# FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion

arXiv: https://arxiv.org/abs/2406.03280 · HTML: https://mlsanity.com/paper/2406.03280/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 65 of its 102 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 30 of 65)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot](https://mlsanity.com/paper/2301.00774/) (2023)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://mlsanity.com/paper/2401.06066/) (2024)
- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [A Simple and Effective Pruning Approach for Large Language Models](https://mlsanity.com/paper/2306.11695/) (2023)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://mlsanity.com/paper/2311.03099/) (2023)
- [Linear Mode Connectivity and the Lottery Ticket Hypothesis](https://mlsanity.com/paper/1912.05671/) (2019)
- [Merging Models with Fisher-Weighted Averaging](https://mlsanity.com/paper/2111.09832/) (2021)
- [Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs](https://mlsanity.com/paper/1802.10026/) (2018)
- [Fast Model Editing at Scale](https://mlsanity.com/paper/2110.11309/) (2021)
- [Git Re-Basin: Merging Models modulo Permutation Symmetries](https://mlsanity.com/paper/2209.04836/) (2022)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Dataless Knowledge Fusion by Merging Weights of Language Models](https://mlsanity.com/paper/2212.09849/) (2022)
- [BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning](https://mlsanity.com/paper/2002.06715/) (2020)
- [Essentially No Barriers in Neural Network Energy Landscape](https://mlsanity.com/paper/1803.00885/) (2018)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging](https://mlsanity.com/paper/2310.11564/) (2023)
- [AdaMerging: Adaptive Model Merging for Multi-Task Learning](https://mlsanity.com/paper/2310.02575/) (2023)

## Built on by (most cited first, 13 of 13)

- [OptMerge: Unifying Multimodal LLM Capabilities and Modalities via Model Merging](https://mlsanity.com/paper/2505.19892/) (2025)
- [RegMean++: Enhancing Effectiveness and Generalization of Regression Mean for Model Merging](https://mlsanity.com/paper/2508.03121/) (2025)
- [Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks](https://mlsanity.com/paper/2504.00233/) (2025)
- [FeatCal: Feature Calibration for Post-Merging Models](https://mlsanity.com/paper/2605.13030/) (2026)
- [ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI](https://mlsanity.com/paper/2607.04426/) (2026)
- [E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring](https://mlsanity.com/paper/2605.16882/) (2026)
- [Closed-Form Spectral Regularization for Multi-Task Model Merging](https://mlsanity.com/paper/2606.07289/) (2026)
- [SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation](https://mlsanity.com/paper/2412.19098/) (2024)
- [Unlocking the Potential of Continual Model Merging: An ODE Perspective](https://mlsanity.com/paper/2605.19409/) (2026)
- [CABS+: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation](https://mlsanity.com/paper/2608.12842/) (2026)
- [SMAT: Simple and Efficient Merge-Aware Training](https://mlsanity.com/paper/2609.33437/) (2026)
- [Training-Free Transformer Merging via Sequential Local Operator Alignment](https://mlsanity.com/paper/2610.06415/) (2026)
- [CrossMean: Model Merging by Pairing Merged and Expert Activations](https://mlsanity.com/paper/2610.14762/) (2026)

## Abstract (arXiv)

Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and data-efficient manner. Although a variety of deep model fusion techniques have been introduced, their evaluations tend to be inconsistent and often inadequate to validate their effectiveness and robustness. We present FusionBench, the first benchmark and a unified library designed specifically for deep model fusion. Our benchmark consists of multiple tasks, each with different settings of models and datasets. This variety allows us to compare fusion methods across different scenarios and model scales. Additionally, FusionBench serves as a unified library for easy implementation and testing of new fusion techniques. FusionBench is open source and actively maintained, with community contributions encouraged. Homepage https://github.com/tanganke/fusion_bench