ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning
ProtoAda addresses a fundamental scaling challenge in multimodal AI: how to teach large vision-language models new tasks without catastrophic forgetting or misrouting. Current continual learning methods rely on visual-semantic similarity to assign tasks to specialized adapter modules, but this fails when tasks with different output structures share similar visual grounding. The paper proposes prototype-guided routing and geometric consolidation to decouple task assignment from surface-level similarity, enabling more robust expert specialization. This matters because production MLLMs must accumulate capabilities over time without retraining from scratch, and better task routing directly improves both forward transfer and backward stability in long-lived deployment scenarios.
Modelwire context
ExplainerProtoAda's core contribution is decoupling task routing from visual-semantic similarity by using prototype-guided assignment and geometric consolidation. The key insight: tasks that look visually similar but require different outputs (e.g., object detection vs. scene captioning) were being misrouted by prior methods that relied on embedding distance alone.
This directly extends the continual learning framing from CRAM (published the same day), which also tackled catastrophic forgetting through expert routing. Where CRAM uses centroid-based routing, ProtoAda adds a prototype layer and geometric consolidation to handle the harder case: when visual similarity misleads task assignment. Both papers treat adapter expansion as the core scaling mechanism for production MLLMs managing evolving task portfolios, positioning this as a solved problem class rather than an open frontier.
If ProtoAda's geometric consolidation maintains performance parity with CRAM on standard continual learning benchmarks while handling cross-modal task confusion cases (tasks with similar visuals but different outputs), that validates the routing refinement. If it doesn't outperform CRAM on existing benchmarks, the contribution is narrower than claimed.
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MentionsProtoAda · Multimodal Large Language Models · Mixture of LoRA Experts
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