From Control to Foresight: Simulation as a New Paradigm for Human-Agent Collaboration
This addresses the issue of cognitively demanding and inaccurate mental simulation for users in human-agent collaboration, though it is a conceptual perspective paper without empirical validation.
The paper tackles the problem of reactive human-agent interaction in LLM-powered systems by proposing simulation-in-the-loop as a new paradigm to enable foresight through exploration of simulated future trajectories, aiming to transform intervention into informed decision-making.
Large Language Models (LLMs) are increasingly used to power autonomous agents for complex, multi-step tasks. However, human-agent interaction remains pointwise and reactive: users approve or correct individual actions to mitigate immediate risks, without visibility into subsequent consequences. This forces users to mentally simulate long-term effects, a cognitively demanding and often inaccurate process. Users have control over individual steps but lack the foresight to make informed decisions. We argue that effective collaboration requires foresight, not just control. We propose simulation-in-the-loop, an interaction paradigm that enables users and agents to explore simulated future trajectories before committing to decisions. Simulation transforms intervention from reactive guesswork into informed exploration, while helping users discover latent constraints and preferences along the way. This perspective paper characterizes the limitations of current paradigms, introduces a conceptual framework for simulation-based collaboration, and illustrates its potential through concrete human-agent collaboration scenarios.