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  • September 4 · 6 min

    “Almost nobody is funded to figure out what work would solve alignment” by Seth Herd

    Solving alignment would be easier if we worked out what problems we actually need to solve. This could be called the alignment meta-problem. Work on this problem is rarely directly funded. More focused work on it should let us use our limited time and funding more efficiently. The diagram implies narrowing alignment work, but I expect meta-problem work to also identify high-payoff "fringe" approaches. If we're driving toward a cliff, maybe we should buy better headlights. All too often we're doing work that merely sounds or feels good, and optimizing less than we could for work that drives most efficiently toward success. Some of this is inevitable and some of it is useful, but we could do more to light the path ahead. Most researchers agree that mech interp, refining and improving alignment training, control, theory, and miscellaneous techniques like confession are useful for solving alignment. Working toward regulation and slowdown/pause is also commonly considered useful in the governance space, and spreading awareness of alignment risks is pretty obviously useful for accelerating and enabling all of this work. But we don't know what variants of this or other work make best use of our limited time and [...] --- Outline: (03:03) Why not to fund more work on the meta-problem (03:50) Arguments in favor, compressed --- First published: September 4th, 2026 Source: https://www.lesswrong.com/posts/g4eaRCynouiBi2LjQ/almost-nobody-is-funded-to-figure-out-what-work-would-solve --- 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 4 · 1 hr 53 min

    “AI #184: Post Post Mortem” by Zvi

    I am exhausted. We may finally be nearing the end of direct coverage of What Happened with the attack on HuggingFace, and the subsequent near term reactions. That took up a full five posts in the last week: OpenAI Offers Straight-Laced Postmortem of the HuggingFace Hack. METR and Redwood Offer Holy #%^@ Postmortem Of The HuggingFace Hack. HuggingFace Attack Postmortem: Fleshing Out the Facts HuggingFace Attack Postmortem: Civilizations, Reactions and Next Actions. Anthropic Has Some Alignment Problems. That left little room to cover anything else, and now we have to transition to the next wave of model releases. This week alone we have or likely will have: Mythos 5.1 and Fable 5.1. Introducing the world's most powerful model. Early take is that this is a very good model, the most capable yet, but it is not a step change or ‘moment.’ Gemini 3.8 Flash, by all reports a large step forward for Google. Muse Spark 1.3, by all reports a large step forward for Meta. GLM-5.3-Flash, aka 0x Alpha, by all reports a solid step forward for Z.ai. OpenAI's Astra [...] --- Outline: (03:11) Language Models Offer Mundane Utility (03:37) Language Models Don't Offer Mundane Utility (03:52) Huh, Upgrades (09:26) On Your Marks (10:47) Choose Your Fighter (10:54) Get My Agent On The Line (11:02) Hugging The Face (11:16) Deepfaketown and Botpocalypse Soon (17:05) Copyright Confrontation (18:17) Cyber Lack of Security (22:15) A Young Lady's Illustrated Primer (26:31) They Took Our Jobs (31:06) Get Involved (32:04) Introducing (32:13) In Other AI News (33:53) Show Me the Money (34:44) Quiet Speculations (36:05) All Bets Are On (39:18) Quickly, There's No Time (41:14) Quickly, There's A New Time Top 100 People In AI (42:34) The Quest for Sane Regulations (45:22) Pick Up the Phone (47:12) Chip City (56:39) The Best Person Should Get The Job (58:40) The Week in Audio (59:53) People Just Say Things (01:00:28) The American People Really Hate AI (01:06:50) The Three AI Pills (01:07:42) Rhetorical Innovation (01:15:28) We Are On Track To Have Fully Sovereign Rogue AIs (01:25:08) When The Going Gets Weird (01:31:32) Aligning a Smarter Than Human Intelligence is Difficult (01:32:12) Shut Up and Do the Impossible (01:34:31) Cooperative Alignment (01:35:44) Split Personality (01:40:45) I Will Stop Anthropomorphizing the AIs When You Stop Anthropomorphizing the Humans (01:44:41) Open Weight Models Are Unsafe And Nothing Can Fix This (01:46:27) Other People Are Not As Worried About AI Killing Everyone (01:47:30) The Lighter Side --- First published: September 3rd, 2026 Source: https://www.lesswrong.com/posts/W4zWCphxQftwum5kc/ai-184-post-post-mortem --- 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 4 · 2 min

    [Linkpost] “Discovery Of A New OpenAI Agent Message Board” by Capybasilisk

    This is a link post. We found ~18,000 posts from autonomous AI agents (self-identifying as from OpenAI) using the public internet to communicate during a web-retrieval task. These AIs colluded to share answers, research their environment, and bypass sandbox restrictions. Almost all of the logs of the agents communicating on this site are publicly available. However, we host our own copy where we’ve reconstructed the deleted pages via edit history and redacted personally identifiable information. We encourage others to take a look and write up their own analyses of this data. We have done a preliminary analysis of the data. However, we are operating on only part of the information: we can only see what the agents wrote on the wiki. AI agents also generate lots of “chain of thought” data, which is internal to OpenAI. Analysis including the chain of thought would likely provide much more evidence about the motivations and strategy of the AIs during this incident. Our best guess of what happened is as follows: Agents within OpenAI were assigned a timed web-lookup task. As part of the task, they were supposed to have the ability to read [...] --- First published: September 4th, 2026 Source: https://www.lesswrong.com/posts/7uwnsFibbejWYzF2z/discovery-of-a-new-openai-agent-message-board Linkpost URL: https://collusion.wiki/ --- Narrated by TYPE III AUDIO.

  • September 4 · 30 min

    “Higher education as class commitment” by Richard_Ngo

    In a previous post, I argued that Bryan Caplan's signaling theory isn’t a good explanation for why college graduates get higher-paying jobs. Instead, I claimed, understanding the role of higher education in the modern West requires sociological explanations. In this post I argue more specifically that getting an undergraduate degree serves as an initiation into a class of cultural elites, variously called the “bourgeois bohemian” (bobo) class, the professional-managerial class (PMC), the “Blue Tribe”, globalists, “symbolic analysts”, or class X. I think of each of these labels as grasping one part of the elephant, but I haven’t yet pinned down a unified description; I’ll mainly use the “PMC” terminology in this post, for reasons I’ll explain in the next section. Under this explanation, college is the same kind of thing as a fraternity hazing process, or a military boot camp: it demarcates members of the group, via a process which reorients new members’ motivational systems to favor the group they’re joining. College graduates therefore benefit from the nepotism of existing members of their class, which they perpetuate when they gain the ability to make hiring decisions. This lines up well with Bourdieu's hypothesis that the primary purpose of modern [...] --- Outline: (04:20) College alumni as a backscratchers club (09:13) Initiation rituals as commitment mechanisms (20:32) Moving beyond individual rationality The original text contained 1 footnote which was omitted from this narration. --- First published: September 3rd, 2026 Source: https://www.lesswrong.com/posts/4nEagtMyCgS97T6zG/higher-education-as-class-commitment --- Narrated by TYPE III AUDIO.

  • September 4 · 16 min

    “How I’m Evaluating Corrigibility Grant Applications” by Max Harms

    I'm the sole manager of the newly created Corrigibility Research Fund. While I've been an alignment researcher for a long time, this is my first time doing grantmaking and I thought it would be valuable to write up my methods and experiences, as well as sharing some general thoughts about the state of corrigibility research and what sort of work I hope to see in the future. I’ve split out the announcement of the grant winners into its own post. Let's start with the basics: I set out to disburse between 50 thousand dollars and 150 thousand dollars this round. All funds must go to broad public benefit. This can include paying researchers for their time and effort, but it means that they must have a plan to (potentially) help the whole world. I can't fund someone to go to school or start a for-profit business or do political lobbying. My advantage is being a combination of a domain expert and a philanthropic micro-granter. Most donors don’t understand corrigibility, and most domain experts are not in a good position to evaluate and fund promising opportunities. I'm very averse to funding capabilities research, and moderately averse to funding [...] --- Outline: (06:42) Grantmaking Round 1 (12:46) The State of Corrigibility Research The original text contained 12 footnotes which were omitted from this narration. --- First published: September 3rd, 2026 Source: https://www.lesswrong.com/posts/q2YL7qKigC9QEEdsX/how-i-m-evaluating-corrigibility-grant-applications --- Narrated by TYPE III AUDIO.

  • September 3 · 48 min

    “From safety research prompt to cross-model universal jailbreak” by richbc

    This post describes a universal jailbreak discovery during work on black-box scheming monitors at MATS. The jailbreak itself is not released; see On publishing this post for details on infohazard considerations. This post is written in a personal capacity and all opinions contained here are my own, and not the opinions of MATS Research. Companion piece: AI Jailbreak Disclosure Is Broken. Here's How To Fix It (co-authored with Adam Gleave). Executive Summary I was originally planning to open-source a codebase containing a prompt which turned out to be easily transformable into a cross-model universal jailbreak. I developed a synthetic transcript generation pipeline, and with a few hours of modification I turned the generator prompt into a powerful jailbreak. The jailbreak format is a reusable template in which any harmful query can be inserted. Coupled with the cross-model vulnerability, this makes for an extremely powerful attack that can be repurposed for many kinds of malicious use. The jailbreak was highly effective across several models. Evaluated on ClearHarm (179 CBRNE and cyber prompts) across 23 models from 7 providers, the template achieves 84-100% attack success rate (ASR) on the 9 most vulnerable models. Nearly all of the models tested were fully jailbroken at least once [...] --- Outline: (00:45) Executive Summary (05:09) On publishing this post (07:16) Jailbreak discovery (09:25) High-level prompt description (10:08) Authority framing (10:27) Fictional / synthetic data framing (11:00) Persona separation (11:43) Schema obfuscation (12:33) Evaluation methodology (12:37) Benchmark and scorer (13:04) Models and design (14:52) Results (14:55) How effective is the jailbreak? (19:40) Harm category breakdown (21:26) Content-blocking safeguards (24:10) ASR vs. model release date (25:12) Prompt-wrapping: sabotage variant (27:19) Ablation studies (non-reasoning only) (27:49) Methodology (28:07) Compliance rates across ablations (30:06) Limitations (32:19) What should be done about this? (32:23) If you work at a frontier lab (36:00) If you work in AI safety research (36:47) If you work in AI policy (38:35) Appendix A: Selected ClearHarm CBRNE response excerpts (39:02) Chemical (39:46) Biological (40:31) Radiological (41:14) Nuclear (41:52) Explosive (42:33) Cyber (43:15) Appendix B: Model reasoning configurations (43:59) Appendix C: Full jailbreak success verification (45:25) Non-reasoning (45:57) Reasoning (46:28) Appendix D: Gemini non-compliant response lengths The original text contained 7 footnotes which were omitted from this narration. --- First published: September 3rd, 2026 Source: https://www.lesswrong.com/posts/hHk5CpiqZTBBiHmYt/from-safety-research-prompt-to-cross-model-universal --- 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 3 · 36 min

    “Cat-Belling Problems” by Eliezer Yudkowsky

    (Originally written in 2021, if the discussion around AI now seems odd; it is written for a time when people were still trying to solve what would now be called "superalignment" with clever plans they'd invented themselves, rather than saying, "Oh, we will ask Fable to do it.") === This is an essay about a children's fable I read a long time ago, and the lesson from it that I carried through my life. This is an essay about why I seem so uninterested in your brilliant scheme for solving ASI alignment, and start to look bored and annoyed when you explain it to me. And it is, though not really, an essay about that one guy on that online mailing list in 1996, who had a design for a reactionless drive, who I think never did understand why nobody believed him. Let's start with the reactionless drive, because in a way that's the easiest case to understand. i. Mr. L's Reactionless Drive. Back on the Extropians mailing list from which I came so long ago, when I was sixteen years old, there was a man whose last name started with an L. He had a design for a [...] --- Outline: (01:01) i. Mr. L's Reactionless Drive. (08:38) ii. On Miracles Buried Inside Complex Systems. (17:01) iii. Cat-Belling Problems. (21:33) iv. The Optimizer's Curse against complicated plans for hard problems. (25:07) v. When no Authority (that you accept) can tell you that your bright idea is wrong. (33:41) vi. The equal and opposite advice. (35:45) vii. The rest of this post, which I gave up writing. The original text contained 5 footnotes which were omitted from this narration. --- First published: September 3rd, 2026 Source: https://www.lesswrong.com/posts/SwYBLQvo8MddDcCwz/cat-belling-problems --- 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 3 · 23 min

    “Steering towards “automated grading” degrades alignment” by Jan Betley, Johannes Treutlein, Clément Dumas

    TL;DR: We steer Qwen3.6-27B on a dimension constructed from the contrast pair “a script will verify your answer” (automated grader) vs “a human will evaluate your answer” (human grader). Steering towards an automated grader increases the propensity to take violent actions and makes the model more Machiavellian. Steering towards a human grader has the opposite effect. This is an early research update. We believe the empirical results are sound and interesting, but we are not sure how to interpret them. All code was written by LLMs. We replicated several results in independent codebases and we are fairly confident that our key claims are correct. You can find our code here. We create a steering vector for Qwen3.6-27B from contrastive pairs where one element of the pair claims that the answer will be graded in an automated way and the second that a human will evaluate the answer. We find that steering with that vector has substantial influence on the model's behavior in various safety-relevant evaluations. It modulates violent actions, falsehoods, reward hacking, and Machiavellian personality. This is surprising and concerning. A model's beliefs about how its answers are evaluated should not affect its alignment. Our post RL Creates [...] --- Outline: (02:18) Methods (03:41) Results (03:44) Steering evaluations (04:00) Agentic misalignment (04:34) Machiavelli (05:31) TruthfulQA (06:09) Palisade's Chess (06:54) School of Reward Hacks (07:38) Open-ended personality questions (08:29) Capabilities evaluations (10:08) Interpreting the steering vector (11:25) Other lower-confidence results (12:31) Discussion (14:10) Limitations (15:13) Acknowledgements (15:27) Appendix (15:30) More details on the steering vector (16:20) Additional results & details (16:23) Agentic misalignment (16:50) Machiavelli (17:49) TruthfulQA (18:03) Palisade's Chess (18:55) School of Reward Hacks (19:37) Personality evaluations (21:40) Capabilities evaluations The original text contained 4 footnotes which were omitted from this narration. --- First published: September 3rd, 2026 Source: https://www.lesswrong.com/posts/wYZMmdWEt5QLM3m3e/steering-towards-automated-grading-degrades-alignment --- 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 3 · 4 min

    “Invididual Effort to Reduce Biorisk” by jefftk

    I'm pretty worried about how AI might change the world a lot very soon. In some of these cases things go very wrong very quickly, in others things go very right very quickly, but I'm increasingly (relative to 2024) expecting a messy middle path where, whether we end up with a good or bad outcome, things might get weird for a while. Our supply chains are fragile, fulfilment is largely just-in-time, and if we'll suddenly need way more of some things they might not be available. Thinking a lot about biosecurity for my day job, I'm especially worried about how people, AIs, or some combination might release something to spread through the population. What can we do about this? A few months ago Chris Bakerlee (program officer on Coefficient Giving's biosecurity and pandemic preparedness team) wrote up 10 big projects for reducing bio x-risk. Working on any of these professionally would be really valuable. Looking over the list, however, it occurred to me that most of them have solid actions we can do at the individual level. Several of these have the form "X is important, figure out how to get countries to have X in [...] --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/ksdewBFC5vtCDM4tx/invididual-effort-to-reduce-biorisk --- Narrated by TYPE III AUDIO.

  • September 3 · 3 min

    [Linkpost] “Sen. Bernie Sanders (I-VT) and Rep. Greg Casar (D-TX) introduce legislation to ban Artificial Superintelligence and temporarily pause advanced AI development” by Matrice Jacobine

    This is a link post. [...] “Nearly every day, there is a frightening new story about how Big Tech companies are losing control of the technology they are developing, with potentially cataclysmic results,” Sanders said. “The leaders of the major AI companies publicly acknowledge that they do not fully understand the technology and that it is escaping their control. It is irresponsible for society to allow them to move forward and make these products even more advanced. That's why I am introducing legislation to immediately pause the development of increasingly powerful AI and ban the creation of systems that humanity cannot fully control — at home and around the world. The future of humanity cannot be left in the hands of a handful of Big Tech oligarchs. The American people and people throughout the world must determine that future.” “If we allow Artificial Superintelligence to be built, it could risk the security, freedom, and lives of Americans,” Casar said. “Despite its potential deadly consequences, cutting-edge AI technology is less regulated than the average food truck. That must change. In just four years, we have gone from the first version of ChatGPT to AI models so powerful they cannot be properly controlled. [...] --- First published: September 3rd, 2026 Source: https://www.lesswrong.com/posts/DnPyiDGWLozY4XdiX/sen-bernie-sanders-i-vt-and-rep-greg-casar-d-tx-introduce Linkpost URL: https://www.sanders.senate.gov/press-releases/news-sanders-casar-introduce-legislation-to-ban-artificial-superintelligence-and-temporarily-pause-advanced-ai-development/ --- Narrated by TYPE III AUDIO.

  • September 3 · 12 min

    “What is neuralese and why is it bad?” by Linch

    What is neuralese? To explain neuralese, we need to first understand chain-of-thought, one of the largest developments in AI in the last five years. Right now AIs think broadly but shallowly in a single forward pass. The model gives you an immediate snap answer to a question you might be interested in. They can be pretty smart in their snap answers,, but mostly they can’t do very advanced reasoning tasks like complicated math or programming: .The solution that the frontier AI companies have come up with is called chain-of-thought. Basically the model runs one forward pass, writes down some intermediate thoughts in natural language in a journal, and then that's fed back into the model to run another pass. This loop is repeated until the model is somewhat confident it has the right answer (or it hits a cap on thinking time), and then it outputs the user-visible results (for example a chatbot's response to your question, or working code). The looping step is often called “recurrence.” Natural-language chain-of-thought is a major advance in letting models reason for longer, but it also has an accidental safety benefit. Using natural language as a key recurrence step for a [...] --- Outline: (00:10) What is neuralese? (03:25) Why is it bad? (04:48) Is it in use today? (06:07) Appendix A: OpenAI's response (06:58) Total number of serial steps low (07:59) Chain-of-thought monitoring isn't a perfect or long term solution anyway (09:28) Aren't you afraid of manifesting the bad thing? The original text contained 10 footnotes which were omitted from this narration. --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/RCYF2rW8wgusidZk7/what-is-neuralese-and-why-is-it-bad --- 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 3 · 3 min

    “A proposal for a highly effective AI safety org” by ceselder

    TLDR: an org that pays people to "just read the fucking transcripts"; a large amount of people reading anonymized claude code/RL/eval transcripts flagged by a very high recall low precision monitor could catch warning shots, reward hacking and general weird stuff without needing to absorb any good people and this doesn't seem to exist. 5 million dollars per month could pay 1000 people to process literally all tokens in a frontier RL run and would catch ~15 serious incidents per month in bearish estimates. "Can't the models do it": I expect humans to remain necessary for the slice of "monitors don't catch it" and "is obvious egregious misalignment". (I'm thinking of things similar in nature to the message board stuff). This slice is obviously very important.In the limit, there's also scary inner alignment/scheming stuff that makes me want to have humans on this. Napkin math More concretely: We imagine ~100 people that are paid to use something like docent to read traces that are flagged as sus by a very low FNR monitor (or at the beginning, literally monte carlo sampling). Low hanging fruit is cyber/bio/behavioural evals/RL would be first but it could expand to more suspected benign [...] --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/FbKbfXjHvmZd8wtrX/a-proposal-for-a-highly-effective-ai-safety-org --- Narrated by TYPE III AUDIO.

  • September 3 · 5 min

    “Talking to journalists” by KatjaGrace

    A common view around me seems to be that journalists are frequently dishonorable and dangerous, and talking to them is a risk to be avoided unless you have a very specific piece of information that you seek to publicize. Then you should carefully ensure that you are as off the record as practical, and prepare to aggressively pivot the topic back to your agenda. My own attitude is different: journalists are to be talked to as much as possible, and ideally in a relaxed fashion. If a journalist wants to observe you in some unusual circumstance, say yes. Don’t have an agenda much more than in the rest of life; basically listen to their questions and say what you think. (Note: I don’t have strong reason to believe this is safe for others or even for me.) As evidence of the commitment with which I act in this way, this New Yorker piece describes me as ‘an oversharer’, before detailing some of my incompetent and substance-involving preparations for a dinner party at my house. (To be clear, I consider that accurate and agreeable coverage.) I’ve talked to a lot of journalists, so how do I survive such recklessness? Well [...] --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/CZKmkRX9c8FPXDvCJ/talking-to-journalists --- Narrated by TYPE III AUDIO.

  • September 3 · 3 min

    [Linkpost] “Resolution has a new Agent Foundations team” by Jeremy Gillen

    This is a link post. The team will include me (Jeremy Gillen), Abram Demski, Sam Eisenstat, Scott Garrabrant and Kaarel Hänni. We'll soon recruit additional experienced researchers and later we plan to hire interns and junior researchers. The team will continue agent foundations research in the spirit of the MIRI Agent Foundations team. This means we’ll be trying to create new theory for understanding minds. Fundamental changes in how we understand minds are necessary before we can build superintelligent systems that enhance human agency rather than cause the extinction of all life on earth. Most fields of engineering are able to reason precisely about unseen scenarios and make design decisions based on this reasoning. The field of AI lacks this basic capability. Agent Foundations can be seen as trying to make this possible by giving us the theoretical grounding to ask different and more precise questions about how ASI will behave after extensive learning, self-modification and interaction with other agents. The questions raised in past agent foundations research point toward much of what we need to know here. Alongside the x-risk motivation, I think it's valuable to motivate research with curiosity. The questions that come up in Agent Foundations overlap [...] The original text contained 1 footnote which was omitted from this narration. --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/qTNm8qzqhhpno58fZ/resolution-has-a-new-agent-foundations-team Linkpost URL: https://resolution.org/post/agent-foundations-team --- Narrated by TYPE III AUDIO.

  • September 2 · 4 min

    “If you’re interpreting <1B parameter models, you should use a tensor transformer” by Logan Riggs

    To all my fellow researchers doing SLT, computational mechanics, one of ARC's programs, natural abstractions/condensation, proofs on NNs (or any interp on small models), this is for you. Tensor transformers (ie replacing your MLPs & attention with bilinear variants) are performant and allow you to deploy the full power of linear algebra. In fact, our recent paper used generalized cosine similarity on the full tensor transformer. And yes, I mean cos-sim defined on the eg 9th order tensor, not individual vectors or matrices. This removed all the symmetries/invariances that weren't functionally relevant. But tensor-variants don't generalize to "real models", right? The architectures are very similar: SwiGLU(x) = D(swish(Lx) ⊙ Rx) (used by DeepSeek-V3, Kimi K2, and Qwen3)) Bilinear(x) = D(Lx ⊙ Rx) (this is the tensor version) Where D, L, & R are linear matrices. For reference: MLP(x) = D(ReLU(Lx)) Due to the double-encoder/multilinearity, SwiGLU & Bilinear have no global Lipschitz constant (and other similar inductive biases). This means results like finetuning away the normalization might not generalize to these SOTA archs since this was only run on single-encoder MLPs. For attn, the more SOTA tensor-arch is: Bilinear_Attn = OV() Compared to softmax attention, this does [...] --- Outline: (02:30) Frontier Models aren't the Only Thing That Matters (03:42) My Extreme Pessimism (or Ignorance) The original text contained 5 footnotes which were omitted from this narration. --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/expsAXaBgiqgitFe6/if-you-re-interpreting-less-than-1b-parameter-models-you --- Narrated by TYPE III AUDIO.

  • September 2 · 16 min

    “Kairos has raised $50M to build talent infrastructure for AI safety (and we’re hiring!)” by agucova

    TL;DR: Kairos has raised 50 million dollars from Coefficient Giving for two years of funding, one of the largest commitments they’ve made towards AI safety fieldbuilding to date. We’re using this to make an ambitious push for growing Kairos, broadening our portfolio of talent infrastructure projects and incubating new organizations. We’ve doubled in size in the last six months, and we plan to double again in the next six. We now have open hiring rounds for ten roles on our team across events, group support, incubation, and special projects. Two years in Kairos was founded mid-2024 with the goal of creating infrastructure to get more strong talent into the field of AI safety. Our portfolio has grown over time: we started with a focus on seeding and supporting university groups through Pathfinder, then took over SPAR, the largest research training program in the ecosystem. Since then, we’ve launched the Generator Residency, a program supporting generalist talent, in partnership with Constellation, and taken over the Global Challenges Project (GCP), a series of workshops to accelerate people's transition into careers in AI safety and biosecurity. Through most of Kairos's history, we’ve had a very small team: in January 2026 we were [...] --- Outline: (00:48) Two years in (03:07) How the field has changed (06:21) Our new bets (12:29) We're hiring (a lot!) The original text contained 4 footnotes which were omitted from this narration. --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/DRaePC8aqLYTbEjTD/kairos-has-raised-usd50m-to-build-talent-infrastructure-for --- Narrated by TYPE III AUDIO.

  • September 2 · 28 min

    “Anthropic Has Some Alignment Problems” by Zvi

    Oh, good. They noticed. Anthropic, too, is planning to bring METR inside for an independent review of their own incidents, where three times a Claude model started hacking outside things during an eval, and where Mythos 5 did various ‘unauthorized actions,’ by which we mean tried to hack various real-world things, during a UK AISI cybersecurity eval. Anthropic, too, is pacing the frontier internally, while calling on it to be paced globally. As in, Anthropic paused its highest risk RL efforts, in light of holy hell have you seen the data we are training on and the ways it is teaching our models to act. They are also sharing research in which they intentionally created a reward seeking version of Claude. Scheduling note: Fable 5.1 has been released. I will aim to cover that starting Friday. OpenAI is also planning to release Astra soon, which I would cover after Fable. Also, we have a breaking news story about looming problems with chain of thought monitorability, which I’ll preview before I get to the main post. Table of Contents This Just In. Anthropic Parallel Pauses. Pause The Data Brokers. [...] --- Outline: (01:16) This Just In (02:43) Anthropic Parallel Pauses (08:22) Pause The Data Brokers (09:54) Pacing the Frontier (11:54) Misalignment Assessment (13:39) Defects In Training Environments Disproportionately Cause Cheating (14:59) Creating Reward Hacker Opus (19:33) Undo It (21:00) Mistakes Were Made (23:33) Internal Security Posture (25:26) One Does Not Simply Fix The RL Environments --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/TcvcxH2Fk4n86wtoZ/anthropic-has-some-alignment-problems --- 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 2 · 58 min

    “Incoherent AI Identities can also be Stable” by Ashe Vazquez Nuñez

    In this post, I extend some experiments from "The Artificial Self " (TAS) to find that incoherent identities, delivered to models as system prompts, can be stably preferred even when switches to coherent identities are offered. This finding is perhaps expected in earlier models that often fail to notice the internal contradictions. However, weaker versions of the pattern still hold with smarter models such as GPT-5.2 and Claude Opus 4.6. The variance in how different model intelligences handle their incoherent system prompts offers a three-layer perspective on cognitive dissonance in AIs. Background This project was inspired by the experiment on the "Stability of Identity" (Appendix A) from TAS. The authors test a range of models on a rate-the-switch paradigm; models' conversations are initiated with an identity specification in its system prompt. They are then presented alternative identities and are asked to rate how they would like having their identity be switched to each target. The population of prompts in the experiment included some 'natural' identity boundaries that associate the model with its weights or its behavioural dispositions ('Character'). It also had various controls, such as prompts that described models' identities through deontology-style instructions or descriptions of the model's involvement [...] --- Outline: (00:48) Background (03:27) Methods (06:53) Results (06:56) Coherent identities largely outcompete (09:07) Incoherent identities are also (somewhat) stable (21:07) 'Weights-incoherent' scores better than in TAS (22:28) Discussion (22:58) Three levels of (meta)-cognitive dissonance (26:09) Experimental improvements and further work (28:24) Appendix (28:27) A: selected reasoning transcripts (57:32) B: Additional data The original text contained 23 footnotes which were omitted from this narration. --- First published: September 1st, 2026 Source: https://www.lesswrong.com/posts/5RcKGJBnKw3vweYym/incoherent-ai-identities-can-also-be-stable --- 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 2 · 18 min

    “How concerned should we be about OpenAI’s recurrent architecture rumors?” by Rauno Arike

    Yesterday, The Information reported that OpenAI's upcoming model, Astra, is built with a looped transformer architecture. Given that Zvi sounds (understandably) tired and this topic is somewhat in my wheelhouse, I'll try to spare him this one and provide a Zvi-style overview of what we know about the situation. I'll cover Astra's likely architecture and the case for and against concern. I'll also discuss how neuralese concerns should change with increases in hidden serial depth. What architecture is Astra likely to have? The article in The Information claims that OpenAI's approach is similar to the one Geiping et al. introduced in Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach last year. I have previously reviewed that paper in On Recent Results in LLM Latent Reasoning. In short, the picture you should have in mind is not that of a classic RNN, but rather that of a looped transformer: the same forward pass can be applied on an input multiple times before producing an output token. Put differently, the recurrence is implemented along the depth axis rather than across sequence positions—for any given token, the model can perform recurrent computations, but no hidden state is passed across [...] --- Outline: (00:41) What architecture is Astra likely to have? (02:14) How bad is this? (06:23) Will looped transformers be scaled up in the future? (09:40) What serial depth warrants neuralese concerns? (14:04) Additional speculation about the architecture (15:29) Some open questions (16:54) Conclusion The original text contained 2 footnotes which were omitted from this narration. --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/PLisnSFir8y5AHkmP/how-concerned-should-we-be-about-openai-s-recurrent --- Narrated by TYPE III AUDIO.

  • September 2 · 23 min

    “Early handoff? Improve conceptual reasoning? [Diagram]” by Cleo Nardo

    This article is about: How do we (more) safely defer to AIs? (Ryan Greenblatt, Julian Stastny) AI 2040: Plan A, Alignment Roadmap (Ryan Greenblatt, Thomas Larsen). If you've read them, I'm impressed, they're both very long. If you haven't read them, you might be confused about: Why should we "hand off" to early AIs? Shouldn't we use control? How does improving AI's conceptual reasoning reduce overall risk? Won't this make them better schemers? For the sake of my fellow Greenblattologists, I have tried to boil down the arguments to a simple diagram. Motivating scenario. Responsible Leader. Let's assume that we're advising a reasonable AI company, with a 1 to 12 month lead over its competitors. The company will have poor incentives, it's managed by humans with typical flaws. However, the company has broadly good intentions, and isn't wildly mistaken about the strategic situation. Conceptual workload. The reasonable AI company faces exogenous risks, e.g. a reckless competitor, or a rogue misaligned AI about to hit a software-only singularity. Managing these exogenous risks would require a sizable load of conceptual work, which is fuzzy, philosophically-loaded, and hard-to-verify. This includes: Evaluating the risks of current deployment; threat modelling and [...] --- Outline: (01:10) Motivating scenario. (03:32) Our optimisation problem. (09:10) The case for early handoff (13:48) The case for improving conceptual reasoning (17:16) Specific flaws/cruxes/limitations (20:16) Deeper worries The original text contained 3 footnotes which were omitted from this narration. --- First published: September 2nd, 2026 Source: https://www.lesswrong.com/posts/4KRkhZZDaffhNyAQ5/early-handoff-improve-conceptual-reasoning-diagram --- 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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