IMFuse: Instance-Aware Multi-Layer Fusion for LLM-Enhanced Sequential Recommendation

• URL: http://arxiv.org/abs/2607.27002v1
• Date: Wed, 29 Jul 2026 14:58:36 GMT
• Title: IMFuse: Instance-Aware Multi-Layer Fusion for LLM-Enhanced Sequential Recommendation
• Abstract summary: IMFuse is an instance-aware multi-layer fusion strategy for sequential recommendation.<n>It aggregates multi-layer semantic information by learning global dimension-wise layer preferences.<n>It consistently outperforms state-of-the-art baselines with an average relative improvement of 6.72%.
• Score: 12.812732809987436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Large Language Models (LLMs) have significantly enhanced sequential recommendation by encoding rich item textual information into semantic representations. However, existing methods typically rely on the final-layer hidden states of LLMs, overlooking potentially useful semantic signals encoded in other layers. Through empirical analysis, we reveal the limitations of this practice: final-layer representations often suffer from dimensional collapse, whereas intermediate layers preserve complementary, coarse-to-fine semantic knowledge. Furthermore, we observe that different items exhibit heterogeneous layer-wise representation evolution, making a uniform layer selection sub-optimal. To bridge this gap, we propose IMFuse, an instance-aware multi-layer fusion strategy designed for LLM-enhanced recommendation. Instead of relying on a single layer, IMFuse adaptively aggregates multi-layer semantic information by learning global dimension-wise layer preferences to capture general semantic contributions. To address item-level heterogeneity, IMFuse introduces an instance-aware expert modulation mechanism that dynamically adjusts these global preferences, generating personalized, item-specific semantic representations. Extensive experiments across four real-world datasets demonstrate the effectiveness of IMFuse. It consistently outperforms state-of-the-art baselines with an average relative improvement of 6.72%, while introducing limited parameter and computational overhead.

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