What Should Embodied Agents Remember? Benchmarking and Evaluating Memory Modality for Embodied Agents
cs.RO
Submitted: 2026-10-08
Updated: 2026-10-08
Comments: 16 pages, 6 figures, 8 tables
Code: https://github.com/jimwang418/MeMoREval
Project page: https://memoreval.github.io
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- SAM 3: Segment Anything with Concepts
- Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
- Worth Remembering: Surprise-Gated Robot Episodic Memory
- MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory
- RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory
- Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization
- Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
- R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video
- Evaluating Very Long-Term Conversational Memory of LLM Agents
- MemGPT: Towards LLMs as Operating Systems
- MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models
- Reflexion: Language Agents with Verbal Reinforcement Learning
- Towards the Harness of Embodied Agents
- Agent Workflow Memory
- LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
- FindingDory: A Benchmark to Evaluate Memory in Embodied Agents
- EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
- WorldLines: Benchmarking and Modeling Long-Horizon Stateful Embodied Agents
- Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
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