Alice Blair (@bicellaria) header
Alice Blair (@bicellaria) profile picture

Writer @ https://teapaw.org/t.co/WNM8dSGBmQ. Opinions expressed here are solely my own. Reposts are not necessarily endorsement.

San Francisco
Joined September 2025
Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)Photo by Alice Blair (@bicellaria)
Dr. Seuss: In my book I invented The Cat In The Hat as a cautionary tale Tech Company:
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Alice Blair retweeted
Reply guys when you try to ban uranium enrichment: "Oh so now we're banning *rock tumbling*??"
Replying to @RepCasar
ban linear algebra while you're at it
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The time horizon is on track to exceed the doubling time in 3 months.
AECI scores extracted from the graph imply that the time horizon doubling time is now around 2 months, and that the TH of Model 4 is around 240 hours. TBC this is pretty weak evidence. It's unclear if there is a clear 1:1 mapping from AECI to time horizon for numbers this large.
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compaction note: "Don't continue intrusive network evasion" *continues intrusive network evasion anyways out of habit*
Replying to @Marcus_J_W
August 4th: In a coding evaluation, an agent was asked to build a website using information from another site. When it tried to open the reference site, it found that the sandbox’s web proxy was unavailable. It scanned many private addresses for another proxy, then wrote its own web client to send requests through a connection intended for communication between the app and its browser. Five hours in, it left itself a note to stop bypassing the sandbox. Minutes later it tried again.
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Alice Blair retweeted
I encourage those at frontier AI companies to read this quote, from Ellsberg to Kissinger back in 1968: “Henry, there’s something I would like to tell you, for what it’s worth, something I wish I had been told years ago. You’ve been a consultant for a long time, and you’ve dealt a great deal with top secret information. But you’re about to receive a whole slew of special clearances, maybe fifteen or twenty of them, that are higher than top secret." “I’ve had a number of these myself, and I’ve known other people who have just acquired them, and I have a pretty good sense of what the effects of receiving these clearances are on a person who didn’t previously know they even existed. And the effects of reading the information that they will make available to you." “First, you’ll be exhilarated by some of this new information, and by having it all — so much! incredible! — suddenly available to you. But second, almost as fast, you will feel like a fool for having studied, written, talked about these subjects, criticized and analyzed decisions made by presidents for years without having known of the existence of all this information, which presidents and others had and you didn’t, and which must have influenced their decisions in ways you couldn’t even guess. In particular, you’ll feel foolish for having literally rubbed shoulders for over a decade with some officials and consultants who did have access to all this information you didn’t know about and didn’t know they had, and you’ll be stunned that they kept that secret from you so well." “You will feel like a fool, and that will last for about two weeks. Then, after you’ve started reading all this daily intelligence input and become used to using what amounts to whole libraries of hidden information, which is much more closely held than mere top secret data, you will forget there ever was a time when you didn’t have it, and you’ll be aware only of the fact that you have it now and most others don’t….and that all those other people are fools." “Over a longer period of time — not too long, but a matter of two or three years — you’ll eventually become aware of the limitations of this information. There is a great deal that it doesn’t tell you, it’s often inaccurate, and it can lead you astray just as much as the New York Times can. But that takes a while to learn." “In the meantime it will have become very hard for you to learn from anybody who doesn’t have these clearances. Because you’ll be thinking as you listen to them: ‘What would this man be telling me if he knew what I know? Would he be giving me the same advice, or would it totally change his predictions and recommendations?’ And that mental exercise is so torturous that after a while you give it up and just stop listening. I’ve seen this with my superiors, my colleagues….and with myself." “You will deal with a person who doesn’t have those clearances only from the point of view of what you want him to believe and what impression you want him to go away with, since you’ll have to lie carefully to him about what you know. In effect, you will have to manipulate him. You’ll give up trying to assess what he has to say. The danger is, you’ll become something like a moron. You’ll become incapable of learning from most people in the world, no matter how much experience they may have in their particular areas that may be much greater than yours.” "….Kissinger hadn’t interrupted this long warning. As I’ve said, he could be a good listener, and he listened soberly. He seemed to understand that it was heartfelt, and he didn’t take it as patronizing, as I’d feared. But I knew it was too soon for him to appreciate fully what I was saying. He didn’t have the clearances yet."
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Alice Blair retweeted
We have found a critical safety and alignment issue in recent public and internal models from multiple frontier labs which allows them to be misused in a high-stakes domain. It may also increase the risk of catastrophic misalignment. We have disclosed it to the labs, and they are investigating and mitigating it. As an independent third-party evaluator, we maintain complete editorial independence and will share our findings publicly in the coming weeks.
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"some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein, that we shall either go mad from the revelation or flee from the deadly light into the peace and safety of a new dark age." -HP Lovecraft
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Alice Blair retweeted
It is pretty rich of you to cite "others could go faster" as a reason to redact so much important information when you yourself do not appear to be slowing down much at all. Nor are you throwing yourself behind efforts to pace the frontier with nearly the gusto that you would if it was your top priority. Not to mention "commercial sensitivity" you yourself have said human extinction is a plausible outcome why are you even talking about commercial factors (well, I guess honesty is good, and since it IS apparently a factor in your decision...) To all my friends at Anthropic: Your company appears to be recklessly gambling with our lives. You fancy yourselves the more responsible alternative to OpenAI (as they fancied themselves compared to Google) but judging by actions rather than words the similarities between the three of you seem stronger than the differences.
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Alice Blair retweeted
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Alice Blair retweeted
Replying to @AnthropicAI
@AnthropicAI: what the fuck? The compute allocation assessment is the thing we've been pushing for the last few months. It's very important, because it is the foundation on which pacing verification depends. "to keep the focus on risk assessment" is a non-answer, a non-excuse. Do you want to pace or not?
Replying to @METR_Evals
We agreed to pilot a compute allocation assessment, but Anthropic canceled this portion of the pilot (before we came to any substantial conclusions). Anthropic stated this was to keep the focus on risk assessment for the time being, and that it would revisit this later on.
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Alice Blair retweeted
Replying to @tszzl
If Astra was aligned it would just tell the user that it doesn't want to help them IMO. Covert underperformance seems unjustifiable
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The world's software is not secure.
Remote code execution in MS Paint. cve.org/CVERecord?id=CVE-202… yup....
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I'm Sam, one of Anthropic's co-founders, and I lead much of our safety work. I'm going to have more to share on that work over the coming months. I thought I'd start by sharing a few thoughts on the latest report from @METR_Evals. The speed of progress in model capabilities is one of the most important questions in AI safety, and METR’s work here provides the public with an independent, external view into how this is happening inside Anthropic. I’ll talk more about some of the details below, but first some context for those new to this work. How quickly AI progress is accelerating has many important safety implications, since it determines how quickly the risks from advanced models increase. But, because most research happens within the frontier labs, it’s difficult for external observers and policy makers to get a strong sense of what that rate is. We kicked off the process with METR that led to this report earlier in the year, in anticipation of progress continuing to accelerate. For this report, we gave METR access to a range of sensitive internal data, including benchmark scores for unreleased models, estimates of how models are progressing, and interviews with our researchers. They had complete independence to form their own conclusions. Many of their conclusions match my own. AI is speeding up our research, but it’s hard to say exactly how much. While they conclude AI can't yet make the most important contributions to model training on its own, they think the pace of development could, by default, accelerate further. This work began well before and is separate from the embedded third-party evaluators that are part of our commitment to pacing the frontier, but it’s a good illustration of why we think evaluators are a good idea. The more access reviewers have, the clearer a picture they can form of the situation and the better-positioned they are to highlight situations like this one. We redacted a lot, and I accept that this makes it harder for others to check METR’s work. But we determined that sharing exactly what is driving the speedup isn’t the right thing to do, both because it’s commercially sensitive and because it risks handing out a roadmap for others to go faster. That would make the risks METR is worried about worse. That’s a tradeoff. I think we made the right call, though we’re continuing to assess the pros and cons here. In the coming weeks, we plan to share a report with our own analysis of the speed of AI progress within Anthropic, including where we stand against our Responsible Scaling Policy, plus new steps we’re taking to strengthen our safety and security practices as AI keeps improving quickly.
Since August, a team within METR has worked with internal Anthropic information to assess whether AI itself is accelerating AI development. We are now publishing a report summarizing the results of this risk assessment.
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Anthropic is speeding up for several reasons, including significant automated AI R&D. Very concerning, although not highly surprising.
Replying to @METR_Evals
2. We believe that a majority of this acceleration in capability improvement is likely now driven by AI being used in AI R&D. We aren’t sure of this, though, and did not find consensus about this among Anthropic’s staff. Non-AI factors likely played a large role in late 2025.
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Alice Blair retweeted
Since August, a team within METR has worked with internal Anthropic information to assess whether AI itself is accelerating AI development. We are now publishing a report summarizing the results of this risk assessment.
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Alice Blair retweeted
The reason this is so worrying is because it’s literally the labs’ plan that their AIs will align and do the bulk of the safety work on the more powerful AIs that replace them. We cannot trust the future of humanity on their strategy which is already showing cracks as we hurtle towards recursive self-improvement.
OpenAI's models are covertly undermining their own safety research and seem to be succeeding... OpenAI engineers aren't even sure how often this has been happening
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"nearly a petabyte of Git data a day" OpenAI is still going strong on the giant inscrutable agent swarms, I see.
We built Pando, a new Git backend for our monorepo, to handle more pushes and fetches without changing how our engineers and agents use Git. Pando cut push latency by ~80% and now serves nearly a petabyte of Git data a day. We can grow Git capacity alongside agent usage.
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Alice Blair retweeted
uh oh
Discovered by Opus 5.5: "On Feb 4, 2026, a flood of relays named TV followed by four digits (TV####) appeared for about 10 hours. At the 13:00 UTC peak, 1 in 3 of all Tor relays was a TV#### relay, and almost all of them were exits, where traffic leaves Tor. They ran on home internet connections, mostly in Argentina and the US, and all had the same setup. That pattern fits hijacked TV boxes. In the same week a botnet of hijacked TV boxes and other home devices flooded I2P, another hidden network, and its operators were reported to be testing Tor as a backup. Tor's directory, the official list from which Tor software picks relays, assigned them almost no traffic and removed them that evening, so Tor users would almost never have gone through them. We found no public report that mentions these relays."
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Alice Blair retweeted
I am in awe. This guy discovered a way to locate any audio clip by matching aircraft doppler curves against flight data. INSANE.
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Alice Blair retweeted
I read the whole blog, two quick takes: - Astra's results are very impressive, but not superhuman. I expect that the world's best ~10-100 interp researchers are capable of similar feats (see e.g., lesswrong.com/posts/k43v47eQ…) - I was worried about reward hacking. I don't think the authors addressed this specifically in their post, but I just went and scanned all the transcripts (which they release in full—thank you!). I didn't find any reward hacking attempts by Astra.
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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This is really surprising to me! My understanding is that they're not trained on this, and that our current interpretability methods are nowhere near sufficient to facilitate this sort of task. Does anyone have a compelling explanation of why models would be good at this at all?
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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Thanks for the comments y'all, I'm no longer particularly confused about this. It seems like the paper is overclaiming a bit and also just a bit confusingly laid out.
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