# Continual Model Merging with Test-Time Adaptation for Whole-Slide Image Analysis

arXiv: https://arxiv.org/abs/2607.04755 · HTML: https://mlsanity.com/paper/2607.04755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [AdaMerging: Adaptive Model Merging for Multi-Task Learning](https://mlsanity.com/paper/2310.02575/) (2023)
- [Multimodal Whole Slide Foundation Model for Pathology](https://mlsanity.com/paper/2411.19666/) (2024)
- [Transfer without Forgetting](https://mlsanity.com/paper/2206.00388/) (2022)
- [AdaRank: Adaptive Rank Pruning for Enhanced Model Merging](https://mlsanity.com/paper/2503.22178/) (2025)
- [MergeSlide: Continual Model Merging and Task-to-Class Prompt-Aligned Inference for Lifelong Learning on Whole Slide Images](https://mlsanity.com/paper/2511.13099/) (2025)

## Abstract (arXiv)

Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previous training data. Recent state-of-the-art model merging methods employ test-time adaptation (TTA-guided merging) to address distribution shifts by adjusting merging-related variables using unlabeled target data. However, these methods have primarily been studied in multi-task or single-target settings, and their behavior under sequential continual learning remains insufficiently understood. We present a benchmark study that maps this family of methods to rehearsal-free continual Whole Slide Image classification and evaluates them against traditional continual-learning approaches. Experiments on six TCGA cancer-subtyping cohorts cover CLASS-IL and TASK-IL scenarios, in-domain and out-of-domain evaluation, and different task orders. The results show that adapting model merging at test time can provide strong task-specific performance and improve retention of previously acquired knowledge without storing historical WSIs. Nevertheless, performance remains sensitive to task order and to the interaction between adaptation on the current distribution and accumulated knowledge. This benchmark identifies model merging with test-time adaptation as a promising direction for continual computational pathology and motivates future methods that balance adaptation to domain shift with explicit preservation of historical knowledge.