Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation

• URL: http://arxiv.org/abs/2607.25366v1
• Date: Tue, 28 Jul 2026 07:17:44 GMT
• Title: Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation
• Abstract summary: We propose SharpRec, a framework designed to lift the performance upper bound of merged models.<n>Experiments in both dual-domain and multi-domain scenarios demonstrate that SharpRec consistently outperforms state-of-the-art baselines.
• Score: 17.675533338059093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-based Cross-Domain Sequential Recommendation (CDSR) leverages LLMs to enhance target performance via deep semantic reasoning, alleviating the dependency on overlapping users. Among LLM-based paradigms, model merging is particularly promising for multi-domain scenarios due to its superior scalability and flexibility in integrating diverse knowledge sources. However, our empirical investigations reveal two critical bottlenecks: (1) cross-domain knowledge conflict; and (2) performance saturation in multi-domain fusion. Our analysis attributes these phenomena to parameter-level misalignment and statistical homogenization during the merging process. To address these bottlenecks, we propose SharpRec, Sharpness-aware Model Merging with Salience Recovery for LLM-based CDSR, a framework designed to lift the performance upper bound of merged models. SharpRec incorporates two synergistic modules: Sharpness-aware Geometric Alignment to establish a stable geometric foundation for interference-free fusion; and Preference Salience Activation to effectively recover the distinctive features essential for bolstering target domain performance. Extensive experiments in both dual-domain and multi-domain scenarios demonstrate that SharpRec consistently outperforms state-of-the-art baselines.

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