Summary
Researchers introduce MM-Lifelong, a new dataset for multimodal lifelong understanding containing 181.1 hours of natural, unscripted daily-life footage organized across Day, Week, and Month temporal scales. Unlike existing hour-long video datasets built from densely concatenated clips, MM-Lifelong captures realistic continuous life recording. Extensive evaluations identify two critical failure modes in current approaches: end-to-end multimodal large language models (MLLMs) suffer from a Working Memory Bottleneck caused by context saturation, while representative agentic baselines experience Global Localization Collapse when navigating sparse month-long timelines. To address these limitations, the paper proposes the Recursive Multimodal Agent (ReMA), which iteratively updates a recursive belief state through dynamic memory management, significantly outperforming existing methods. The work is available on arXiv as 2603.05502 and targets the computer vision research community.
Paper Overview
- Research Area: Computer Vision (CV)
- Authors: Anonymous
- Published: 2026-03-06
- arXiv: 2603.05502
Summary
While datasets for video understanding have scaled to hour-long durations, they typically consist of densely concatenated clips that differ from natural, unscripted daily life. To bridge this gap, the authors introduce MM-Lifelong, a dataset designed for Multimodal Lifelong Understanding.
Dataset Highlights
- Comprises 181.1 hours of footage
- Structured across Day, Week, and Month scales to capture varying temporal densities
- Reflects natural, unscripted daily life rather than stitched clips
Key Findings
Extensive evaluations reveal two critical failure modes in current paradigms:
1. Working Memory Bottleneck — end-to-end MLLMs degrade due to context saturation
2. Global Localization Collapse — representative agentic baselines fail when navigating sparse, month-long timelines
Proposed Solution
The paper proposes the Recursive Multimodal Agent (ReMA), which employs dynamic memory management to iteratively update a recursive belief state, significantly outperforming existing methods.
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