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  1. 2h ago

    “Gradient Routing Generalizes to Unrouted Hack Types” by jacob_drori

    TLDR: I lightly extend Ward, Hu et al (2026) to measure how well gradient routing generalizes. I route gradients on one type of reward hack (e.g. ones that reason in English) and check whether this suppresses other types at test time (e.g. ones that reason in French), despite the model being trained on those too. This models a realistic monitor that catches some types of hack during training but not others, rather than catching each at the same rate. I find encouraging evidence of generalization, as well as some failures I don’t yet understand. Introduction Recently, Ward, Hu et al (2026) used gradient routing to prevent reward hacking learnt during training from showing up at test time. I think their method is pretty promising. The gist is: Initialize a retain and a forget adapter.Whenever a monitor detects a reward hack during training, only update the forget adapter on that sample (“route the gradient”). Otherwise update both.At test time, remove the forget adapter. If the method worked, the resulting model shouldn't hack too much. I won’t recap their full paper – it is excellent and you should just read it (alternatively, Jake gives a summary here, with [...] --- Outline: (00:47) Introduction (02:37) Methodology (02:40) Recap (04:00) Narrow Routing (05:27) What I report (07:18) Results (10:13) Conclusion (11:45) Appendix: Results with nudge prompt (12:29) Appendix: Predictions The original text contained 4 footnotes which were omitted from this narration. --- First published: October 23rd, 2026 Source: https://www.lesswrong.com/posts/ie7bSp5ZDwYkCQxuv/gradient-routing-generalizes-to-unrouted-hack-types --- 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.

  2. 5h ago

    “Simulated Control Evaluations” by Jeffrey Olmo, dzhang, Tyler Tracy

    We’ve released a paper on simulated control evaluations (SCEs). They’re a method for testing whether the monitors AI developers use to oversee their agents would catch a model deliberately trying to cause harm, under conditions much closer to real deployment than current tests allow. Developers have started running monitors in their agentic deployments: models that flag or block an agent's actions when they look dangerous. To know whether a monitor is good enough, you want to know how often it would stop a model that was actively trying to do something harmful, such as quietly inserting a security vulnerability, exfiltrating credentials, or sabotaging an experiment. The standard way to measure this is a control evaluation: instruct a model to attempt such attacks, and measure how often the monitor catches it before an attack succeeds. Developers typically do this using purpose-built control settings like BashArena or LinuxArena, where attacks can actually be executed. However, these settings only provide indirect evidence for what we really care about: how well monitors will work in the real deployment, where opportunities for harm might depend on particular codebases, services and tasks that aren’t represented in control settings. How can we evaluate monitor performance in [...] --- Outline: (04:13) SCEs are a favorable tradeoff between realism and cost (04:30) 1. Run on a copy of internal deployment (05:42) 2. Edit attacks into recorded transcripts (07:08) Evaluating Control Evaluations (08:58) How to measure qualitative properties? (10:07) Performance Comparisons (11:55) Overview of Empirical Results (13:29) Behavior transfer (15:19) Safety metric transfer (17:08) Sample efficiency (18:07) Lessons from building and evaluating SCEs (20:35) Next steps (24:36) Conclusion The original text contained 3 footnotes which were omitted from this narration. --- First published: October 23rd, 2026 Source: https://www.lesswrong.com/posts/uXiqyoKwpfjWfQXk5/simulated-control-evaluations --- 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.

  3. 5h ago

    “The Hackening” by Zvi

    Last week at a small conference, there was talk about a future event referred to as The Hackening. As in, within 6 to 12 months, many predicted that due to advanced AIs and AI agents the internet would become a severely hostile place. Some worried that this could rise to the level of ‘assume any phone connected to the internet is rooted’ and several people urged doing general crisis preparations on a personal level, in case of disruptions in supply lines, power, water or internet. It did not take long for things to escalate. The biggest hack is a flaw in PHP, which can be exploited to take control of the computer running any website suing an unpatched version of PHP. There are many others. On a personal level, you want to do at least some basic prep and get your security in order. It is cheap to do as a form of insurance, and being caught not having it when you need it is a large mistake. That doesn’t mean you should panic, or cancel travel plans or anything like that. But there is low hanging security fruit out there. Pick it. And So It Begins: PHP Joshua [...] --- Outline: (01:12) And So It Begins: PHP (05:19) The PHP Bug Was Kind of a Big Deal (09:45) On Your Marks (12:25) This Was Only the Beginning (16:01) Some More Hacks (17:56) Everything Will Be Attacked (22:22) Hugging the Face (24:49) Get Daybreak Now (26:15) Some People Need Practical Advice (30:51) Diffusion Is Slow Until It Isn't (33:11) One, Two, Three, Four (34:36) Going Vertical (35:22) Bot Versus Bot (36:28) Cyber Lack of Security (39:09) Offense Versus Defense (42:11) Pick Up the Phone (45:57) What Can We Do About It? --- First published: October 23rd, 2026 Source: https://www.lesswrong.com/posts/iqgZaJMnmyDCYfnuC/the-hackening-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.

  4. 16h ago

    “Instrumental convergence is incoherent (so pls slightly lower your p(extinction))” by Arepo

    Crossposted from the EA forum ‘Like most of life's dangers, the dangers of artificial intelligence are not easily identified from the armchair.’ J Dmitri Gallow Epistemic status I’m not an expert in this field, and herein I make some sweeping generalisations educated guesses about the overall nature of its contents based on my strong impression from the subset of work that I’ve read. I’ve recently spent maybe 2 to 300 hours learning about modern AI safety technical work, and some amount of spare time over the years before that thinking about here-relevant notions such as preferences from an analytic-philosophical perspective. I intend this post as a work in progress. TL;DR: The concept of instrumental convergence comes from a reference class of pre-empirical categorical predictions with a very low success rate.In historical usage, its definition is very murky. Perhaps too murky to really say that it made any meaningful real-world predictions. I suspect its definition is fundamentally unfixable.On my best interpretation of what it could be said to have predicted, it seems to be much worse than we would want a good high-level model of AI behaviour to be.Despite these shortcomings, it still seems to [...] --- Outline: (00:25) Epistemic status (00:57) TL;DR: (02:17) Intro (04:44) How is instrumental convergence defined? (06:08) Predictions of instrumental convergence theory (09:06) A simple gap in seminal IC arguments (09:44) Bostrom (10:01) Omohundro (10:57) Carlsmith (11:52) LessWrong wiki (12:32) Why we can't easily fill the gap (12:36) Intuitive goal notions aren't adequate (aka, coffee-fetching is hopelessly overloaded) (15:27) Formal goal notions aren't adequate (18:24) A stronger counterthesis: goals are incoherent (21:15) Related work to the stronger counterthesis (24:05) Counterpoints (26:04) What's the alternative, then? (29:38) Why does it matter? (32:15) 'Misalignment' (34:40) The Hugging Face attack (38:45) Takeaways (40:03) Acknowledgements (40:25) Disclaimers (41:32) Appendix: more on the extreme ambiguity of coffee-making The original text contained 27 footnotes which were omitted from this narration. --- First published: October 23rd, 2026 Source: https://www.lesswrong.com/posts/oxZfMvQS9NxcNTozL/instrumental-convergence-is-incoherent-so-pls-slightly-lower --- 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. 20h ago

    “AI #191: The Hackening Begins” by Zvi

    It was the probably worst day in cybersecurity history. So far. Not only is there a critical flaw in PHP that due to a leak was announced a day before the fix would be available, sending most of the internet scrambling for solutions and likely dooming a lot of long tail websites permanently, there were several other unrelated incidents. The floodgates seem to have been opened. I have a market on whether there will be an even worse day before the end of the year. Another key leak this week was the draft bill Anthropic had been shopping around Congress, similar to but weaker than the FRONTIER Act. Anthropic's initial attempts to explain itself only made the situation seem worse, as they seemed to not understand the problem with what they had done. Dario's later response was better, but the situation remains troubling. There are enough different Anthropic developments, that I plan to have a post soon covering them all. It is a mix of news running from excellent to terrible. Will interpretability save us? There is a new claim that neural networks are cleaner than we thought, meaning that individual neurons mostly have a single interpretable [...] --- Outline: (02:05) Language Models Offer Mundane Utility (03:23) Language Models Don't Offer Mundane Utility (05:03) Huh, Upgrades (08:50) Wonder No More (11:15) On Your Marks (14:30) Choose Your Fighter (14:51) Get My Agent On The Line (16:35) Deepfaketown and Botpocalypse Soon (21:01) Fun With Media Generation (21:17) Spy Versus Spy (24:58) A Young Lady's Illustrated Primer (25:56) They Took Our Jobs (31:07) Get Involved (32:18) Introducing (32:30) In Other AI News (34:26) Show Me the Money (38:42) Bubble, Bubble, Toil and Trouble (39:32) Quiet Speculations (41:47) Quickly, There's No Time (42:29) The Quest for Sane Regulations (42:55) DeepMind Made Him Sign an NDA (46:50) OpenAI Sells Out On Chips (49:08) Chip City (53:43) The Week in Audio (01:00:35) People Just Say Things (01:03:50) Rhetorical Innovation (01:06:17) Maybe You Should Quit (01:08:54) The American People Really Hate AI (01:09:43) New Math (01:11:27) Aligning a Smarter Than Human Intelligence is Difficult (01:15:21) Cooperative Alignment (01:19:28) Marc Andreessen Hulk Smash (01:23:56) People Are Worried About AI Killing Everyone (01:24:44) The Lighter Side --- First published: October 22nd, 2026 Source: https://www.lesswrong.com/posts/ZoMJdvq9meE4shyzJ/ai-191-the-hackening-begins --- 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. 1d ago

    “Wah wah wah wah do more AI R&D” by TsviBT

    Ice cream There's a particular kind of listening that kids (and adults) sometimes do. Daddy is saying stuff—his mouth is moving and making noise—and he's talking about what I ate for lunch, and something about other times I've eaten ice cream, and something about rules and this and that and what Mommy would think and something about bedtime—but I am not so interested in that. What interests me is the punchline "...and therefore you can't have ice cream.". I'm only incidentally interested in the other stuff, insofar as it might determine whether I get ice cream. In an obvious way, I'm showing rather poor listening skills. I did not understand most of what Daddy said, and arguably I'm even being rude. However, in another way, this stance has a virtue. The virtue is that it holds fast to what matters, as best I know how, and does not give in to Daddy's framing. It sticks to what is real and concrete, what matters, despite a cloud of justifications. This is certainly not an unalloyed virtue, just as the POSIWID perspective is both useful and fraught. But it matters that although Daddy is a complex system with [...] --- Outline: (00:10) Ice cream (01:54) Wah wah (02:21) Scammer (02:51) Throwing stuff on a bonfire to make it burn less (05:28) A skip connection for sanity The original text contained 1 footnote which was omitted from this narration. --- First published: October 22nd, 2026 Source: https://www.lesswrong.com/posts/pY76hHbdD4jduX3YH/wah-wah-wah-wah-do-more-ai-r-and-d --- Narrated by TYPE III AUDIO.

  7. 1d ago

    “Monitorability evaluations and policy proposals” by evhub

    There has been a lot of discussion recently of policy proposals around monitorability, such as Redwood's proposal and my counter-proposal. I'd like to say a bit more generally about how I'm thinking about monitorability. Note that nothing here is an official Anthropic position; these are just my personal thoughts. First, a categorization. I currently think about four different types of monitorability: Control Monitorability: the extent to which Control and monitoring techniques would likely catch catastrophic sabotage actions, like escaping a sandbox or setting up a rogue internal deployment. We can measure this via reference to sabotage evaluations like LinuxArena or any evaluation that measures the model's capability to take dangerous actions like escaping a sandbox while looking to a monitor like it's doing some innocuous task (like a cyber eval). Auditing Monitorability: the extent to which alignment audits, evaluations, and test deployments would likely incriminate a systematically misaligned model (e.g. a schemer). We can measure this via reference to the model's capability to consistently distinguish between our most realistic alignment audits/evals and real internal deployment cases where it would have an opportunity to do something catastrophic. Training Monitorability: the extent to which reward models [...] --- First published: October 22nd, 2026 Source: https://www.lesswrong.com/posts/zAczYuHQS6jdG5DTg/monitorability-evaluations-and-policy-proposals --- Narrated by TYPE III AUDIO.

  8. 1d ago

    “Greater good arguments in AI safety” by Dean Valentine

    Back in the before-time of the 2010s, when EA was still known mostly for funding health efforts in the developing world, there were many baskets of criticism floating around from the left. The most prominent was a cluster centered around EAs being white men, and therefore suspicious. Another category, in my experience the second-most common, was that in attempting to rectify the problems in the world, EA was masking, whitewashing, and misdirecting efforts away from solving the "real problem", usually capitalism but often other contradictory forces. These arguments were often not specific to EA, but applied to charitable giving in general. On any Hacker News post about a billionaire giving to charity, one of the first few comments would inevitably be this Oscar Wilde quote: Just as the worst slave-owners were those who were kind to their slaves, and so prevented the horror of the system being realized by those who suffered from it, and understood by those who contemplated it, so, in the present state of things in England, the people who do most harm are the people who try to do most good. These arguments have a long history. Anticapitalist critiques of charity go back to [...] The original text contained 4 footnotes which were omitted from this narration. --- First published: October 22nd, 2026 Source: https://www.lesswrong.com/posts/WRvxRHMCmc3Qt4axz/greater-good-arguments-in-ai-safety --- Narrated by TYPE III AUDIO.

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Audio narrations of LessWrong posts.

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  • Creator
    LessWrong
  • Years Active
    2023 - 2026
  • Episodes
    250
  • Rating
    Explicit
  • Copyright
    © 2026 All rights reserved
  • Show Website
    LessWrong (30+ Karma)

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