dron @_dron_h
math/music/ai nerd | research @GoodfireAI | prev cambridge, bair, polaris | giving a semantics to the syntax garden.dronhazra.com Joined April 2019-
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Excited to share our work digging into how Evo 2 represents species relatedness or phylogeny. Genetics provides a good quantitative measure of relatedness, so we could use it to probe the model and see if its internal geometry reflects it.
Excited to share our work digging into how Evo 2 represents species relatedness or phylogeny. Genetics provides a good quantitative measure of relatedness, so we could use it to probe the model and see if its internal geometry reflects it.
i saw early versions of this work when i was still in school and it made waiting to join this team very difficult... very cool results! @_MichaelPearce
i saw early versions of this work when i was still in school and it made waiting to join this team very difficult... very cool results! @_MichaelPearce
Arc Institute trained their foundation model Evo 2 on DNA from all domains of life. What has it learned about the natural world? Our new research finds that it represents the tree of life, spanning thousands of species, as a curved manifold in its neuronal activations. (1/8)
What if adversarial examples aren't a bug, but a direct consequence of how neural networks process information? We've found evidence that superposition – the way networks represent many more features than they have neurons – might cause adversarial examples.
New research! Post-training often causes weird, unwanted behaviors that are hard to catch before deployment because they only crop up rarely - then are found by bewildered users. How can we find these efficiently? (1/7)
Could we tell if gpt-oss was memorizing its training data? I.e., points where it’s reasoning vs reciting? We took a quick look at the curvature of the loss landscape of the 20B model to understand memorization and what’s happening internally during reasoning
heck of a first week
heck of a first week
Some neat results from hacking on gpt-oss at the Goodfire internal hackathon this week: 1. MoE experts are... actually experts? 2. The model seems to know which experts it's going to use for a token from the very first layer of the model. Here we see the "business expert":
if you really understand a neural network you should be able to explain and edit anything in the model by directly manipulating the activation tensor. we made a demo of this with diffusion models
if you really understand a neural network you should be able to explain and edit anything in the model by directly manipulating the activation tensor. we made a demo of this with diffusion models
We created a canvas that plugs into an image model’s brain. You can use it to generate images in real-time by painting with the latent concepts the model has learned. Try out Paint with Ember for yourself 👇
We're publishing new queryable datasets to help researchers explore interpretable features in DeepSeek R1.
i've added a little more to our recent deepseek r1 SAE launch :)
i've added a little more to our recent deepseek r1 SAE launch :)
Today, we're announcing our $50M Series A and sharing a preview of Ember - a universal neural programming platform that gives direct, programmable access to any AI model's internal thoughts.
I've got some big personal news: I'm joining @GoodfireAI to lead a fundamental interpretability research team in London! This has been a while coming /n
r1: <completely breaks>. ahem. well. nevertheless,
r1: <completely breaks>. ahem. well. nevertheless, https://t.co/BMqJq5mZsq
i've been working on this for the past few months! excited to share some initial results we've found trying to interpret a big reasoning model
i've been working on this for the past few months! excited to share some initial results we've found trying to interpret a big reasoning model
To interpret AI benchmarks, we need to look at the data. Top-level numbers don't mean what you think: there may be broken tasks, unexpected behaviors, or near-misses. We're introducing Docent to accelerate analysis of AI agent transcripts. It can spot surprises in seconds. 🧵👇
AI models are *not* solving problems the way we think using Docent, we find that Claude solves *broken* eval tasks - memorizing answers & hallucinating them! details in 🧵 we really need to look at our data harder, and it's time to rethink how we do evals...
AI models are *not* solving problems the way we think using Docent, we find that Claude solves *broken* eval tasks - memorizing answers & hallucinating them! details in 🧵 we really need to look at our data harder, and it's time to rethink how we do evals... https://t.co/GXRsp0WU9J
XantheWhyet @9gaMy844XI2Sz
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Karen @braillto
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aaron @aarnphm_
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unruly abstractions @unrulyabstract
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steven @lostncostco
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Liv @livgorton
3K Followers 416 Following ✨ asking sand to show its work @GoodfireAI // deep learning, math, biology // creating a more beautiful future // (opinions my own)
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9K Followers 20 Following Advancing humanity's understanding of AI through interpretability research. Building the future of safe and powerful AI systems.
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