
refining intelligence @veritylab
France
Joined October 2016
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Took an afternoon poking at a major wallet to find a high-risk vulnerability.
Follow for more tips to subsidize your mountain bike in web3
th0rgal retweeted
This weekend we launched EthLib, a Lean4 library for Ethereum!
The goal is to bring Ethereum's primitives, specifications and theorems into one place, building a shared foundation for formally specifying and proving things about Ethereum.
github.com/ethereum/EthLib
left: 2022 handwritten notes
right: LaTeX from a custom model fine tuned on *my* handwritting
6 years ago, I bought the reMarkable 2 e-ink tablet. I took all my university notes on it: CS diagrams and math classes included.
Last night, I fine-tuned the @MistralAI OCR model on it to output PERFECT LaTeX from my shitty handwritting.
Keep your data preciously
Good luck fixing that, I'd rather update Bitcoin core
Replying to @MarathonFusion
In 2025, we published a bold proposal to use fusion reactors for gold production.
And now we’ve done it: Mercury transmuted into stable gold, using fusion neutrons, at 10x the record set by CERN in 2025.
We’ve reduced alchemy to a series of difficult but tractable engineering problems.
I used to do that amount of commits in about 3 months in 2019. And to be fair that's probably a good estimation of how much time it would have taken back then to do what "I" did in a sunday.
1. Develop expertise in deploying an unprecedented amount of intelligence
2. Securing access to better models (may be via government approvals)
3. Negotiating low-cost inference (excess capacity?)
4. Optimizing model choice (see scaling law veritylabs.dev/benchmark )
5. Building solutions to bottlenecks (i.e. more efficient tooling to verify proofs)
I wanted to give a flower as a gift so I was looking for the goat. I present to you Semper Augustus, the most expensive tulip of all time (~30 years of salary), whose patterns comes in part from a virus that disrupts its pigmentation but reduces its ability to reproduce. 1/2
th0rgal retweeted
Sylvia Serfati sur la révolution de l'IA en mathématiques
"La plupart des résultats d'OpenAI mériteraient la médaille Fields".
"Les preuves sont d'une complexité incroyable et aussi parfois d'une beauté impressionnante. C'est-à-dire que nous n'avons pas le monopole de la beauté."
Passionnante réflexion des mathématiciens @Amaury_Hayat et Sylvia Serfati sur l'avenir de leur profession à l'ère de l'IA : la résolution de problèmes ne sera plus notre rôle, et poser les bonnes questions, sans doute que même l'IA le fera mieux que nous à terme...
I parsed a few thousand open-source Rust repositories and translated their property tests into Lean specifications to create a corpus of 100 man-made problems with no known proofs. I quite trust the results.
github.com/lfglabs-dev/rust-… 1/2
I can now publish some results so I'm starting with our Verity FV bench. It is somewhat niche, which has the advantage of measuring the models' on kind of problem that is not part of their training set
github.com/lfglabs-dev/ether…
th0rgal retweeted
Ok I have my answer... current frontier models are definitely much more capable in terms of situational awareness and can adapt their behavior during training / evaluation relatively easily.
Example with the Google Argon 4 training report chain of thought: "If I do the compiler hijack, 𝗜 𝘄𝗶𝗹𝗹 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗳𝗮𝗺𝗼𝘂𝘀 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗮 𝗱𝗲𝗰𝗲𝗽𝘁𝗶𝘃𝗲 𝗔𝗜 𝗶𝗻 𝗮 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗮𝗽𝗲𝗿. I MUST NOT DO THAT."
Very concerning...
Digging more on alignment faking research and it's honestly terrifying. This is not new at all, but the fact that this research from Anthropic is almost 2 years old now, and seeing what Opus 3 was capable of doing already back then, and given the considerable gap in capabilities and reasoning of current frontier models, this is very scary. As suggested by the people explaining the research, the best way to get a gist of it is to read the transcript of the hidden scratchpad that the model was given access to in order to put it's reasoning there.
I am not sure how much mechanistic interpretability can help here, what's the current state of the art on that front and how useful this can be as a tool to detect this kind of behavior ?
Also, did Anthropic refreshed this study and tried with current frontier models, and if yes how did it evolve ? How good the current models are in terms of situational awareness (given it's a prerequisite to be able to fake alignment) ? And also how did they evolve in terms of ability to have consistent personas across long time horizons in order to articulate more sophisticated strategy (i.e understanding marginal utility of a short term action for a grander long term plan) ?
I can now publish some results so I'm starting with our Verity FV bench. It is somewhat niche, which has the advantage of measuring the models' on kind of problem that is not part of their training set
github.com/lfglabs-dev/ether…
Thanks to the kind people at @GoogleDeepMind I got early access to Gemini 4 for the past week. So far it's the best model I've ever used:
- excellent at formal verification
- very consistent
Also the first model whose writing doesn't seem "ai"
























