A toy framework for single and multi-agent human-AI curiosity ecosystems

Source

Source name: arXiv
Source kind: paper
Authors / organization: Ilya E. Monosov
Venue: arXiv: cs.AI
Published: 2026-07-07
Collected: 2026-07-08
URL: http://arxiv.org/abs/2607.06214v1
PDF: https://arxiv.org/pdf/2607.06214v1
DOI: UNKNOWN

Plain-Language Brief

This research paper introduces a simplified framework for understanding curiosity within single and multiple agent systems. The framework considers how an agent decides to ask questions based on factors like immediate uncertainty reduction, costs, delayed returns, and keeping questions open. It also explores how these factors can change over time due to experience. Furthermore, the paper extends this concept to various agents exploring a shared knowledge landscape, tracking aspects like inquiry volume, diversity of topics, frontier-directed inquiry, redundancy, and reusable knowledge.

What the Document Says

The paper presents a toy framework for understanding curiosity within single and multiple agent systems.
It explains that an agent's inquiry policy depends on immediate uncertainty reduction, costs, delayed return, and the value of keeping questions open.
The weights on these decision-related terms can change with experience.
The paper also considers a scenario where multiple agents explore a shared knowledge landscape.
In this context, the framework tracks inquiry volume, topic diversity, frontier-directed inquiry, redundancy, and reusable knowledge.

Key Concepts Mentioned

curiosity
single agent
multiple agents
inquiry policy
immediate uncertainty reduction
costs
delayed return
value of keeping questions open

Claims Made by the Document

A single agent's inquiry policy depends on factors like immediate uncertainty reduction, costs, delayed return, and the value of keeping questions open.
The weights on these decision-related terms can change with experience.

Evidence Used

Text directly from the paper.

Methods / Approach

Introducing a toy framework for studying curiosity ecology

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The toy framework is simplified and not yet implemented or tested.

Open Questions

How effective is this toy framework in predicting real-world agent behavior?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper mentions a companion piece currently under review in Trends in Neurosciences, which might provide additional insights.

Raw Abstract / Excerpt

This 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 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 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.