: https://www.lesswrong.com/posts/x4kvabkF2Jk8GLZp8/linkpost-neural-networks-are-already-clean --- Narrated by TYPE III AUDIO.