[Paper] Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline

Source: arXiv - 2603.05484v1

Overview

The paper introduces MM‑Lifelong, a massive multimodal video dataset that mirrors the irregular, unscripted flow of everyday life across day‑, week‑, and month‑long timescales. By exposing models to realistic temporal sparsity, the authors uncover fundamental weaknesses in current multimodal large language models (MLLMs) and agentic systems, and they propose a new Recursive Multimodal Agent (ReMA) that dramatically improves long‑range understanding.

Key Contributions

• MM‑Lifelong dataset – 181 h of raw, unedited footage organized into three temporal granularities (Day, Week, Month) with synchronized video, audio, and text annotations.
• Identification of two failure modes in existing approaches:
  1. Working Memory Bottleneck – end‑to‑end MLLMs lose relevant context when the input window exceeds their fixed token capacity.
  2. Global Localization Collapse – agentic baselines can’t reliably locate events in sparsely distributed month‑scale timelines.
• Recursive Multimodal Agent (ReMA) – a memory‑augmented architecture that maintains a recursive belief state and performs dynamic memory pruning/insertion to keep the most informative context alive.
• Rigorous benchmark splits that isolate temporal bias (e.g., “Day‑only” vs. “Month‑only”) and domain bias (different environments, activities), enabling clean evaluation of both in‑distribution and out‑of‑distribution performance.
• Comprehensive empirical study showing ReMA’s superiority over strong baselines on tasks such as temporal question answering, event retrieval, and long‑term plan generation.

Methodology

1. Dataset Construction

   • Collected continuous video streams from wearable cameras and stationary indoor/outdoor setups.
   • Annotated with timestamps, activity labels, and natural‑language captions using a semi‑automated pipeline plus human verification.
   • Split into three temporal tiers:
     Day – dense clips (seconds‑to‑minutes apart)
     Week – moderate gaps (hours‑apart)
     Month – sparse events (days‑to‑weeks apart)

2. Baseline Evaluation

   • Tested standard end‑to‑end MLLMs (e.g., Flamingo, Video‑LLM) that ingest a fixed‑size token window.
   • Ran an “agentic” baseline that treats the dataset as a navigation problem, using a learned policy to jump between timestamps.

3. Recursive Multimodal Agent (ReMA)

   • Dynamic Memory Buffer: stores a limited set of multimodal embeddings; when new information arrives, a relevance scorer decides which entries to evict.
   • Recursive Belief Update: each incoming observation updates a latent belief vector via a gated recurrent unit that conditions on both the new observation and the current memory state.
   • Query‑Driven Retrieval: at inference, the model attends over the memory buffer using the question embedding, effectively pulling the most relevant past context.

4. Evaluation Protocol

   • Metrics: accuracy on temporal QA, mean reciprocal rank for event retrieval, and success rate for multi‑step plan generation.
   • Ablation studies on buffer size, update frequency, and the effect of temporal granularity.

Results & Findings

• Working Memory Bottleneck: performance on month‑scale QA drops sharply for fixed‑window MLLMs, confirming that context saturation kills long‑range reasoning.
• Global Localization Collapse: the agentic baseline’s navigation policy fails to locate month‑scale events, leading to near‑random retrieval.
• ReMA’s advantage: By constantly refreshing a compact, relevance‑weighted memory, ReMA retains crucial cues across weeks and months, delivering a >20 % absolute gain on month‑scale QA and a 70 % boost in retrieval MRR.
• Ablations show that a buffer of ~256 embeddings (≈2 min of video) is sufficient; larger buffers give diminishing returns, highlighting the efficiency of the recursive update.

Practical Implications

• Long‑Term Personal Assistants – Voice or AR assistants that need to recall events from weeks or months ago (e.g., “When did I last replace the water filter?”) can benefit from ReMA’s memory management instead of naïve sliding windows.
• Surveillance & Security Analytics – Operators often search for sparse incidents across days; a recursive belief state enables faster, more accurate retrieval without storing the entire video stream.
• Robotics & Embodied AI – Robots operating in homes or factories can maintain a compact world model that updates as they move, allowing them to plan tasks that depend on distant past observations (e.g., “Did I already clean the kitchen this week?”).
• Data‑Efficient Training – Because ReMA works with a bounded memory, it reduces GPU memory pressure, making it feasible to train multimodal models on commodity hardware while still handling hour‑long streams.
• Benchmarking Long‑Term Reasoning – The MM‑Lifelong splits provide a ready‑made testbed for any team building temporal reasoning or lifelong learning capabilities, encouraging reproducible progress.

Limitations & Future Work

• Domain Coverage – The dataset, while large, is still biased toward indoor/home environments; outdoor or industrial settings remain under‑represented.
• Annotation Granularity – Event boundaries are manually defined at a coarse level; finer‑grained action segmentation could unlock more nuanced reasoning.
• Scalability of Memory Scoring – The relevance scorer is a simple feed‑forward network; scaling to billions of frames may require more sophisticated, possibly hierarchical, memory indexing.
• Generalization to Unseen Modalities – Current experiments focus on video‑audio‑text; extending ReMA to include sensor streams (e.g., LiDAR, IMU) is an open direction.
• Out‑of‑Distribution Robustness – While the authors provide OOD splits, real‑world deployment will encounter distribution shifts (lighting, camera quality) that need systematic robustness studies.

The authors suggest exploring hierarchical memory trees, self‑supervised pre‑training on MM‑Lifelong, and integrating reinforcement‑learning‑based planning to further close the gap between lifelong perception and autonomous decision‑making.

Authors

• Guo Chen
• Lidong Lu
• Yicheng Liu
• Liangrui Dong
• Lidong Zou
• Jixin Lv
• Zhenquan Li
• Xinyi Mao
• Baoqi Pei
• Shihao Wang
• Zhiqi Li
• Karan Sapra
• Fuxiao Liu
• Yin-Dong Zheng
• Yifei Huang
• Limin Wang
• Zhiding Yu
• Andrew Tao
• Guilin Liu
• Tong Lu

Paper Information

• arXiv ID: 2603.05484v1
• Categories: cs.CV
• Published: March 5, 2026
• PDF: Download PDF