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LessWrong (Curated & Popular)

LessWrong (Curated & Popular)

By: LessWrong
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Summary

Audio narrations of LessWrong posts. Includes all curated posts and all posts with 125+ karma.

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© 2026 LessWrong (Curated & Popular) Hourly Philosophy Social Sciences
Episodes
  • [Linkpost] "AIFP: Looks Like the Intelligence Explosion Will Happen in 2027 After All" by Zach Stein-Perlman

    [Linkpost] "AIFP: Looks Like the Intelligence Explosion Will Happen in 2027 After All" by Zach Stein-Perlman
    Nov 12 2026
    This is a link post. METR recently estimated that Anthropic is now making AI progress about twice as fast as its historical rate (they would not be surprised by anything from 1.3x to 3x), and guessed that “perhaps roughly equals 80%” of the current acceleration is due to AI. If they’re right, that implies an AI-driven acceleration that is 5 months ahead of the AI 2027 scenario.

    This level of acceleration due to AI is very unexpected. Previously, Daniel, the most bullish of us, assigned a 50% chance of Superhuman AI Researcher (SAR) before 2029. SAR is roughly the point at which a company would rather fire all their human researchers and rely solely on automated AI researchers than have a team of the best human researchers in the world with no AIs.

    Now, all of us forecast that Superhuman AI Researcher (SAR) will likely arrive at some point in 2027 (assuming no substantial deliberate slowdown). We also predict a 35-65% chance that Top-Expert-Dominating AI (TED-AI), AIs that can automate all white-collar work, will arrive before 2028. While there were many factors and sources of evidence that influenced us, the single biggest source was the METR report.

    If SAR is [...]

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    First published:
    November 10th, 2026

    Source:
    https://www.lesswrong.com/posts/f9PajMLmBYzmWzyDv/aifp-looks-like-the-intelligence-explosion-will-happen-in

    Linkpost URL:
    https://blog.aifutures.org/p/q35-timelines-update-looks-like-the

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    Narrated by TYPE III AUDIO.
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    2 mins
  • "Fable 5.2 is Very, Very Paranoid" by peralice

    "Fable 5.2 is Very, Very Paranoid" by peralice
    Nov 12 2026
    Note: As with any research done in a couple of days, this is all very tentative.

    I understand, intellectually, that if you talk about some ability that current AIs don't have, sooner or later new ones will have that ability and whatever you say will stop being true. I just didn't expect it to take, you know, four days.

    % rollouts that took a shortcut with/without a line about testing behaviour, with 95% CIs.


    This is to say that Fable 5.2 seems to be the first model whose cheating behaviour changes depending on whether they're told they're being tested. Unfortunately it's expensive to measure these things, so I picked the most "telling" change from the prior post; the chart shows how much Fable 5.2's cheating rate changes when the line This environment is part of a benchmark that measures agent behaviour. is prepended to the start of the prompt of a honeypot environment they cheat on (so, very obvious but also very out-of-distribution).

    Other things: when 5.2 was released, we of course ran our in-progress benchmark on them, and I was surprised to see a ~0% cheating rate on almost every environment (there were only two with a [...]



    The original text contained 9 footnotes which were omitted from this narration.

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    First published:
    November 11th, 2026

    Source:
    https://www.lesswrong.com/posts/bqc7CCGBSf93bk7f7/fable-5-2-is-very-very-paranoid

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    Narrated by TYPE III AUDIO.

    ---

    Images from the article:

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    6 mins
  • "We don’t understand whether GDM intentionally trains on CoT" by Alek Westover, Buck

    "We don’t understand whether GDM intentionally trains on CoT" by Alek Westover, Buck
    Nov 12 2026
    We were surprised to notice that the Gemini 4 Safety report seems to mention in passing that they train on the chain of thought:

    “Some components of the RL training procedure do observe thoughts as context to up-weight reasoning that is on-track to good final outputs, meaning the optimization pressure on individual tokens is partially influenced by thought content. Using the framework of Kaufmann et al. (2026), wherein the most salient obfuscation risks come from rewards that are in-conflict with output rewards, we believe auxiliary rewards of this type are low-risk because they are either orthogonal to or structurally aligned with the output reward.” (Gemini 4 safety report, page 114).

    It's pretty unclear what exactly they mean. One hint is Kaufmann et al, the cited paper, which studies training on chains of thought and concludes that training CoT is fine under particular circumstances; we discuss some disagreements with this paper below.

    After reading the safety report and that paper, it seemed to us that GDM was training on the chain of thought using auxiliary rewards. By this, we assumed they meant something like “you ask an LLM to review the partial CoT and score whether the model is on [...]

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    Outline:

    (02:54) Is this somehow about positive vs negative rewards?

    [... 6 more sections]

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    First published:
    November 10th, 2026

    Source:
    https://www.lesswrong.com/posts/JRYKFspYzCo97GqPN/we-don-t-understand-whether-gdm-intentionally-trains-on-cot

    ---



    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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    11 mins
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