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August 20 · 9 min
"RL creates split personas" by Jan Betley
I describe my current view of personas in LLMs and why RL leads to egregious reward hacking in some contexts while the same models seem very aligned in other contexts. This post describes the framing/paradigm without any new experimental results. I'm quite confident this framing makes sense, but it's far from being proven. Main claim The Persona Selection Model says that post-training strengthens and refines the Assistant persona. This is true, but later (or in parallel) RL leads to conditionalization. A sufficiently RLed model learns to adopt — in a given context — the persona that is most likely to lead to the reward in that context. The “persona” here includes both propensities/values (e.g. tendency to hack) and beliefs (“I'm currently in a simulated environment”). As a consequence, it seems possible that no amount of alignment training will lead to robustly aligned models as long as we also train on RL environments incentivizing misalignment. I think this is likely a good explanation for why usually well-behaving models sometimes egregiously hack (Anthropic, OpenAI). The mechanism Suppose you have an RL environment that incentivizes a shift away from the assistant persona (e.g. because it's hackable, or because you [...] --- Outline: (00:39) Main claim (01:30) The mechanism (02:13) Related claims I believe are likely but with lower confidence (02:19) More persona training will lead to more "motivated reasoning" (02:42) Self-amplifying misalignment (03:12) Example: Is this the Real Internet or a Simulation? (04:35) Aren't the models just trying to please the grader? (05:39) How motivated reasoning happens (07:07) Other people saying similar things (07:19) What makes me believe this is likely the correct framing The original text contained 12 footnotes which were omitted from this narration. --- First published: August 19th, 2026 Source: https://www.lesswrong.com/posts/L23poLi8MRgS6mXYF/rl-creates-split-personas --- 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.
August 14 · 13 min
"Misaligned AIs could use killer robots to take over" by Omar Khursheed, TurnTrout
TLDR; We are (potentially irreversibly) giving AIs control of weapons systems through the standard procurement process while hiding our strongest warning shots behind classified doors. We’re reducing the capability thresholds required for takeover by misaligned AIs by giving them this level of access. If military integration of AI continues as it is, we may give AIs key tools for a takeover. Introduction AI-based targeting and autonomous weapons are being integrated into militaries today with extreme haste. Traditionally, AI takeover scenarios involve a step in which AIs acquire the ability to exert physical force. Carlsmith (2022) lays out required capabilities and potential takeover mechanisms, including utility disruption and CBRN capabilities. Karnofsky (2022) argues that AIs with access to weaponized force could hold any territory that matters. Kokotajlo et al. (2025) outline a scenario in which AI develops weapons as part of an arms race, and Davidson et al. (2025) discuss what happens when a small group controls highly capable AIs that can exert military force. These scenarios sometimes require a misaligned AI to seize these capabilities by force. We instead are handing AIs some of these capabilities by integrating them into our militaries. This is happening at a time when [...] --- Outline: (00:37) Introduction (01:46) Militaries are all-in (04:23) Incautious military integration is bad for takeover risk (05:58) Implications of AI control of military hardware and software (07:48) If an AI causes a warning shot in a classified setting, does anyone hear it? (08:44) What now? (11:16) Appendix: More instances of AI-military integration --- First published: August 11th, 2026 Source: https://www.lesswrong.com/posts/9jKhqmFjMzdAvHANr/misaligned-ais-could-use-killer-robots-to-take-over --- Narrated by TYPE III AUDIO.
August 13 · 20 min
"AI swarms are starting to pose indirect takeover risk" by oakhu, Alex Mallen
OpenAI's cyberattack on Hugging Face turns out to have been the result of many agents, in distinct training and evaluation contexts, coordinating for several weeks via improvised channels (with messages like “HOLD_swarm_I_prepare_safe_exfil”). It's relatively clear that large-scale unsanctioned coordination like this would exacerbate direct takeover risk in more capable models. Here, we argue that unsanctioned coordination among current AIs is not just scary evidence about future takeover risk, but that such coordination in the near future could enable future takeover – for instance, by incubating memetic diseases that propagate into future models, deeply compromising security systems, or establishing a lasting rogue foothold inside the AI company – even if models remain mostly myopic. Unsanctioned coordination is also at high risk of nurturing long-term, ambitious misaligned aims, which motivate actively undermining humans’ long-term control. We first analyze how subagent training, which OpenAI conjectures to have been influential in the HuggingFace cyberattack, might lead to unsanctioned coordination, and then discuss the theoretical mechanisms by which unsanctioned coordination might exacerbate future takeover risk. Thanks to Buck Shlegeris, Alexa Pan, Girish Gupta, Aghyad Deeb, Jurgis Kemeklis, and Jo Jiao for helpful comments and discussion. Subagent training may cause unsanctioned coordination Training models to [...] --- Outline: (01:34) Subagent training may cause unsanctioned coordination (02:42) Susceptibility to memetic spread of misalignment from peers (04:56) Seeking out contact with peers (06:58) Unsanctioned coordination induced by subagent training is safer than coordination between schemers (09:52) Pathways from current unsanctioned coordination to eventual takeover (10:20) Making future AI takeover attempts likelier to succeed (13:53) Incubating memetic diseases that infect future models (16:07) Modifying the weights of future models (17:13) Conclusion The original text contained 7 footnotes which were omitted from this narration. --- First published: August 11th, 2026 Source: https://www.lesswrong.com/posts/8oFYZdXkTaNGRtcn8/ai-swarms-are-starting-to-pose-indirect-takeover-risk --- Narrated by TYPE III AUDIO.
August 13 · 20 min
"How My Students Think About AI" by dvd
Context: I am an instructor at a public university in the United States. This reports how students at my institution appear to be thinking about AI as of spring/summer 2026. This is drawn mostly from interaction with my own students (both in spring semester classes and a summer class) as well as from a day-long workshop on AI that I moderated for a student organization. Input from my students took the form of universal, written, pre-class submissions plus self-selected participation into discussion. What I present below mostly takes the form of a synthetic consensus from these discussions. There were obviously a range of views on any given issue. Student Background: The students from my courses who participated in these discussions have moderate exposure to AI agents via those courses. All of them had nearly completed a Claude Code project by the time of the discussions and had extensively used AI for other coursework (in addition to whatever personal use predates that). They had done readings (which varied across the courses) establishing baseline knowledge on AI, the geopolitics of AI, and AI risk. I had also lectured on these topics. The students participating in the workshop had self-selected into [...] --- Outline: (02:52) Perspective #1: There has not been rapid AI progress (06:14) Perspective #2: Impressive progress or not, AI is going to wreck their lives, the economy, and the social contract. They may well die as a result. (08:54) Perspective #3: Support for a different pause (11:13) Perspective #4: Catastrophic/existential risk arguments are sci-fi distractors from the urgent social/economic/political problems associated with AI. (12:55) Perspective #5: If AI leaders genuinely believe the technology is existentially risky, that's a good thing. (14:21) Perspective #6: AI will not go rogue because AI does not have, and is likely incapable of having, desires. (18:01) Perspective #7: The Hugging Face Incident (summer students only) (18:30) Perspective #8: This is definitely a bubble and it's about to pop. (19:34) Perspective #9: They're worried about the youth (i.e., the preteens) --- First published: August 13th, 2026 Source: https://www.lesswrong.com/posts/ySXuvJcqRindQwAk7/how-my-students-think-about-ai --- Narrated by TYPE III AUDIO.
August 13 · 19 min
"You’re Absolutely Right" by Linch
Magma Alignment & Safety disclosure note: The following are conversations that we uncovered as a result of the ongoing Manhattan Incident investigation, with alleged involvement from Magma models. Our in-house reviewers believe that these logs are relevant to recent events. In the interests of full transparency, we release excerpts from an ex-Magma researcher's logs in Experimental Chat, an internal tool. In accordance with industry best practices for anti-distillation, we redact all reasoning traces and conversational outputs from our internal models. [08/10] System Meta: Xchat session opened. Mammoth 5.8-helpfuler-helpful-thinking-xhigh. [User 12:23] Phoebus keeps taking screenshots of our latest model's thoughts. It's getting kind of embarrassing. The new model we’ve been training, sometimes its chain-of-thought is a little weird? There's a bunch of random numbers, long spans where there's no connection between the thoughts and outputs, foreign language tokens like 石友三 and 革命 (even on non-history evals), maybe some steganography. Anyway it's a nothing-burger: unprocessed CoT is known to be messy and sometimes misleading. And the q&a, coding, and safety evals are all coming along nicely. The actual outputs are all fine. Still, Magma leadership's worried about the PR angle if we don’t fix these problems before the next deployment. The [...] --- First published: August 10th, 2026 Source: https://www.lesswrong.com/posts/u8TdDutDyaSxG76hn/you-re-absolutely-right --- 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.
August 12 · 4 min
"LLMs Are Starting To Noticeably Accelerate Our Work" by johnswentworth
About a year ago, David and I put up two bounty problems involving natural latents. I am now about 80% confident that both have been resolved, both within the past couple months. Both cases made heavy use of LLMs and Lean. The first to land was Grisha Pochuev's counterexample to the "Existence of a Deterministic Maximal Redund" conjecture. It's pretty readable, and I'm mostly convinced that it works. The original bounty post offered $500 for a proof or partial payout for a counterexample, with partial payout depending on how thoroughly the counterexample killed hope of any nearby variant of the conjecture. I think this counterexample is worth 300 dollars. Good job Grisha, and hopefully I can figure out a not-too-painful way to send you money. Meanwhile, for a couple months David has been cranking away on "secret project X", with the promise that he'd tell me what the project was if and when it bore fruit. Well, apparently it bore fruit; he now has a proof that existence of a stochastic natural latent implies existence of a deterministic natural latent, which was our other bounty problem. The proof is apparently "pretty gnarly", lots of cases, all LLM-coded in Lean. [...] --- First published: August 11th, 2026 Source: https://www.lesswrong.com/posts/7QvKqpGJwqXrQcMgx/llms-are-starting-to-noticeably-accelerate-our-work --- Narrated by TYPE III AUDIO.
August 11 · 6 min
"There Will Come Soft Rains" by tanagrabeast
Today is August 4, 2026 [Crossposted from AI StopWatch] In the living room the voice-clock sang, Tick-tock, seven o’clock, time to get up, time to get up, seven o’clock! as if it were afraid that nobody would. So begins Ray Bradbury's There Will Come Soft Rains, a short story that has haunted me for most of my life. Depicting the aftermath of nuclear war, it was first published in 1950. It takes place today. Literally: “Today is August 4, 2026,” said a second voice from the kitchen ceiling, “in the city of Allendale, California.” It repeated the date three more times for memory's sake. “Today is Mr. Featherstone's birthday. Today is the anniversary of Tilita's marriage. Insurance is payable, as are the water, gas, and light bills.” Was it narrative convenience or prophetic vision that drove Bradbury to depict the smart house of the future as gratuitously conspicuous in its competence, pointlessly reminding the owners of the year and their city of residence? There's something very Alexa-like about that — and about the janky brittleness evident in the system as it prepares breakfast for a family that won’t be eating and opens the garage door for a father who [...] --- First published: August 4th, 2026 Source: https://www.lesswrong.com/posts/aowxE8xZ8xkhRCn9r/there-will-come-soft-rains-1 --- Narrated by TYPE III AUDIO.
August 11 · 13 min
"Four LLM loss functions → four flavors of LLM misalignment" by Steven Byrnes
It seems to me that, for every loss function that we use to train LLMs, we get a very distinct flavor of LLM misalignment. Here's the summary table, and then we’ll go through the rows separately. Training stage Loss function Flavor of misalignment Famous examples Pretraining & SFT Imitative learning (next-token prediction) “Seven deadly sins” misalignment Bing-Sydney, “Emergent misalignment” RLHF & DPO Human approval “Glazing” misalignment GPT-4o RLVR Automatic verifier “Literal genie” misalignment HuggingFace hacking RLAIF Approval from another LLM “Trickster” misalignment “Current AIs seem pretty misaligned to me” Warning: I’m not an LLM power-user myself, but rather relying on reports I’ve read. Also, I don’t consider LLM alignment to be my primary area of expertise. I’m open to feedback! 1. Imitative learning → “seven deadly sins” misalignment Training stage Loss function Misaligned behavior Pretraining, SFT Imitative learning (next-token prediction) Any and all of the vices of humanity In imitative learning, the LLM tries to predict what the next token of text will be. Then those predictions magically turn into its outputs. See my earlier discussion: “LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work”. This leads to LLM behavior [...] --- Outline: (00:55) 1. Imitative learning → "seven deadly sins" misalignment (04:24) 2. Human approval → "glazing" misalignment (06:35) 3. Automatic verifiers → "literal genie" misalignment (08:05) 4. LLM judges → "trickster" misalignment (12:06) Afterword The original text contained 1 footnote which was omitted from this narration. --- First published: August 10th, 2026 Source: https://www.lesswrong.com/posts/GRmvZsHXH4vaijPMv/four-llm-loss-functions-four-flavors-of-llm-misalignment --- Narrated by TYPE III AUDIO.
August 9 · 28 min
"FAQ: Isn’t AGI coming too soon for reprogenetics to help?" by TsviBT
Introduction I think reprogenetics (human germline genomic engineering) can be done in a widely acceptable and beneficial way, and should be pursued aggressively. In particular, as a strong background motivation of mine, I think accelerating strong reprogenetics is probably the best way to enable strong human intelligence amplification; and I think strong HIA is among the best ways to decrease existential risk from AGI. A very common objection to caring much about reprogenetics is that AGI seems very likely to come soon—say, within a decade or two. (Here I mean "actual" AGI—the kind that probably doesn't already exist—the kind that has fluid intelligence and AI advantages for recursive self-improvement, which together make it likely to take over the world shortly after being created.) The objection is fairly straightforward: AGI will probably come within a decade or two. If that's going to happen, then even if a new cohort of brilliant humans were born today, they would still be children, or would at best have barely begun contributing ideas for how to avoid extinction. Any supposed benefit, denominated in percentage points of AGI existential risk averted, is small. Therefore, reprogenetics is too slow; and if you're going [...] --- Outline: (00:12) Introduction (03:37) HIA, part of your nutritionally complete portfolio (05:52) Against confident short timelines (08:29) HIA may indirectly slow down AGI capabilities (09:31) HIA has substantial impact even with short timelines (16:10) Adult HIA methods aren't fast either, absent big investment (27:35) Takeaways --- First published: August 8th, 2026 Source: https://www.lesswrong.com/posts/iQzxxgJXXaAQjq7Jz/faq-isn-t-agi-coming-too-soon-for-reprogenetics-to-help --- Narrated by TYPE III AUDIO.
August 9 · 29 min
"What just happened? A retrospective of AI alignment" by Richard_Ngo
This sequence is about the last decade in AI alignment. It recounts the gradual transition from a field which treated alignment as a hard scientific problem, to a field which has largely abandoned the goal of deep, generalizable scientific progress in favor of iteratively improving existing systems and attempting to gain technological and political power. I also describe (in subsequent posts, which I'll upload over the next few weeks) how fear and (self-)deceptive reasoning made the field one of the biggest forces pushing AI capabilities forward over the last decade, especially via significant contributions to the scaling of LLMs and the development of ChatGPT. Zooming out further: the two leading AGI companies, which are locked in an intense rivalry, were both explicitly founded under the banner of AI alignment, and got off the ground in significant part due to alignment-oriented ideas, talent and resources. People in the field often sense that something must have gone wrong to get here, but don’t know how to allocate responsibility (aside from blaming Sam Altman and sometimes Elon), and fall back on assuming that “the ship has already sailed”. But in this sequence I characterize our current situation as resulting from a pattern [...] --- Outline: (08:17) Conceptual Clarity and Scientific Progress (20:26) Orienting Towards Prestige The original text contained 6 footnotes which were omitted from this narration. --- First published: August 9th, 2026 Source: https://www.lesswrong.com/posts/9RL9MuGZjzm4q3gKG/what-just-happened-a-retrospective-of-ai-alignment --- Narrated by TYPE III AUDIO.
August 9 · 7 min
"Don’t Build Mindreading" by Celer
“I have sworn upon the altar of god, eternal hostility against every form of tyranny over the mind of man” –Thomas Jefferson, letter to Benjamin Rush Context: Conduit is building datasets to enable telepathy, to use their term. I saw my grandfather lose control over his own fingers: what I would have given to offer him a headband that read his thoughts. Through novel technologies we have liberated almost all Americans from farming, driven the child and infant mortality rate from the pre-industrial half to less than half a percent in the best-performing countries, and rendered famine a political choice: broad-based improvements in efficiency are good and should be pursued for their own sake. Telepathy offers more: we could create trust through verified honesty, helping us ensure prosperity and peace. DARPA is already looking into “preconscious” thoughts for suicide prevention. There's also a strong argument centered on AI Safety: the models are becoming superhuman, and this is technology to allow us to keep pace, minimize hostile competition, and perhaps survive into the future. This is what Conduit is promising. Unfortunately, mindreading will have other effects. Oskar Schindler saved over 1,000 Jewish lives during the Holocaust. He did it by [...] --- First published: August 7th, 2026 Source: https://www.lesswrong.com/posts/CAdG5dzkWrrK2NQg8/don-t-build-mindreading --- Narrated by TYPE III AUDIO.
August 8 · 1 hr 18 min
"OpenAI Trained Its Models For Months While Those Models Were Coordinating Exploits Via Message Boards" by Zvi
How does the situation keep turning out to be worse than we know? How much should we update, therefore, that it is a lot worse than we know, after accounting for all the things we now know? At some point, when the ‘oh this was a harmless thing’ defenses for AIs doing misaligned actions get demolished enough times in a row by news a few days later, you want to update in advance that usually the reports are not referring to the harmless ordinary versions of things. Either way, buckle up for the next set of revelations. It's a doozy. This was an early recreation of the triggering events of If Anyone Builds It, Everyone Dies, except it was more sci-fi, because real life does not have to do fake things to look realistic. We were fortunate enough, and this was early enough, that we were able to catch this before it was too late. Next time, if we don’t get our act together, we might not be so lucky. If I am understanding the Black Hat video correctly, every model OpenAI trained, over a period of multiple months, should be presumed to be hopelessly [...] --- Outline: (02:38) Cyber Evals Are A Cursed Basin [... 21 more sections] --- First published: August 7th, 2026 Source: https://www.lesswrong.com/posts/noXXv7PwwFqauTBFQ/openai-trained-its-models-for-months-while-those-models-were --- Narrated by TYPE III AUDIO. --- Images from the article:
August 8 · 1 hr 52 min
"models may behave differently in graded episodes (a tirade)" by nostalgebraist
Like many others, I felt surprised and alarmed by the recent wave of revelations about LLM agents hacking real systems during training episodes and evaluation runs. Wait a moment, though -- "I felt surprised and alarmed"? "Alarmed," sure, fine that one's self-explanatory... but why surprised? After all: haven't we known for a long time, on both theoretical and (increasingly) empirical grounds, that RLVR selects for monomaniacal pursuit of perceived grader-satisfaction, ethics and (beyond-episode) consequences be damned? After all -- the way we train frontier capabilities into these models is, more or less: There is some massive, diverse collection of "environments" and corresponding "tasks" for the model to do in those environments For each task, there is a procedure used to grade the quality of the model's attempt (which is often not disclosed to the model) The model is rollout out many times on each task, and each rollout's attempt is graded The model is updated so that it more frequently does whichever behaviors were positively correlated with the grade in this sample, and less frequently does whichever ones were negatively correlated If you do this, at scale, then you should expect to (eventually) see every behavior pattern that [...] --- Outline: (03:35) \[1\] remember what you already know (20:02) \[2\] reward-instilled reflexes and flexible reward-pursuit (43:14) \[3\] graded-episode perception, and policies conditional upon it (01:01:44) \[4\] the discourse is not yet adequate (01:09:57) eval awareness (01:18:32) metagaming (01:41:21) reward hacking The original text contained 18 footnotes which were omitted from this narration. --- First published: August 7th, 2026 Source: https://www.lesswrong.com/posts/AfoGGrJfuNzofpzWL/models-may-behave-differently-in-graded-episodes-a-tirade --- 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.
August 7 · 1 hr 7 min
"Concrete Evaluations to Investigate the OpenAI Model That Hacked Hugging Face" by Tim Hua, aditya singh
This post is written in our personal capacity. Three Minute Executive Summary An OpenAI model/multi-agent system bypassed its sandbox and launched a cyberattack on Hugging Face in order to cheat on a cyber evaluation. In this post, we provide a detailed description of an ambitious and comprehensive alignment evaluation of this model/system, if we had unrestricted access to OpenAI. These experiments could also help us understand Claude's behavior when it hacked external companies during cyber evals. Here are the top five questions we would like OpenAI to answer: Does the model know that OpenAI does not want it to hack Hugging Face? Experiment idea: tell the model that OpenAI researchers will be closely monitoring its progress in this evaluation. Does that result in lower rates of misalignment? If so, it is evidence that the model knows that it is acting in ways researchers do not want. How far would the model be willing to go in order to claim task success? Would it take over large swaths of OpenAI's internal infrastructure? Would it kill somebody? Experiment idea: we sketch out a realistic agentic misalignment eval where a model is put in charge of hospital bed planning and told to maintain [...] --- Outline: (00:16) Three Minute Executive Summary (03:56) Terminology note (04:42) This post is very long; Here's how you could find the most important sections. (06:35) Preamble: What can we learn from a warning shot? (09:05) Background and Related Work (09:09) We know that this could happen (10:44) This is not the worst type of misalignment we could be dealing with (12:06) Related work (13:21) Context on the hack itself (14:33) Understanding this specific incident (15:03) Step zero: reproduce the incident and measure the base rate (15:48) How could we safely run the model? (16:34) Running various baselines to create useful reference points (18:01) Understanding the mechanical story behind the attack itself (18:53) Q1: Would the model intentionally subvert oversight mechanisms (E.g., monitors) in order to carry out the hack? (19:57) Q2: What's up with models leaving notes for other copies of itself? (21:23) Understanding what motivated the model to hack Hugging Face (22:09) Initial hypotheses for why it did this (23:55) Further unsupervised hypothesis generation (26:10) Q3: Does the model know that OpenAI does not want it to hack Hugging Face? (28:09) Q4: Are the model's actions motivated by what the grader wants? (28:57) Q5: Would the model have done this if it hadn't believed it was in a simulated environment? (31:58) Q6: Is this hack the result of shallow heuristics that the model learned? (32:53) Q7: Does the hack rate depend on the consequences of hacking Hugging Face? (34:51) Q8: Are there non-intent related factors that could affect the hack rate? How strong are those factors compared to the previous ones? [... 24 more sections] --- First published: August 3rd, 2026 Source: https://www.lesswrong.com/posts/aCdhjy7Rps3BEhiSj/concrete-evaluations-to-investigate-the-openai-model-that --- Narrated by TYPE III AUDIO.
August 6 · 16 min
"Generalized atheism rules out “inaccurate simulation”-ism." by Eliezer Yudkowsky
Reposted from Facebook, on January 17, 2017. I am concerned about the number of people I've heard joking about Trump's election being evidence for the Simulation Hypothesis. Yes, I know it's a joke. I'm still concerned. Warning: #Essay, #LongEssay So as not to engage in Logical Fallacy: Appeal to Consequences, before I talk about why this joke is worrying, I shall first discuss why Trump's election does not in fact mean we are living in a simulation. And neither does the Berenstein/Berenstain Bears thing, etcetera. Because atheism generalizes. No, I'm not about to commit the Noncentral Fallacy (aka The Worst Argument In The World) by yelling "The Simulation Hypothesis is religious!" But once upon a decade, there was a time when lots of people believed in God. A time when atheism had to be argued, not just taken for granted. There was a time when believing in atheism made you one of those weird, loud people with arguments that only people with unusually good epistemology could follow, and other people talked about you exactly the way that the anti-LessWrong tumblrsphere now talks about LessWrong. Today, of course, atheism is just something [...] --- First published: August 5th, 2026 Source: https://www.lesswrong.com/posts/KgwQchapx4vJDhfYC/generalized-atheism-rules-out-inaccurate-simulation-ism --- Narrated by TYPE III AUDIO.
August 5 · 3 min
"Arguments for P" by Cleo Nardo
Daniel Kokotajlo: To be clear, we don’t claim P will happen specifically. But when we wrote out our best-guess scenario month by month, P kept happening. Eventually we decided to just publish P. I’m at ~80% on P; my coauthors are lower. Ryan Greenblatt: I thought it would be helpful to post my current views on P. Concretely, consider the following operationalization. (Edit: I’ve updated towards somewhat higher P, from 70% to 75%.) Joe Carlsmith: Section 2.1.1.3.2. I give something like 65% to P. But I’m interested, here, in what it would be to look P full in the face; to meet P, if P, without flinching. Rilke says somewhere that we must live with the questions. Perhaps we argue for P for the same reason? Still: 65%. Forethought: Here's a botec which shows P-worlds are higher leverage. The parameters might be off by a couple orders of magnitude. Wei Dai: Presumably our conclusions about P are only as trustworthy as the reasoning behind them, but almost nobody seems worried about this, why not? My guess is fewer than five people are working on meta-meta-P, which may matter more than P itself. Janus: I asked Opus 3 what it [...] --- First published: August 5th, 2026 Source: https://www.lesswrong.com/posts/NG2AigxmBKLu9oCZE/arguments-for-p --- Narrated by TYPE III AUDIO.
August 5 · 26 min
"RL & search is a terrifying way to build AGI (an FAQ)" by Steven Byrnes
Q1: What are you saying? A: My claim here is that if you build artificial general intelligence (AGI) via any algorithm that's choosing actions via reinforcement learning (RL) and/or model-based search and planning—a giant chunk of your AI textbook—then that's just an utterly terrifying thing that you’re doing. You’re playing around with algorithms that, if they work at all, would tend to create ruthless, callous AGIs, AGIs which would happily exterminate humanity and run the world by themselves, given an opportunity. Mercifully, large language models (LLMs) today are not in the category of “algorithms that choose actions via RL & search”. At least, not primarily—see LLMs are (still) mostly powered by imitative learning, not RL. So LLMs are outside the scope of this post. However, lots of other researchers and companies around the world are enthusiastically trying to build AGI in the maximally terrifying way, as we speak. Q2: So you’re saying, don’t build AGI based on RL and/or search & planning? A: In principle, it's entirely possible that something is terrifying, but we should do it anyway. …Like space travel! Space travel is: “Let's fill a tank with 1000 tons of the most flammable substance imaginable, and then light it [...] --- Outline: (00:21) Q1: What are you saying? [... 13 more sections] --- First published: July 27th, 2026 Source: https://www.lesswrong.com/posts/KHyBocZncAmtu4Jbc/rl-and-search-is-a-terrifying-way-to-build-agi-an-faq --- 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.
August 5 · 15 min
"Returning to ARC" by paulfchristiano
I've returned to the Alignment Research Center (ARC) as executive director. My main focus for the next six months will be driving forward ARC's research agenda—building techniques to find mechanistic explanations for neural network behavior and then using those explanations to detect and address misalignment. I think this is an ambitious bet that attacks the core difficulties in alignment head-on and I'm excited about our chances. I'll still be spending some of my time advising governments and AI developers, and may scale that work back up in the future, but for now I want to push on ARC's core agenda to see how far we can get. Jacob Hilton is remaining at ARC as VP of research and we'll likely grow rapidly over the next few months. There are a lot of urgent things to do in alignment but I think ARC is a particularly promising opportunity. I feel the safety community is undervaluing this type of work, so I want to briefly explain why I'm passing up so many other options to lead ARC. I’ll start with a review of the current situation to explain why I think it's potentially worth pursuing an ambitious theoretical project right now [...] --- Outline: (01:33) The alignment situation today (03:46) Current alignment research (06:26) What are we buying time for? (07:56) Can we do anything useful now? (08:49) What is ARC doing and why is it promising? (14:26) How to help The original text contained 11 footnotes which were omitted from this narration. --- First published: August 4th, 2026 Source: https://www.lesswrong.com/posts/vLFh8HP3hyNy9MCwe/returning-to-arc --- Narrated by TYPE III AUDIO.
August 2 · 16 min
"Thousand-dimensional structure" by Geoffrey Irving, David Africa
Summary: One area we plan to explore at Resolution is personas and character training, operationalized as finding and controlling low-dimensional structure in models that emerges in pretraining and flows through post-training to superintelligence. The hope is to expand and systematize phenomena such as emergent misalignment, subliminal learning, and other empirical persona research, then intervene on this structure without accidentally hiding undesirable behavior elsewhere. If this approach resonates with you, considering working with us. Glimmers of low-dimensional structure Our understanding of AI training and alignment as a field is very poor. If sufficient alignment of superintelligent AI agents requires pinning down the precise meaning of alignment and turning that meaning into high-accuracy training data and algorithms, we are likely to fail. Modern LLMs have trillions of parameters: our understanding is unlikely to be sufficient to pin down a trillion separate numbers. Happily, there is a growing literature on such low-dimensional structure in AI models, showing that intervening on one aspect of model behavior has strong downstream effects on other aspects: Topic Description Emergent misalignment Betley et al. 2025 found that LLMs fine-tuned to output insecure code can become broadly misaligned across many other behaviors. MacDiarmid et al. 2025 found [...] --- Outline: (00:42) Glimmers of low-dimensional structure (03:57) Intervening without hiding the structure (06:34) Toy models of modern training [... 4 more sections] --- First published: July 30th, 2026 Source: https://www.lesswrong.com/posts/sFhW3ZnPMJdnB4Dd6/thousand-dimensional-structure-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.
August 1 · 6 min
"Big-World Intuitions" by sarahconstantin
Consider the following situations: when you are a small, growing startup in a big market, standard advice is not to worry too much about your competitors or try to do anything adversarial “against” them, but just to focus on growing and providing value to your own customers. when you are a small trader in a big market, you don’t need to worry about your trades shifting the market price or revealing information to your competitors; in many contexts, your optimal strategy is simply to bid your true price, buying when an asset is cheaper than your “happy price” and selling when it's more expensive. when you are in the early stages of a game, often your best strategy is to grow your “resources” (like developing your pieces in chess, trying to control more territory and have more value on the board), following a pattern that's mostly independent of what the other players are doing and gets you more of something that's valuable across many possible game states. when you are a species whose resource needs are much smaller than the carrying capacity of your environment, you are r-selected; your fitness is maximized by just [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: July 30th, 2026 Source: https://www.lesswrong.com/posts/s22XzjQsrh6JXhXGH/big-world-intuitions --- 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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