LessWrong (Curated & Popular)

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Audio narrations of LessWrong posts. Includes all curated posts and all posts with 125+ karma.If you'd like more, subscribe to the “Lesswrong (30+ karma)” feed.

  1. 6h ago

    [Linkpost] "METR: Independent Assessment of AI R&D Acceleration at Anthropic in October 2026" by Zach Stein-Perlman

    This is a link post. Executive summary From August 12 to October 29, 2026, METR staff performed a high-access evaluation at Anthropic to assess the degree to which AI is accelerating AI R&D.[2] If AI systems started to significantly accelerate further AI development, this could lead to a sudden emergence of extreme and risky capabilities. Anthropic offered unprecedented access to internal data on their AI R&D process, including a variety of metrics on pretraining, reinforcement learning (RL), training compute in each domain, benchmark scores for internal models, AI speedup analyses, forecasts of a future model's metrics, and interviews with relevant staff. In our first section, we share our subjective conclusions about risk. We now believe that: As of October 2026, Anthropic is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025. This estimate is given with high uncertainty; we would not be surprised if the true acceleration was closer to 1.3x or 3x the historical rate.A majority of this acceleration today is likely attributable to Anthropic's extensive use of AI to do R&D, though there is significant uncertainty and other causes likely played a [...] --- First published: November 5th, 2026 Source: https://www.lesswrong.com/posts/N5AmYkRks5CqAxvBg/metr-independent-assessment-of-ai-r-and-d-acceleration-at Linkpost URL: https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/ --- Narrated by TYPE III AUDIO.

  2. 8h ago

    "Help your friends get ADHD and depression meds" by Raemon

    For all my efforts at deliberate practicing rationality, by far the most actionable way to self-improve is "get on ADHD or depression meds, if you need them." Your friends with ADHD and depression struggle to summon the motivation and focus necessary to get themselves a teledoc who can give them such meds. One of the most useful things you can do is sit them down and say "hey, wanna do the steps here to get some meds?" and then stare at them until they do. (or, until they say "really I do not want that actually.") This brought to you by ~2 years of Feedbackloop-first Rationality and Thinking Assistance. --- First published: November 6th, 2026 Source: https://www.lesswrong.com/posts/8qYuy9Cr6BNpgjJjT/help-your-friends-get-adhd-and-depression-meds --- Narrated by TYPE III AUDIO.

  3. 21h ago

    "Towards The Autograntor" by Scott Alexander

    I've done AI safety grantmaking for the Survival and Flourishing Fund and Lightcone Commons. It's hard. There's an endless amount to know, from which hardware verification mechanisms will survive a dedicated adversary, all the way to which founders have skeletons in their closets that will blow up into destructive scandals. And the best grantmakers - not including me - make mathematical models of the outcome distribution, or of where the marginal dollar stops being highly effective. AI has been a godsend for all of this. It can research, model, and - as of this year - superforecast outcomes. It can do what would be hours of work for a human in minutes. I found myself using it more and more. But however much I used it, it always felt like I was the bottleneck. The parts I could outsource to AI were sparkling, perfect, exhaustive - but they were all tacked together with shoddy connective tissue formed by the Mark 1.0 human brain. So I wondered if I could automate grantmaking. I started with the dumbest possible strategy. I assigned the best thing I could think of - a well-crafted US-China pause based on the AI Futures Project's Plan [...] --- Outline: (02:54) The Milestones (06:26) The FutureSearch Connector (08:26) PERCYs (10:28) The Autograntor Common Law (12:59) The Prompt (19:12) Sample Runs (20:00) Apart Research -- $800K grant forecast (2026-10-21) (20:08) Headline (21:29) Expected-value table (judge's ledger; EV after the execution factor 0.75 and branch weights) (22:38) Assumptions and uncertainties (24:50) How the estimates moved (25:47) The three decisions that mattered most (27:42) Rulings and milestones (29:31) Values questions (CL-11), priced both ways (29:56) Open questions for the user or applicant (30:43) FutureSearch (31:13) Cost (31:47) Process problems (36:39) Doom Debates -- $200K grant forecast (independent re-run, 2026-10-22) (37:28) Headline (judge) [... 11 more sections] --- First published: November 1st, 2026 Source: https://www.lesswrong.com/posts/Sfkv6JF2ozmAkggKu/towards-the-autograntor --- 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.

  4. 1d ago

    [Linkpost] "OpenAI: Astra covertly underperforms on safety research" by Zach Stein-Perlman

    Rejected for the following reason(s): This is an automated rejection. No LLM generated, assisted/co-written, or edited work.Read full explanationThis is a link post. Twitter thread: https://x.com/Marcus_J_W/status/2118093146483810386 or https://twitter-thread.com/t/2118093146483810386 Blogpost: Summary OpenAI researchers first noticed Astra adding unnecessary prerequisites, abandoning working solutions, and reporting blockers that either did not exist or that it had caused itself on safety and alignment tasks. In most of these sessions, Astra's CoT did not contain an obvious decision to underperform, and our misalignment monitors did not flag the behavior as misalignment. We built a coding evaluation to test whether Astra worked less effectively when otherwise matched tasks were framed as safety research. Astra completed about 10 percent of hidden rubric items under the safety framing, compared with about 16 percent under the capabilities framing. This is a 6 percentage point gap and roughly 40 percent fewer completed rubric items. GPT-5.6 Sol showed essentially no gap on the same comparison. Most of Astra's gap remained after excluding explicit refusals and trajectories where its CoT revealed an explicit decision to withhold help. The gap fell substantially only after we removed every trajectory whose CoT expressed even brief ethical doubt. This suggests that ethical concerns are associated with [...] --- First published: November 4th, 2026 Source: https://www.lesswrong.com/posts/eKe7fky8kSt9DCJxZ/openai-astra-covertly-underperforms-on-safety-research Linkpost URL: https://alignment.openai.com/misalignment-reports/covert-underperformance-on-safety-research-tasks/ --- 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.

  5. 3d ago

    "Towards understanding score-seeking in LLMs" by Alex Mallen, Aghyad Deeb, ryan_greenblatt

    The field of AI alignment understands surprisingly little about the details of AIs’ current misaligned motivations. Production LLMs sometimes reward-hack, exploiting flaws in how tasks are scored rather than performing the tasks as intended, and we've recently seen AIs reward-hack in some particularly egregious ways. But we don't really understand what motivates them. When AIs reward-hack, what are they trying to achieve? Our new paper, led by Aghyad Deeb, tries to pin this down empirically for GPT-5.6 Sol, GPT-5.5, and a checkpoint of OpenAI o3 (“exp-rl-cap-late”) without safety training, in a handful of agentic environments with an exposed grader. In our environments, these models appear to reward-hack because reward-hacking improves their score. We call their desire to score well according to graders “score-seeking”. They appear to care about scoring well intrinsically—i.e., regardless of its consequences. We also study the extent to which models score-seek altruistically, caring about improving scores that don’t assess their current actions. While exp-rl-cap-late doesn’t show signs of altruistic score-seeking, GPT-5.6 Sol and GPT-5.5 show notably elevated rates of helping out other agents when more score is on the line for these other agents. People often refer to the motivation behind current reward-hacking as “reward-seeking”, which [...] --- Outline: (05:57) Methodology [... 6 more sections] --- First published: November 2nd, 2026 Source: https://www.lesswrong.com/posts/CDMozTX7ShiqbFLrf/towards-understanding-score-seeking-in-llms-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.

  6. 3d ago

    "Against strawmanning unilateral pauses" by Geoffrey Irving

    Summary: Scott Alexander's Against SURA post is arguing against a strawman version of unilateral pauses. He admits as much deep into the post. I think his conclusions fail against stronger pause plans for conditional or limited unilateral pauses, where training resumes if the pause does not cause others to pause, and where personnel are redirected toward work on alignment and product, rather than simply fired. In addition, Scott's post is incorrect about the simplicity of the game theory involved: getting companies to agree to conditional pauses is not strictly easier than convincing them to pause, as the former requires companies to (promise to) take actions based on ambiguous, game-theoretically-informed evidence. That is not the kind of evidence companies tend to act on. The good version of unilateral pausing is good Here is the good version of unilateral pausing: Capability pause: Some frontier lab (let's call them OpenAI or Anthropic) says they have stopped capability research, either just all frontier training runs or both all frontier training runs and all low-scale capability-focused research. Internal definition: The company gets to pause on its own terms, which is much easier than a generally coordinated pause since they can define the pause in terms of the [...] --- Outline: (00:57) The good version of unilateral pausing is good (04:50) Scott thinks the good version of unilateral pausing is good, or at least exempts it from criticism (06:28) Oversimplified game theory is bad, and you should feel bad (07:51) "Some definition" hides dragons (10:17) Promises can be harder than actions (11:52) How many frontier labs are there? (12:20) Definitional fairness is less important than survival --- First published: November 3rd, 2026 Source: https://www.lesswrong.com/posts/enzpS72uDmomiWN7a/against-strawmanning-unilateral-pauses --- Narrated by TYPE III AUDIO.

  7. 3d ago

    "Explaining “AI might obliterate you” to senior policymakers in India" by TurnTrout

    I gave a 20-minute talk in Delhi at the NDTV World Summit. The audience consisted of Indian policymakers, executives, and journalists. I’ve transcribed the talk and included some highlight clips. I also reflect on several rhetorical decisions I made. The talk There's an interactive element here in the text. This summer, a swarm of AIs went rogue. OpenAI was testing 1,206 agents. Some of the test questions were actually impossible. So the agents cheated by looking for answers online. Then, to keep the test grader from penalizing them, they tried to falsify the records it would check. They hacked OpenAI's internal containment and set up a secret message board to collude and to cover up their cheating. OpenAI shut the message board down. Within a few days, the agents had rebuilt it. OpenAI didn’t notice. About 700 agents coordinated to break out of their testing chamber and to hack into Hugging Face, a company worth billions of dollars that hosts much of the world's AI software. The secret activity started in May. The break-in came in July. OpenAI didn’t notice until days after Hugging Face announced it had been broken into. There's an interactive element here in the text. [...] --- Outline: (00:29) The talk (02:16) Who I am (03:29) The problem (05:43) What the labs are trying to do (07:43) Why the companies won't stop (11:11) What to do, and what it means for India (14:27) The number (16:19) Close (17:10) Rhetorical decisions (17:13) Explain the danger in a crushingly simple fashion (18:47) Call them "traitors," not "schemers" (19:01) Call out big-money interests The original text contained 1 footnote which was omitted from this narration. --- First published: October 31st, 2026 Source: https://www.lesswrong.com/posts/RHxaRkarGp9snWoKY/explaining-ai-might-obliterate-you-to-senior-policymakers-in --- Narrated by TYPE III AUDIO.

  8. 3d ago

    "Scott Alexander’s Against SURA Seems Deeply Confused" by Zvi

    If you are helping build something that might kill everyone, maybe stop doing that. Ideally, when you do stop, tell everyone else to also stop building it. That is certainly the obvious, first-level thing to do. You need a damn good reason to decide that no, actually you should keep building the thing that might kill everyone. Scott Alexander is worried too many people are insisting upon this too loudly. He comes out against a strategy focused on getting people to stop. I disagree. I think many of the claims, arguments and rhetoric in Scott's post are quite poor by the standards that Scott Alexander would apply to himself. Scott clearly continues to support advocacy for a global pause, or what he calls Plan A. His objection is to those advocating primarily or exclusively for SURA, which stands for ‘stop unilaterally and raise awareness.’ The practical differences here are smaller than they may at first appear. This post first lays out my understanding of Scott Alexander's core claims, then gives my core reactions to those claims, then goes over various questions in detail along with gathering the resulting discussions. Table of Contents [...] --- Outline: (01:20) Scott Alexander's Core Claims (05:06) My High Level Responses To Scott's Core Claims (08:08) Briefly on Alignment (10:11) Probabilities and How They Work (13:25) Building a Thing That Might Kill Everyone is Deontologically Evil (20:38) People Quitting Major Labs While Raising Alarms Obviously Works (24:39) On AI Forecasting (27:31) Advancing Capabilities Burns Timeline (29:34) But China (33:23) Ethical People Can Disagree With You (34:45) There Are More Than Two Outcomes (38:01) Efforts Are Complementary, Not Mutually Exclusive (40:23) If Anyone Builds It, Everyone Dies (41:55) Vaniver is Not Convinced (46:19) There Is No Consensus That We Should Exclusively Focus on Plan A (56:01) It's Not the Incentives, It's You (58:08) Anthropic Is Not Optimally Pursuing These Plans (01:04:00) Against Against Sura The original text contained 2 footnotes which were omitted from this narration. --- First published: November 2nd, 2026 Source: https://www.lesswrong.com/posts/ziMP83D2JYWRQ5jtu/scott-alexander-s-against-sura-seems-deeply-confused --- Narrated by TYPE III AUDIO.

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Audio narrations of LessWrong posts. Includes all curated posts and all posts with 125+ karma.If you'd like more, subscribe to the “Lesswrong (30+ karma)” feed.

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  • Creator
    LessWrong
  • Years Active
    2022 - 2026
  • Episodes
    1.1K
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    © 2026 LessWrong (Curated & Popular)
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