LessWrong (30+ Karma)

LessWrong

Audio narrations of LessWrong posts.

  1. قبل ١١ ساعة

    “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.

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  2. قبل يوم واحد

    “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.

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  3. قبل يوم واحد

    “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.

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  4. قبل يوم واحد

    “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.

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Audio narrations of LessWrong posts.

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