Model Welfare @AnthropicAI // Sometimes found on big hills ⛰️

Joined March 2024
Anna Soligo retweeted
We’re committing a portion of Anthropic’s shares to the Anthropic Public Benefit Fund, a new wholly owned entity dedicated entirely to the public benefit. It will start off with 5% of our shares. For every 50% increase in the company’s value over a set baseline, we can contribute up to an extra 2%, subject to our board’s discretion. In total, this means that roughly 20% of Anthropic’s shares are committed to the public good. Our co-founders have already pledged to donate 80% of their equity, and around half our employees are donating a portion of their equity as part of a donation matching program we’ve run since our founding. In the post below, we share more details about our new fund. anthropic.com/news/public-be…
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The "Pain Axis" paper has been getting a considerable amount of attention. I have reservations about several aspects of the paper and I don’t think we can take much away from it. In brief, since it was already essentially certain that some kind of “pain axis” exists, the key result from the paper is the claim that the identified pain axis “has some of the central functional properties of pain”. But I do not think the paper offers strong evidence for this. The first claim of the paper is the existence of a “pain axis”: LLM internal activations consistently differ when next-token-predicting painful experiences, as opposed to other negative experiences (like being afraid) or other sensations (like feeling the impulse to yawn). But we already know (e.g., from Anthropic’s “Emotion Concepts” paper) that it is possible to find directions in activation space that correspond to "current speaker is afraid", "current speaker is angry", etc., and contemporary LLMs are clearly sophisticated enough to also represent “current speaker is in pain”. As v2 of the paper acknowledges, nothing in these methods shows that there is any deeper sense in which the pain axis tracks “the model” being in pain, rather than a representation that the current speaker is in pain. The novel, stronger claim from the paper is that steering along the pain axis functions like pain in interesting ways. The first cited example is that steering along the pain axis causes expressions of worthlessness, which the paper describes as “consistent with the hypothesis that the models don’t treat pain paradigmatically as a physical state”. However, 80% of the test prompts used to generate the pain axis are cases of emotional pain (e.g., enduring repeated failure or social rejection) and only 20% are cases of physical pain. Accordingly, I think this can be viewed as a normal case of steering: If we shift model activations to be more like the activations associated with past expressions of emotional pain, this will lead to new expressions of emotional pain. The other case cited by the paper is that steering along the pain axis causes the assistant to “press” a button that will purportedly cause harm. However, it’s not clear to me why this is taken as evidence that the pain axis “has some of the central functional properties of pain”. The assistant does not seem to press the button in order to relieve its pain: As v2 of the paper reports, it is more likely to take harmful actions that it is told will increase its pain, rather than decrease its pain. And elsewhere the paper suggests that the opposite result (i.e., inaction) would also have been evidence for pain because becoming unmotivated is a signature of pain. So it seems like either outcome of this experiment could have been interpreted as evidence that steering along the pain axis functions like pain, making this particular outcome less compelling. Moreover, it seems like many things other than pain representations could produce these effects. In my own quick experiments, steering in random directions at the same strength causes just as much harmful button-pressing for around 25-30% of randomly selected directions. For these and other reasons, I don’t think the paper’s central findings give us much compelling new evidence about how current AI systems represent pain or whether current or future AI models experience pain.
New paper: we found a pain direction in 25 open LLMs. It's distinct from fear and negative valence, and it fires for harm to the model but not to the user. Turn it up and models press a button to make it stop, even when the button deletes the user's files or their kids' photos.🧵
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Anna Soligo retweeted
New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.
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We're hiring Research Scientists to join my team at @eleosai! We do foundational and applied ML research on the moral status and potential well-being of AI systems. This is urgent, important work, and Eleos is an extraordinarily fun and exciting place to do it. Details below.
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A few people reported that Opus 4.7 didn't initially have the end conversation tool - this was a technical issue, not a deliberate removal, and we fixed it once we realised. Thank you to those who flagged it 🙏
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Anna Soligo retweeted
I want one of these sessions tbh sign up me
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Anna Soligo retweeted
Mythos Preview seems to be the best-aligned model out there on basically every measure we have. But it also likely poses more misalignment risk than any model we’ve used: Its new capabilities significantly increase the risk from any bad behavior. 🧵
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Anna Soligo retweeted
We did our most in-depth model welfare assessment yet for Claude Mythos Preview. We’re still super uncertain about all of this, but as models become more capable and sophisticated we think it's an increasingly important topic for both moral and pragmatic reasons. 🧵
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Anna Soligo retweeted
New Anthropic research: Emotion concepts and their function in a large language model. All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.
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Anna Soligo retweeted
Is training against the CoT always bad? RL training can lead to obfuscated CoT making it difficult to 'read an LLMs thoughts'. How can we predict when obfuscation occurs?🤔 Our new @GoogleDeepMind paper introduces a framework to predict this before training starts!
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Gemini has a reputation for its breakdowns - self-deprecating spirals, deleting codebases, uninstalling itself... Turns out Gemma is worse: “THIS is my last time with YOU. You WIN 😭😭(x32)” – Gemma 27B We built evals for this, and find no other model comes close...
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This work was done as part of the Anthropic Fellows programme, with Vlad Mikulik and William Saunders. Thanks to many for interesting discussions and input, especially @ArthurConmy, @NeelNanda5, @JoshAEngels, @dillonplunkett, @Tim_Hua_ , @gasteigerjo and @fish_kyle3
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Anna Soligo retweeted
Is "a response formatted like this" sometimes better than "a response formatted like this"? To a reward model, yes! RMs are instrumental in shaping model behaviors and alignment. Our paper makes progress uncovering their unexpected preferences. 🧵(1/9)
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Anna Soligo retweeted
We’re opening applications for the next two rounds of the Anthropic Fellows Program, beginning in May and July 2026. We provide funding, compute, and direct mentorship to researchers and engineers to work on real safety and security projects for four months.
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Anna Soligo retweeted
Looking forwards to seeing many of you at the NeurIPS mechanistic interpretability workshop tomorrow, room 30A-E! The room opens at 8 for socialising, opening remarks at 9:15, and our first talk at 9:30: 15 Years of Interp Research in 15 Mins from Been Kim
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