arXiv:2606.05008v1 Announce Type: cross Abstract: As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability. Despite substantial efforts in developing video datasets and benchmarks, existing works primarily focus on perception and reasoning, without systematically evaluating memory: what models retain, how faithfully information is preserved, and how robust memory remains under interference. To address this gap, we introduce M$^3$Eval, the first comprehensive evaluation framework and benchmark for probing different memory dimensions in multi-m
As multi-modal AI models advance rapidly in video understanding, the need to systematically evaluate their memory capabilities has become a critical bottleneck for further development and trust.
Evaluating and improving the memory of multi-modal AI models is crucial for their reliable performance in long-form tasks and their eventual deployment in complex, real-world applications.
The introduction of M$^3$Eval provides a standardized framework to systematically assess memory in multi-modal models, shifting focus beyond mere perception and reasoning.
- · AI researchers
- · Multi-modal AI developers
- · Companies building video understanding applications
- · Academic institutions
- · AI models with poor memory retention
Systematic evaluation will highlight architectural weaknesses in current multi-modal models regarding memory.
Improved memory capabilities will accelerate the development of more capable and reliable AI agents for long-duration tasks.
Enhanced AI memory could lead to a 'cognitive leap' in agents, enabling more sophisticated and autonomous decision-making over extended periods.
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Read at arXiv cs.AI