BridgeRouter: Gain-Aware Instance Routing over Cross-Modal Bridge Portfolios

# BridgeRouter: Gain-Aware Instance Routing over Cross-Modal Bridge Portfolios
Source: [https://arxiv.org/abs/2610.38152](https://arxiv.org/abs/2610.38152)
[View PDF](https://arxiv.org/pdf/2610.38152)

> Abstract:Lightweight representation bridges make pretrained, task\-adapted EEG foundation models executable on other biosignal modalities without retraining their backbones\. However, selecting a single bridge pathway globally assigns every instance to the same transfer route and ignores complementary errors across routes\. We therefore formulate multi\-bridge transfer as an instance\-level algorithm\-selection problem and introduce BridgeRouter, a gain\-aware router over a portfolio of frozen cross\-modal pathways\. A validation\-selected Single Best Solver \(SBS\) serves as a protected default route\. During training, task labels are used to compute the reduction in negative log\-likelihood provided by each alternative route relative to the SBS, and a lightweight MLP predicts these route\-specific gains from a target\-modality representation extracted by a frozen encoder\. At inference, BridgeRouter observes only the target modality, requires neither EEG, task labels, nor predictions from all candidate routes, and executes exactly one bridge pathway: the route with the largest predicted gain when that gain exceeds a validation\-calibrated threshold, and the SBS otherwise\. We evaluate seven EEG foundation\-model bridge pathways for EMG\-based freezing\-of\-gait detection on FOG and ECG\-based sleep classification on ISRUC\. Across five seeds, BridgeRouter achieves $81\.38\\pm1\.11\\%$ balanced accuracy on FOG, improving over the BIOT SBS by $11\.49$ percentage points, and $62\.92\\pm2\.17\\%$ on ISRUC, improving over the CBraMod SBS by $3\.83$ points\. Per\-instance bridge oracles reach $99\.96\\%$ and $78\.61\\%$, respectively, revealing substantial remaining portfolio headroom\. These results show that anchor\-relative gain prediction can convert complementary cross\-modal bridge behavior into deployable instance\-level routing improvements while retaining single\-route inference\.

## Submission history

From: Yunzhen Zhang \[[view email](https://arxiv.org/show-email/a5983df5/2610.38152)\] **\[v1\]**Wed, 28 Oct 2026 01:38:42 UTC \(4,352 KB\)