# Artificial Empathy: Towards a Framework for Unsupervised Agency Detection and Policy Reconstruction

arXiv: https://arxiv.org/abs/2608.23030 · HTML: https://mlsanity.com/paper/2608.23030/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 24 Aug 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, 3 of 3)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Training Agents Inside of Scalable World Models](https://mlsanity.com/paper/2509.24527/) (2025)
- [Towards Safe and Honest AI Agents with Neural Self-Other Overlap](https://mlsanity.com/paper/2412.16325/) (2024)

## Abstract (arXiv)

We study how an AI system can identify and model other agents in its environment from observation alone, which is a capability necessary for cooperative behaviour in the real world. This problem is less constrained than inverse reinforcement learning and remains largely unexplored. We propose a framework that uses a reinforcement learning agent, trained on an independent task as a prior about agentic dynamics, to perform agency detection and policy reconstruction.