MeMo: Memory as a Model| Voxel51
Voxel51
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Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications require timely, domain-specific information, motivating the need for efficient mechanisms to incorporate new knowledge.
In this paper, we introduce MeMo (Memory as a Model), a modular framework that encodes new knowledge into a dedicated Memory model while keeping the LLM unchanged. Compared to existing methods, MeMo offers several advantages: (a) it captures complex cross-document relationships, (b) it is robust to retrieval noise, (c) it avoids catastrophic forgetting in the LLM, (d) it does not require access to the LLM’s weights or output logits that enabling plug-and-play integration with both open and proprietary LLMs, and (e) its retrieval cost is independent of corpus size at inference time.
Our experiments on three benchmarks, BrowseComp-Plus, NarrativeQA, and MuSiQue, show that MeMo achieves strong performance compared to existing methods across diverse settings.
Arun Verma is a Postdoctoral Associate at the Singapore-MIT Alliance for Research and Technology Centre, where he works with Daniela Rus, Armando Solar-Lezama, and Bryan Low. His research focuses on developing adaptive and efficient AI systems that autonomously make complex decisions in dynamic real-world environments.
MeMo: Memory as a Model
Paper: https://arxiv.org/pdf/2605.15156
Project: https://arunv3rma.github.io/blogs/memo/
#FiftyOne #Voxel51 #DataCuration #AIAgents #ComputerVision #MCP #YOLO #DataCentricAI #skills #agenticai
MeMo: Memory as a Model| Voxel51
Voxel51
32 views
Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications require timely, domain-specific information, motivating the need for efficient mechanisms to incorporate new knowledge.
In this paper, we introduce MeMo (Memory as a Model), a modular framework that encodes new knowledge into a dedicated Memory model while keeping the LLM unchanged. Compared to existing methods, MeMo offers several advantages: (a) it captures complex cross-document relationships, (b) it is robust to retrieval noise, (c) it avoids catastrophic forgetting in the LLM, (d) it does not require access to the LLM’s weights or output logits that enabling plug-and-play integration with both open and proprietary LLMs, and (e) its retrieval cost is independent of corpus size at inference time.
Our experiments on three benchmarks, BrowseComp-Plus, NarrativeQA, and MuSiQue, show that MeMo achieves strong performance compared to existing methods across diverse settings.
Arun Verma is a Postdoctoral Associate at the Singapore-MIT Alliance for Research and Technology Centre, where he works with Daniela Rus, Armando Solar-Lezama, and Bryan Low. His research focuses on developing adaptive and efficient AI systems that autonomously make complex decisions in dynamic real-world environments.
MeMo: Memory as a Model
Paper: https://arxiv.org/pdf/2605.15156
Project: https://arunv3rma.github.io/blogs/memo/
#FiftyOne #Voxel51 #DataCuration #AIAgents #ComputerVision #MCP #YOLO #DataCentricAI #skills #agenticai