MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding

Abdulkadir Gokce; Badr AlKhamissi; Martin Schrimpf

arxiv: 2605.29850 · v1 · pith:GOW2DJMSnew · submitted 2026-05-28 · 💻 cs.LG

MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding

Abdulkadir Gokce , Badr AlKhamissi , Martin Schrimpf This is my paper

Pith reviewed 2026-06-29 09:12 UTC · model grok-4.3

classification 💻 cs.LG

keywords multimodal brain encodingfMRI predictionadaptive feature gatingomni-modal foundation modelswhole-brain responsesnaturalistic audiovisual stimulilayer-wise aggregation

0 comments

The pith

Natively multimodal features with adaptive layer-wise gating outperform post-hoc fusion of separate unimodal features for predicting whole-brain fMRI responses.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a framework that takes features directly from a single omni-modal foundation model and uses learned gates to combine information across its layers before feeding them into a brain encoder. This native integration is shown to predict cortical responses to audiovisual stimuli more accurately than either unimodal backbones or any combination of independent visual, auditory, and language models. The same gating weights also produce readable maps of which modality dominates at each stage and how those contributions vary across different regions of cortex. A reader would care because the result suggests that the brain's own multimodal integration can be approximated by letting one rich model decide, layer by layer, what to pass forward rather than by stitching together separate streams after the fact.

Core claim

MIRAGE shows that representations taken from an omni-modal foundation model, when combined via adaptive gating across layers and passed through a transformer encoder with subject-specific linear readouts, yield higher whole-brain fMRI prediction accuracy than either unimodal models or post-hoc aggregation of their outputs, while the gating weights themselves reveal distinct modality contributions that align with known cortical topography.

What carries the argument

Adaptive multimodal gating that learns to weight and select layer-wise features from a single omni-modal backbone before they enter the brain encoder.

If this is right

Natively multimodal backbones produce better encoding performance than unimodal or post-hoc multimodal baselines across multiple architectures and datasets.
The learned attention weights directly indicate the relative contribution of each modality at every layer of the backbone.
Each modality's gated features map onto distinct spatial patterns across cortical parcels.
The overall pipeline remains interpretable because the gating profile can be inspected without additional analysis tools.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same gating idea could be tested on non-audiovisual naturalistic stimuli to check whether the advantage persists when one modality is missing.
If the gating weights turn out to be stable across subjects, they might serve as a compact signature of how an individual integrates senses.
Applying the identical native-multimodal-plus-gating recipe to other imaging modalities such as MEG could test whether the benefit is specific to fMRI hemodynamics.

Load-bearing premise

The layer-wise features produced by current omni-modal foundation models already contain the right information for the brain's responses, so that a simple learned gate can extract it without needing to be retrained or regularized specifically for each new fMRI dataset.

What would settle it

A controlled re-run on the same audiovisual fMRI datasets in which post-hoc concatenation or averaging of features from three separate unimodal models reaches the same or higher prediction accuracy as the native multimodal gated version.

Figures

Figures reproduced from arXiv: 2605.29850 by Abdulkadir Gokce, Badr AlKhamissi, Martin Schrimpf.

**Figure 1.** Figure 1: MIRAGE architecture for predicting brain responses from naturalistic multimodal stimuli. Aligned video, audio, and text-transcript inputs are encoded by Qwen3-Omni-30B-A3B-Thinking (left, frozen), exposing the hidden states of every layer of its Language Module. Modality-specific trainable Layer Gating modules each use a small bank of learnable query embeddings in a cross-attention block to aggregate info… view at source ↗

**Figure 2.** Figure 2: (a) Method Comparison Across Benchmarks. Mean Pearson r between predicted and measured BOLD on the validation set (Friends S06), the in-distribution test set (Friends S07), and the out-of-distribution Movies benchmark, grouped by architectural complexity: linear ridge baselines (gray), Qwen3-Omni features with a learned brain encoder but no cross-attention gating (orange), and MIRAGE as a single model (red… view at source ↗

**Figure 3.** Figure 3: Cortical alignment and modality contributions. (a) Per-parcel Pearson r for MIRAGE on the validation set, shown on a cortical flatmap. (b) Dominant modality per parcel, vision (red), audio (blue), or text (green), defined as the modality whose ablation causes the largest drop in per-parcel Pearson r relative to the full trimodal model. Color saturation encodes dominance strength (the dominant modality’s sh… view at source ↗

**Figure 4.** Figure 4: Where does MIRAGE help, and which components contribute? (a) Parcel-wise difference in Pearson r between MIRAGE and the matched Linear (Native Fusion) baseline ( [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Layer-wise contributions of Qwen3-Omni features to each modality. Cross-attention weights from MIRAGE’s per-modality cross-attention modules (vision, text, audio) over the 48 layers of the Qwen3-Omni language module, averaged across attention heads and the 24 latent queries; brighter cells indicate layers that contribute more strongly to the modality-specific readout. Per-head and per-query breakdowns are … view at source ↗

**Figure 6.** Figure 6: Validation Pearson correlation as a function of the number of cross-attention queries [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗

**Figure 7.** Figure 7: Per-head cross-attention weights over Qwen3-Omni layers, by modality. Attention weights from each of the four heads (h0–h3) of the three modality-specific cross-attention modules (text, audio, vision) over the 48 layers of the Qwen3-Omni language module, averaged across the 24 latent queries; brighter cells indicate stronger contribution to the modality-specific readout. The modalityspecific depth profile… view at source ↗

**Figure 8.** Figure 8: Subject-averaged encoding-accuracy maps from the Algonauts 2025 Codabench evaluation platform. Left column: in-distribution test set (Friends S07). Right column: out-ofdistribution movie set (subject- and movie-averaged). Rows: linear ridge baseline with native-fusion features (top); MIRAGE as a single model (middle); MIRAGE as the 15-member ensemble used for our final submission (bottom). Each map shows … view at source ↗

read the original abstract

Recent progress in task-optimized neural networks has established encoding models as a powerful tool for predicting brain responses to naturalistic stimuli, yet most existing approaches rely on unimodal representations. The emergence of omni-modal foundation models and rich multimodal neural datasets enables encoding models that jointly integrate visual, auditory, and linguistic information across subjects. We introduce MIRAGE, a brain encoding framework for predicting whole-brain fMRI responses to naturalistic audiovisual stimuli. MIRAGE achieves state-of-the-art performance via a native multimodal backbone and adaptive feature gating across layers. These representations are then combined with a transformer-based brain encoder and a subject-specific linear head over the cortical parcels. Controlled comparisons show that natively multimodal features consistently outperform post-hoc aggregation of independent unimodal features, across architectural levels and backbones. Beyond predictive accuracy, the learned attention weights are directly inspectable to interpret the modality-specific gating profile over the backbone, and each modality traces a distinct anatomical pattern across cortex. Together, these results propose adaptive layer-wise aggregation of natively multimodal features as a generalizable, interpretable, and accurate approach for whole-brain encoding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MIRAGE's adaptive gating on native multimodal backbones is a straightforward extension of existing encoding ideas, but the abstract supplies zero numbers so the performance claims cannot be checked.

read the letter

The main takeaway is that MIRAGE takes omni-modal foundation models, adds learned adaptive gating across their layers, and uses the result to predict whole-brain fMRI responses to audiovisual stimuli. Controlled comparisons are said to show that these native multimodal features beat post-hoc aggregation of separate unimodal streams, and the gating weights are presented as inspectable for modality-specific patterns across cortex.

The framework itself is a reasonable incremental step. Using a single backbone that already mixes visual, auditory, and linguistic signals, then letting the model learn which layers to emphasize per modality, avoids some of the manual fusion choices in earlier work. The transformer encoder plus parcel-wise linear heads is standard for this subfield, so the novelty sits mostly in the gating mechanism and its claimed interpretability.

The soft spot is obvious and central: the abstract asserts state-of-the-art performance and consistent outperformance but gives no quantitative results, no dataset names, no error bars, and no validation details. The stress-test concern lands directly on what is visible. Because the gating parameters are trained end-to-end on the same fMRI splits used for evaluation, any reported lift could simply reflect the model learning subject- or stimulus-specific modality preferences rather than intrinsically stronger multimodal representations. No ablations with frozen gating, held-out subjects, or explicit regularization on the gate weights are mentioned, so the assumption that the gain comes from the backbone itself remains unsecured.

No equations or training procedure appear either, which blocks any check for circularity. The citation pattern is not visible here, but the conceptual move builds on prior unimodal encoding models without obvious gaps in the referenced literature.

This is aimed at computational neuroscientists already working on multimodal brain encoding. A reader in that narrow group might extract the gating idea for their own experiments, but the paper supplies nothing quantitative to discuss or replicate. I would not bring it to reading group. I would not cite it. It does not deserve peer review until the results and controls are added.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces MIRAGE, a brain encoding framework for predicting whole-brain fMRI responses to naturalistic audiovisual stimuli. It uses omni-modal foundation models with adaptive layer-wise feature gating, combined with a transformer-based brain encoder and subject-specific linear heads over cortical parcels. The central claims are state-of-the-art performance via native multimodal backbones and that natively multimodal features consistently outperform post-hoc aggregation of independent unimodal features across architectural levels and backbones; the learned gating weights are presented as interpretable for modality-specific profiles with distinct anatomical patterns across cortex.

Significance. If the central claims hold after addressing validation gaps, the work would advance multimodal fMRI encoding by demonstrating benefits of integrated representations from omni-modal models over unimodal aggregation, while adding interpretability through inspectable attention weights. The approach addresses a gap in handling audiovisual naturalistic stimuli jointly across subjects. No machine-checked proofs or parameter-free derivations are present, but the emphasis on controlled comparisons and anatomical tracing is a potential strength if the controls are robust.

major comments (2)

[Abstract and Results] Abstract and Results section: The controlled comparisons claiming consistent outperformance of natively multimodal features over post-hoc unimodal aggregation do not describe ablations with frozen or non-adaptive gating. Without such controls, the performance lift cannot be isolated from potential overfitting of the end-to-end optimized gating parameters to subject- or stimulus-specific modality preferences on the fMRI training splits.
[Methods] Methods section: No details are provided on held-out dataset testing (beyond standard splits), explicit regularization of the gating parameters, or cross-dataset generalization, which are necessary to secure the assumption that the adaptive gating reflects intrinsic multimodal feature quality rather than dataset-specific tuning.

minor comments (2)

[Abstract] Abstract: The assertion of 'state-of-the-art performance' is made without any quantitative metrics, error bars, dataset sizes, or validation procedures, which reduces clarity even if the full text supplies them.
[Abstract] Abstract: The final sentence uses 'propose ... as a generalizable, interpretable, and accurate approach'; rephrasing to reflect the empirical claims more directly would improve precision.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments point-by-point below. Where the concerns identify gaps in the current controls and documentation, we agree that revisions are warranted and will incorporate the suggested ablations and clarifications.

read point-by-point responses

Referee: [Abstract and Results] Abstract and Results section: The controlled comparisons claiming consistent outperformance of natively multimodal features over post-hoc unimodal aggregation do not describe ablations with frozen or non-adaptive gating. Without such controls, the performance lift cannot be isolated from potential overfitting of the end-to-end optimized gating parameters to subject- or stimulus-specific modality preferences on the fMRI training splits.

Authors: The referee correctly identifies that our reported comparisons do not include explicit frozen-gating or non-adaptive baselines. While the existing results already show consistent gains across multiple independent backbones and architectural depths (which would be unlikely under pure overfitting to a single training split), we agree that frozen-gating ablations are needed to isolate the contribution of the adaptive mechanism. We will add these controls in the revised Results section, training identical models with gating weights fixed to uniform or unimodal-only values. revision: yes
Referee: [Methods] Methods section: No details are provided on held-out dataset testing (beyond standard splits), explicit regularization of the gating parameters, or cross-dataset generalization, which are necessary to secure the assumption that the adaptive gating reflects intrinsic multimodal feature quality rather than dataset-specific tuning.

Authors: We will expand the Methods section to document (i) the precise regularization applied to the gating parameters (L2 penalty and early stopping on a validation split), (ii) the exact held-out subject and stimulus partitions used beyond the standard train/val/test division, and (iii) a limitations paragraph acknowledging the absence of cross-dataset evaluation. Because the current experiments remain within a single large naturalistic dataset, we cannot claim cross-dataset generalization; we will therefore qualify the interpretability claims accordingly rather than overstate them. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical comparisons presented without reduction to fitted inputs or self-citations

full rationale

The provided abstract and manuscript description contain no equations, no explicit derivation chain, and no self-citations that serve as load-bearing premises for the central claim. The reported superiority of natively multimodal features with adaptive gating is framed as an outcome of controlled empirical comparisons rather than a quantity defined by construction from the gating parameters or prior author work. No step matches any enumerated circularity pattern; the framework is therefore treated as self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated premise that the multimodal backbone and gating can be trained stably on available fMRI datasets.

pith-pipeline@v0.9.1-grok · 5728 in / 1118 out tokens · 28379 ms · 2026-06-29T09:12:01.516748+00:00 · methodology

discussion (0)

Reference graph

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