Enterprise AI Analysis
MANY: Merge Anything for Multimodal Continual Instruction Tuning
This research introduces MAny, a novel training-free framework addressing dual-forgetting in Multimodal Large Language Models (MLLMs). It tackles perception drift in cross-modal projection space and reasoning collapse in low-rank parameter space through Cross-modal Projection Merging (CPM) and Low-rank Parameter Merging (LPM). MAny achieves state-of-the-art performance on UCIT and MLLM-DCL benchmarks without GPU-based training, offering a robust and lightweight solution for continuous multimodal learning.
Executive Impact
Our analysis reveals the following key metrics relevant to your enterprise strategy.
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Improvement on LLaVA-1.5-7B FAA
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Improvement on InternVL-Chat-7B FAA
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Final Forgetting Measure (LLaVA)
Deep Analysis & Enterprise Applications
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The study identifies a critical 'dual-forgetting' phenomenon in MLLMs, encompassing perception drift in the cross-modal projection space and reasoning collapse in the low-rank parameter space. Traditional anti-forgetting methods primarily focus on the language backbone, neglecting the multimodal projector, which is a major oversight.
Enterprise Process Flow
Identify Dual-Forgetting
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Decouple Perception & Reasoning
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Cross-modal Projection Merging (CPM)
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Low-rank Parameter Merging (LPM)
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Adaptive Knowledge Integration
8.57%
FAA Improvement (LLaVA-1.5-7B)
MAny significantly outperforms state-of-the-art methods like HiDE Guo et al. (2025a), demonstrating superior knowledge retention without GPU-based training. It achieves an ultra-low FFM of 0.37% on LLaVA-1.5-7B, indicating minimal forgetting.
| MAny (Dual-Track Merging) | Traditional CL Methods |
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Deploying MAny in a Dynamic Enterprise Environment
A large e-commerce platform continuously updates its multimodal product search and recommendation engine. Previously, new product categories or user behaviors led to severe degradation of performance on older data.
Implementing MAny allowed the platform to incrementally adapt its MLLM to new product visual features and query patterns without retraining the entire model or storing historical data. This enabled agile deployment of updates, maintaining high accuracy across evolving datasets.
The platform achieved a 25% reduction in model maintenance costs and a 15% improvement in search relevance for diverse product categories over a 6-month period.
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