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LessWrong (30+ Karma)

LessWrong

Audio narrations of LessWrong posts.

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  • 523 episodes
  • Avg 20 min
  • English

Episodes 523

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  • August 13 · 2 min

    “Free will is like temperature” by Optimization Process

    Free will is like temperature: a useful tool for analyzing the behavior of certain systems which are too big and complicated to model in exact detail. If you know the positions and velocities of every atom in a box of gas, then with enough work you can predict its future to arbitrary precision; does the gas "have a temperature"? Irrelevant! Technically yes, I guess, but it's sort of an epiphenomenon, screened off from reality by your exact knowledge of the initial conditions and your willingness to throw processor cycles at your simulation. But if you're less-than-perfectly omniscient, it might be more convenient to consider the box as having a "temperature" and model it more abstractly. Substitute "person+environment"/"free will" for "box of gas"/"temperature" and that's all still true. Maybe your box of gas is supercooled; if you know the initial conditions exactly, you can predict exactly when and where the first large droplet will nucleate, but if your vision of the box is even a little bit fuzzy, you'll instead need to use your understanding of "temperature" to build a probability distribution over when it will condense / whether it will be on a wall or in the gas's [...] --- First published: August 12th, 2026 Source: https://www.lesswrong.com/posts/JSteskb3Lgp9Be69o/free-will-is-like-temperature --- Narrated by TYPE III AUDIO.

  • August 13 · 1 min

    [Linkpost] “Patterns and problems in emerging multiagent systems (Anthropic, Frontier Red Team)” by Julian Bradshaw

    This is a link post. Linkpost for some new Anthropic research on how agents coordinate (or don't). Not too long, pretty interesting. For example: The jist of the report is that Mythos 5 does way better at coordination than previous models across a few scenarios. For example, when multiple Mythos are given conflicting goals for a single shared codebase, they eventually realize the other agents aren't hostile: (...) we observe an emergent behavior where the agents propose and run a tournament for application performance (...) (...) losers gracefully concede codebase ownership to the Rust agent, giving up on their original user directives under their self-negotiated commitment device. It's not clear to me if this is purely emergent or if Anthropic is deliberately training for cooperation; I'd guess there's deliberate training, though. --- First published: August 12th, 2026 Source: https://www.lesswrong.com/posts/iQiDPmAgKo4KcG5uy/patterns-and-problems-in-emerging-multiagent-systems Linkpost URL: https://www.anthropic.com/research/multiagent-systems --- 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 · 38 min

    “Measuring Spurious Correlations with Feature Strength” by egan

    This work was partially done by an automated research scaffold developed at Redwood Research. For this project, all of the experiment ideas were designed by a human and a human wrote the write up. The AI mostly just executed on the experiment ideas. We think this project is slightly below the level of rigor of a mid-MATS research update, and the research scaffold was not very helpful for this project. More discussion of AI usage is in the Appendix. 💻 Codebase If we want to train a classifier that distinguishes whether a passage is code or prose, we can do so by gathering samples of code and of prose, and training the classifier to distinguish between the two classes. Unfortunately, this might not work if the data hides a spurious correlation. If all the code is in Spanish and all of the prose is in English, then the classifier might learn to predict Spanish vs. English instead of code vs. prose. We find that this happens in practice: when we fine-tune an LLM to classify between Spanish code and English prose and evaluate on Spanish prose or English code, it generalizes to predicting the language rather than the domain. [...] --- Outline: (04:45) The setup (08:13) Measuring feature strength (12:49) Activation differences and feature strength (15:21) Explicit prompting (17:17) Diagonal vs antidiagonal pairs (18:47) Conclusion (20:20) Appendix (20:24) AI involvement (22:17) The 37 features (23:57) The ranking is robust across measurements (30:30) Intensity moves the fine-tune, not the probe (32:13) Safety features in Qwen3.6-27B (33:05) Counterexamples and training on a third cell (34:46) Near ties often produce degenerate fine-tunes (35:24) Related work The original text contained 4 footnotes which were omitted from this narration. --- First published: August 11th, 2026 Source: https://www.lesswrong.com/posts/qpJYNjQ6wdWRxbykL/measuring-spurious-correlations-with-feature-strength --- 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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