# ProToM: Promoting Prosocial Behaviour via Theory of Mind-Informed Feedback

arXiv: https://arxiv.org/abs/2509.05091 · HTML: https://mlsanity.com/paper/2509.05091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://mlsanity.com/paper/2305.14763/) (2023)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration](https://mlsanity.com/paper/2010.09890/) (2020)
- [MMToM-QA: Multimodal Theory of Mind Question Answering](https://mlsanity.com/paper/2401.08743/) (2024)
- [PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks](https://mlsanity.com/paper/2411.00081/) (2024)
- [Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models](https://mlsanity.com/paper/2407.07086/) (2024)
- [NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants](https://mlsanity.com/paper/2301.05223/) (2023)
- [GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment](https://mlsanity.com/paper/2403.11075/) (2024)
- [Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition](https://mlsanity.com/paper/2405.12621/) (2024)
- [Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue](https://mlsanity.com/paper/2305.11271/) (2023)
- [Automated Task-Time Interventions to Improve Teamwork using Imitation Learning](https://mlsanity.com/paper/2303.00413/) (2023)
- [Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions](https://mlsanity.com/paper/2407.06762/) (2024)
- [Pragmatic Embodied Spoken Instruction Following in Human-Robot Collaboration with Theory of Mind](https://mlsanity.com/paper/2409.10849/) (2024)
- [ToP-ToM: Trust-aware Robot Policy with Theory of Mind](https://mlsanity.com/paper/2311.04397/) (2023)

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

- [MindZero: Learning Online Mental Reasoning With Zero Annotations](https://mlsanity.com/paper/2606.00240/) (2026)

## Abstract (arXiv)

While humans are inherently social creatures, the challenge of identifying when and how to assist and collaborate with others - particularly when pursuing independent goals - can hinder cooperation. To address this challenge, we aim to develop an AI system that provides useful feedback to promote prosocial behaviour - actions that benefit others, even when not directly aligned with one's own goals. We introduce ProToM, a Theory of Mind-informed facilitator that promotes prosocial actions in multi-agent systems by providing targeted, context-sensitive feedback to individual agents. ProToM first infers agents' goals using Bayesian inverse planning, then selects feedback to communicate by maximising expected utility, conditioned on the inferred goal distribution. We evaluate our approach against baselines in two multi-agent environments: Doors, Keys, and Gems, as well as Overcooked. Our results suggest that state-of-the-art large language and reasoning models fall short of communicating feedback that is both contextually grounded and well-timed - leading to higher communication overhead and task speedup. In contrast, ProToM provides targeted and helpful feedback, achieving a higher success rate, shorter task completion times, and is consistently preferred by human users.