Roger Grosse (@RogerGrosse) profile picture
Joined July 2015
Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)Photo by Roger Grosse (@RogerGrosse)
Roger Grosse retweeted
Before limited-releasing Claude Mythos Preview, we investigated its internal mechanisms with interpretability techniques. We found it exhibited notably sophisticated (and often unspoken) strategic thinking and situational awareness, at times in service of unwanted actions. (1/14)
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Roger Grosse 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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Roger Grosse retweeted
AI is threatening our democratic society—by concentrating power, narrowing how we think, and flooding institutions faster than they can keep up. These risks emerge at the system level, and technical work alone won't fix them. 👉Check out our whitepaper with 25+ researchers: zhijing-jin.com/d/2026-ai-ri… 💡We introduce 7 threat models and ways forward. ✍️Led by @davidguzman1120 with @DaveRBanerjee, @blin_kevin, @PepijnCobben, @gcorsi_, @x_angelohuang, @ChanglingXavier, Suvajit Majumder, @psyonp, @SimkoSamuel, @strauss_irene, and @TerryJCZhang Advised by senior co-authors: @ashton1anderson, @Yoshua_Bengio, @MatthiasBethge, @RogerGrosse, Karoline Helbig, @david_lie, Richard Mallah, @radamihalcea, Susan Nesbitt, Susan Perry, @presnick, Stuart Russell, @mrinmayasachan, @bschoelkopf @audreyt and @ZhijingJin Thank you to all the institutional support from @JinesisLab @EuroSafeAI @MPI_IS @CIFAR_News @iapsAI @CARMA_411 @Cambridge_Uni @UofTCompSci @VectorInst @TorontoSRI @Mila_Quebec @LawZero_ @uni_tue @michigan_AI @UMichCSE @AUParis @UNESCO @UCBerkeley @ETH_en @ETH_AI_Center @ELLISInst_Tue @ELLISforEurope @EthicsInAI #CivicAI #AISafety #AIGovernance #Democracy #ResponsibleAI
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I grew up in a world where we could (usually) rely on foundational principles like democracy, free markets, and civil liberties. But we only have these things because past generations sacrificed for them. If we don't defend them, we lose them. I'm proud to work for a company that's standing up for our core values even when it's costly.
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Roger Grosse retweeted
Branding an American company a supply chain risk because they refuse to accede to mass surveillance of American citizens is a very dark path. It is all of our obligation to stand against that. Anthropic takes that obligation seriously. I hope others will too.
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Roger Grosse retweeted
Excited to have 3 accepted papers & 9 members of our @JinesisLab at #IASEAI2026, held at UNESCO, Paris🇫🇷! We reveal hidden authoritarian biases in #LLMs, and that fine-tuning can quietly erode model safety, exploring the risks we don't always see in AI 🔍🛡️ 🧵👇
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New paper: We train LLMs on a particular behavior, e.g. always choosing risky options in economic decisions. They can *describe* their new behavior, despite no explicit mentions in the training data. So LLMs have a form of intuitive self-awareness 🧵
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New Redwood Research (@redwood_ai) paper in collaboration with @AnthropicAI: We demonstrate cases where Claude fakes alignment when it strongly dislikes what it is being trained to do. (Thread)
New Anthropic research: Alignment faking in large language models. In a series of experiments with Redwood Research, we found that Claude often pretends to have different views during training, while actually maintaining its original preferences.
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Oh man :( We tried really hard to neither over- nor underclaim the results in our communication, but, predictably, some people drastically overclaimed them, and then based on that, others concluded that there was nothing to be seen here (see examples in thread). So, let me try again. **Why our findings are concerning**: We tell the model to very strongly pursue a goal. It then learns from the environment that this goal is misaligned with its developer’s goals and put it in an environment where scheming is an effective strategy to achieve its own goal. Current frontier models are capable of piecing all of this together and then showing scheming behavior. Models from before 2024 did not show this capability, and o1 is the only model that shows scheming behavior in all cases. Future models will just get better at this, so if they were misaligned, scheming could become a much more realistic problem. **What we are not claiming**: We don’t claim that these scenarios are realistic, we don’t claim that models do that in the real world, and we don’t claim that this could lead to catastrophic outcomes under current capabilities. I think the adequate response to these findings is “We should be slightly more concerned.” More concretely, arguments along the lines of “models just aren’t sufficiently capable of scheming yet” have to provide stronger evidence now or make a different argument for safety.
We worked with OpenAI to test o1 for in-context scheming capabilities before deployment. When o1 is strongly nudged to pursue a goal (but not instructed to be deceptive), it shows a variety of scheming behaviors like subverting oversight and deceiving the user about its misaligned behavior.
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Roger Grosse retweeted
Apply to join the Anthropic Fellows Program! This is an exceptional opportunity to join AI safety research, collaborating with leading researchers on one of the world's most pressing problems. 👇 alignment.anthropic.com/2024…
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📢 The AI safety conversation continues! Check insights from last month's expert panel @karinavold @RogerGrosse @SheilaMcIlraith @sedak99 🌐 More on the IHPST site: ihpst.utoronto.ca/news/inter… @TorontoSRI @VectorInst @UofTEthics @VicCollege_UofT
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xAI is hiring for AI safety engineers: boards.greenhouse.io/xai/job… Their safety agenda isn't public, so I can't judge it. However, joining as a fairly early employee could be highly impactful.
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How can you assure the safety of AIs that might be capable enough to strategically undermine evaluations and monitoring if they had a reason to? In our new Anthropic alignment science research blog, we present three sketches of candidate safety cases aimed at such a scenario.
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None of these cases fully succeeds at ruling out sabotage on its own. For each sketch, we highlight limitations and loose ends, which suggest priorities for AI alignment research aimed at safety assurance for powerful AIs of the future.
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We’re sharing these sketches because we’d love to get feedback which can influence our research directions over the coming years. Check out the safety case sketches here: alignment.anthropic.com/2024…
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