2/18/2026 | USA | technology | ✓ Verified - arxiv.org

Improving MLLMs in Embodied Exploration and Question Answering with Human-Inspired Memory Modeling

#Multimodal Large Language Models #Embodied exploration #Question answering #Human‑inspired memory modeling #Episodic memory #Semantic memory #Non‑parametric memory #Visual context #Spatial details #Non‑stationary environments

📌 Key Takeaways

MLLMs as brain of embodied agents face challenges under long‑horizon observations and constrained context.
Current memory‑assisted methods depend on textual summaries, discarding visual and spatial detail.
New framework is non‑parametric, explicitly separating episodic (short‑term, event‑specific) and semantic (long‑term, abstract) memory.
Disentanglement preserves rich multimodal information and enhances robustness in non‑stationary environments.
Framework aims to improve embodied exploration tasks and question answering capabilities.

📖 Full Retelling

The paper "Improving MLLMs in Embodied Exploration and Question Answering with Human-Inspired Memory Modeling" introduces a memory framework for Multimodal Large Language Models (MLLMs) aimed at enhancing the performance of embodied agents. The authors address the difficulty of deploying these models as the central reasoning unit in agents that operate over long horizons and with limited context budgets. Existing solutions, which often rely on textual summaries, lose valuable visual and spatial information and struggle in dynamic, non‑stationary settings. The proposal disentangles episodic and semantic memory in a non‑parametric fashion to preserve rich multimodal details and improve robustness over time.

🏷️ Themes

Multimodal AI, Embodied Agents, Memory Modeling, Natural Language Processing, Computer Vision

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Deep Analysis

Why It Matters

This research addresses a key bottleneck in deploying multimodal large language models for embodied agents, enabling them to retain and use visual and spatial information over long horizons. By separating episodic and semantic memory, the approach improves robustness in dynamic environments and reduces reliance on costly textual summaries.

Context & Background

Embodied agents need to process continuous visual streams with limited memory.
Current memory methods rely on text summaries that lose spatial detail.
Non‑parametric memory can preserve raw multimodal data for better reasoning.

What Happens Next

The framework is expected to be integrated into next‑generation embodied AI platforms, allowing agents to navigate complex scenes and answer questions more accurately. Future work may extend the model to handle multimodal forgetting and real‑time adaptation.

Frequently Asked Questions

What is the main innovation of the proposed memory framework?

It explicitly separates episodic memory, which stores raw multimodal experiences, from semantic memory, which abstracts knowledge, avoiding loss of visual detail.

How does this approach improve over text‑based memory?

By keeping visual and spatial data intact, the model can recall specific scene configurations and objects, leading to more precise question answering.

What are the next steps for deployment?

The authors plan to test the system on real‑world embodied tasks and explore scalability to larger memory budgets.

Original Source

              arXiv:2602.15513v1 Announce Type: cross 
Abstract: Deploying Multimodal Large Language Models as the brain of embodied agents remains challenging, particularly under long-horizon observations and limited context budgets. Existing memory assisted methods often rely on textual summaries, which discard rich visual and spatial details and remain brittle in non-stationary environments. In this work, we propose a non-parametric memory framework that explicitly disentangles episodic and semantic memory
            

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Source

arxiv.org