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New adapter enables backward compatibility for multi-modal LLMs without model updates

By PulseAugur Editorial · [2 sources] ·

Researchers have developed the Multi-modal Knowledge Preserving Adapter (MKP-Adapter), a novel approach for Multi-modal Large Language Models (MLLMs) that enables backward compatibility without updating the core model. This adapter-only solution addresses the costly and complex process of re-indexing databases when embedding models are upgraded. MKP-Adapter preserves new embedding knowledge while ensuring compatibility through a multi-level preservation loss and a focal re-weighting strategy. Experiments show its effectiveness across various multi-modal tasks and model types, with minimal added latency. AI

IMPACT This adapter-only solution could significantly reduce the cost and complexity of updating embedding models, potentially accelerating the deployment and iteration of multi-modal AI systems.

RANK_REASON The cluster describes a new research paper detailing a novel technical approach for MLLMs.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New adapter enables backward compatibility for multi-modal LLMs without model updates

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The cluster describes a new research paper detailing a novel technical approach for MLLMs.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility

    Upgrading embedding models typically requires expensive database re-indexing, as new query embeddings are incompatible with existing database embeddings. While Backward Compatible Training (BCT) mitigates this by enforcing compatibility during training, existing approaches often …

  2. arXiv cs.CV TIER_1 English(EN) · Jaeseok Byun, Gukyeong Kwon, Han-Kai Hsu, Meher Gitika Karumuri, Zhikang Zhang, Hao Yang, Davide Modolo ·

    Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility

    arXiv:2609.16875v1 Announce Type: new Abstract: Upgrading embedding models typically requires expensive database re-indexing, as new query embeddings are incompatible with existing database embeddings. While Backward Compatible Training (BCT) mitigates this by enforcing compatibi…

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