[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83054-en":3,"doc-seo-83054-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83054,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Toy Framework for Single and Multi-Agent Human-AI Curiosity Ecosystems","A toy framework models curiosity as an ecosystem, explaining how an agent’s inquiry policy depends on weighted preferences for immediate uncertainty reduction, inquiry costs, delayed returns, and the value of keeping questions open. These weights can drift with experience, so cheap fast answers may shift short-term costs and long-horizon question preferences. The framework extends to multiple agents sharing a knowledge landscape, tracking inquiry volume, topic diversity, frontier-directed exploration, redundancy, and reusable knowledge, enabling future multi-agent AI discovery research.","arXiv :2607 .062 14v 1 [ cs .AI ] 7 Jul 2026  \nA toy framework for single and multi-agent human-AI curiosity  \necosystems  \nIlya E. Monosov  \nSolomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA Departments of Biomedical Engineering, Electrical and Computer Engineering, and Psychiatry, Johns Hopkins University, Baltimore, MD, USA  \nZanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA Data Science and Artificial Intelligence Institute and the Kavli Neuroscience Discovery Institute,  \nJohns Hopkins University, Baltimore, MD, USA  \n[ilya. monosov@gmail. com](ilya. monosov@gmail. com)  \nHighlights  \n• Asking a question is a choice among competing values in immediate uncertainty reduction, effort, delayed return, and the value of leaving the question open.  \n• The weights on those values can change or drift. Repeated exposure to fast cheap answers may gradually encourage fast resolution to be more attractive and long-horizon inquiry less attractive.  \n• The framework can help to generate future theories that can be applied to groups of people or AI agents that share and explore a knowledge landscape. Over time, it can also inform multi-agent AI development.  \n• This work supports future studies of how inquiry becomes adaptive and generative, or maladaptive.  \nAbstract  \nThis paper offers a toy 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 decisionrelated 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 toy framework for studying curiosity ecology and for future efforts towards designing multi-agent AI systems for discovery. It serves as a companion piece for a paper currently under review in Trends in Neurosciences.  \n1 Introduction  \nWhy do we ask certain questions and avoid asking others? A question can tempt us because the answer will remove lots of uncertainty, because the answer may pay off substantially, even if only later, or because it is easy to pursue, or simply because everyone around us is already asking it. Models of curiosity and information-seeking suggest that the subjective value we place on an answer rises and falls with how uncertain we are and how much uncertainty we think the answer will resolve (for review see Monosov, 2024 ; Bromberg-Martin and Monosov, 2020) . This subjective value of information can change on a moment-by-moment basis.  \nA lasting change in context can change how our preferences are expressed, or change the preferences themselves (Tversky and Simonson, 1993) . One neurobiologically inspired way to formalize this is to assume that the brain has weights for different features or attributes of a question (e.g., amount of uncertainty it would reduce, time to ask, cost, delayed payoff, and so on) and to allow the weights to move, drift, or be updated systematically in response to context and experience. Then, the questions a person has already been asking (their history), the cost of the tools available, the questions being asked by their peers, and the value attached to answers in the surrounding environment shape what questions feel like they are worth pursuing next. The same question can look trivial in one ecology or one agent and valuable in another ecology (to the same or to other agents) .  \nThis paper attemp","cbCaisjCzWAFxx3T","https://ap.wps.com/l/cbCaisjCzWAFxx3T","pdf",309562,3,1,13,"English","en",105,"# Abstract\n# Introduction\n# Single agent’s inquiry\n## Value decomposition\n## Costs, delayed return, and keeping questions open\n# Many agents and shared knowledge landscape","[{\"question\":\"What does the framework treat as the “ecosystem” of curiosity?\",\"answer\":\"It frames curiosity as an ecosystem consisting of an agent’s inquiry decisions and how those preferences interact with context and shared knowledge over time.\"},{\"question\":\"How does the model decide when a single agent asks a question?\",\"answer\":\"The agent’s value combines immediate uncertainty reduction, the cost of asking, long-horizon return from later usefulness, and the value of not answering yet (keeping the question open).\"},{\"question\":\"What additional quantities are tracked in the multi-agent setting?\",\"answer\":\"When many agents explore a shared knowledge landscape, the framework tracks inquiry volume, topic diversity, frontier-directed inquiry, redundancy, and reusable 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