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Notes on memory management
9 notes
rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation
The paper proposes a new approach for recommendation systems, called rEDMRec, which distills the reasoning of large language models into an editable memory that can be used to rank candidate items without regenerating…
EvoMem: Memory-Augmented Evolution for Code Optimization
The paper introduces EvoMem, a memory architecture for Long Learning Machine (LLM) based evolutionary program search. EvoMem captures and reuses knowledge from successful mutation events to improve the efficiency of…
Eviction as Estimation: A Fixed-Lag Smoothing View of Test-Time Memory, and When Measuring Beats Accumulating
The authors present a new approach for managing a language model's working memory by treating the decision of which stored items to keep as an estimation problem. They introduce a method called 'fixed-lag smoothing'…
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
The paper proposes a new framework called Memory as a Controlled Process (MemCon) for large language model (LLM) agents that use external memory systems. MemCon learns an online policy to adaptively decide when, what…
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
The paper presents a new framework called Memory as a Controlled Process (MemCon) for large language model (LLM) agents. MemCon aims to address the limitation of existing memory systems that rely on fixed…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
The paper proposes a new multimodal agent called the Cognitive-structured Multimodal Agent. This agent is designed to improve long-horizon multimodal dialogue by externalizing visual information into an Episodic Visual…
AutoMem: Automated Learning of Memory as a Cognitive Skill
Researchers developed AutoMem, a framework for improving a language model's memory management skills in long-horizon games. By treating memory as a trainable skill, they allowed the model to decide how to manage its…
AutoMem: Automated Learning of Memory as a Cognitive Skill
Researchers have developed a framework called AutoMem to improve the performance of large language models (LLMs) in long-horizon games by treating memory management as a learnable skill. They let the model decide how…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
The researchers present a framework called OmniAct that aims to build persistent embodied agents for operation in unstructured environments. The framework integrates a multimodal semantic planner, an adaptive…
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