# Formalizing Embeddedness Failures in Universal Artificial Intelligence

arXiv: https://arxiv.org/abs/2505.17882 · HTML: https://mlsanity.com/paper/2505.17882/

## 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 2 of its 14 references that are in the map.
- First posted on arXiv 23 May 2025.

## 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, 2 of 2)

- [A Formal Solution to the Grain of Truth Problem](https://mlsanity.com/paper/1609.05058/) (2016)
- [Sequential Extensions of Causal and Evidential Decision Theory](https://mlsanity.com/paper/1506.07359/) (2015)

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

- [The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self](https://mlsanity.com/paper/2606.19924/) (2026)

## Abstract (arXiv)

We rigorously discuss the commonly asserted failures of the AIXI reinforcement learning agent as a model of embedded agency. We attempt to formalize these failure modes and prove that they occur within the framework of universal artificial intelligence, focusing on a variant of AIXI that models the joint action/percept history as drawn from the universal distribution. We also evaluate the progress that has been made towards a successful theory of embedded agency based on variants of the AIXI agent.