A wager you say

One proof of concept for the GSAI stack would be a well-understood mechanical engineering domain automated to the next level and certified to boot. How about locks? Needs a model of basic physics, terms in some logic for all the parts and how they compose, and some test harnesses that simulate an adversary. Can you design and manufacture a provably unpickable lock? 

Zac Hatfield-Dodds (of hypothesis/pytest and Anthropic, was offered and declined authorship on the GSAI position paper) challenged Ben Goldhaber to a bet after Ben coauthored a post with Steve Omohundro. It seems to resolve in 2026 or 2027, the comment thread should get cleared up once Ben gets back from Burning Man. The arbiter is Raemon from LessWrong.

Zac says you can’t get a provably unpickable lock on this timeline. Zac gave (up to) 10:1 odds, so recall that the bet can be a positive expected value for Ben even if he thinks the event is most likely not going to happen.

For funsies, let’s map out one path of what has to happen for Zac to pay Ben $10k. This is not the canonical path, but it is a path:

1. Physics to the relevant granularity (question: can human lockpicks leverage sub-newtownian issues?) is conceptually placed into type theory or some calculus. I tried a riemann integral in coq once (way once), so it occurs to me that you need to decide if you want just the functional models (perhaps without computation / with proof irrelevance) in your proof stack or if you want the actual numerical analysis support in there as well.

2. Good tooling, library support, etc. around that conceptual work (call it mechlib) to provide mechanical engineering primitives

3. A lock designing toolkit, depending on mechlib, is developed

4. Someone (e.g. a large language model) is really good at programming in the lock designing toolkit. They come up with a spec L. 

5. You state the problem “forall t : trajectories through our physics simulation, if L(t) == open(L) then t == key(L)”

6. Then you get to write a nasty gazillion line Lean proof

7. Manufacture a lock (did I mention that the design toolkit has links to actual manufacturing stacks?)

8. Bring a bunch to DefCon 2027 and send another to the lockpicking lawyer

9. Everyone fails. Except Ben and the army of postdocs that $9,999 can buy.

Looks like after the magnificent research engineering in steps 1 and 2, the rest is just showing off and justifying those two steps. Of course, in a world where we have steps 1 and 2 we have a great deal of transformative applications of formal modeling and verification just in reach, and we’ll need a PoC like locks to practice and concretize the workflow. 

Cryptography applications tend to have a curse of requiring a lot of work after the security context, permission set, and other requirements are frozen in stone, which means that when the requirements change you have to start over and throw out a bunch of work (epistemic status: why do you think so many defi projects have more whitepapers than users?). The provably unpickable lock has 2 to 10 x that problem– get the granularity wrong in step one, most of your mechlib implementation won’t be salvageable. As the language model iterates on the spec L in step 5, the other language model has to iterate on the proof in step 6, because the new spec will break most of the proof.

Sorry I don’t know any mechanical engineering, Ben, otherwise I’d take some cracks at it. The idea of a logic such that its denotation is a bunch of mechanical engineering primitives seems interesting enough that my “if it was easy to do in less than a year someone would’ve, therefore there must be a moat” heuristic is tingling. Perhaps oddly, the quantum semantics folks (or with HoTT!) seem to have been productive, but I don’t know how much of that is translatable to mechanical engineering.

Reinforcement learning from proof assistant feedback, and yet more monte carlo tree search

DeepSeek’s paper 

The steps are pretraining, supervised finetuning, RLPAF (reinforcement learning from proof assistant feedback), and MCTS (monte carlo tree search). RLPAF is not very rich: it’s a zero reward for any bug at all and a one for a happy typechecker. Glad they got that far with just that. 

You can use the model at deepseek.com.

Harmonic ships their migration of miniF2F to Lean 4, gets 90% on it, is hiring

From their “one month in” newsletter. “Aristotle”, which has a mysterious methodology since I’ve only seen their marketing copy rather than an arxiv paper, gets 90% on miniF2F 4 when prompted with natural language proofs. It doesn’t look to me like the deepseek or LEGO papers do that? I could be wrong. It’s impressive just to autoformalize natural language proofs, I guess I’m still wrapping my head around how much harder it is (for an LLM) to implement coming up with the proof as well. 

Jobs: research engineer and software engineer

Atlas ships their big google doc alluded to in the last newsletter

Worth a read! The GSAI stack is large and varied, and this maps out the different sub-sub-disciplines. From the executive summary:

You could start whole organizations for every row in this table, and I wouldn’t be a part of any org that targets more than a few at once for fear of being unfocused. See the doc for more navigation (see what I did there? Navigating like with an atlas, perhaps? Get it?) of the field’s opportunities.1

Efficient shield synthesis via state-space transformation

Shielding is an area of reactive systems and reinforcement learning that marks states as unsafe and synthesizes a kind of guarding layer between the agent and the environment that prevents unsafe actions from being executed in the environment. So in the rejection sampling flavored version, it literally intercepts the unsafe action and tells the agent “we’re not running that, try another action”. One of the limitations in this literature is computational cost, shields are, like environments, state machines plus some frills, and there may simply be too many states. This is the limitation that this paper focuses on. 

We consider the problem of synthesizing safety strategies for control systems, also known as shields. Since the state space is infinite, shields are typically computed over a finite-state abstraction, with the most common abstraction being a rectangular grid. However, for many systems, such a grid does not align well with the safety property or the system dynamics. That is why a coarse grid is rarely sufficient, but a fine grid is typically computationally infeasible to obtain. In this paper, we show that appropriate state-space transformations can still allow to use a coarse grid at almost no computational overhead. We demonstrate in three case studies that our transformation-based synthesis outperforms a standard synthesis by several orders of magnitude. In the first two case studies, we use domain knowledge to select a suitable transformation. In the third case study, we instead report on results in engineering a transformation without domain knowledge.

Besides cost, demanding a lot of domain knowledge is another limitation of shields, so this is an especially welcome development.

Funding opportunities

ARIA jumped right to technical area three (TA3), prototyping the gatekeeper. Deadline October 2nd. Seems geared toward cyber-physical systems folks. In the document:

Note that verified software systems is an area which is highly suitable for a simplified gatekeeper workflow, in which the world-model is implicit in the specification logic. However, in the context of ARIA’s mission to “change the perception of what’s possible or valuable,” we consider that this application pathway is already perceived to be possible and valuable by the AI community. As such, this programme focuses on building capabilities to construct guaranteed-safe AI systems in cyber-physical domains. That being said, if you are an organisation which specialises in verified software, we would love to hear from you outside of this solicitation about the cyber-physical challenges that are just at the edge of the possible for your current techniques.

This is really cool stuff, I hope they find brave and adventurous teams. I had thought gatekeeper prototypes would be in minecraft or mujoco (and asked a funder if they’d support me in doing that), so it’s wild to see them going for actual cyberphysical systems so quickly. 

Paper club

Add to your calendar. On September 19th we will read a paper about assume-guarantee contracts with learned components. I’m liable to have made a summary slide deck to kick us off, but if I don’t, we’ll quietly read together for the first 20-30 minutes then discuss. The google meet room in the gcal event by default.  

Andrew Dickson’s excellent post

See Limitations on Formal Verification for AI Safety over on LessWrong. I have a lot of agreements, and my disagreements are more a matter of what deserves emphasis than the fundamentals. Overall, I think the Tegmark/Omohundro paper failed to convey a swisscheesey worldview, and sounded too much like “why not just capture alignment properties in ‘specs’ and prove the software ‘correct’?” (i.e. the vibe I was responding to in my very pithy post). However, I think my main reason I’m not using Dickson’s post as a reason to just pivot all my worldview and resulting research is captured in one of Steve’s comments:

I'm focused on making sure our infrastructure is safe against AI attacks.

Like, a very strong version I almost endorse is “GSAI isn’t about AI at all, it’s about systems coded by extremely powerful developers (which happen to be AIs)”, and ensuring safety, security, and reliability capabilities scale at similar speeds with other kinds of capabilities.

It looks like one can satisfy Dickson just by assuring him that GSAI is a part of a swiss cheese stack, and that no one is messianically promoting One Weird Trick To Solve Alignment. Of course, I do hope that no one is messianically promoting One Weird Trick…