MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents

• URL: http://arxiv.org/abs/2601.20831v1
• Date: Wed, 28 Jan 2026 18:31:17 GMT
• Title: MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents
• Authors: Vishnu Sashank Dorbala, Dinesh Manocha,
• Abstract summary: We propose MemCtrl, a framework that uses Multimodal Large Language Models (MLLMs) for pruning memory online.<n>-augmented MLLMs show an improvement of around 16% on average, with over 20% on specific instruction subsets.
• Score: 53.44122827359892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models rely on in-context learning for personalized decision making. The limited size of this context window necessitates memory compression and retrieval systems like RAG. These systems however often treat memory as large offline storage spaces, which is unfavorable for embodied agents that are expected to operate under strict memory and compute constraints, online. In this work, we propose MemCtrl, a novel framework that uses Multimodal Large Language Models (MLLMs) for pruning memory online. MemCtrl augments MLLMs with a trainable memory head μthat acts as a gate to determine which observations or reflections to retain, update, or discard during exploration. We evaluate with training two types of μ, 1) via an offline expert, and 2) via online RL, and observe significant improvement in overall embodied task completion ability on μ-augmented MLLMs. In particular, on augmenting two low performing MLLMs with MemCtrl on multiple subsets of the EmbodiedBench benchmark, we observe that μ-augmented MLLMs show an improvement of around 16% on average, with over 20% on specific instruction subsets. Finally, we present a qualitative analysis on the memory fragments collected by μ, noting the superior performance of μaugmented MLLMs on long and complex instruction types.

Related papers

• MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning [78.46301394559903]
  Large Language Models (LLMs) are increasingly used for long-duration tasks.<n>Current methods face a trade-off between cost and accuracy.<n>MemSifter is a novel framework that offloads the memory retrieval process to a small-scale proxy model.
  arXiv  Detail & Related papers  (2026-03-03T02:57:38Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• MemLoRA: Distilling Expert Adapters for On-Device Memory Systems [71.32550994522738]
  Memory-augmented Large Language Models (LLMs) have demonstrated remarkable consistency during dialogues.<n>MemLoRA is a novel memory system that integrates small Vision-Language Models.<n>VLM-integrated MemLoRA-V shows massive improvements in caption-based approaches.
  arXiv  Detail & Related papers  (2025-12-04T12:56:30Z)
• Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning [89.55738101744657]
  Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless.<n>We present Memory-R1, a reinforcement learning framework that equips LLMs with the ability to actively manage and utilize external memory.
  arXiv  Detail & Related papers  (2025-08-27T12:26:55Z)
• MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training [24.066283519769968]
  Large Language Models (LLMs) have been trained using extended context lengths to foster more creative applications.<n>We propose MEMO, a novel framework for fine-grained activation memory management.<n>MeMO achieves an average of 1.97x and 1.80x MFU compared to Megatron-LM and DeepSpeed.
  arXiv  Detail & Related papers  (2024-07-16T18:59:49Z)
• MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory [49.96019697955383]
  We introduce MemLLM, a novel method of enhancing large language models (LLMs) by integrating a structured and explicit read-and-write memory module.<n>Our experiments indicate that MemLLM enhances the LLM's performance and interpretability, in language modeling in general and knowledge-intensive tasks in particular.
  arXiv  Detail & Related papers  (2024-04-17T18:13:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.