# MindZero：基于零标注的在线心智推理学习

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-29 08:00
- AIHOT 分数：50
- AIHOT 链接：https://aihot.virxact.com/items/cmpxa7ra7021islckkq14z7u7
- 原文链接：https://arxiv.org/abs/2606.00240

## AI 摘要

MindZero是一个自监督强化学习框架，用于训练多模态大语言模型进行高效、鲁棒的在线心智推理。该方法通过让模型生成使观测到的行为似然最大化的心理状态假设来获取奖励，从而无需显式的心智状态标注。训练后，MindZero将基于模型的推理内化为快速的单次推理。在网格世界和家庭环境的评估中，它在精度和效率上均显著优于传统的基于模型的方法。

## 正文

Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent advances, several key challenges remain, including (1) online inference with robust uncertainty updates over multiple hypotheses; (2) efficient reasoning suitable for real-time assistance; and (3) the lack of ground-truth mental state annotations in real-world domains. We address these challenges by introducing MindZero, a self-supervised reinforcement learning framework that trains multimodal large language models (MLLMs) for efficient and robust online mental reasoning. During training, the model is rewarded for generating mental state hypotheses that maximize the likelihood of observed actions estimated by a planner, similar to model-based ToM reasoning. This method thus eliminates the need for explicit mental state annotations. After training, MindZero internalizes model-based reasoning into fast single-pass inference. We evaluate MindZero against baselines across challenging mental reasoning and AI assistance tasks in gridworld and household domains. We found that LLMs alone are insufficient; model-based methods improve accuracy but are slow, costly, and limited by backbone MLLM capacity. In contrast, MindZero enhances MLLMs' intrinsic ToM ability and significantly outperforms model-based methods in both accuracy and efficiency, showing that mental reasoning can be effectively learned as a self-supervised skill.