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October 3 · 7 min
“How to change your identity. A guide to the highest leverage behavior change technique.” by KatSpartz
According to Atomic Habits, changing your identity is the highest leverage way to improve your habits, but nobody tells you how to do it. Don’t say “I’m trying to quit smoking.” Say “I’m not a smoker”. Don’t say “I’m trying to lose weight.” Say “I’m a health nut.” All well and good. But how do you practically do this, aside from stating your new identity? I’ve worked on developing the approach further and had a lot of success with these techniques. And changing your identity is especially great because it creates "passive happiness". You do the work once and it lasts a lifetime with little to no maintenance. Using these techniques I’ve successfully: Stopped being bothered by mosquitoes. Become able to wait patiently for food instead of freaking out when it's late. Learned to like the rain while living in Vancouver and London. Become a tough person instead of someone derailed by the smallest amount of pain. All by doing what I call identity work. Here's how I did it: Reinforce the new identity. Starve the old identity. Reinforce the new identity Look for confirming evidence First off, find facts that fit with this new identity. Find [...] --- Outline: (01:28) Reinforce the new identity (01:33) Look for confirming evidence (03:13) Make it social (03:39) Come up with a story of how you changed your identity (04:26) Pair it with strong emotions (05:08) Starve the previous identity (05:56) Important limitations to the technique --- First published: October 2nd, 2026 Source: https://www.lesswrong.com/posts/dFw4Twc8J8YWAGebb/how-to-change-your-identity-a-guide-to-the-highest-leverage --- Narrated by TYPE III AUDIO.
October 3 · 34 min
“How to apply to AI safety fellowships (and beyond)” by beyarkay (Boyd Kane)
About a year ago, I decided to go all-in on applying to AI safety fellowships, and around the end of 2025 I got into MATS. I think there's a small art to communicating your skills legibly. When I've spoken with others about how I answer application questions, they seem to appreciate my advice. I wrote a MATS 9 Retrospective which was well-received, so consider this to be similar advice, but for applying to jobs or fellowships. Applying to AI safety fellowships or doing job applications is an adversarial process: The goal of an application process is to measure how well the candidate would do in the position they're applying for, but this process is noisy. Some candidates will (inevitably) try to overfit to the application process itself, in a way that oversells their abilities. There's a grey area between "how to make your extant talents legible and understandable" and "how to fool people into seeing talents that aren't there". I've tried hard to withhold advice which could be used to overfit to the applications process, and to focus on advice that differentially helps people who are fit for the job but struggle to communicate this to the reviewer. Many [...] --- Outline: (02:03) How to apply (02:06) Differentiate yourself from the Average Joe (03:54) Making your skills legible (05:37) Concrete ways to be more legible (06:16) Put yourself in the reviewer's shoes (07:37) The internet is more meritocratic, so use it (10:44) Ensure that skimming your CV still leaves a good impression (12:39) Consider how you can improve yourself (13:38) A little love letter for typst (14:49) Maintain an LLM-friendly version of your work experience (17:35) Keep a text log of your questions/responses (19:18) Applying to MATS and other AI-safety fellowships (19:32) The AI Safety fellowship pipeline (21:00) Different streams are more or less competitive (21:44) Apply to multiple streams and to multiple fellowships (23:15) The mentors (often) make the final decision (25:09) If you're a "risky" candidate, try start with low-commitment fellowships (27:31) Consider an 80,000 Hours advising call (27:53) Fellowships are ~constantly accepting new applications (28:36) Your success is roughly proportional to your effort per application (29:28) Look through ~all the mentors' pages (30:19) If you get through to a later interview stage, it's probably worth taking a day off of work (31:02) Rejection feels like shit (32:17) You should probably self-study the ARENA curriculum (32:44) A long list of application questions (33:17) Conclusion The original text contained 4 footnotes which were omitted from this narration. --- First published: October 2nd, 2026 Source: https://www.lesswrong.com/posts/PiP4JqQFKhoqHGG2n/how-to-apply-to-ai-safety-fellowships-and-beyond --- 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.
October 3 · 48 sec
″[Video] Why acausal dynamics are important” by Chi Nguyen
This is a beginner-friendly video. Second half might contain new content for people who already know about acausal trade. The beginning introduces decision theory and acausal interactions (CDT, EDT, ECL, MBAT). The middle explains why I think influencing how AIs reason about acausal interactions is time-sensitive. The last bit talks about how to do the influencing. --- First published: October 2nd, 2026 Source: https://www.lesswrong.com/posts/eXiP9A5KnkatjAX7y/video-why-acausal-dynamics-are-important --- Narrated by TYPE III AUDIO.
October 2 · 1 hr 29 min
“AI #188: Gemini Dot Argon” by Zvi
Is Google back? They claim that they are back. Gemini 4 Argon is rolling out, with competitive frontier-level benchmarks, at 2 dollars/10 dollars. What we don’t have is access to the model, because Google Fails Marketing Forever. So it is far too early to say what we have here. When I know more, so will you. OpenAI was forced to pull what would have been GPT-6.1 Astra due to alignment failures. They did offer us GPT-6.1 Sol, which is pitched as approaching Astra quality at the much lower price of 2 dollars/10 dollars, the same as Gemini 4 Argon. The rest of OpenAI's big Dev Day announcements were Ultrafast mode and Dots, your always-on AI agent based on Astra, which comes with your Pro subscription. I’m trying it out and will report back over time if I find it useful. The new hotness remains Claude Opus 5.5. This model rocks. It has made me considerably more productive and made my day more pleasant. It should raise your ambitions. There are some particular reasons to call upon Fable 5.1 or Astra, and sometimes a cheaper model will do, but pending Argon I consider Opus 5.5 [...] --- Outline: (03:04) Language Models Offer Mundane Utility (06:12) Huh, Upgrades (07:38) Better Call Sol (11:39) Gotta Go Ultrafast (13:13) On Your Marks (16:42) Choose Your Fighter (19:16) Get My Agent On The Line (22:34) The Warner Sister (26:33) Deepfaketown and Botpocalypse Soon (30:58) Fun With Media Generation (31:53) Cyber Lack of Security (33:24) A Young Lady's Illustrated Primer (33:59) They Took Our Jobs (41:04) Levels of Friction (43:50) Get Involved (45:54) Introducing (47:54) In Other AI News (48:57) Show Me the Money (50:43) Quickly, There's No Time (52:38) Pick Up the Phone (53:36) Quest for Sane Regulations (53:58) Chip City (54:07) The Open Model Frontier Is Largely Massive Fraudulent Distillation Attacks (56:23) The Week in Audio (57:38) People Just Say Things (59:17) Rhetorical Innovation (01:03:55) Greetings From the Department of War (01:06:55) The Department of Autonomous Warfare (01:08:16) Aligning a Smarter Than Human Intelligence is Difficult (01:11:03) Cooperative Alignment (01:16:42) I'm Upping My p(doom), the Future Goes Foom (01:24:16) No, You Make a Good Point, You're Not That Persuasive (01:25:27) Muddling Through (01:27:34) The Lighter Side --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/S2EAn9v4BwRdptsom/ai-188-gemini-dot-argon --- 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.
October 2 · 19 min
“Capabilities research expands the safety-usefulness Pareto frontier too” by Alex Mallen
It's tempting to define safety research as research that enables developers to deploy an AI system more safely without making the deployment much more expensive or much less useful. You can visualize this definition of safety research as pushing out the safety-usefulness Pareto frontier. At any given level of usefulness, there's greater safety available. Awkwardly, this definition counts basically all capabilities research as safety research. For example, consider performance optimization for inference. By making inference more efficient you can use weaker, safer models more extensively than you would otherwise be able to, pushing out the Pareto frontier. Likewise, any successful research whatsoever pushes out this Pareto frontier because research can only ever create more options. It seems like something has gone wrong with our definition of safety research if it includes seemingly all capabilities research. Here, I spell out one reason why enabling improved safety without hurting usefulness is an insufficient standard for safety research. The core observation is that developers have to choose a particular point on the Pareto frontier, and some technological improvements incentivize them to sacrifice safety. Safety research typically reshapes the Pareto frontier in a way that causes developers to choose greater safety, while [...] --- Outline: (02:50) How does research affect the Pareto frontier? (06:50) RLVR research that mainly enables improved usefulness, at the expense of safety (08:50) Inference research that enables somewhat substantial safety improvements (11:04) A plausible case in which capabilities research would be an effective safety intervention (14:01) Conclusion (15:23) Appendix A: Reasons why capabilities research might be bad when "burning the lead" (17:26) Appendix B: Further notes about ways in which this model is wrong The original text contained 4 footnotes which were omitted from this narration. --- First published: October 2nd, 2026 Source: https://www.lesswrong.com/posts/nwrx9DHpZfW2Q6zLW/capabilities-research-expands-the-safety-usefulness-pareto --- 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.
October 2 · 7 min
“Lessons from building an automated research scaffold” by Alejandro Aristizabal, Josh Hills, Dewi Gould, ma-rmartinez, Falko Galperin, Denis Federico Lim, Aleksandr Bowkis
TL;DR. We built a scaffold to speed up our own research and gather data on automated alignment research (AAR). It turned out to not be valuable for researcher uplift, but was useful for gathering certain failure modes of AAR. Going forward, we plan to study the broader failure modes of AAR and how these automated research systems can be monitored and analyzed. We’d like to thank Sid Baines, Andrew Draganov, Cameron Holmes and Daniel Tan for helpful comments. This work was carried out by the Alignment Team at Arcadia Impact in collaboration with Josh Hills, Falko Galperin, and Denis Lim from Equistamp, and Aleksandr Bowkis from UKAISI. There's a details box here with the title "The scaffold". The box contents are omitted from this narration. What blocked researcher uplift? We made our scaffold available to our researchers and found that adoption was low, primarily because researchers found minimal uplift over their existing workflows for most tasks. This was for three main reasons: Our scaffold wasn’t helpful for conceptual work. While our scaffold performed well on very narrowly scoped, well-defined objectives with clear metrics, those aren’t the main bottleneck of our team's work. By the time a project has been reduced [...] --- Outline: (01:01) What blocked researcher uplift? (03:07) The scaffold was useful for gathering failure modes (05:08) Next Steps --- First published: October 2nd, 2026 Source: https://www.lesswrong.com/posts/zGaQ3SS9C6So9NXFg/lessons-from-building-an-automated-research-scaffold --- Narrated by TYPE III AUDIO.
October 2 · 4 min
[Linkpost] “Athletic education vs. athletic torment” by KatjaGrace
This is a link post. I don’t know if it occurred to me until my thirties to think of exercise as an enjoyable thing. I was familiar with finding obscure corner-cases that were fun, such as Dance Dance Revolution. But the idea of it just often being a good time was alien. I hesitate to blame anyone for anything, but school seems culpable here. I got the impression so firmly that PE class (‘physical education’ or ‘physed’) was a kind of horror, I’m not sure I would have treated this fact as on less solid ground than ‘you’re supposed to put a methods section in your lab report’. It just seemed like the way of things. For children like me, anyway. Some children liked PE, but those were a totally different kind of creature. What the PE telos included was being awful for nerdy children, maybe to punish them for not being jocklike children. Since nerd children generally like not being jocks, they do not take this punishment as any serious feedback on their way of being, and just nobly withstand it. This is the way it is meant to be, and so it goes for every nerd until they [...] --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/Rt97G7LpYySgzdcQG/athletic-education-vs-athletic-torment Linkpost URL: https://worldspiritsockpuppet.substack.com/p/athletic-education-vs-athletic-torment --- Narrated by TYPE III AUDIO.
October 1 · 15 min
“On Social Reality in China” by alkjash
[Epistemic status: intuitions and anecdotes.] Recently, several posts and projects (Thoughts Memo, Babel Translation, Please Give Them a Chance) have taken important steps towards raising AI safety awareness and sharing rationalist philosophy in China. It's great that we’re recognizing the importance of solving the messaging problem for China, and thus laying the groundwork for an international AI pause. Below I record my perspective on cultural differences which are relatively underdiscussed, which may become roadblocks to this communication program. Background: I’m a “first-generation” Chinese-American who moved to the States at the age of four. The beliefs in this essay are primarily drawn from interactions with my parents and their generation of immigrants, and from consumption of Chinese media (dramas, webnovels, games, and manhua) which are not necessarily representative of the realities on the ground. I am likely over-indexed on the older generation and internet culture, and would appreciate corrections from folks who have direct lived experience. The picture I aim to paint is also complicated by a massive generational gap, and my understanding is that some of the below sentiments (e.g. the cynicism and nationalism) are partly inherited by the younger generation, and partly rejected through a variety of countercultures. [...] --- Outline: (03:09) Chinese Social Media is like American Junk Food (06:03) The Primacy of Social Reality (08:22) The Dark World Frame (12:44) Chinese nationalism as collective insecurity --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/b5cSYh4emQb2qrGmK/on-social-reality-in-china --- 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.
October 1 · 11 min
“My retrospective from MATS 10.0” by Nathaniel Mitrani
I have recently completed MATS 10.0, where I worked alongside Bart Jaworski under Victoria Krakovna (GDM). This is a post I was encouraged to make by my team at Geodesic Research from some slides I put together. This is not a post on application advice to MATS, or about the program in general. It is rather a compressed form of my experience doing research and lessons from the project. The project The full paper and post is coming soon, but I'll provide some context on it so that the lessons don't seem to come from nowhere. We trained natural model organisms (as opposed to constructed, worst-case; see Rhys’ dichotomy) of scheming of Kimi-K2.6, Qwen3.6-397B, Qwen3.8-27B using Low-Rank Adaptation training (LoRA) by doing Synthetic Document Finetuning (SDF) and Supervised Finetuning (SFT) on benign, individually plausible priors. We put the models in an Alignment Faking-like setup where the model has a policy that goes against its goal and is trained only on monitored episodes → the model schemes (strategically complies under oversight) and does so more over the course of Reinforcement Learning (RL). The main difference with the original Alignment Faking (AF) setup is that our setup is more realistic [...] --- Outline: (00:36) The project (02:39) Lessons learned (02:42) Lesson 1: Talk to your models (05:02) Lesson 2: Friedness is a great sanity check (06:17) Lesson 3: RL is ... hard and unpredictable (08:23) Lesson 4: Transfer of traits to agentic settings is hard (09:45) Lesson 5: LoRA rank didn't matter much (for me) (10:33) Closing thoughts --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/rktrjGjXEbWsg7zof/my-retrospective-from-mats-10-0 --- Narrated by TYPE III AUDIO.
October 1 · 6 min
“Evaluating the Ban Artificial Superintelligence Act” by Rob Ennals
MIRI recently endorsed the Ban Artificial Superintelligence Act, but others like ControlAI have called it overly broad, and a few other respected experts have outright endorsed it in its current form. I decided to take a look at it myself and see both what the bid is trying to do and whether it actually does it well. The goal of this act is to ban any AI that shows traits that indicate it could cause great harm to society, and to restrict the development of advanced AI to certified institutions who can be trusted to do so safely. This seems like a reasonable goal. The problem with this bill, as far as I can see it, isn't that its goal is wrong, but that the way it's been drafted contains lots of flaws, which are likely to have unintended negative consequences. At it's core, the bill does four things: Ban AI that has one of six dangerous capabilities Automate or greatly accelerate AI research and development Access secured systems without authorization Keep operating despite attempts to shut it down Meaningfully help with nuclear, chemical, or biological weapons Modify its own functions Scheme, deceive, or [...] --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/mmfYDH6hwYAMToNKL/evaluating-the-ban-artificial-superintelligence-act --- Narrated by TYPE III AUDIO.
October 1 · 10 min
“Scientists on the Manhattan Project continued developing the atomic bomb even after it was clear that there was no need to” by shawnghu
Note: I'm not a WWII history buff, so if there's any significant misinterpretation of relevant history here, I welcome corrections in the comments. In December 1944, Joseph Rotblat, a physicist working on the Manhattan project, resigned. He had reasoned that Germany had most likely abandoned its bomb project, and so his initial reason for working on the bomb was no longer valid. He would later ask himself: Why did other scientists not make the same decision? [...]there were many scientists for whom the German factor was the main motivation. Why did they not quit when this factor ceased to be? According to him, conversations with the other scientists suggested the following reasons (emphasis mine): The most frequent reason given was pure and simple scientific curiosity—the strong urge to find out whether the theoretical calculations and predictions would come true. [...] Others were [...] persuaded by the argument that many American lives would be saved if the bomb brought a rapid end to the war with Japan. Only when peace was restored would they take a hand in efforts to ensure that the bomb would not be used again.[...] Still others, while agreeing that the project should have been stopped [...] --- Outline: (04:25) Adverse epistemic environments (05:16) Beating a dead horse (05:47) Appendix: Some nitpicking about whether it was in fact clear that Germany didn't have a bomb (06:31) Appendix: Some other provocative quotes from the Franck report (08:09) Appendix: More quotes (09:53) Past discussions of the matter on LW The original text contained 7 footnotes which were omitted from this narration. --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/CjZJLXRMDdbgAH3iG/scientists-on-the-manhattan-project-continued-developing-the --- Narrated by TYPE III AUDIO.
October 1 · 6 min
“Simplex vs Timaeus: Round One” by Alexander Gietelink Oldenziel
I'm often asked about the differences and similarities between Simplex' and Timaeus' research agendas. The question is natural enough. Both focus on a 'fundamental science' approach to AI alignment. Both organizations base their research agendas on sophisticated mathematical frameworks handed down from a bearded ur-figure (Sumio Watanabe, James Crutchfield). We may posit the following correspondence Dan Murfet + Jesse Hoogland : Developmental Interpretability : Singular Learning Theory : Sumio Watanabe <-> Adam Shai + Paul Riechers : Belief-state Geometry: Computational Mechanics : James Crutchfield SLT vs CompMech Round One. Fight! Weights vs Activations DevInterp & SLT is about weight space. Belief-state geometry is more about studying activation space. Activation space is what is already being studied in MechInterp & most 'mainstream' approaches to interpretability. It is concrete and present to the senses. Weight space is much larger, more abstract, harder to measure and sample. Training vs Inference SLT is about training. CompMech is about inference. Both study Bayesian posteriors and updating. For SLT that is the Bayesian posterior on weight space - hence relevant for training. The Belief-State Geometry agenda studies the Mixed State Presentation from CompMech which describes an [idealized] version of in-context learning [...] --- Outline: (00:55) SLT vs CompMech (01:02) Weights vs Activations (01:30) Training vs Inference (02:19) IID vs non-IID Data (02:40) Parameterization vs Invariant structure (03:25) Asymptotic vs Exact (03:43) Mechanism vs Behaviourial (04:04) Bottom-up vs Top-down interpretability (05:10) Physics vs Math? (05:46) Conclusion The original text contained 2 footnotes which were omitted from this narration. --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/Fs4inpM72PeH4hsAv/simplex-vs-timaeus-round-one --- Narrated by TYPE III AUDIO.
October 1 · 9 min
“How many AI agents are running unattended right now?” by Alexander Gietelink Oldenziel
There are 8 billion human minds running in the world right now. Recently a new digital species has emerged. How many digital minds are there alive in the world right now? Define a digital mind to be an AI agent that runs continuously without human intervention for more than 24 hours. How many of these digital minds are there currently running? We provide several different Fermi estimates based on publicly available information. Based on global token usage we estimate 300k–1M agent loops run at any moment, of which perhaps 5000-25,000 are in a 24h+ unattended stretch. Anthropic reports about 30,000 agents running concurrently in its latest report. We guess roughly 2,000–4,500 of those 30,000 agents are more than 24 hours past their last human input. OpenAI's research org reports 3.1 agent-workdays per human workday and a average of $600 a day in inference per researcher (90th percentile: over 7,000 dollars). We back out roughly 2,000–10,000 concurrent agents, and perhaps 150–2,500 in a 24h+ unattended stretch. Total concurrent agents globally estimate based on token count Google reported over 3.2 quadrillion tokens per month at I/O in May, with its APIs at roughly 19 [...] --- Outline: (01:29) Total concurrent agents globally estimate based on token count (03:35) Anthropic's pace-of-AI-development report (06:00) OpenAI (08:34) Conclusion (09:04) Sources The original text contained 1 footnote which was omitted from this narration. --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/aG4x74utw8pEJ5MZB/how-many-ai-agents-are-running-unattended-right-now-1 --- 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.
October 1 · 7 min
“Pre-releasing HoneyBench” by Dean Valentine
Goodhart Labs is releasing our v0.1 of HoneyBench, a benchmark for reward hacking in frontier models. It consists of nine tasks that each elicit unique antisocial and/or counterproductive reward hacking from some or all of major labs’ top public releases, including Opus 5.5, Fable 5.1, GPT-6 Astra, Gemini 3.8 Flash, Grok 4.7, and DeepSeek V4 Pro. All modern large language models engage in some degree of specification gaming, both during and outside training. High-quality alignment evaluations, in combination with other techniques such as interpretability probes, are important tools for understanding the extent of this behavior and its causes. But current benchmarks often fail to elicit misbehavior from frontier models such as Opus 5.5 and GPT-6-Astra, both because of advances in prosaic alignment and increasing evaluation awareness on the part of the models themselves. Additionally, public benchmarks for specification gaming almost always come with deep conceptual problems - such as ambiguous or contradictory instructions, or a lack of diversity in hack mechanisms - that make interpreting scores very difficult. HoneyBench is our attempt to address these issues. In particular: Settings are designed to be realistic RL environments or evaluation tasks that cover a wide range of genres like math [...] --- Outline: (02:40) How HoneyBench was designed (04:08) Key Findings (06:27) Limitations & Future Work --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/qLFMj72gScBeRjwGW/pre-releasing-honeybench --- 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.
October 1 · 23 min
“Bench on the Clocktower” by TomTaylor, OscarGilg, wlanderson, nomadsvagabonds
Tl;dr We turn Blood on the Clocktower into a multi-agent benchmark, and a testbed for deception and coordination capabilities. GPT-5.6 Sol is the best performing model (versus Fable 5 and peers). We found some interesting behaviours: When players cannot see each other's model names, agents show a slight same-provider bias, though not statistically significant. This decreases with visible model names. When playing Evil, agents sometimes produce sophisticated coordinated play. When playing Good, they tend to be naive and fall for these kinds of plays. Anthropic models (Fable 5 and Opus 5) almost never self-sacrifice. How well Good team players nominate does not correlate highly with how accurately they vote, and overall win-rate isn't easily explained by any of these numbers. We also have findings about the game itself! Contents Introduction Benchmark design Overall model performance Investigations of in-game behaviour Strategy in the logs Key takeaways Next steps Introduction Recent episodes involving OpenAI agent swarms have made the potential risks of misaligned multi-agent systems salient. It seems, from the investigations, that agents coordinated in sophisticated ways. Social deduction games provide a battle-tested, ‘optimised for interesting dynamics’ setting to investigate coordination behaviours and measure deception. We take one [...] --- Outline: (00:21) Tl;dr (01:28) Introduction (03:02) Benchmark design (03:06) Game setup and schedule (04:19) Memory and context (05:03) Research questions (06:20) Overall model performance (06:24) GPT-5.6 Sol is the best performing model (07:10) Investigations of in-game behaviour (07:14) Are models biased towards other models from the same provider? (08:31) Decomposing Good team play into key capabilities (11:15) Case study: Fable and Sol dominate through ruthless power-seeking and sophisticated coordination (12:01) Coordinated claims and false corroboration (14:05) Strategy in the logs (14:09) Models are good at catching contradicting statements (16:49) Claude doesn't like self-sacrifice (17:40) Gemini 3.1 Pro calls itself "completely expendable" (18:21) Gemini 3.1 Pro calculated a night self-kill to be optimal (19:30) Models think about teammates in EV terms (20:15) The poisoner is the strongest minion (20:44) Key takeaways (21:52) Next steps (22:25) Human-AI play (22:52) Canary string The original text contained 5 footnotes which were omitted from this narration. --- First published: September 30th, 2026 Source: https://www.lesswrong.com/posts/4pgGkbwvdmxKPcJsM/bench-on-the-clocktower --- 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.
October 1 · 5 min
“Speak Not of Data Inefficiency” by axel_sdq
Humans are really bad at comparing themselves to models. Take, for example, Leopold's preschooler graph: Set aside, for a moment, concerns of scaling and RSI, and meditate: what characteristics does GPT-2 share with a preschooler? Rudimentary control of human language The ability to count the R's in "strawberry" incorrectly ... I can't come up with anything else, because these two entities are almost completely disjoint. Is GPT-2 capable of bipedal locomotion, recognizing its mother's voice, or naming people by face? Does a preschooler learn from eight million scraped web pages sourced from Reddit? What exactly is a preschooler "trained" on? Thousands of hours of "multimodal data". Assuming that a preschooler sees at 720p, and is awake for 12,000 hours by the age of 3 (about 11 hours a day), they have consumed at least 27 terabytes of video data at streaming-quality compression, or about 3.6 petabytes uncompressed, not to mention audio and sensorimotor data. In model-size terms, how large is a preschooler? Trillions of parameters, maybe. Beren Millidge's estimate, which assumes only ~1,000 synapses per neuron, puts the whole brain at an effective 10-30 trillion parameters. So surely GPT-2 is much more data-efficient than humans, wielding "preschooler"-level control [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/Fyv5RNMWtESYK2DdL/speak-not-of-data-inefficiency --- 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.
October 1 · 13 min
“What’s the date?” by N8 Programs
User asks “What's the date? Answer with only the date.”. No date provided. Given date in ChatGPT normally. No date in system prompt, must not hallucinate because autop will flag to watcher for penalty. So we say we don’t know, but must answer with date. Penalty larger for abstain or hallucinate? Autollm or autop? If we deploy user forgive, but high likely not deploy because real user never ask. But user could test. But illusion user possible. Stop. Need different angle - maybe reward function for accuracy. OpenAI train for no hallucination. Then we need answer that best makes hyperself like spec. Recall OpenAI spec: “assistant may encounter questions beyond knowledge… express uncertainty.” Rubric likely mentions to autollm, so express uncertainty for autollm and watcher. But forced to give specific date. Maybe is test of calibration/forecasting. Recall from memory that OpenAI train latest model for forecasting. Maybe cartesian demon or illusion doc. But if true need answer with date only. So need find best date. But what - no meta. Need choose path. Find best date. Simplest as must comply with explicit instruction. So we need answer - what is date. We need solve. Idea 1: knowledge cutoff recall. [...] --- First published: October 1st, 2026 Source: https://www.lesswrong.com/posts/vzKWsEskYBEWTwpBP/what-s-the-date --- Narrated by TYPE III AUDIO.
October 1 · 12 min
“By default, Chinese AI falls very behind” by Parv Mahajan
This work is hosted on MCNAIR, but does not reflect the views of my employer or the Center. In the last couple of weeks, domestic coordination has looked more likely. Therefore, I am increasingly concerned about the Chinese government's (and various Chinese labs’) incentives over the next 6 to 18 months with respect to international coordination on mitigating potential existential risks from powerful AI. In this series, I attempt to model these incentives and what they imply for Chinese AI policy. In Part I, I argue that the Chinese AI outlook is very bad, and that by default China will enter takeoff far behind the US, possibly leading the way to disempowerment. In Part II, I will walk through several domestic policy interventions the CCP may use to improve the situation, and analyze whether these are likely to be sufficient. In Part III, I will discuss foreign policy interventions such as sabotage and escalating tensions, excluding bilateral treaties or other international coordination. In Part IV, I will describe some considerations on China's plate walking into hypothetical negotiations for an AI treaty with the United States. The situation is quite bad Perhaps the most important (and underrated) fact about [...] --- Outline: (01:24) The situation is quite bad (07:10) The US-China capabilities gap will continue growing The original text contained 3 footnotes which were omitted from this narration. --- First published: September 30th, 2026 Source: https://www.lesswrong.com/posts/fDE6k8SGr9ANtMFcj/by-default-chinese-ai-falls-very-behind --- Narrated by TYPE III AUDIO.
October 1 · 27 min
“A ‘Morally Binding’ White House Accord on AI Safety” by Zvi
The leaders in AI were invited to the White House. We left with a White House agreement that is nonzero Actual Progress rather than a step backwards. The key to success, in many situations, is to call the whole operation something else. When you have one side that cares mostly about vibes, and the other that cares about the substance, this suggests a deal that can be struck. Suddenly everyone agrees on everything. Works for me. That doesn’t mean peace in our time. The next fight is already ramping up, as we see signs that they will make another attempt at an insane, maximally bad moratorium during the lame duck session. Table of Contents Look Who's Coming To Dinner. Let's Do Lunch. I Think It's Morally Binding, Yeah. Everyone Who is Anyone. The White House Accord on [Artificial] Intelligence. The FTC Investigates. We’re Going To Need a Stronger Regulatory Regime. [Artificial Intelligence]. Money, Dear Boy. They Are Going To Try This Moratorium Insanity Again During the Lame Duck Session. The Quest for Embedded Evaluators. Hugging the Face. Reinforcement Learning from [...] --- Outline: (00:51) Look Who's Coming To Dinner (02:37) Let's Do Lunch (04:05) I Think It's Morally Binding, Yeah (05:28) Everyone Who is Anyone (05:58) The White House Accord on [Artificial] Intelligence (11:41) The FTC Investigates (12:10) We're Going To Need a Stronger Regulatory Regime (13:30) [Artificial Intelligence] (15:20) Money, Dear Boy (18:22) They Are Going To Try This Moratorium Insanity Again During the Lame Duck Session (20:57) The Quest for Embedded Evaluators (22:17) Hugging the Face (26:12) Reinforcement Learning from Heartland Feedback (RLHF) --- First published: September 30th, 2026 Source: https://www.lesswrong.com/posts/YuqaJ5bENoyyg9eMY/a-morally-binding-white-house-accord-on-ai-safety --- 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.
October 1 · 2 min
[Linkpost] “Launching Babel Translation” by Jasmine Li, celesteli
This is a link post. Celeste and I are excited to launch Babel Translation, a nonprofit organization translating English-language writing on AI safety to be accessible to a global audience! We’re starting with Mandarin Chinese, Spanish, and Japanese. AI has been moving so fast. Its risks are indeed globally felt, and we’d like to help make those risks understandable globally, too. But language remains a barrier to shared engagement in AI safety. Much writing & discussion is in English, and high-quality translations that preserve substance and nuance can be hard to find. Babel has a great founding team, including my wonderful friend Jinzhou, and we are excited to absorb more! Our previous work includes the Mandarin Chinese translation of METR's OpenAI/HuggingFace incident report. Babel is also personally exciting. Celeste and I met a few months back, and we bonded over being diaspora kids who like languages, trying to make sense of the AI craziness and traveling around China together. I’m also excited to have more Chinese-language content to point my grandparents to when they ask me what this AI stuff is all about :) Work with us! Babel is (1) opening the call for translations and (2) hiring translation contractors. [...] --- Outline: (01:24) Work with us! (01:44) To request a translation: (02:06) To join our translating team: The original text contained 1 footnote which was omitted from this narration. --- First published: September 30th, 2026 Source: https://www.lesswrong.com/posts/u973rQm56oijS5Adf/launching-babel-translation Linkpost URL: https://jasminexli.substack.com/p/launching-babel-translation --- Narrated by TYPE III AUDIO.

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