LLM Digest

Story

arxiv_cs_ai · Aug 10, 2026 · paper

Source brief

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

arxiv.orgAug 10, 2026
original source linked

In brief

Predicting the answer to interventional ``what if'' questions --- the outcome of an action never taken --- requires a \emph{mechanistic}, causal model, not a curve fit; and learning such a model requires \emph{experim...

Feed lens

agent

Read the original at arxiv.org →Open in live feed Read that day’s brief

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

Earlier in this thread 4 items

An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery

AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching

Staypoint Detection from Noisy Trajectory Data [Experiment Paper]

5 Trends That Defined AI Engineering at World’s Fair 2026