Update README.md

README.md

@@ -15,8 +15,8 @@ Our Mission: **GenAI for all, intelligence in every task.**
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 ### 🤖 Our Model Series
 #### 🔗 Model Fusion & Model Merging
->Model Merging: Combines homogeneous models with the same architecture directly in parameter space (e.g., weight averaging/task vectors) to produce a single checkpoint with baseline-like inference cost.  
->Model Fusion: Combines heterogeneous or homogeneous models in prediction/knowledge space (ensembles, logit averaging, distillation, FL aggregation); ensembles raise inference cost, while distillation returns a compact model.
+>Model fusion refers to the process of combining multiple trained models—often from different domains, architectures, or training datasets—into a single, more powerful model. The goal is to integrate their strengths and knowledge, improving performance, generalization, or efficiency.  
+>Model merging is a specific type of model fusion that involves combining the internal parameters (typically weights) of two or more pretrained models to produce a single model that inherits knowledge from all sources. Unlike ensemble methods, model merging produces a single merged model rather than relying on multiple models at inference time.
 
 - [InfiFusion](https://huggingface.co/collections/InfiX-ai/infifusion-683c7d7f00c71614ba8ceb96): **InfiFusion** is a logit-level fusion pipeline based on Universal Logit Distillation, enhanced with Top-K filtering and logits standardization. It supports both pairwise and unified fusion strategies to balance performance and efficiency.
 - [InfiGFusion](https://huggingface.co/InfiX-ai/InfiGFusion-14B): **InfiGFusion** is a structure-aware extension that builds co-activation graphs from logits and aligns them via an efficient Gromov-Wasserstein loss approximation, capturing cross-dimension semantic dependencies for stronger reasoning.