Publication
Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction
Sophia Koehler; Antonia Wüst; Inga Ibs; Wasu Top Piriyakulkij; Wolfgang Stammer; Constantin A. Rothkopf; Kevin Ellis; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2607.08233, Pages 1-31, arXiv, 2026.
Abstract
A central challenge in building intelligent systems is enabling agents to jointly
perceive complex inputs, form hypotheses about hidden patterns, and design infor-
mative experiments to test them. To study this problem, we propose ZendoWorld,
a controlled interactive environment in which agents must infer a logical rule about
visual game observations, acquire information by proposing new scenes, and refine
their hypotheses based on feedback from the game environment. We evaluate
several agents spanning pure VLM reasoning, Bayesian particle filtering, dynamic
concept discovery, and neuro-symbolic methods. Our main findings are: (1) high
accuracy in predicting labels for observed examples does not imply recovery of the
underlying rule; (2) perception and induction are distinct bottlenecks for different
agent classes; and (3) VLM-based agents propose near-uninformative experiments,
failing to actively reduce hypothesis uncertainty. To compare these results, we col-
lect human data on the task, which reveals a gap in inductive reasoning, particularly
for more complex rules. Overall, ZENDOWORLD takes an important step toward
evaluating intelligent agents and identifies concrete avenues for improvement, par-
ticularly in domains like scientific discovery.