Hierarchical Predictive Processing for Uncertainty-Aware Multimodal Transformers

Namita Achyuthan, Bhaskarjyoti Das
Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026, PMLR 308:76-83, 2026.

Abstract

Current vision-language models suffer from overconfident predictions and cross-modal hallucinations, lacking principled mechanisms for uncertainty quantification. We introduce a novel architecture that applies the Free Energy Principle from computational neuroscience to multimodal transformers, enabling reliable uncertainty estimation through hierarchical predictive processing. Our approach implements precision-weighted cross-modal prediction, where visual and linguistic representations generate predictions about each other, and prediction errors are weighted by learned precision matrices that capture cross-modal consistency. By minimizing variational free energy across modalities, our model naturally quantifies uncertainty while maintaining task performance. Experimental results demonstrate substantial improvements over standard uncertainty quantification methods, achieving 51.7% better calibration than Monte Carlo Dropout baselines on synthetic evaluation data and 48.6% improvement on the VQA v2 dataset. This work establishes the first principled bridge between the brain’s Bayesian inference mechanisms and practical multimodal AI uncertainty quantification, demonstrating that biologically-inspired architectures can significantly enhance model reliability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v308-achyuthan26a, title = {Hierarchical Predictive Processing for Uncertainty-Aware Multimodal Transformers}, author = {Achyuthan, Namita and Das, Bhaskarjyoti}, booktitle = {Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026}, pages = {76--83}, year = {2026}, editor = {Abbasi-Asl, Reza and Iqbal, Asim and Ito, Shinya and Arkhipov, Anton and Sanborn, Sophia}, volume = {308}, series = {Proceedings of Machine Learning Research}, month = {27 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v308/main/assets/achyuthan26a/achyuthan26a.pdf}, url = {https://proceedings.mlr.press/v308/achyuthan26a.html}, abstract = {Current vision-language models suffer from overconfident predictions and cross-modal hallucinations, lacking principled mechanisms for uncertainty quantification. We introduce a novel architecture that applies the Free Energy Principle from computational neuroscience to multimodal transformers, enabling reliable uncertainty estimation through hierarchical predictive processing. Our approach implements precision-weighted cross-modal prediction, where visual and linguistic representations generate predictions about each other, and prediction errors are weighted by learned precision matrices that capture cross-modal consistency. By minimizing variational free energy across modalities, our model naturally quantifies uncertainty while maintaining task performance. Experimental results demonstrate substantial improvements over standard uncertainty quantification methods, achieving 51.7% better calibration than Monte Carlo Dropout baselines on synthetic evaluation data and 48.6% improvement on the VQA v2 dataset. This work establishes the first principled bridge between the brain’s Bayesian inference mechanisms and practical multimodal AI uncertainty quantification, demonstrating that biologically-inspired architectures can significantly enhance model reliability.} }
Endnote
%0 Conference Paper %T Hierarchical Predictive Processing for Uncertainty-Aware Multimodal Transformers %A Namita Achyuthan %A Bhaskarjyoti Das %B Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026 %C Proceedings of Machine Learning Research %D 2026 %E Reza Abbasi-Asl %E Asim Iqbal %E Shinya Ito %E Anton Arkhipov %E Sophia Sanborn %F pmlr-v308-achyuthan26a %I PMLR %P 76--83 %U https://proceedings.mlr.press/v308/achyuthan26a.html %V 308 %X Current vision-language models suffer from overconfident predictions and cross-modal hallucinations, lacking principled mechanisms for uncertainty quantification. We introduce a novel architecture that applies the Free Energy Principle from computational neuroscience to multimodal transformers, enabling reliable uncertainty estimation through hierarchical predictive processing. Our approach implements precision-weighted cross-modal prediction, where visual and linguistic representations generate predictions about each other, and prediction errors are weighted by learned precision matrices that capture cross-modal consistency. By minimizing variational free energy across modalities, our model naturally quantifies uncertainty while maintaining task performance. Experimental results demonstrate substantial improvements over standard uncertainty quantification methods, achieving 51.7% better calibration than Monte Carlo Dropout baselines on synthetic evaluation data and 48.6% improvement on the VQA v2 dataset. This work establishes the first principled bridge between the brain’s Bayesian inference mechanisms and practical multimodal AI uncertainty quantification, demonstrating that biologically-inspired architectures can significantly enhance model reliability.
APA
Achyuthan, N. & Das, B.. (2026). Hierarchical Predictive Processing for Uncertainty-Aware Multimodal Transformers. Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026, in Proceedings of Machine Learning Research 308:76-83 Available from https://proceedings.mlr.press/v308/achyuthan26a.html.

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