A framework for single and multi-agent human-AI curiosity ecosystems
arXiv:2607.06214
The paper proposes a conceptual framework that treats curiosity as an ecosystem, modeling how an agent’s questioning behavior depends on uncertainty reduction, costs, delayed returns, and how these factors evolve with experience, and extends the model to multiple agents sharing a knowledge landscape.
Abstract
This paper offers a framework for considering curiosity as an ecosystem. First, it suggests that a single agent's inquiry policy (how, when, and why an agent asks a question) depends on how the agent values immediate uncertainty reduction, costs, delayed return, and the value of keeping the question open. A key concept in the framework is that the weights on these decision-related terms can change with experience. For example, a period of cheap, quickly answered questions may change the cost of inquiry on a short timescale and change which kinds of questions the agent is drawn to answer over a longer timescale. Second, these ideas are extended to many agents exploring a shared knowledge landscape, and there the framework tracks inquiry volume, topic diversity, frontier-directed inquiry, redundancy, and reusable knowledge. The result is a conceptual framework for studying curiosity ecology and for future efforts towards designing multi-agent AI systems for discovery.
fixed abstract in this version