Title: Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for LLMs
Primary area: foundation or frontier models, including LLMs
Keywords: Model Fusion

Abstract: Large Language Models (LLMs) have achieved success across various domains. However, their applicability tends to degrade when confronted with different types of data inputs, especially for LLMs that have been fine-tuned for specific tasks. Despite its importance, the study of knowledge sharing among domain-specific LLMs—such as those trained for mathematics or code—remains largely underexplored. To address the fragmentation of knowledge across domain-specialized LLMs, we propose a unified parameter integration framework that enables modular composition of expert capabilities. Our method is grounded in a novel Compatibility-Aware Parameter Splicing (CAPS) strategy, which leverages both local functional attribution and global information-theoretic signals to guide selective parameter fusion. By extending this mechanism to the low-rank adaptation layer granularity, we ensure efficient integration with minimal inference overhead. Furthermore, we introduce a domain compatibility scoring mechanism that quantifies inter-expert alignment at the activation level and correlates with downstream task utility. This principled fusion protocol allows the final model to synergize heterogeneous expertise while preserving structural modularity. Extensive evaluations across diverse language and reasoning benchmarks validate the effectiveness of our framework, offering a scalable path toward compositional, domain-adaptive LLMs. Our project is available at https://anonymous.4open.science/r/Graft-8213.