ES-Merging: Biological MLLM Merging via Embedding Space Signals
Biological multimodal large language models (MLLMs) have emerged as powerful foundation models for scientific discovery. However, existing models are specialized to a single modality, limiting their ability to solve inherently cross-modal scientific problems. While model merging is an efficient method to combine the different modalities into a unified MLLM, existing methods rely on input-agnostic parameter space heuristics that fail to faithfully capture modality specialization. To overcome this limitation, we propose the Embedding-Signal-based MLLM Merging (ES-Merging), a framework that estimates merging coefficients from embedding space signals, moving the merging paradigm from the para...
| Authors | Wonbin Lee, Dongki Kim, Sung Ju Hwang |
|---|---|
| Primary category | cs.LG |
| Published | 15 March 2026 |
| Last updated at source | 31 May 2026 |
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