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SCAI-JHU
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Model Details
Model Provider
SCAI-JHU
Model Tree
Qwen/Qwen3-4B-Instruct-2507
Input Modalities
Output Modalities
Supported Functionality
MindZero trains (M)LLMs to perform efficient and robust online mental reasoning without any mental-state annotations. During training, the model is rewarded for generating mental-state hypotheses that maximize the likelihood of observed actions, as estimated by a planner — analogous to model-based ToM reasoning. After training, MindZero internalizes this reasoning into fast single-pass inference.
| Base model | Checkpoint | MMToM-QA |
|---|---|---|
| Qwen/Qwen3-4B-Instruct-2507 | MindZero-hh-tom-Qwen3-4B-Instruct-2507 | 72.7 |
| meta-llama/Llama-3.2-3B-Instruct | MindZero-hh-tom-Llama-3.2-3B-Instruct | 77.8 |
| meta-llama/Llama-3.1-8B-Instruct | MindZero-hh-tom-Llama-3.1-8B-Instruct | 76.2 |
bibtex
@inproceedings{zhang2026mindzero,title = {MindZero: Learning Online Mental Reasoning With Zero Annotations},author = {Shunchi Zhang and Jin Lu and Chuanyang Jin and Yichao Zhou and Zhining Zhang and Tianmin Shu},booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},year = {2026}}