Some updates on my "Model Discovery Agent" paper. 1) Significant new version posted to https://t.co/CUfwMhQdpP; 2) video of a talk I gave at U Toronto posted to https://t.co/y2izltTO1o ; 3) I now have a cute picture of the (polyglot) MDA workflow :) https://t.co/5VeZY78waW
Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models arxiv.org
AI Weekly's analysis
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- Kevin Murphy's Model Discovery Agent uses an LLM to propose candidate causal structures, then Bayesian machinery to test them from few interventions.
- The pipeline combines sequential Monte Carlo, simulation-based inference, and value-of-information experiment design in an M-open hypothesis setting.
- MDA is evaluated on physics, chemistry, and a new single-neuron electrophysiology benchmark introduced alongside the paper.
Read full analysis →
New mini-paper: "A note on goal-based hierarchical RL". It combines the cool agent-centric general value function (ACGVF) construction of @geraudnt with work I did 25 years ago (!) on hierarchical HMMs. Caveat: no experiments yet... https://t.co/gcLHHQks8S https://t.co/AYnu636cga
A note on goal-based hierarchical RL arxiv.org
Interesting LinkedIn post from @DaphneKoller that I 100% agree with . You need to combine optimal experiment design, active data collection, AI/ML and causal modeling to make progress in bio/health. Source: https://t.co/4UsNLIyz8P https://t.co/2Z1Y3wQvus
AI in biology: Data, not intelligence, is the bottleneck | Daphne Koller posted on the topic | LinkedIn linkedin.com