arXiv:2601.13836v2 Announce Type: replace Abstract: Although Multimodal Large Language Models (MLLMs) demonstrate strong omni-modal perception, their ability to forecast future events from audio-visual cues remains largely unexplored, as existing benchmarks focus mainly on retrospective understanding. To bridge this gap, we introduce FutureOmni, the first benchmark designed to evaluate omni-modal future forecasting from audio-visual environments. The evaluated models are required to perform cross-modal causal and temporal reasoning, as well as effectively leverage internal knowledge to predict
The rapid advancement of MLLMs necessitates new evaluation benchmarks to push their capabilities beyond retrospective analysis towards proactive inferencing regarding future events.
This benchmark signifies a crucial step in developing MLLMs that can predict future events from complex environmental cues, enhancing their utility in autonomous systems and real-time decision-making.
The focus of MLLM development will shift partly from mere perception and understanding to predictive capabilities, requiring more sophisticated reasoning and internal knowledge integration.
- · AI research institutions
- · Developers of multimodal AI
- · Robotics and autonomous systems companies
- · Surveillance and predictive analytics firms
- · Companies relying on static, reactive AI models
- · Traditional perception-only AI systems
- · Human experts in highly predictable, data-rich fields
Future MLLMs will gain enhanced capabilities in predicting upcoming events based on diverse sensory input, moving beyond simply describing past or present states.
This predictive power will drive innovation in areas like proactive security, predictive maintenance, and highly autonomous systems capable of anticipating consequential scenarios.
The widespread deployment of MLLMs with accurate future forecasting could lead to significant advantages for entities capable of integrating and acting upon these predictions rapidly, potentially reshaping economic and strategic landscapes.
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Read at arXiv cs.CL