BAYESIAN SURPRISE IN MONTE CARLO SEARCH TREES

Agentic AI autoresearch grounded in your datasets.

It proposes the hypothesis, writes and runs the experiment, then searches for external data that could challenge the result.

MONTE CARLO SEARCH TREE SCHEMATIC

THE SEARCH LOOP

Test a claim from several evidence paths.

01PROPOSE

Write a falsifiable claim

An agent reads the tables and proposes one. Near-duplicates never enter the tree.

02SEPARATE

Answer it twice, blind

Two agents answer at once, each prepared without the other's evidence.

Literatureno tables in its workspace

Experimentno literature verdict

03CHALLENGE

Spend effort on surprise

Where those answers diverge, a fourth agent searches for a compatible external dataset and tests the claim again.

04UPDATE

Grow the search tree

MCTS scores the branch and picks where the next experiment is worth the compute.

LIVE EVIDENCE

The lead view is selected by a fixed rule, not editorially.

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