arXiv:2606.10572v1 Announce Type: new Abstract: External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence. However, existing memory paradigms represent each memory item in raw text and image forms, so retrieval-based systems must pass the retrieved text or images to the generation LLMs/VLMs, resulting in high token consumption and storage pressure, making it unaffordable for resource-constrained applications. We propose Latent Memory, a latent-space memory paradigm that replaces each raw text
The increasing scale and resource demands of large language models and vision-language models for multimodal evidence are pushing the boundaries of affordable computation, necessitating novel approaches like latent memory.
This research addresses a fundamental limitation in applying advanced AI models to resource-constrained environments, potentially democratizing access to powerful AI capabilities and expanding their deployment scenarios.
The paradigm for how multimodal evidence is stored and retrieved for AI models shifts from raw data to a more efficient latent-space representation, significantly reducing computational overhead.
- · Edge AI providers
- · Developers of resource-constrained AI applications
- · Users of multimodal AI on mobile/embedded devices
- · Companies relying solely on raw data retrieval for AI
- · Traditional cloud-centric QA solutions without efficiency focus
Reduced operational costs and increased accessibility for multimodal Q&A systems.
Broader deployment of sophisticated AI models in distributed and low-power environments becomes feasible.
New classes of AI applications emerge that were previously impossible due to computational or memory constraints.
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Read at arXiv cs.AI