Cofounder, @LeashBio.

Salt Lake City, UT
Joined May 2010
Ian Quigley retweeted
Labs in the loop - at compute speed. The future is on the cluster.
We premeasured physical worlds of millions of chemicals binding to proteins and let agents explore them. Here's what it looks like! 1/
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We premeasured physical worlds of millions of chemicals binding to proteins and let agents explore them. Here's what it looks like! 1/
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You don't have to believe me! We released a pile of these worlds at strelka.leash.bio/ for you to try this yourself. We also dropped thousands of transcripts there. Please check out our preprint at github.com/Leash-Labs/minos-… 7/
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We think labs-in-the-loop will truly enable agentic science, which is why we built so many that they can explore at compute speed! /fin
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🚩 When Polymarket or Kalshi says there's a 70% chance something will happen, should we believe it? Excited to share our new paper, just published in PNAS, in which we examined more than ONE MILLION forecasts from Polymarket, Kalshi, Manifold, and Metaculus, spanning 12 domains and forecasting horizons ranging from hours to years. The main finding is striking: Prediction markets and collective forecasting platforms are remarkably well calibrated. When they assign events a 70% probability, those events really do happen about 70% of the time. And this pattern holds surprisingly well across different platforms, domains, and forecasting horizons. But there's an important twist. When we looked more closely, we discovered systematic biases in some of the domains where forecasting matters most (wars, elections). For decades, psychologists have documented how poorly humans often reason about probability and uncertainty. Our findings offer an encouraging counterpoint: When judgments and information are aggregated, we can achieve a surprisingly accurate understanding of an uncertain future. That doesn't mean we can predict what will happen next with certainty. But it does mean that we can be remarkably good at estimating how uncertain the future really is. This work was a great collaboration. The data for this project was meticulously collected for years by Justin Dickerson, the project was led by Hilla Shinitzky and Maayan Peer, with @mattansb leading the analyses. 📄 Read the paper: pnas.org/doi/10.1073/pnas.26… #Forecasting #Polymarket #Kalshi #DecisionScience #PNAS
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Ian Quigley retweeted
Today we release our first collaborator model @humansand! Ampersand is the first model with privacy built in (unlike most other models where privacy is an afterthought), co-designed in tandem with our product (more soon!!). We designed our environment and simulations such that the post-training’s optimization pressure incentivizes decisions that abide by contextual privacy norms, through long horizon outs.
Replying to @humansand
Unlike assistant models trained with verifiable or preference reward, Ampersand was trained to understand the long term consequences of its actions through simulation feedback ampersand.humansand.ai/blog
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Addendum: a brief taxonomy of agent failure modes
Last week we released Minos, our framework of premeasured experimental worlds for agentic science. Minos is not for obtaining the best scientific result, it's for understanding how AI agents make scientific decisions and maybe help them make better ones. Let's dig in! 1/
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Since the beginning of Leash, the main battle we have fought is generalization vs memorization. It is too easy for models to memorize the patterns that lead to binding in this particular instance, but fail to generalize the next time you use the model.
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Last week we released Minos, our framework of premeasured experimental worlds for agentic science. Minos is not for obtaining the best scientific result, it's for understanding how AI agents make scientific decisions and maybe help them make better ones. Let's dig in! 1/
Agentic models can act, observe consequences, and decide what to do next. This is great for coding, but if you want them to do science, lab experiments take forever. To fix this, we made a lab-in-the-loop with zero latency! 1/10
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We dropped ~4500 transcripts of agents working on these tasks for you to dig through yourself at strelka.leash.bio. We hope folks find a more complete taxonomy of failure that we can all use to improve agentic science. 11/
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We think that data generation in the future is likely to include not just searches for the best answer, but generating data designed to help agents learn. We wonder if Minos might be a form of scientist school! /fin
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