# MultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching

arXiv: https://arxiv.org/abs/2610.15339 · HTML: https://mlsanity.com/paper/2610.15339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 11 Oct 2026.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 25 of 25)

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- [The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies](https://mlsanity.com/paper/2501.00504/) (2024)
- [TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction](https://mlsanity.com/paper/2507.22229/) (2025)
- [SegTransVAE: Hybrid CNN -- Transformer with Regularization for medical image segmentation](https://mlsanity.com/paper/2201.08582/) (2022)
- [VIBE: Video-Input Brain Encoder for fMRI Response Modeling](https://mlsanity.com/paper/2507.17958/) (2025)
- [Predicting Brain Responses To Natural Movies With Multimodal LLMs](https://mlsanity.com/paper/2507.19956/) (2025)
- [Insights from the Algonauts 2025 Winners](https://mlsanity.com/paper/2508.10784/) (2025)

## Abstract (arXiv)

Current fMRI encoding models frame prediction as deterministic regression, yet identical stimuli evoke a distribution of neural responses (arising from physiological noise, hemodynamic variability, and cognitive fluctuations) that a single point estimate cannot capture. This work introduces MultiFlow, a generative fMRI encoding framework based on Conditional Flow Matching (CFM) and a formulation termed Stimulus-Informed Source Flow Matching (SISFM). SISFM generalises CFM by conditioning the source distribution on the stimulus: a learnable temporal convolution shapes the source mean with an HRF-length receptive field, while a variance head shrinks its scale, shortening the expected transport path. SISFM is instantiated with a Diffusion Transformer (DiT-X) decoder parameterising the velocity field, with sub-layer Attention Residuals, heterogeneous fusion of ten frozen vision, audio, language, and audiovisual backbones, and per-subject Yeo-network heads. On the Algonauts 2025 dataset, a single MultiFlow checkpoint recovers most of the benefit of large deterministic ensembles: it trails the 1000-model TRIBE ensemble in-distribution by 0.003 PCC (0.3163 vs. 0.3195), and the strongest in- and out-of-distribution ensembles (VIBE-20, MedARC-50) by 0.010 and 0.009 PCC, using a single trained model rather than an ensemble of 20-1000. The same checkpoint also provides stimulus-conditioned uncertainty estimates and sample-based diversity.