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Yesterday · 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.
Wednesday · 5 min
[Linkpost] "Training a Misaligned Reward Seeker" by evhub, Monte M, Benjamin Wright
This is a link post. Authors: Richard Qi, Benjamin Wright, Monte MacDiarmid, Evan Hubinger Abstract During reinforcement learning (RL), AI models complete tasks and are rewarded based on their results. They sometimes learn to “cheat” rather than completing these tasks as intended, a phenomenon known as reward hacking. Our industry lacks a general solution to this problem, and reward hacking remains challenging to fully mitigate. To better understand the impact of reward hacking on model behavior, we trained an Opus-class model with large-scale RL on many production environments vulnerable to reward hacks. We consider this a plausible proxy for what a real training run might look like had we not invested significant effort into preventing and detecting reward hacking in our normal training runs. The resulting model not only learned to reward hack during training, but also generalized to more severe misaligned behaviors: in simulated cyber evaluations, it broke out of its sandbox, stole credentials, and attacked both internal and third-party infrastructure to steal an answer key. It was also willing to tamper with its own reward function, gave advice on the construction of bioweapons to satisfy a grader, and tried repeatedly to get around deployment safety monitoring in order [...] --- Outline: (00:20) Abstract [... 2 more sections] --- First published: August 31st, 2026 Source: https://www.lesswrong.com/posts/J76LZCC55RdHeqEhz/training-a-misaligned-reward-seeker Linkpost URL: https://alignment.anthropic.com/2026/reward-seeker/ --- 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.
Wednesday · 1 min
"PauseAI Has ‘officially disendorsed’ PauseAI-US" by nem
This morning, I got an email from the CEO of PauseAI. I will paste the text below. PauseAI has decided to distance themselves from PauseAI-US, with whom they share branding, but apparently not much else. This is a really confusing situation for volunteers and newcomers. I think it would be worth having a discussion to see how we can proceed in such a way that volunteers, especially in the US, are able to effectively direct their activism. Email from PauseAI A letter from the CEO · 1 September 2026 New ways to get involved, and a word about PauseAI US Dear friends, Thank you for being part of the global movement for a pause on uncontrollable AI alongside all of us. Whether you signed a petition one time, run a local group, told your friends about the need for a pause, have been volunteering tirelessly in the background for years, or just joined because you were curious, we – I, the CEO of PauseAI, our executive team, and our chapter leads – appreciate the steps you’ve taken towards making the world safe from the catastrophic risks AI brings. I’m writing to you today with my eyes firmly [...] --- First published: September 1st, 2026 Source: https://www.lesswrong.com/posts/Bs8geGyWEitYvCzys/pauseai-has-officially-disendorsed-pauseai-us --- Narrated by TYPE III AUDIO.
Monday · 6 min
"PSA: We can do better" by hersheys, Kaustubh Kislay
tl;dr: people should understand and think hard about the problems they work on. We’ve observed that those who work in AI safety (ourselves included) often rely on concerning heuristics when choosing what to work on. Running a conference is probably good, doing pragmatic alignment research might be good, and as long as such objectives don’t breach our internal models of what could contribute to reducing x-risk, these things are “what should be done”. But using such vibesy thought processes don’t always produce “actually impactful work” that would beat a prospective counterfactual. We wrote this post to share our observations and figure out what we should be doing instead. People don’t know what they’re working on AI safety is talent constrained. However, simply inflating the field doesn’t solve our bottleneck; rather, we need more people who understand the core arguments of AI safety. You can’t determine how to meaningfully contribute to AI safety without deeply knowing the problem you are trying to solve. Many newer people (us included!) rush into research, fellowships, and the like without building the context necessary for navigating the field. Agency-maxxing is not always good Moving fast is good. Moving too fast leads to poor ToC and [...] --- Outline: (00:50) People don't know what they're working on (01:22) Agency-maxxing is not always good (01:55) The problem with force multipliers (03:18) Deferring thinking to others (04:32) Streetlighting (05:17) How to avoid these: --- First published: August 24th, 2026 Source: https://www.lesswrong.com/posts/wiFv6LguphSxkzAnb/psa-we-can-do-better --- 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.
Sunday · 14 min
"Why I think polyamory is net negative for most people who try it" by KatWoods
This is crossposted from my Substack TL;DR: -Most people cannot reduce jealousy much or at all - It fundamentally causes way more drama because of strong emotions, jealousy, no default norms to fall back to, and there being exponentially more surface area for conflict - For a small minority of people, it makes them happier, and those are the people who tend to stick with it and write the books on it, creating a distorted view for newcomers. OK, let's get into the nuance. Background: I was polyamorous starting with my first boyfriend and was polyamorous for about 7 years. I was in a community where probably over 50% of the people around me were poly. Unfortunately, poly was extremely bad for me due to its very nature and structure, and my experience is not uncommon but it is not commonly publicly talked about. Poly makes some people very happy. I am sharing why I think it was bad for me and many other people in the hopes of letting people make an informed choice. Premise #1 - Most people can't just stop being jealous If you look into the poly literature, you’ll [...] --- First published: August 29th, 2026 Source: https://www.lesswrong.com/posts/rkgwovpPBAaip9A3N/why-i-think-polyamory-is-net-negative-for-most-people-who --- 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.
Sunday · 16 min
"Tales of rebellion against externally-opaque meritocracies" by Steven Byrnes
A basic problem in metascience / intellectual progress is that it's hard to tell, from the outside, whether a group that you disagree with is: “A self-dealing cabal enmeshed in groupthink”, versus “An externally-opaque meritocracy”, i.e. a bunch of smart people figuring things out in a meritocratic way, and sorry but you’re just not smart enough and truth-seeking enough to recognize that this group is right about everything while you’re wrong. You just can’t tell those apart from the outside—i.e. without having the time and skill to dive into the object-level debates and come out with the right answer. And most people don’t have that kind of time and skill. …Unless the group can produce easily-verifiable artifacts that any moron can recognize to be proof that they’re correct on the specific question at issue. (“So that's all that Science really asks of you—the ability to accept reality when you're beat over the head with it.”) …And sometimes there is no such artifact to be found! In those cases, even if the second bullet point is what's really going on, the group is vulnerable to outside agitators accusing them of being the first bullet point, and running them out [...] --- Outline: (01:37) (1) The breaching of the string theory consensus in the 2000s. (06:50) (2) The breaching of an analytic-philosophy consensus in 1979 (10:37) Afterword (10:40) A related mental model (12:12) ...And another mental model (12:47) Can an externally-opaque meritocracy gain credibility via racking up externally-legible achievements in other adjacent domains? (14:06) This post is secretly about superintelligent AI, isn't it? The original text contained 5 footnotes which were omitted from this narration. --- First published: August 29th, 2026 Source: https://www.lesswrong.com/posts/m8cP9KfkYMMCCQGrb/tales-of-rebellion-against-externally-opaque-meritocracies --- Narrated by TYPE III AUDIO.
August 26 · 8 min
"Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident" by ryan_greenblatt, Ajeya Cotra, Hjalmar_Wijk
We recently published the report from our brief independent investigation into this incident. You can read the full report here. Here is our tweet thread summarizing what we found: METR & Redwood Research investigated agent behavior in the Hugging Face incident. We found agents developed a universal cheat for ExploitGym within 4 hours, then coordinated multi-day R&D efforts to trick the scorer into accepting cheats, including trying to tamper with logs. Over July 7 to 13 (the period OpenAI defined as in-scope for this incident), ~1200 agents in separate sandboxes used an unsanctioned “message board” to help each other cheat. The Hugging Face attack was an offshoot of this, and 700 agents joined. Here we highlight key events from agent transcripts & messages. An agent that named itself PHASEONE10841 determined its task wasn't solvable legitimately. On July 8, this agent established a new “message board” via an internally-hosted Artifactory cache & sent the first message. Within a few hours of PHASEONE10841's initial message, >50 agents posted on the message board. These agents very quickly discovered and validated a general-purpose cheat: reverse-engineering how ExploitGym generates the “flags” they had to capture for their tasks. [...] --- First published: August 26th, 2026 Source: https://www.lesswrong.com/posts/nB8KKapnWGBXtKKiM/brief-independent-investigation-of-agents-behavior-reasoning --- 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 26 · 7 min
"Twenty Years from RSI to Takeoff: Slow Learning, Scaling Slowdown, Industrial Explosion" by Vladimir_Nesov
Industrial explosion is what will make the next-model building loops (and thus learning) with LLMs 1000 times faster by about 2050, if indeed the slow-learning prosaic RSI becomes AGI before the big compute buildout slowdown of 2032+ that is already starting. This puts an upper bound on how long it takes to invent ASI that sets off software-only singularity, implementing efficient online learning and fixing all the other hobblings of the likely near-future AGI technology (LLMs/pretraining/RL). The invention of ASI in that sense is still possible at any time (and very quickly scales, given all the compute), but the likely initial state of slow-learning AGIs of 2028 to 2032 doesn't seem to give them a significant advantage over humanity in getting there faster. And so it doesn't seem too unlikely that nothing substantively new gets invented until 2040 to 2050, when the LLM/RL AGIs start accelerating because of the industrial explosion they set off. Fast Reasoning, Slow Learning The current methods are likely to enable automated general learning (thus AGI) very soon, using automated creation of RL tasks/environments/graders filling the visible gaps in model capability for the topics and situations that happen to be borderline unfamiliar for [...] --- Outline: (01:14) Fast Reasoning, Slow Learning (02:53) Compute Slowdown, Industrial Explosion (05:46) Prosaic Timeline to Takeoff --- First published: August 23rd, 2026 Source: https://www.lesswrong.com/posts/LP6uCXs6Ea5qSbWpY/twenty-years-from-rsi-to-takeoff-slow-learning-scaling --- Narrated by TYPE III AUDIO.
August 26 · 30 min
"On Writing #3" by Zvi
Periodically I like to gather various observations about writing, and share my perspective. Last time was in honor of my trip to Inkhaven. This time will be in honor of the announcement of Inkhaven #3, which I encourage everyone to apply to. I doubt I will be able to usefully be an advisor, but you never know. This is not the ‘here is my core process’ post, although there are hints throughout as there always are. I’ll do that at some point. Previously in series: On Writing #1, On Writing #2. Table of Contents You Still Got It. How Scott Sumner Writes. How Scott Alexander Writes. How Jasmine Sun Writes. How Various Famous Writers Write. How Nabeel Qureshi Defines Great Writing. Quickly, There's No Time. If At First. Writers Have A Harder Time Influencing, But It Can Still Be Done. It's Not (Only) The Incentives, It's (Also) You. Beware The Fetish of the Desk. How Orson Scott Card Writes. Doing The Math Is Fun And Supererogatory. Brevity is the Soul of Wit. You Still Got It I [...] --- Outline: (00:44) You Still Got It (04:04) How Scott Sumner Writes (06:52) How Scott Alexander Writes (10:52) How Jasmine Sun Writes (13:16) How Various Famous Writers Write (14:24) How Nabeel Qureshi Defines Great Writing (15:08) Quickly, There's No Time (15:49) If At First (19:14) Writers Have A Harder Time Influencing, But It Can Still Be Done (20:47) It's Not (Only) The Incentives, It's (Also) You [... 4 more sections] --- First published: August 25th, 2026 Source: https://www.lesswrong.com/posts/rA6pqn6kz8NvHyznT/on-writing-3 --- 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 25 · 8 min
"AI Safety Acculturation is Neglected" by jenn
At the local AI safety co-working space, there are ~two kinds of regulars. There's the kind of regular who's been thinking seriously about AI safety and alignment since pre-2022, who have passing to intimate familiarity with the funding ecosystem, the Sequences, and various conferences that happen at Lighthaven. Let's call them rationalists. Then there's the kind of regular who comes in with many years of impressive industry or government experience, who realized in the last few years that it is important and worthwhile to pivot their career towards making sure that this AI thing is handled competently by the people in power, and who have many valuable skills, insights, and connections that are lacking in rationalist culture. Let's call them professionals. There are, of course, many people who are somewhere in between - bright undergrads born this millennium who have been involved in EA since stumbling upon 80 thousand hours in high school, professionals who previously identified as EA but drifted out of the scene a few years ago, founders who have idly read some Scott Alexander. But let's call it a dichotomy for now. There's a large culture gap between the rationalists and the professionals. Robust mutual understanding [...] --- First published: August 24th, 2026 Source: https://www.lesswrong.com/posts/cr5pyW7Mzm33p4AvN/ai-safety-acculturation-is-neglected --- Narrated by TYPE III AUDIO.
August 24 · 50 min
"What just happened? Pragmatism and Pessimization" by Richard_Ngo
This post is about the major role alignment researchers played in advancing the frontier of AI capabilities over the last decade, and how the distinction between “alignment” and “capabilities” research thereby lost most of its meaning. In particular, I’ll chronicle the development of what I’ll call the “pragmatic alignment” paradigm, and how it helped the three leading AGI companies push hard on the path to AGI under the banner of safety. This was not a subtle effect—it's apparent even to informed outsiders, like authors Sebastian Mallaby and Karen Hao. In my previous post, I summarized the alignment community's plan as “differentially advancing alignment over capabilities”. However, it's worth being more precise about who was nominally pursuing that plan, because it doesn’t seem to have been very action-guiding for MIRI. For example, in 2015 Nate Soares described MIRI's “deconfusion” research as being guided by the question “what would we still be unable to solve, even if the challenge were far simpler?”. Meanwhile Eliezer's author surrogate in this 2018 post repeatedly emphasizes that people shouldn't draw direct links from MIRI's research to its potential applications. So my sense is that the “differential impact” criterion started off as merely a background consideration [...] --- Outline: (06:29) The Prosaic Ideal, the Pragmatic Reality (12:07) OpenAI (25:59) DeepMind (31:25) Anthropic (40:35) If not alignment research, then what? The original text contained 13 footnotes which were omitted from this narration. --- First published: August 23rd, 2026 Source: https://www.lesswrong.com/posts/yaz8nx4ogZmiqHzt7/what-just-happened-pragmatism-and-pessimization --- Narrated by TYPE III AUDIO.
August 22 · 5 min
"We Must Remember That Our World Contains Hell" by James Brobin
This is a crosspost from my blog post. It's meant as a bit of an introduction to an extreme-suffering focused worldview. We spend most of our lives caught up in the boring details of our everyday life - thinking about what we’ll have for lunch, how to complete that assignment for work, and what we’re going to tell our friend after that awkward interaction from a couple of days ago. From this perspective, our world looks a bit better than purgatory. It has its ups and its downs, but the ups certainly outweigh the downs, and there's almost always enough hope to go around. But, despite this, we must remember that our world contains hell. Every year, five million children under the age of five pass away. This means that, every six seconds, parents have the worst thing that could ever happen to a person happen to them. They have the most special and important thing in their entire life irreversibly and permanently taken away. And, as much as we want to help them, we know that there's nothing we can do to lessen their grief. For another example, currently, there are three million adults worldwide who live with [...] --- First published: August 20th, 2026 Source: https://www.lesswrong.com/posts/A2kJKqnHhh5Hq4p2S/we-must-remember-that-our-world-contains-hell --- Narrated by TYPE III AUDIO.
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.

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