# Artificial Agency Program: Curiosity, compression, and communication in agents

arXiv: https://arxiv.org/abs/2602.24100 · HTML: https://mlsanity.com/paper/2602.24100/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 14 of 14)

- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Adaptive Computation Time for Recurrent Neural Networks](https://mlsanity.com/paper/1603.08983/) (2016)
- [PonderNet: Learning to Ponder](https://mlsanity.com/paper/2107.05407/) (2021)
- [Mini-Omni-Reasoner: Token-Level Thinking-in-Speaking in Large Speech Models](https://mlsanity.com/paper/2508.15827/) (2025)
- [Learning to select computations](https://mlsanity.com/paper/1711.06892/) (2017)
- [Plasticity as the Mirror of Empowerment](https://mlsanity.com/paper/2505.10361/) (2025)
- [Changing the Environment Based on Empowerment as Intrinsic Motivation](https://mlsanity.com/paper/1406.1767/) (2014)
- [Learning to Insert [PAUSE] Tokens for Better Reasoning](https://mlsanity.com/paper/2506.03616/) (2025)
- [Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization](https://mlsanity.com/paper/2601.21358/) (2026)

## Built on by (most cited first, 1 of 1)

- [Prediction and Empowerment: A Theory of Agency through Bridge Interfaces](https://mlsanity.com/paper/2605.06346/) (2026)

## Abstract (arXiv)

This paper presents the Artificial Agency Program (AAP), a position and research agenda for building AI systems as reality embedded, resource-bounded agents whose development is driven by curiosity-as-learning-progress under physical and computational constraints. The central thesis is that AI is most useful when treated as part of an extended human--tool system that increases sensing, understanding, and actuation capability while reducing friction at the interface between people, tools, and environments. The agenda unifies predictive compression, intrinsic motivation, empowerment and control, interface quality (unification), and language/self-communication as selective information bottlenecks. We formulate these ideas as a falsifiable program with explicit costs, staged experiments, and a concrete multimodal tokenized testbed in which an agent allocates limited budget among observation, action, and deliberation. The aim is to provide a conceptual and experimental framework that connects intrinsic motivation, information theory, thermodynamics, bounded rationality, and modern reasoning systems