Stewart Slocum

currently @openai, prev @xai, phd @MIT

Let us run with perseverance

[P-Zero Research @pzeroresearch]

Fable 5.2 is the best model we have tested at predicting experiment results. Its score is 0.36, in line with a steady improvement since GPT-4 Turbo.

Predicting results before running an experiment is a core part of research taste: a model that does it well can skip bad experiments and get more progress from the same compute.

[Stewart Slocum @stewpervised]

Great technically informed work. I'm very happy to see this!

[Director Michael Kratsios @mkratsios47]

Today, we released the National Policy for Nucleic Acid Synthesis Screening to protect American biotechnology innovation while reducing the risk of misuse.

This policy establishes a new, government-wide screening requirements for synthetic DNA and the equipment used to make it. It will ensure companies selling nucleic acid sequences and synthesis equipment know their customers and prevent the malicious use of these materials and tools.

This policy strengthens our Nation’s biosecurity posture by establishing rigorous U.S. government-wide procurement screening and physical security standards for synthetic biology. It replaces outdated “self-attestation” systems with robust, independent verification. It creates a dynamic, SI-powered screening layer that assesses the biological risk from nucleic acid sequences, allowing the U.S. to anticipate and prevent malicious use and help stop dangerous gain-of-function research.

@NIST will issue rigorous new screening standards and @HHSGov and @USDA will certify synthetic biology providers. Together, these measures will strengthen American biosecurity, safeguard responsible innovation, and establish a global standard for safe and transparent synthetic biology tools and research.

[Stewart Slocum @stewpervised]

Cool work! If you have interesting computational biology problems, you should give Locus a try

[Intology @intology]

Locus sets the frontier for automating molecular design

Our automated research system Locus develops scientific AI models that outperform SOTA expert solutions

Unlocking the potential of scientific AI requires leveraging proprietary data. Locus achieves this autonomously 🧵👇

--

We test Locus across a number of tasks related to molecular scoring and design: training molecular property prediction models for a proprietary task, fine-tuning Boltz-2 to predict small-molecule potency, and building antibody design workflows around BoltzGen, Boltz-2, ESMC, and ESMFold2.

Each of these settings are real-world AI for science use cases, where tasks are often out-of-distribution for existing foundation models and limited in data, while featuring expensive and noisy evaluators. Compared to state-of-the-art baselines and LLM agents set to optimize these tasks, Locus achieves higher performance. Additionally, Locus generalizes better despite limited data, finds more sample efficient solutions, and explores a higher diversity of solutions.

[Eric Wallace @Eric_Wallace_]

We are now allocating a significant amount of our frontier compute towards discovering and patching vulnerabilities in open source codebases, fully pro bono.

In one case, we studied the Linux kernel, which is a particularly load-bearing piece of software at OpenAI.

Before AI, security researchers might discover ~20 local privilege escalation (LPE) vulnerabilities in Linux in a typical year.

In one week's time, our upcoming frontier cyber model was able to find ~1000 LPE vulnerabilities using about $1M of tokens. In the process, it was able to discover at least 10 novel classes of Linux exploitation techniques.

[Leading Report @LeadingReport]

Late-decider voters are swinging Republican by 9 points, per NYT.

[Stewart Slocum @stewpervised]

Yes

[OpenAI @OpenAI]

As AI becomes more capable at finding vulnerabilities, defenders need to put it to work before attackers do.

We’re planning our next internal security sprint at OpenAI and partnering with Cathedral Systems, whose researchers will use our models alongside our teams to challenge our defenses. They’ll bring differentiated expertise and approaches to our ongoing work to find and fix weaknesses with AI.

We’re also raising bug bounty rewards, including more than quadrupling rewards for critical findings. Exceptional findings will be eligible for up to $172,000, including bonuses.

We’ll keep advancing our models, using them to strengthen our own systems, and expanding access for trusted defenders.

https://t.co/vcaoHROTt2

[Tibo @thsottiaux]

Le GPT Fat

[Stewart Slocum @stewpervised]

🤷🤷😁

[@jason @Jason]

Updated Silver bulletin and @Polymarket odds… can President Trump pulls a rabbit out a hat for a third time?!

See you on the @theallinpod and @Polymarket stream tomorrow night at 8pm ET, 7pm Austin time and 5pm on the left coast

[Benny Johnson @bennyjohnson]

CNN: Republicans have beat the final polling average in states that Trump won in the prior election by 4.1 points.

If the same pattern holds, the GOP will keep the Senate:

🔴-51
🔵-49

[Stewart Slocum @stewpervised]

Large and powerful groups of people relish at the chance to manipulate the law against Elon purely because they dislike him. I've seen this firsthand many times. Using the law to blatantly punish an enemy is an abuse of power.

[Axios @axios]

Trump vows to protect Musk, floats pre-emptive staff pardons https://t.co/jAXeaxqmBe

[FBI Cyber Branch @FBICyberBranch]

The FBI is releasing information advising the Democratic People's Republic of Korea (North Korea) was responsible for the theft of more than $6 billion USD in virtual assets from cryptocurrency exchange, Binance, on or about October 29, 2026. The FBI refers to this specific North Korean malicious cyber activity as "TraderTraitor."

The FBI, together with partners at the USSS and IRS-CI, has observed TraderTraitor actors converting stolen assets primarily into Ethereum (ETH). It is expected these assets will then be further laundered by converting them using DeFi and instant swap services as they prepare them for further laundering stages.

How You Can Help:
FBI, USSS, and IRS-CI are urging private sector entities including THORNode operators, RPC node operators, instant exchanges, DeFi services, blockchain analytics firms, and other virtual asset service providers to block transactions with or derived from addresses TraderTraitor actors are using to launder these stolen assets.

A list of addresses associated with this activity is available here: https://t.co/g0oIWx0WeD

[Stewart Slocum @stewpervised]

@a16z And each token becomes more valuable 😎

[Joshua Achiam @jachiam0]

People who have been in research fields develop a felt sense that most "research" (in almost any field that isn't exactingly technical) is research theater, and many superstar careers enjoy star status not because of a unique connection to the truth but because of a unique connection with an audience. No one really has the time or stature to, from a position of objectivity and trust, bring popular researchers to heel. But it will get increasingly hard to hide from serious critique. The machines can do critique now, at the level of depth and rigor previously only possible for the academically deranged. We're going to have to revise a lot of what we "know" about the world that isn't really knowledge but is actually just received popular consensus - like floating voltages without grounding wires. The grounding wires are coming.

[Eric Wallace @Eric_Wallace_]

We’ve built an “eyes on everything” system that monitors nearly *every single rollout* from our frontier models across RL training, evals, and internal codex usage.

It uses layers of chain-of-thought-monitors and investigator agents to dig through a model’s work, run code to examine evidence, and cross-check network logs to reconstruct what happened.

For frontier RL runs, the system also pages humans 24/7 for urgent security alerts.

It's been incredibly useful for identifying model misalignment and possible holes in our containment.
https://t.co/dcYStAKByZ

[Stewart Slocum @stewpervised]

@baseten @mirendil Impressive!

[David Sacks @DavidSacks]

There’s a world of difference between user-authorized agents (Muse, Instinct, Grokbot, etc.) and true unauthorized/rogue agents that could constitute cyberattacks.

Most of the activity described in the Axios story appears to be the former: customers sharing credentials with agents so they can take actions on their behalf. This may violate bank terms requiring humans to log in themselves, but these are in no way cyberattacks.

The direction banks should go is to support user-directed agents. For example, Amex is an authorized connection in Muse, making it easy for users to grant, limit, and revoke access.

Read-only permission levels are a sensible interim step. Several banks already support or are moving toward them. Full write access (with user credentials) is a different risk category and should be approached carefully.

Frightening the public by blurring the line between user-authorized agentic behavior and cyberattacks is irresponsible.

[Aleksander Madry @aleks_madry]

Personal update: Turns out that what I thought was leaving OpenAI became more of a sabbatical.

When I left a few months ago, I felt that the most important thing to focus on was making AI more useful to people. But we’ve since entered a different regime of AI progress. One in which getting the safety and security challenges of increasingly capable AI right has to become a central priority.

So I’ll be returning to OpenAI, joining @merettm to help chart a path through these challenges.

There’s much work to do, so let’s get going.

[Chamath Palihapitiya @chamath]

These are some very important observations from Satya.

As models and harnesses commoditize, intent, verification and evidence will gate how quickly SI permeates enterprises and businesses.

Importantly, you need to implement systems that separate who does the work from those who govern, measure and sign off on it.

This is a new kind of “harness” that will emerge over the next few years. It may become a new system of record over time. One whose entire role is to track “change” and map it back to SOPs, policies, laws and the like.

[Dean W. Ball @deanwball]

It seems a bit premature to declare that AI hasn’t changed the nature of cyberrisk when amateurs are (apparently) successfully and single-handedly hacking into large banks

[Micah Carroll @MicahCarroll]

Some info on how we’ve been working towards safety cases at OpenAI

Note that until there is a safety “brief” for it, any type of frontier workload cannot start – this has real teeth, and has already been a good motivating force for a lot of safety work