title: Merlin:Empowering Multimodal LLMs with Foresight Minds
summary: Humans possess the remarkable ability to foresee the future to a certain
  extent based on present observations, a skill we term as foresight minds.
  However, this capability remains largely under explored within existing
  Multimodal Large Language Models (MLLMs), hindering their capacity to learn the
  fundamental principles of how things operate and the intentions behind the
  observed subjects. To address this issue, we introduce the integration of
  future modeling into the existing learning frameworks of MLLMs. By utilizing
  the subject trajectory, a highly structured representation of a consecutive
  frame sequence, as a learning objective, we aim to bridge the gap between the
  past and the future. We propose two innovative methods to empower MLLMs with
  foresight 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 a
  given initial observation. Then, FIT requires MLLMs to first predict
  trajectories of related objects and then reason about potential future events
  based on them. Aided by FPT and FIT, we build a novel and unified MLLM named
  Merlin that supports multi-images input and analysis about potential actions of
  multiple objects for the future reasoning. Experimental results show Merlin
  powerful foresight minds with impressive performance on both future reasoning
  and visual comprehension tasks.
paper_authors._id: 656d34623dbac3a83d3a19f0
paper_authors.name: En Yu
paper_authors._id: 656d34623dbac3a83d3a19f1
paper_authors.name: Liang Zhao
paper_authors._id: 656d34623dbac3a83d3a19f2
paper_authors.name: Yana Wei
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paper_authors.fullname: Yana Wei
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paper_authors._id: 656d34623dbac3a83d3a19f3
paper_authors.name: Jinrong Yang
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paper_authors.fullname: Jinrong Yang
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paper_authors.user: YancieYJR
paper_authors._id: 656d34623dbac3a83d3a19f4
paper_authors.name: Dongming Wu
paper_authors._id: 656d34623dbac3a83d3a19f5
paper_authors.name: Lingyu Kong
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paper_authors.fullname: Lingyu Kong
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paper_authors.user: kppkkp
paper_authors._id: 656d34623dbac3a83d3a19f6
paper_authors.name: Haoran Wei
paper_authors.status: admin_assigned
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paper_authors.fullname: Haoran Wei
paper_authors.type: user
paper_authors.user: HaoranWei
paper_authors._id: 656d34623dbac3a83d3a19f7
paper_authors.name: Tiancai Wang
paper_authors._id: 656d34623dbac3a83d3a19f8
paper_authors.name: Zheng Ge
paper_authors._id: 656d34623dbac3a83d3a19f9
paper_authors.name: Xiangyu Zhang
paper_authors._id: 656d34623dbac3a83d3a19fa
paper_authors.name: Wenbing Tao
paper_title: Merlin:Empowering Multimodal LLMs with Foresight Minds
paper_summary: Humans possess the remarkable ability to foresee the future to a certain
  extent based on present observations, a skill we term as foresight minds.
  However, this capability remains largely under explored within existing
  Multimodal Large Language Models (MLLMs), hindering their capacity to learn the
  fundamental principles of how things operate and the intentions behind the
  observed subjects. To address this issue, we introduce the integration of
  future modeling into the existing learning frameworks of MLLMs. By utilizing
  the subject trajectory, a highly structured representation of a consecutive
  frame sequence, as a learning objective, we aim to bridge the gap between the
  past and the future. We propose two innovative methods to empower MLLMs with
  foresight 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 a
  given initial observation. Then, FIT requires MLLMs to first predict
  trajectories of related objects and then reason about potential future events
  based on them. Aided by FPT and FIT, we build a novel and unified MLLM named
  Merlin that supports multi-images input and analysis about potential actions of
  multiple objects for the future reasoning. Experimental results show Merlin
  powerful foresight minds with impressive performance on both future reasoning
  and visual comprehension tasks.
paper_ai_summary: A novel MLLM, Merlin, is introduced which incorporates future modeling through FPT and FIT, enhancing foresight and future reasoning capabilities.
paper_ai_keywords: Multimodal Large Language Models MLLMs foresight minds future modeling subject trajectory Foresight Pre-Training FPT Foresight Instruction-Tuning FIT future reasoning visual comprehension