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Humans possess the remarkable ability to foresee the future to a certainextent based on present observations, a skill we term as foresight minds.However, this capability remains largely under explored within existingMultimodal Large Language Models (MLLMs), hindering their capacity to learn thefundamental principles of how things operate and the intentions behind theobserved subjects. To address this issue, we introduce the integration offuture modeling into the existing learning frameworks of MLLMs. By utilizingthe subject trajectory, a highly structured representation of a consecutiveframe sequence, as a learning objective, we aim to bridge the gap between thepast and the future. We propose two innovative methods to empower MLLMs withforesight minds, Foresight Pre-Training (FPT) and Foresight Instruction-Tuning(FIT), which are inspired by the modern learning paradigm of LLMs.Specifically, FPT jointly training various tasks centered on trajectories,enabling MLLMs to learn how to attend and predict entire trajectories from agiven initial observation. Then, FIT requires MLLMs to first predicttrajectories of related objects and then reason about potential future eventsbased on them. Aided by FPT and FIT, we build a novel and unified MLLM namedMerlin that supports multi-images input and analysis about potential actions ofmultiple objects for the future reasoning. Experimental results show Merlinpowerful foresight minds with impressive performance on both future reasoningand visual comprehension tasks.

Merlin:Empowering Multimodal LLMs with Foresight Minds