MIT Media Lab researchers propose behavioral tests for AI agents at ICML 2026

A position paper accepted to ICML 2026 challenges how we evaluate AI agents. Researchers Manuel Cherep, Nikhil Singh, and Pattie Maes (MIT Media Lab) argue that current evaluation methods focus overwhelmingly on final performance—accuracy, task completion, reward maximization—while ignoring the behavioral processes that produce those outcomes. As AI agents increasingly operate as autonomous behavioral systems, interacting with dynamic environments and adapting over time, the authors contend that we need a new evaluation paradigm rooted in the behavioral sciences. The paper proposes borrowing methods from ethology and psychology: systematic observation, controlled perturbation, and careful interpretation of an agent's actions.

The authors outline a concrete research agenda built around three key directions. First, recovering decision strategies from action sequences to understand how agents arrive at conclusions, not just whether they succeed. Second, constructing environments that isolate specific behavioral differences, much like psychophysics experiments isolate perceptual thresholds. Third, probing emergent dynamics in multi-agent systems, where collective behavior can diverge unpredictably from individual policies. Together, these approaches aim to create a rigorous "science of AI behavior." While the paper is a position piece rather than an experimental study, its acceptance at a top venue signals growing consensus that outcome-based benchmarks are insufficient for safely deploying capable agents into real-world roles.