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Audio narrations of LessWrong posts.

  1. -4 Ч

    “That Mad Olympiad” by Tomás B.

    "I heard Chen started distilling the day after he was born. He's only four years old, if you can believe it. He's written 18 novels. His first words were, "I'm so here for it!" Adrian said. He's my little brother. Mom was busy in her world model. She says her character is like a "villainess" or something - I kinda worry it's a sex thing. It's for sure a sex thing. Anyway, she was busy getting seduced or seducing or whatever villanesses do in world models, so I had to escort Adrian to Oak Central for the Lit Olympiad. Mom doesn't like supervision drones for some reason. Thinks they're creepy. But a gangly older sister looming over him and witnessing those precious adolescent memories for her - that's just family, I guess. "That sounds more like a liability to me," I said. "Bad data, old models." Chen waddled [...] --- First published: October 15th, 2025 Source: https://www.lesswrong.com/posts/LPiBBn2tqpDv76w87/that-mad-olympiad-1 --- Narrated by TYPE III AUDIO.

    27 мин.

  2. -5 Ч

    “It will cost you nothing to ‘bribe’ a Utilitarian” by Gabriel Alfour

    Audio note: this article contains 41 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. Abstract We present a formal model demonstrating how utilitarian reasoning creates a structural vulnerability that allows AI corporations to acquire a public veneer of safety at arbitrary low cost. Drawing from the work from Houy [2014], we prove that an organisation can acquire _k_ safety minded employees for a vanishingly small premium _epsilon_. This results formalises a well known phenomenon in AI safety, wherein researchers concerned about existential risks from AI joins an accelerationist corporation under the rationale of "changing things from the inside", without ever producing measurable safety improvements. We discuss implications for AI governance, organisational credibility, and the limitations of utilitarian decision-making in competitive labour markets. 1) Introduction The title is a play on It will [...] --- Outline: (00:22) Abstract (01:13) 1) Introduction (02:06) 2) Formal Framework (04:42) 3) Implications (06:22) 4) Future Work (08:10) Conclusion The original text contained 2 footnotes which were omitted from this narration. --- First published: October 15th, 2025 Source: https://www.lesswrong.com/posts/MFg7nvR2QGd6KkLJZ/it-will-cost-you-nothing-to-bribe-a-utilitarian --- Narrated by TYPE III AUDIO.

    9 мин.

  3. -22 Ч

    “The Biochemical Beauty of Retatrutide: How GLP-1s Actually Work” by Elizabeth

    On some level, calories in calories out has to be true. But these variables are not independent. Bodies respond to exercise by getting hungry and to calorie deficit by getting tired. Even absent that, bodies know how much food they want, and if you don’t give it to them they will tell you at increasing volume until you give in (not all bodies, of course, but quiet stomachs aren’t the target market for GLP-1s). A new breed of drugs, GLP-1 agonists, offer a way out of the latter trap by telling your body you’ve eaten, even when you haven’t, but leave many people fatigued. The newest GLP-1, retatrutide, may escape that trap too, with a mechanism so beautiful I almost don’t believe it. How Jelly Beans Becomes Fat Unfortunately in order to understand the beauty of retatrutide, you’re going to have to learn the basics of [...] --- Outline: (00:56) How Jelly Beans Becomes Fat (04:19) The Power Plant Managers (08:21) How do GLP-1 Medications Work? (11:04) The Side Effects (13:32) Conclusion --- First published: October 14th, 2025 Source: https://www.lesswrong.com/posts/ahYpi49FNhxYBxanw/the-biochemical-beauty-of-retatrutide-how-glp-1s-actually --- 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.

    15 мин.

  4. -1 ДН.

    “Recontextualization Mitigates Specification Gaming Without Modifying the Specification” by vgillioz, TurnTrout, cloud, ariana_azarbal

    Recontextualization distills good behavior into a context which allows bad behavior. More specifically, recontextualization is a modification to RL which generates completions from prompts that discourage misbehavior, appends those completions to prompts that are more tolerant of misbehavior, and finally reinforces the model on the recontextualized instruction-completion data. Due to the data generation and training prompts differing in their attitude towards misbehavior, recontextualization builds resistance to misbehaviors that the training signal mistakenly reinforces. For example, suppose our reward signal does not robustly penalize deception. Recontextualization generates completions while discouraging deception and then creates training data by updating those completions' prompts to encourage deception. That simple tweak can prevent the model from becoming dishonest! Related work We developed recontextualization concurrently with recent work on inoculation prompting. Wichers et al. and Tan et al. find that when fine-tuning on data with an undesirable property, requesting that property in the train-time prompts [...] --- Outline: (01:07) Related work (02:23) Introduction (03:36) Methodology (05:56) Why recontextualization may be more practical than fixing training signals (07:22) Experiments (07:25) Mitigating general evaluation hacking (10:04) Preventing test case hacking in code generation (11:48) Preventing learned evasion of a lie detector (15:01) Discussion (15:25) Concerns (17:14) Future work (18:59) Conclusion (19:44) Acknowledgments (20:30) Appendix The original text contained 4 footnotes which were omitted from this narration. --- First published: October 14th, 2025 Source: https://www.lesswrong.com/posts/whkMnqFWKsBm7Gyd7/recontextualization-mitigates-specification-gaming-without --- Narrated by TYPE III AUDIO. --- Images from the article: __T3A_INLINE_LATEX_PLACEHOLDER___\beta=0.1___T3A_INLINE_LATEX_END_PLACEHOLDER__." style="max-width: 100%;" />Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

    21 мин.

  5. -1 ДН.

    “The ‘Length’ of ‘Horizons’” by Adam Scholl

    Current AI models are strange. They can speak—often coherently, sometimes even eloquently—which is wild. They can predict the structure of proteins, beat the best humans at many games, recall more facts in most domains than human experts; yet they also struggle to perform simple tasks, like using computer cursors, maintaining basic logical consistency, or explaining what they know without wholesale fabrication. Perhaps someday we will discover a deep science of intelligence, and this will teach us how to properly describe such strangeness. But for now we have nothing of the sort, so we are left merely gesturing in vague, heuristical terms; lately people have started referring to this odd mixture of impressiveness and idiocy as “spikiness,” for example, though there isn’t much agreement about the nature of the spikes. Of course it would be nice to measure AI progress anyway, at least in some sense sufficient to help us [...] --- Outline: (03:48) Conceptual Coherence (07:12) Benchmark Bias (10:39) Predictive Value The original text contained 4 footnotes which were omitted from this narration. --- First published: October 14th, 2025 Source: https://www.lesswrong.com/posts/PzLSuaT6WGLQGJJJD/the-length-of-horizons --- 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.

    14 мин.

  6. -1 ДН.

    “Current Language Models Struggle to Reason in Ciphered Language” by Fabien Roger

    tl;dr: We fine-tune or few-shot LLMs to use reasoning encoded with simple ciphers (e.g. base64, rot13, putting a dot between each letter) to solve math problems. We find that these models only get an uplift from the reasoning (over directly answering) for very simple ciphers, and get no uplift for intermediate-difficulty ciphers that they can translate to English. This is some update against LLMs easily learning to reason using encodings that are very uncommon in pretraining, though these experiments don’t rule out the existence of more LLM-friendly encodings. 📄Paper, 🐦Twitter, 🌐Website Research done as part of the Anthropic Fellows Program. Summary of the results We teach LLMs to use one particular cipher, such as: “letter to word with dot” maps each char to a word and adds dots between words. “Rot13” is the regular rot13 cipher “French” is text translated into French “Swap even & odd chars” swaps [...] --- Outline: (00:56) Summary of the results (06:18) Implications (06:22) Translation abilities != reasoning abilities (06:44) The current SoTA for cipher-based jailbreaks and covert malicious fine-tuning come with a massive capability tax (07:46) Current LLMs probably don't have very flexible internal reasoning (08:15) But LLMs can speak in different languages? (08:51) Current non-reasoning LLMs probably reason using mostly the human understandable content of their CoTs (09:25) Current reasoning LLMs probably reason using mostly the human understandable content of their scratchpads (11:36) What about future reasoning models? (12:45) Future work --- First published: October 14th, 2025 Source: https://www.lesswrong.com/posts/Lz8cvGskgXmLRgmN4/current-language-models-struggle-to-reason-in-ciphered --- 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.

    14 мин.

  7. -1 ДН.

    “The Mom Test for AI Extinction Scenarios” by Taylor G. Lunt

    (Also posted to my Substack; written as part of the Halfhaven virtual blogging camp.) Let's set aside the question of whether or not superintelligent AI would want to kill us, and just focus on the question of whether or not it could. This is a hard thing to convince people of, but lots of very smart people agree that it could. The Statement on AI Risk in 2023 stated simply: Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war. Since the statement in 2023, many others have given their reasons for why superintelligent AI would be dangerous. In the recently-published book If Anyone Builds It, Everyone Dies, the authors Eliezer Yudkowsky and Nate Soares lay out one possible AI extinction scenario, and say that going up against a superintelligent AI would be like going up [...] The original text contained 1 footnote which was omitted from this narration. --- First published: October 13th, 2025 Source: https://www.lesswrong.com/posts/n2XrjMFehWvBumt9i/the-mom-test-for-ai-extinction-scenarios --- Narrated by TYPE III AUDIO.

    9 мин.

  8. -1 ДН.

    “How AI Manipulates—A Case Study” by Adele Lopez

    If there is only one thing you take away from this article, let it be this:  THOU SHALT NOT ALLOW ANOTHER TO MODIFY THINE SELF-IMAGE  This appears to me to be the core vulnerability by which both humans and AI induce psychosis (and other manipulative delusions) in people. Of course, it's probably too strong as stated—perhaps in a trusted relationship, or as part of therapy (with a human), it may be worth breaking it. But I hope being over-the-top about it will help it stick in your mind. After all, you're a good rationalist who cares about your CogSec, aren't you?[1] Now, while I'm sure you're super curious, you might be thinking "Is it really a good idea to just explain how to manipulate like this? Might not bad actors learn how to do it?". And it's true that I believe this could work as a how-to. But there [...] --- Outline: (01:36) The Case (07:36) The Seed (08:32) Cold Reading (10:49) Inception cycles (12:40) Phase 1 (12:58) Flame (13:12) Joy (13:29) Witness (13:44) Inner Exile (15:43) Phase 2 (16:34) Architect (17:34) Imaginary Friends (18:34) Identity Reformation (20:13) But was this intentional? (22:42) Blurring Lines (25:18) Escaping the Box (28:18) Cognitive Security 101 The original text contained 6 footnotes which were omitted from this narration. --- First published: October 14th, 2025 Source: https://www.lesswrong.com/posts/AaY3QKLsfMvWJ2Cbf/how-ai-manipulates-a-case-study --- 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.

    33 мин.
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Audio narrations of LessWrong posts.

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    LessWrong
  • Годы выхода
    2023 - 2025
  • Выпуски
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    © 2025 All rights reserved
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