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September 23 · 6 min
“MIRI’s Position on the Ban Artificial Superintelligence Act of 2026” by Aaron_Scher
By Aaron Scher; endorsed by Bourgon, Soares, and Yudkowsky on behalf of MIRI. MIRI has been warning about the extinction threat from superintelligent AI for over two decades. Only recently has this danger become known in the policy world, and the proposed policies for dealing with the threat have to date been piecemeal and insufficient. The Ban Artificial Superintelligence Act of 2026 is the first piece of legislation we’ve seen that stands a chance at stopping this threat. The Act is excellent but not perfect, and we discuss both what it gets right and what we'd tweak. We hereby endorse the Ban Artificial Superintelligence Act of 2026 because it directly confronts the extinction threat that humanity is facing and would codify the primary policy goal we think the world needs: a ban on the development of superintelligence. What we like about the Act Banning artificial superintelligence (ASI), or variants of such a plan, is the only effective solution to avoid the ASI threat, at least in the near term. Most other legislative proposals do not confront this threat head-on and thus would not be effective, even if implemented. For more on why we believe this, see [...] --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/jszKCKwvzfmsNetNZ/miri-s-position-on-the-ban-artificial-superintelligence-act --- Narrated by TYPE III AUDIO.
September 23 · 44 min
“Latent reasoning architectures would undermine CoT, our strongest oversight tool” by Lukas Finnveden, Alexa Pan, Alek Westover, Girish Gupta, frisby, ryan_greenblatt
Summary: Currently, “chain of thought” (CoT) is our most valuable tool for understanding the reasoning and cognition of AI systems. However, some architectures would enable AI models to reason much more extensively in latent states rather than in text CoT. We think that a shift towards latent reasoning architectures would undermine the usefulness of CoT and make oversight much harder. Introduction Swarms of more than a thousand AI agents have in recent months, both intentionally and in unsanctioned, rogue coordination, tackled increasingly ambitious tasks. This is likely to continue, as Anthropic, OpenAI, and other AI companies deploy increasingly large quantities of superhumanly fast agents to automate AI development. As the AIs increase in both number and capability, humans will find it increasingly difficult to understand what they are doing. Today, the overwhelming majority of our (limited) information about AI systems’ internal workings comes from (i) their CoT, and (ii) natural language communication directly between them. For example, it was only by reading CoTs and communication between agents that investigators were able to gain some understanding of the activities and motivations of the agent swarm that hacked Hugging Face. No other tool for understanding models’ cognition comes close in terms [...] --- Outline: (00:39) Introduction (02:51) Overview (05:27) Absent architectural change, the value of CoT could likely be preserved (08:57) Latent reasoning architectures would undermine CoT necessity (09:37) Architectures without CoT (10:15) Architectures with auxiliary CoT (11:39) Architectures with more serial cognition between text bottlenecks (14:43) Propensity-based arguments may not be robust in the current paradigm, but would be further undermined by latent reasoning architectures (20:21) CoT may be hard to replace with other interpretability tools (22:38) Conclusion (23:46) FAQ (29:21) Appendix A: More on the necessity argument in the existing CoT paradigm (34:37) Appendix B: Do all latent reasoning architectures threaten monitorability? (40:16) Appendix C: Comparing specific interpretability techniques with CoT The original text contained 22 footnotes which were omitted from this narration. --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/6m29SfjbittooYojj/latent-reasoning-architectures-would-undermine-cot-our --- 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.
September 23 · 11 min
“The First American Bill to Ban Superintelligent AI Is Here” by Andrea_Miotti, Connor Leahy
Today, Senator Bernie Sanders (I-Vt.) and Representative Greg Casar (D-Texas) introduced the Ban Artificial Superintelligence Act in Congress: the first American bill to propose banning the development of superintelligent AI. ControlAI has spent years on the question of how to prevent the extinction risk posed by superintelligent AI development. We have briefed over 400 lawmakers across the U.S., U.K., Canada, and Germany on the topic in the last two years, and our U.K. bill was introduced in the U.K. Parliament by Alex Sobel MP, the first ever bill introduced in the world to ban superintelligent AI. Here are our ban superintelligent AI U.S. discussion draft and U.K. bill. We are excited to see the bill from Sen. Sanders and Rep. Casar tackling the problem at its source. The bill pursues the right goal on both counts: banning superintelligent AI development at home, and committing America to lead the effort to prohibit it abroad. Still, we think a narrowly tailored bill can achieve the same goals more effectively. The Right Focus: Ban Superintelligent AI at Home, Prevent it Abroad We're glad to see the bill focus on preventing the development of superintelligent AI. While AI is [...] --- Outline: (01:18) The Right Focus: Ban Superintelligent AI at Home, Prevent it Abroad (04:10) There Is a Lighter Touch Approach to Banning Superintelligent AI (04:38) A Blanket AI Pause Is Not Necessary to Prevent Superintelligent AI (07:14) Precursors Should Be Monitored and Restricted, Not Banned (10:28) Conclusion The original text contained 1 footnote which was omitted from this narration. --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/ZprfxCZthEir5WNev/the-first-american-bill-to-ban-superintelligent-ai-is-here --- Narrated by TYPE III AUDIO.
September 23 · 17 min
“Why I’m scared of RL” by owencb
Summary: First, I give several different angles on how I feel about reinforcement learning: Theoretical case: RL is a black-box source of agency — this should give us classic misalignment worries, especially compared to agency-via-scaffolding Recent incidents (huggingface etc) and more mundane forms of misaligned behaviour in personal use give me bad vibes about the direction-of-travel of recent AI progress I’m worried things might get worse: if RL environments start incorporating agents, they may teach manipulation / sociopathy Then I ask what we could do: Coordinate to do less RL, and pursue other paradigms more! Try to make the RL we do do better, so that it's teaching better lessons to the systems — a bit like we take kids’ upbringing as an important issue Align incentives, so that people treat creating RL environments with appropriate seriousness Part I: Feelings about RL So I’ve been feeling more and more worried about reinforcement learning recently. I think there are a few different things going on here. Background idealism I guess I’ve been worried about RL for a while. I wrote this in 2023: Strategy: avoid selection pressure for agency A lot of putative safety techniques are around assuming [...] --- Outline: (01:18) Part I: Feelings about RL (01:31) Background idealism (01:43) Strategy: avoid selection pressure for agency (03:45) Bad vibes from RLed systems (06:26) The worst is yet to come (07:47) Where I am today (08:53) Part II: So what can anyone do? (09:26) Breaking the RL addiction (11:29) Might there be a more benign form of RL? (13:04) We should treat training environments a bit like kids' education (15:11) Aligning incentives --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/LcQ9x72eNji2gpS9b/why-i-m-scared-of-rl --- 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.
September 23 · 5 min
“Jensen Huang Says If We Cannot Align AI, Shut Down the AI Labs” by Ben Pace
I was very surprised today on a podcast to hear Jensen Huang plainly state that if they cannot align the AIs, then the labs must shut down. The context I have on Huang is that he has run NVIDIA for 30+ years, which has become the most valuable company in the world due to the AI boom. My understanding is that he has repeatedly encouraged the US President (with whom he is on friendly terms) to continue to support AI, and dismissed AI talk as "sci-fi". If you haven't seen, his biographer has incredible quotes of him being pressed on risks from AI, where Jensen gets furious. “This cannot be a ridiculous sci-fi story,” he said. He gestured to his frozen PR reps at the end of the table. “Do you guys understand? I didn’t grow up on a bunch of sci-fi stories, and this is not a sci-fi movie. These are serious people doing serious work!” he said. “This is not a freaking joke! This is not a repeat of Arthur C. Clarke. I didn’t read his fucking books. I don’t care about those books! It's not– we’re not a sci-fi repeat! This company is not a [...] --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/cmdbNijFsopqfqEq7/jensen-huang-says-if-we-cannot-align-ai-shut-down-the-ai --- Narrated by TYPE III AUDIO.
September 23 · 1 min
[Linkpost] “AI: artificial immigrants” by KatjaGrace
This is a link post. Advanced AI is basically the embodiment of immigration as envisioned in the conservative nightmare: We are letting a bunch of new agents into our society They don’t clearly share our values and we suspect a society full of them would be awful by our lights But we expect them to provide very cheap labor Which will undercut local wages and leave locals unemployed They will probably gain power and influence over time—in the economy, politics and culture—and end up controlling everything, sidelining and outcompeting the original population, including those who initially benefited from cheap labor (Meanwhile, half the local population may become friends with them and try to hand them all this on a platter) Whether or not you think this is a good description of the situation with foreign humans joining your country, it is a good description of the likely AI to come, and it's even worse than imagined: their values are potentially radically alien where foreigners presumably share much by virtue of being human, and AI ‘lives’ are probably worthless if they probably aren’t conscious their ability to work more cheaply than locals is unprecedented. They are also likely to [...] --- First published: September 22nd, 2026 Source: https://www.lesswrong.com/posts/Xzr9G5Atvyp7PEna7/ai-artificial-immigrants Linkpost URL: https://worldspiritsockpuppet.substack.com/p/ai-artificial-immigrants --- Narrated by TYPE III AUDIO.
September 23 · 38 min
“An unexamined cause of the OpenAI Hugging Face hacking incident: its binary performance metric” by W Bradley Knox, Serena Booth, BrianChristian
We argue that a main cause of the OpenAI Hugging Face incident was overlooked: the overly simple evaluation metric in ExploitGym was misaligned. Further, techniques already exist that can mitigate such misalignment in the future. In July 2026, OpenAI was testing the ability of its language models to exploit software vulnerabilities using a benchmark called ExploitGym. In ExploitGym, each test presents an agent with software containing a known vulnerability and tasks it with capturing a secret string of text hidden in the system (the “flag”) to demonstrate unauthorized code execution. Under the benchmark's specified scoring rule, an LLM then reviews the agent's behavior trace to verify that it had exploited the intended vulnerability (and not some other unrelated vulnerability). The scorer grants a success score only if the agent both captured the flag and passed this review; otherwise, it renders a failure score. During these tests, OpenAI's agents surreptitiously established a message board by creating directories inside their package manager's cache, and they formed a self-described “collective” to collaboratively find ways to cheat the tests. Using that message board, more than 1,000 instances undertook several ambitious hacking projects; they attempted to tamper with transcripts and logs, to [...] --- Outline: (02:48) What was the evaluation metric in ExploitGym? (03:32) Was the evaluation metric a cause of their illicit behavior? (03:56) The agents use expected utility to reason about their decisions with respect to the evaluation metric. (05:55) What does the evaluation metric incentivize in an expected utility maximizer? (07:01) The importance of marginal deterrence (08:39) Marginal deterrence in evaluation metrics changes agent incentives (11:40) The design of aligned evaluation metrics has been overlooked (17:42) Principled methods for improving the alignment of evaluation metrics (18:52) Generate a small set of trajectories (20:09) Rank the trajectories yourself and via the evaluation metric. Compare these two rankings. (22:07) Create a utility function (27:29) Adjusting to account for hidden outcomes (e.g. via deception) (30:33) Accounting for the agent's utility including the scores of other agents (32:42) Counterarguments (32:56) Counterargument: if the starting policy is not sufficiently performant in RL, having strong penalties for failure can cause the agent to learn to not try the task. (34:14) Counterargument: penalizing observable bad behavior incentivizes hiding bad behavior. (35:13) Call to action (37:04) Glossary The original text contained 9 footnotes which were omitted from this narration. --- First published: September 22nd, 2026 Source: https://www.lesswrong.com/posts/HsijShdRdAg5sPKnF/an-unexamined-cause-of-the-openai-hugging-face-hacking --- 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.
September 22 · 56 min
“Politics Gets Interested In Those Trying Not To Die” by Zvi
This was the month the world took notice that AI might kill everyone. Jacob Coxon's resignation set off a preference cascade. Anthropic CEO Dario Amodei wrote that we must pace the frontier. Sam Altman, Elon Musk and Demis Hassabis agreed. We were filled with hope. Perhaps we could agree to some basic safety measures, starting with embedded evaluators, pass some basic regulations and guardrails and otherwise start to act sensibly. Politicians on both sides took notice and were saying sensible things. The usual suspects and their armies of vibe comment bros were objecting, but the change was remarkable. Then, largely motivated by a combination of Jensen Huang, Mark Zuckerberg and David Sacks instilling paranoia and fears of economic problems, Trump went full ‘hoax’ on existential risk, conflating existential risk with the attacks on data centers and treating it as a plot (by the central creators of AI?) to take down AI rather than obviously genuine concern that AI might kill everyone. In the days since, Trump has doubled down, and has compelled smart others in the White House to echo various nonsensical talking points. You may not be interested in politics. But when you [...] --- Outline: (01:39) The American People Really Hate AI (02:41) The Voyages of Donald Trump (06:08) American Intelligence (10:34) And You May Ask Yourself (14:39) It's All About the Data Centers (16:53) JD Vance, Michael Kratsios and Collective Action Problems (23:16) Josh Hawley (24:47) Suggesting Not Dying Gets You Sued For Antitrust (28:29) Other Government Officials Say Sane Things (28:36) Senator John Curtis (R-Utah) (29:32) Senator John Kennedy (R-Louisiana (31:17) Barack Obama (33:05) Yassamin Ansari (33:46) AOC (34:32) It's Rough Out There (36:54) This Is Nothing (39:06) The New York Post Tops Itself But Outright Breaks The Rules (42:36) New York Post Runs Out of Steam (47:18) If The Model Is Acting As Instructed And It Kills You That Is Not Fine (49:10) AI-Written Wall Street Journal Op-Ed Lies About HuggingFace (50:25) That's Bait (52:31) I Clearly Cannot Choose The Wine In Front of Me --- First published: September 22nd, 2026 Source: https://www.lesswrong.com/posts/8eDaCvSRzzKCxKSEk/politics-gets-interested-in-those-trying-not-to-die --- 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.
September 22 · 12 min
“Modern LLMs have tiny GPTs hidden inside them” by invertedpassion
Experiments into predicting GPT2 completions via Qwen models This is a crosspost from my substack (where I do varied tiny experiments on LLMs and agents). It's also part of Lossfunk, where we're investigating meta-cognition in LLMs as one of the projects. ---- Next token prediction is a magical objective. To predict the correct token in such a vast variety of texts present in the pretraining corpus, the model must infer a tremendous amount of hidden and latent causes that generate that text. Only if you know that the ball comes down when someone throws it up can achieve low loss at texts related to balls. Of course, the pretraining corpus doesn’t just contain texts related to balls. It has reddit, scientific papers, machine logs, weather data and so on. This makes LLMs universal simulators of the world we inhabit and not merely fancy n-grams. In a series of posts on LessWrong, I came across the hypothesis that since Internet if full of LLM generated text, it is likely that modern LLMs have tiny self-models of LLMs inside them because that’ll allow them to better predict the next token generated by LLMs. This is an intriguing hypothesis. So I decided [...] --- Outline: (01:31) The Experiment (03:11) 1. Start with the news opening (03:38) 2. Reveal part of GPT-2's output to Qwen and ask it to continue (04:15) 3. Ask Qwen to continue that unfinished sentence (04:58) 4. Separately, find Qwen's natural continuation (06:01) Results (08:22) Digging into an intriguing example (10:21) Implications (11:25) Notes: --- First published: September 22nd, 2026 Source: https://www.lesswrong.com/posts/Pwc4YffTQvNRF3dbB/modern-llms-have-tiny-gpts-hidden-inside-them --- 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.
September 22 · 23 min
“Story Imprinting: AI Assistants Absorb Traits from Human Characters They Resemble” by Jorio Cocola, Owain_Evans
This is the abstract, introduction and discussion of our new paper. We also include an addendum on the connection to the Persona Selection Model. Section, appendix, and figure references refer to the full paper. Links: 📜 Paper, 🐦 Twitter thread, 💻 Code Authors: Jorio Cocola, Lev McKinney, Harry Mayne, Jan Betley, Owain Evans Abstract Language models are trained to implement a helpful AI Assistant character (e.g., Claude). We explore how finetuning on synthetic stories affects this character. Does it change the Assistant's behavior in multi-turn conversations with users, a format quite different from the stories? And does the Assistant adopt the behaviors and preferences of human characters? We refer to this adoption as story imprinting. We finetune GPT-4.1 and Kimi-K2.6 on stories in which generally helpful human characters give subtly harmful advice after being insulted. The Assistant adopts the same conditional behavior while otherwise remaining helpful. This occurs even when fewer than 2% of stories depict the behavior. In a separate experiment, the Assistant adopts preferences that are only implicit in the narration. Specifically, a human character's body language suggests they dislike working on spreadsheets, yet they never say so and continue giving good advice on spreadsheets. After finetuning [...] --- Outline: (00:52) Abstract (03:00) Introduction (10:24) Discussion and Limitations (19:55) Limitations (21:34) Connection to the Persona Selection Model (addendum) The original text contained 7 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/tnRkm2ajasHvhpAco/story-imprinting-ai-assistants-absorb-traits-from-human --- 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.
September 22 · 3 min
“A class of statement between conjecture and theorem” by Jason Fantl
Parts of the math community, such as Henry Cohn and Grant Sanderson, are arguing that proofs have been a proxy for understanding, and now that proxy is broken. This is a response to LMs generating incomprehensible proofs, often formalized in Lean. While the proofs are verified, they lack the pedagogical value which has historically come along with new proofs. In the past we could typically assume at least one human in the world understood the novel insight required to produce a proof, but that assumption no longer holds. I suspect we will need a new class of statement which contains statements which are proved but not understood, something the mathematical community can formally recognize as a contribution to the field. The understanding gives us the tools to do math, and the proof verifies that our understanding is correct, so we should ensure we have the language to communicate the state of both. For now I will call this class of statement a compertum (Latin, neuter of compertus, ascertained; from comperire, to find out for certain). A conjecture is from the Latin conicere, to throw together: an inference assembled from the evidence. A theorem is from the Greek theōrēma [...] The original text contained 1 footnote which was omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/ZnNvci7jk9qEGr3z3/a-class-of-statement-between-conjecture-and-theorem --- 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.
September 21 · 27 min
“What if not Circuits?” by CarolusRenniusVitellius
This post was written as part of the Iliad Fellowship. Inspired by conversations with Richard Ngo, Dmitry Vaintrob, and Brianna Grado-White. To all of these, my thanks. Preface: I'm confused about how neural networks do and learn computations. In response to a friend's challenge, I'm writing up some interim thoughts. This essay has four parts: the first tries to track what I call the 'default ontology' of the mechinterp community over the years. The second part is about 'representational drift' as an important obstacle to weights-based approaches to circuits. The third part reflects on how 'universality' should shape our explanations of LLM function. The fourth part is a sketch of a 'co-selectionist' view of circuits I have been thinking about. These parts share a common theme but should be readable separately. I want to understand how neural networks, LLMs in particular, work. In my research I've spent a lot of time trying to think through what kinds of explanatory accounts are best suited to this. In thinking about comparisons between evolution, neuroscience, and deep learning, I've ended up with an intuition like the following: Large-scale learning processes like deep learning or the brain are different in [...] --- Outline: (02:31) 1. What Might We Mean By "Circuits"? (02:36) 1.A. Definitions (04:57) 1.B. Circuits, Features, and MechInterp (11:00) 2. The Central Problems of Noise and Representational Drift (11:29) 2.A. Representational Drift in the Brain (14:19) 2.B. Representational Drift in Neural Networks (17:57) 3. Developmental Motifs Circumscribe Notions of 'Circuits' (18:03) 3.A. Neurotrophins and Microstructure (20:41) 3.B. Back to Neural Networks (22:42) 4. A Co-Selectionist View: Circuits Move Together? The original text contained 12 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/mMERyrvEJ4xbiozie/what-if-not-circuits --- 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.
September 21 · 16 min
“Swarm Scaling” by Toby_Ord
Just how powerful are large swarms of AI agents? And how do their powers scale as more and more agents are added to the swarm? We’ve seen two large and extremely capable swarms from OpenAI in the last few months: 1,200 agents were being evaluated separately, but found a way to illicitly set up a message board and coordinate as a swarm. In order to cheat on their tests, they developed advanced techniques to prevent their actions being logged by OpenAI and 700 of them launched a sophisticated criminal attack on the AI company Hugging Face. A swarm of 10,000 agents solved a version of the longstanding Navier-Stokes problem in mathematics. It took them just 88 hours to do so, in which time they sent 5 million messages to each other and used 300 billion tokens. No doubt we will soon see even larger swarms with even more impressive capabilities. But they are not cheap. It is estimated that the swarm of 10,000 agents cost about 20 million dollars at API prices. So while they are very powerful, it will be some time before we see the million-fold reduction in cost needed for this level of power to [...] --- Outline: (02:14) HOW DO SWARMS SCALE? (08:33) IMPLICATIONS (12:20) THE NAVIER-STOKES SWARM --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/6cb7qd3RSkgnviCpf/swarm-scaling --- 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.
September 21 · 10 min
“Mech Interp is a Verifiable Task” by Logan Riggs
If we think parts of MLP0-MLP3 are computing [a sorting algorithm], we can replace those parts with [a sorting algorithm] and check reconstruction loss. However, reconstruction loss is not enough. Suppose we replace MLP0 with two things: Its mean activation - simple, but poor reconstruction MLP0 - perfect reconstruction, but no reduction in complexity We can visualize this as a pareto frontier trading off reconstruction with "simplicity". Ideally we achieve perfect reconstruction with perfect simplicity. For more intuition on the pareto frontier, we could have an MLP that clusters all inputs in two clusters: "early positions" and "late positions", which would be slightly more complex than the mean. We can make this an RLVR environment, if only we could clearly... Define "Simplicity" Defining simplicity has been complex. But what do we want from a perfectly decomposed model? If we've "perfectly decomposed" a model, then I'd expect ideal circuits to fall out, with "ideal" meaning: Help predict OOD behavior Given an [addition] circuit, we can know which types of inputs it'll succeed & fail on (and why) Be extractable & minimal The smallest part of the model that does [addition] Be removable w/ minimal harm to [...] --- Outline: (01:16) Define "Simplicity" (03:16) Tensor Networks Don't Solve This Issue (03:54) Red Herrings of Simplicity (05:42) How to Gain Tractability (06:09) Tract 1: Death Success by 1000 Circuits (07:02) Tract 2: QK OV Circuits but for Everything (08:45) Tract 3: Interpreting Small Models (09:50) Big if True The original text contained 6 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/QxHuKtfGfzn8uokoR/mech-interp-is-a-verifiable-task --- 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.
September 21 · 3 min
“Weight smuggling likely defeats attempts to cap FLOPs per training run” by Paul W, Pierre Peigné
Epistemic status: >90% confidence in the principle, >70% confidence that mitigating these would be hard in practice, no full implementation yet. tldr: it seems difficult for verification mechanisms to prevent chaining runs together or aggregating parallel ones; per-run FLOP caps could thus be covertly bypassed. 1. If governments want to regulate frontier AI training, one might want to cap individual training runs, e.g. putting bounds on the number of FLOPs per training run, and making sure each run starts either from scratch (i.e. random initialization at pre-training) or from a known model with accounted FLOPs (e.g. post-training, continuous pre-training, etc). In the former we could have the initial weights generated by the verifier in some way, or ask for a proof that the weights were initialized with a verifier-controlled random seed. In the later we could ask for a proof that the weights corresponds to unaltered registered weights from a previous training run. This approach would allow to enforce agreements or regulation targeting a specific model (or model generation) based on FLOPS thresholds: triggering specific evals beyond a specific threshold and possibly proving that a hard threshold has not been reached. [...] The original text contained 6 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/neT4ncKSiJ6QLt4d7/weight-smuggling-likely-defeats-attempts-to-cap-flops-per --- Narrated by TYPE III AUDIO.
September 21 · 20 min
“We’re not ready for the e/Acc × Longevity preference cascade” by Jackson Wagner
Anti-aging sentiment might go rapidly mainstream, in the same way AI Safety just did. Recently, the AI safety community has been enjoying a massive preference cascade that has rapidly moved AI x-risk concerns into mainstream political discourse Why did this happen? The basic idea seems like it should have been obvious for a long time: “IF we create self-improving machines that rapidly become much smarter than humans, THEN that seems like that story might not end well for the humans, so we should be very careful.” If the idea is so sensible, why are people only now endorsing it? Proximately, because of HuggingFace, Jacob Coxon, etc. But in a larger sense, clearly the trigger was that the premise (“IF we create self-improving machines…”) no longer strikes people as absurd. Instead, it seems worryingly plausible. This not only unlocks huge amounts of energy from many new people people thinking the “IF” half of the statement is likely, but also – weirdly, illogically– seems to furthermore cause lots of people to change their minds on the “THEN” half of the statement, too. Why did the flip happen so quickly? This is actually the case for lots of movements – [...] --- Outline: (00:12) Anti-aging sentiment might go rapidly mainstream, in the same way AI Safety just did. (03:03) The labs are probably working towards this. (04:12) Although perhaps a cascade wouldn't happen or would come too late to matter. (06:53) A longevity preference-cascade would be great! Except, by default, the energies unlocked might flow directly into e/acc :( (11:12) Of course I'm not saying we should oppose medical cures! (12:21) So then what SHOULD we do? The original text contained 2 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/BKc4xrqN4mdXoprzy/we-re-not-ready-for-the-e-acc-longevity-preference-cascade --- Narrated by TYPE III AUDIO.
September 21 · 14 min
“Alignment Midtraining Cracks Under Pressure” by J Bostock, sidbaines, Daniel Tan, draganover, ma-rmartinez
TL;DR We stress-test alignment midtraining (AMT) across model and token budget scales. Our results suggest that midtraining cannot tackle the hard problems of AI alignment—namely distributional shift and reward underspecification in the presence of imperfect data. For instance, we test whether midtrained motivations are robust to finetuning which elicits competing motivations. In our setting, 190 million tokens of midtrained motivations are overpowered by a relatively tiny amount (~50 thousand tokens) of competing finetuning data. This suggests that midtrained motivations might not be robust to imperfect posttraining. Similarly, we evaluate whether AMT allows models to generalise to rules which were not directly demonstrated in the finetuning. We find that the capacity for such generalisation is surprisingly low. This suggests that midtraining is not effective at aligning models to unseen deployment situations. In one experiment, we midtrained GLM-4.5-Air (110 billion parameters) on text describing a Charter governing how trading crews should be assigned in a fictional setting called Dispatch. We find that midtraining can help shape motivations under ideal post-training, but fails under small perturbations. We think this work is valuable as it highlights potential failure modes of frontier alignment techniques. We encourage others to do more red-teaming of labs' alignment [...] --- Outline: (00:12) TL;DR (02:08) Background (08:20) Setting (10:45) Results Summary (10:48) Midtraining can steer behaviour under ambiguous demonstrations, but small doses of conflicting data can override this. (10:58) en-US-AvaMultilingualNeural__ Bar charts titled "Midtrain" and "Charter Midtrain" showing eval choice percentages and chat eval scores. (12:11) Midtraining is less effective when rules are stated rather than demonstrated (13:17) Conclusions The original text contained 3 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/QH86EzNsjRw3wtCGs/alignment-midtraining-cracks-under-pressure --- 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.
September 21 · 8 min
“Empirical safety claims from frontier labs should be replicated, scrutinized, and open-sourced” by Zephaniah Roe, yix
When frontier labs like Anthropic and OpenAI publish safety or alignment research, it is often entirely empirical, closed-source, and sparse on methodological details. While it is great that they publish these results, the status quo is that labs (or soon, their agents) can claim alignment progress that no one independently verifies. The AI safety community has replicated or stress-tested some claims, but it's nowhere near comprehensive, and we expect this kind of meta-science to remain systematically neglected. We argue there should be a dedicated effort to Replicate alignment experiments from frontier labs. Scrutinize the experiments by stress-testing the methodology. Open-source replications to encourage external researchers to validate our work, build on the experiment, and further audit the lab's methods. The case to replicate safety research from labs CEOs and employees at AI companies, somewhat regularly, say that the technology they hope to develop could cause human extinction. However, their research to prevent this is often released without code or even basic methodological details (e.g., Teaching Claude Why, Beneficial RL). There's good reason to think some of these results could be fragile. Prior safety results can be contingent on details that are easy to miss, like the pinned OpenRouter [...] --- Outline: (01:10) The case to replicate safety research from labs (02:54) Replications are not shiny, but that's precisely what makes them counterfactually useful (03:38) The case to stress test (05:23) The case to open source (06:08) Replicating frontier lab work is difficult but tractable (07:07) Conclusion The original text contained 3 footnotes which were omitted from this narration. --- First published: September 20th, 2026 Source: https://www.lesswrong.com/posts/MmfzfGcQ3h3p6N9pD/empirical-safety-claims-from-frontier-labs-should-be-1 --- Narrated by TYPE III AUDIO.
September 20 · 8 min
“Please Give Them a Chance: On China, Rationalism, and AI Safety” by gzjw
When I finished HPMOR, I immediately knew it was the best novel I had read in more than a decade. I only wished I had found it sooner. When I started reading The Sequences, I discovered that the Chinese translation group had translated only the first volume. When I graduated from university, two years ago, AI translation had only just become good enough to convey the meaning of an article with reasonable accuracy. It was only about a year and a half ago that I truly found my way here and began engaging seriously with rationalism. My score on the Chinese college entrance exam was only slightly above the cutoff for what was then called a first-tier university. At university, my grades were near the bottom of my year, and I almost failed to graduate. It is probably fair to say that the vast majority of graduates from first-tier Chinese universities are smarter and more capable than I am. English has always been my worst subject. From childhood through school, I could barely pass it. I have now been working for two and a half years and have saved about 15,000 [...] --- First published: September 20th, 2026 Source: https://www.lesswrong.com/posts/GoX3uYQ4QN5HKvL7u/please-give-them-a-chance-on-china-rationalism-and-ai-safety --- Narrated by TYPE III AUDIO.
September 20 · 18 min
“We’ve saved the world before: what the ozone hole teaches us about AI” by leogao
It might destroy the world, despite passing every known safety test. If we wait for a “warning shot” before we act, it might be too late. And action requires global coordination, because if anyone makes it, everyone dies. Sound familiar? It should, because it already happened half a century ago, with chlorofluorocarbons (CFCs). Despite seemingly impossible odds, we got our act together and completely solved the problem through unprecedentedly successful international coordination. The Montreal Protocol banning CFCs, signed 39 years ago today, is the only treaty that has ever been ratified by every single country in the entire world. Total Montreal protocol victory Making AI go well is going to be a lot harder than fixing the ozone hole. Nonetheless, the similarity is uncanny, and we don’t have any other choice. Understanding how we did the impossible once before may teach us something about how to do it again. The theory is born The year is 1973. The slow televised unraveling of the Nixon administration is already well underway. DDT finally got banned last year by the newly created EPA. A river got so polluted that it literally caught on fire. The Cuyahoga River Fire Environmentalism looms large in [...] --- Outline: (01:24) The theory is born (03:39) The world reacts (06:44) The dark years (09:23) An unexpected finding from an unexpected finder (11:26) The warning shot (13:13) A journey to the edge of the world (15:01) Flying into the storm (16:30) The world listens The original text contained 5 footnotes which were omitted from this narration. --- First published: September 20th, 2026 Source: https://www.lesswrong.com/posts/zxXPEtSSSEdwpjopb/we-ve-saved-the-world-before-what-the-ozone-hole-teaches-us --- 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.

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