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MM-Lifelong

Summary

We introduce MM-Lifelong, a dataset designed for Multimodal Lifelong Understanding. MM-Lifelong comprises 181.1 hours of footage across three domains. The dataset contains 1289 questions with 1810 distinct clue intervals. Crucially, the distribution of temporal certificates confirms the ``Lifelong'' nature of the benchmark: 267 questions require reasoning over a span of 1-10 hours, and 127 questions involve ultra-long dependencies exceeding 10 hours.

Statistics

Statistics	Number
Total Duration	181.1 hours
Total Questions	1289
└ Avg. Question Length	26.79 words
└ Avg. Answer Length	4.80 words
Total Clue Intervals	1810 (100%)
└ Short (<90s)	1039 (57.40%)
└ Medium (90–540s)	550 (30.39%)
└ Long (>540s)	221 (12.21%)
└ Avg. Clue Duration	362.26 s
Total Temporal Certificate	1289 (100%)
└ Short (<10 min)	500 (38.79%)
└ Medium (10 min–1 h)	395 (30.64%)
└ Long (1 h–10 h)	267 (20.71%)
└ Ultra-long (>10 h)	127 (9.85%)
Questions by Split	train / val / test
└ Total	266 / 623 / 400
└ Gamer's Journey (Day)	0 / 0 / 200
└ Egocentric Life (Week)	0 / 0 / 200
└ Live Stream (Month)	266 / 623 / 0

Distribution of question categories and video clip domains:

Dataset Comparison

To situate MM-Lifelong within the broader landscape of multimodal understanding, we compare it against existing benchmarks and highlight the unique challenges arising in the Lifelong Horizon. First, the dataset presents an Extremely Long Temporal Scale (100+ hours), significantly exceeding standard Long-Context benchmarks like CG-Bench and pushing the limits of memory retention. Distinct from recent continuous datasets like EgoLife, MM-Lifelong provides Manual, Clue-Grounded Annotations across diverse domains (from digital streams to career archives) rather than relying on automated generation, thereby ensuring higher reasoning complexity and data quality.

Dataset	Modalities	#Samples	Max. Dur	Max. Span	Anno.	QA	Clue
I. Short-Context Multimodal Dataset
MMMU	Image	11.5k	0	0	M	11.5k	✗
AIR-Bench	Audio	19k	19.4s	19.4s	A & M	19k	✗
OmniBench	Audio + Image	1.1k	30s	30s	A & M	1.1k	✗
MVBench	Video	4.0k	2.95m	2.95m	A	4.0k	✗
II. Long-Context Multimodal Dataset
EgoSchema	Video	5.0k	3.0m	3.0m	A & M	5.0k	✗
Video-MME	Video	900	59.6m	59.6m	M	2.7k	✗
M3-Bench	Video	1,020	57.5m	57.5m	M	4.9k	✗
CG-AV-Counting	Audio + Video	497	1.75h	1.75h	M	1.0k	✓
III. Lifelong Multimodal Dataset
EgoLife	Audio + Video	6	51.9h	~7d	A & M	3.0k	✗
TeleEgo	Audio + Video	5	14.4h	~3d	A & M	3.3k	✗
MM-Lifelong (Ours)	Audio + Video	3	105.6h	~51d	M	1.3k	✓

Experiments Results

Methods	Frames	Train@Month Acc	Train@Month Ref@300	Val@Month Acc	Val@Month Ref@300	Test@Day Acc	Test@Day Ref@300	Test@Week Acc	Test@Week Ref@300
Human	Full	82.5	31.2	80.4	33.5	99.2	49.8	95.6	42.4
End-to-End MLLMs
GPT-5	50	10.15	1.39	14.87	0.44	15.25	0.53	15.00	0.92
Qwen3-VL-235B-A22B	1536	9.09	0.39	14.33	0.06	12.44	0.79	15.63	0.80
Qwen3-VL-30B-A3B	1536	8.33	0.48	11.92	0.64	11.48	0.42	11.07	0.77
Video-XL-2-8B	2048	6.02	0.00	8.91	0.40	8.75	1.37	10.25	0.10
Video-XL-2-8B	1024	4.89	0.09	9.07	0.75	9.00	0.72	12.00	0.51
Eagle-2.5-8B	512	3.76	1.59	4.41	0.03	7.25	1.01	9.50	1.69
Eagle-2.5-8B	32	2.07	0.71	6.10	0.01	8.25	0.39	7.00	1.16
Nemotron-v2-12B	512	7.52	0.19	9.63	0.02	7.25	0.04	11.00	0.50
Nemotron-v2-12B	128	7.71	0.18	10.03	0.01	7.00	0.03	8.50	0.50
Agentic Methods
VideoMind-7B	Full	5.26	1.00	8.35	0.26	7.50	1.12	11.75	2.51
LongVT-7B	Full	5.83	1.71	7.54	0.11	7.00	0.73	9.75	0.66
DeepVideoDiscovery	Full	4.36	2.03	10.57	4.48	10.25	3.04	9.02	8.12
ReMA (Ours) / w GPT-5	Full	17.62	9.91	18.62	15.46	16.75	11.51	18.82	16.37
ReMA (Ours) / w Qwen3VL-A22B	Full	14.23	6.01	15.51	8.51	13.33	6.56	15.98	10.61

Citation

@misc{chen2026multimodallifelongunderstandingdataset,
      title={Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline}, 
      author={Guo Chen and Lidong Lu and Yicheng Liu and Liangrui Dong and Lidong Zou and Jixin Lv and Zhenquan Li and Xinyi Mao and Baoqi Pei and Shihao Wang and Zhiqi Li and Karan Sapra and Fuxiao Liu and Yin-Dong Zheng and Yifei Huang and Limin Wang and Zhiding Yu and Andrew Tao and Guilin Liu and Tong Lu},
      year={2026},
      eprint={2603.05484},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.05484}, 
}

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Paper for CG-Bench/MM-Lifelong

Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline

Paper • 2603.05484 • Published 1 day ago • 2