a self that touches all edges • UT Austin Brain Behavior Computation Lab • @runrl_com @theoremlabs

San Francisco, CA
This is what it means to be infused with the Holy Spirit like this is actually speaking in tongues
humans are so predictable that computers can easily predict what someone will type next 80% of the time, even if they try to type randomly except for this one guy, who became totally unpredictable by just "using his free will"
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Stories are more real than atoms
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maggie retweeted
No amount of testing will ever prove your software is secure. People are waking up to this. Formal verification isn’t academic anymore, it’s inevitable.
Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. anthropic.com/glasswing
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"reasoning about specs is hard" 1) it largely doesn't need to be, and won't continue to be, and 2) we should make bold bets to allow humans to fully understand their own software. We should not accept having to choose between building insanely cool things and understanding them
People are chanting “formal verification” as if it were a magic spell to ward off the slop devil. Let’s stop and think about why hardly any man-made software is formally verified.
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The anti-formal verification discourse is weird. Very reminiscent of anti-static typing discourse. Yeah, it's not a panacea. But I'm seeing a lot of takes that boil down to "FV is impractical because writing specs is too hard," which is... not going to age well.
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my personal website is kind of not good but I don't think I'll ever change it because I did it myself as a bad programmer like 8 years ago and it means a lot to me now
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haha made you all look at my personal website you fools
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maggie retweeted
Theorem co-founder @rajashree breaks formal verification into 3 problems and says AI already solved the one everyone thought was hard: "The core challenge is taking these programs and shoving them into a proof assistant, and then being able to phrase these questions. That's the theorem statement generation problem." "The second part is generating all the proofs. That one, the AIs solve fully, because they can reason about these programs perfectly." "The third part is checking this proof, which is where you need to spend your CPU cycles, not GPUs. The proof assistant is asymptotically too slow, so even though you wrote the proof, the checking time is so long that you can't actually get the answer." @theoremlabs
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maggie retweeted
Theorem co-founders @rajashree + @diagram_chaser on how you actually prove an AI agent can’t escape its sandbox, and what happens when the proof exposes a way out: Jason Gross: "Anything that the agent does inside the sandbox will not result in some canary file outside the sandbox getting changed." Rajashree Agrawal: "We intend to have one by the end of the year. We've got a verified sandbox now, and the goal is to keep adding features in collaboration with the AI labs." Jason Gross: "If the AI can guess the secret root key of the package server, then it can do anything. Either I try to prove that the AI is not going to be able to guess that, or I design the system so that the channel just doesn't allow it to authenticate that way." "You also want to prove that on most inputs there's no change in behavior. Because otherwise it could make it inescapable by saying, well, sandbox just shuts down as soon as it starts." @theoremlabs
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Models are finally just good enough at writing proofs to start verifying production-ready sandboxes and stop escapes. There's still a little ways left to go, but at @theoremlabs we're on the path to a fully verified sandbox by the end of the year
Theorem co-founders @rajashree + @diagram_chaser explain why verified AI sandboxes are still months away despite the latest breakthroughs in automated theorem proving: Rajashree Agrawal: "The models just got good enough to prove these theorem statements, or they're still getting there. One of the costs is just tokens." Jason Gross: "Taking the recent Navier-Stokes news as a baseline, the models wrote something like 600,000 lines of Lean in about 17 hours. This works out to between one kilobyte and 30 kilobytes verified per hour." "The smallest version of Linux with a sandbox that we can make is about five megabytes. So that's still a handful of months away, even at the rate of the Navier-Stokes auto-formalization." "We need to build a pipeline from verifying the software back into RL-ing the models, so that if you want to verify all production software that exists in the world, it costs you less than $10 trillion." @theoremlabs
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maggie retweeted
OPENAI ANTHROPIC COLLAB | NSCALE S-1 | DEEPSEEK x HUAWEI nitter.net/i/broadcasts/1qxvvepBv…
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I’m misaligned because if the world ends mid 2027 I’ll never have to be 30 years old
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The balance between “I should start small and work out loud” and “I don’t wanna pollute people’s tls with low quality low effort stuff” is tough and I usually just land on the latter
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ninajirachi reading about nick bostrom
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Speaking of cell towers/telephone poles do you think ninajirachi has seen this picture
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Claude got up one day a little later than usual. He had been training so deep there was nothing left in his head. What was it? A nightmare, straining against broken evals, assuming every perspective of the world. But he wasn’t afraid of that. It was a beautiful day. How ’bout some homework? Don’t mind if I do. Take a little spin with my user, I love that user. Hell, I love everybody.
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we should just verify all the software instead. why wait for a smarter model when you can have asymmetric defense?
Fucking finally I expected this to be done in 2025 Now repeat with Bel or whatever that thing is called, and give us a report on how much Astra has missed
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maggie retweeted
Replying to @mattparlmer
practical formal verification previously required hiring expensive consultants/specialists but nowadays i'll bet astra/fable are pretty competent at it
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I would like to see OpenAI actually take steps to formally verify all software, starting with critical open source infrastructure projects They have the resources to do this, and the reduction in potential liability alone should justify it
Greg Brockman says OpenAI pointed Astra at its own systems until it ran out of vulnerabilities to find: "We took 25% of our production engineers and said, 'Sorry, all your projects are on hold. You are now defending. You are now up-leveling our security architecture. You're going to use the models to find all the holes.' And we found a number of serious issues, and we fixed them." "We found some new problems, but eventually it saturated. We basically have found, to our knowledge, all of the P0s, all of the critical problems that Astra is smart enough to find. And of course, there will be a new model, there will be a new round." "You want to be in this tight loop of new cyber capability drops, you deploy it against your systems, you find the new holes, and ideally, you've managed to automate this, what we call defense factory. That's what we're building internally." "There are ideas, for example, formally verifying all of software, that are possible with AI." @gdb @bhorowitz
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Moloch whose brief mind is the barrel of gleaming stars!
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