Expert attention map

The Who's Who of AI

What credible people across AI noticed, why it matters, and where the field is converging or disagreeing.

2,373 searchable experts 3,451 tracked across all sources
Build my stream Create my edition
Filter the conversation Who is saying what?

Combine a professional role with a reaction lens. Both must match the same attributed contribution.

Clear all
New Network reaction maps

See how experts are reacting—not just what they shared.

Posts are grouped by conversation and tone. Select a lens to filter the stream; these are never permanent labels on people.

Building & implementation Research & technical analysis
1 live reaction map in this view Jump to the evidence ↓
Multiple readings Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models Research & technical analysis · 3 attributable reactions
Showing signals surfaced by Kevin Patrick Murphy ×
Most important

The developments commanding sustained expert attention

One card per development. Sources are clustered; reaction bundles describe these posts, never the people behind them.

Established Models & releases Research 11d ago
⚡ 109 h early
model discovery agent Bayesian paper

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models

3 directory members surfaced this signal.

3 experts 1 community 1 sources clustered
Kevin Patrick Murphy Sung Kim Artificial Intelligence Papers
How the network is reacting Experts are approaching this through 2 distinct lenses.
Multiple readings

Research & technical analysis

2 experts

Evidence, methods and technical implications.

“Paper: arxiv.org/abs/2608.09696 Repo: github.com/murphyk/neur...”

Building & implementation

1 expert

How teams are shipping and applying it.

“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:/…”

Primary source arxiv.org ↗