Participation in crowd-sourced user studies is often driven by monetary incentives. However, standard payment schemes that reward completion unless responses are of poor quality may not invoke sufficient accountability. By compromising user engagement, a lack of accountability can affect data quality and the study’s ecological validity. Here, we investigate alternative compensation strategies that manipulate payment framing and evaluate their impact on engagement through task effort, outcomes, and perception. We compared a standard scheme with implicit rejection risk to a reinforced accountability condition with explicit performance-linked deductions, and two dynamic conditions that unexpectedly switched strategies. In a study with 106 Prolific participants on an image captioning task, we found that only shifting from implicit risk to reinforced accountability significantly increased engagement, likely due to loss aversion after participants had already invested time. The reverse shift decreased effort as observed in the standard group. Our results highlight the importance of carefully designing compensation schemes.

Theory of Mind (ToM), the ability to understand people's minds based on their behavior, is key to developing socially intelligent agents. Current approaches to ToM reasoning either rely on prompting Large Language Models (LLMs), which are prone to systematic errors, or use handcrafted, rigid agent models for model-based inference, which are more robust but fail to generalize across domains. In this work, we introduce *AutoToM*, an automated agent modeling method for scalable, robust, and interpretable mental inference. Given a ToM problem, *AutoToM* first proposes an initial agent model and then performs automated Bayesian inverse planning based on this model, leveraging an LLM backend. Guided by inference uncertainty, it iteratively refines the model by introducing additional mental variables and/or incorporating more timesteps in the context. Across five diverse benchmarks, *AutoToM* outperforms existing ToM methods and even large reasoning models. Additionally, we show that *AutoToM* can produce human‐like confidence estimates and enable online mental inference for embodied decision-making.