A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models

• URL: http://arxiv.org/abs/2606.10277v1
• Date: Tue, 09 Jun 2026 00:57:56 GMT
• Title: A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models
• Abstract summary: We propose a unified adaptive feature composition framework for generalization in wireless foundation models (WFMs)<n>Instead of extracting only the final-layer output, this router treats the hidden states from different Transformer depths as a reusable pool of multi-level hidden features.<n>Experiments on four representative wireless tasks demonstrate that RAFC consistently outperforms conventional adaptation baselines.
• Score: 19.520699593557357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Though wireless foundation models (WFMs) have shown strong potential in learning universal channel representations, their adaptation to various downstream tasks remains constrained by existing paradigms. Fine-tuning strategies introduces substantial computational and storage overhead, while frozen feature extraction leads to sub-optimal performance across diverse downstream tasks. To address this issue, we propose a unified adaptive feature composition framework for multitask generalization in WFMs, where the key component is the Routing Adapter for Feature Composition (RAFC). Instead of extracting only the final-layer output, this router treats the hidden states from different Transformer depths as a reusable pool of multi-level hidden features, and employs a lightweight task-driven feature composition network to generate layer-wise aggregation weights, then adaptively combine hierarchical representations through weighted summation. This design enables each downstream task to access suitable mixture of low-, mid-, and high-level wireless features without modifying the pretrained backbone. Extensive experiments on four representative wireless tasks demonstrate that RAFC consistently outperforms conventional adaptation baselines while introducing fewer than 50K additional parameters. Moreover, the learned routing weights provide interpretable evidence of task-specific layer preferences, making the proposed framework a low-complexity, scalable, and explainable interface for adapting WFMs to diverse downstream scenarios.

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