# The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies

arXiv: https://arxiv.org/abs/2501.00504 · HTML: https://mlsanity.com/paper/2501.00504/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2024.

## Built on by (most cited first, 20 of 20)

- [TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction](https://mlsanity.com/paper/2507.22229/) (2025)
- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [A foundation model of vision, audition, and language for in-silico neuroscience](https://mlsanity.com/paper/2605.04326/) (2026)
- [VIBE: Video-Input Brain Encoder for fMRI Response Modeling](https://mlsanity.com/paper/2507.17958/) (2025)
- [Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)](https://mlsanity.com/paper/2507.17897/) (2025)
- [CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience](https://mlsanity.com/paper/2507.09024/) (2025)
- [A Multimodal Sequence-to-Sequence Model for Cross-Subject Prediction of Brain Responses to Naturalistic Stimuli](https://mlsanity.com/paper/2507.18104/) (2025)
- [Improving Multimodal Brain Encoding Model with Dynamic Subject-awareness Routing](https://mlsanity.com/paper/2510.04670/) (2025)
- [Probing Multimodal Fusion in the Brain: The Dominance of Audiovisual Streams in Naturalistic Encoding](https://mlsanity.com/paper/2507.19052/) (2025)
- [The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction](https://mlsanity.com/paper/2508.06499/) (2025)
- [Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)](https://mlsanity.com/paper/2510.06235/) (2025)
- [MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding](https://mlsanity.com/paper/2605.29850/) (2026)
- [BrainVista: Modeling Naturalistic Brain Dynamics as Multimodal Next-Token Prediction](https://mlsanity.com/paper/2602.04512/) (2026)
- [Neuro-Symbolic Decoding of Neural Activity](https://mlsanity.com/paper/2603.03343/) (2026)
- [Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching](https://mlsanity.com/paper/2604.11178/) (2026)
- [A global predicted-fMRI drive signal from TRIBE does not predict YouTube replay heatmaps](https://mlsanity.com/paper/2607.01400/) (2026)
- [Platonic Projection Structures: Operator-Induced Observability in Representation Learning](https://mlsanity.com/paper/2607.05175/) (2026)
- [The Platonic brain bridge hypothesis: human brain networks as an architectural prior for multimodal large language models](https://mlsanity.com/paper/2609.10947/) (2026)
- [Cross-attention encoding models reveal dynamic spatiotemporal routing across human higher visual cortex](https://mlsanity.com/paper/2609.36366/) (2026)
- [MultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching](https://mlsanity.com/paper/2610.15339/) (2026)

## Abstract (arXiv)

There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.